Cancer Translational Medicine

Original Research | Open Access

Vol.9 (2023) | Issue-4 | Page No: 161-182


Construction of Cuproptosis-Related LncRNA Signature as a Prognostic Model Associated with Immune Microenvironment for Clear-Cell Renal Cell Carcinoma

Jiyao Yu1#, Shukai Zhang2#, Qingwen Ran3, Xuemei Li4,5,6*


1. Second Clinical Medical College, Chongqing Medical University, Chongqing, China

2. First Clinical Medical College, Chongqing Medical University, Chongqing, China

3. Fifth Clinical Medical College, Chongqing Medical University, Chongqing, China

4. Department of Pathology, Chongqing Medical University, Chongqing, China

5. Molecular Medicine Diagnostic and Testing Center, Chongqing Medical University, Chongqing, China

6. Department of Pathology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China

# These authors contributed equally to this work.

*Corresponding Author


Address for correspondence: Dr. Xuemei Li, Department of Pathology, Chongqing Medical University, No.1 Youyi Road, Yuzhong District, Chongqing 400042, China. E-mail:

Important Dates  

Date of Submission:   20-Sep-2023

Date of Acceptance:   28-Nov-2023

Date of Publication:   20-Dec-2023


Aim: To determine the effects and mechanisms of cuproptosis on the prognosis of clear-cell renal cell carcinoma (ccRCC).

Methods: We identified and characterized cuproptosis-related long non-coding RNAs (lncRNAs) in RCC. Particularly, a coexpression network of cuproptosis-related mRNAs and lncRNAs was constructed. Univariate and multivariate Cox regression analyses were performed to establish prognostic cuproptosis-related lncRNA signatures. The accuracy of this model was further supported by the receiver operating characteristic (ROC) curve and principal component analysis (PCA). Additionally, GSEA was used to explore the functional enrichment of the gene expression data. Based on this risk model, differences in immune status and responses to different therapies between the high- and low-risk groups were observed.

Results: A prognostic risk model consisting of five cuproptosis-related lncRNAs (AC121338.2,  CD27-AS1, LINC00460, AC026401.3, and LINC00944) was constructed; the high-risk score group was significantly related to poor overall survival. Then, a prognostic nomogram with clinical features was established that indicates a valid prognostic effect for survival risk stratification. Additionally, GSEA revealed that prognostic cuproptosis-related lncRNA signatures were involved in various malignancy-related immunomodulatory pathways. 

Conclusion: Interestingly, we found 35 immune-checkpoint genes were highly expressed in the high-risk group by analyzing the differentially expressed immune-checkpoint genes between the high- and low-risk groups. Among them, LAG3, TIGIT, and CD40 may be potential targets for immunotherapy, and activating CD27-CD70 can serve potential therapeutic target. These results identified a cuproptosis-related lncRNAs prognostic model for ccRCC’s early diagnosis and provided novel insights into the pathogenesis of ccRCC.

Keywords: Clear-cell renal cell carcinoma; Cuproptosis; LncRNA; Prognostic signature; Immune microenvironment


In the past few decades, the global incidence of renal cell carcinoma (RCC) has been astonishingly increasing, its annual mortality ranks first among urinary cancers.[1] As the primary subtype of renal cancer, clear-cell RCC (ccRCC) is one of the most malignant urinary tumors, approximately accounting for 75%-80% of RCC,[2] with a global annual mortality of about 90,000.[3] Histologically, ccRCC is characterized by clear cytoplasm with nested clusters of cells surrounded by a dense endothelial network.[4] Morphologically, ccRCC cells are fraught with lipids and glycogen.[5] Genetically, ccRCC is characterized by the loss of the biallelic gene of the Von Hippel-Lindau (VHL) tumor suppressor gene, which encodes E3 ubiquitin ligase, and the later degrades hypoxia-inducible factor (HIF)1α and HIF2α.[6] Typical radiological features of ccRCC include exogenous (outward) growth, heterogeneity owing to necrosis or hemorrhage in the tumor, and high intake of contrast enhancer.[7] Between 25% and 30% of patients have metastases when their ccRCC is first diagnosed.[8] Targeted therapy is currently one of the standard treatments for ccRCC, nevertheless, almost all patients will eventually develop disease deterioration as long as ccRCC cells can escape drug-induced apoptosis or autophagy.[9] Despite the fact that the mechanisms of cancer development and progression have been extensively studied, the etiology and carcinogenesis of ccRCC remain unclear.[5] Therefore, in view of the high morbidity and mortality of ccRCC, it is of great importance to explore the molecular characteristics with prognostic value for the immune response of ccRCC patients.

Copper is an indispensable cofactor for all organisms, but it becomes toxic under the condition that concentrations of copper exceed a threshold maintained by evolutionarily conserved homeostatic mechanisms, and then it will induce cell death.[10] Cuproptosis, a new research hotspot, was first defined in 2022 as a novel form of cell death via disruption of metabolic pathways, which is dependent on mitochondrial respiration, occurs via direct binding of copper to lipoylated components of the tricarboxylic acid (TCA) cycle, and promotes the aggregation of lipoylated proteins and destabilization of Fe-S cluster proteins, which results in proteotoxic stress and, ultimately, cell death.[10] Nam, found that pharmacologic inhibition of TGF-β restores the expression of TCA cycle enzymes and suppresses tumor growth in an orthotopic model of RCC.[11] Liu, found that SIRT5 deglycosylated PDHA1 at K351 and increased PDC activity to alter the metabolic crosstalk with the TCA cycle and inhibit the Warburg effect, indicating that the SIRT5 knockdown promoted ccRCC tumorigenesis and metastasis.[12] Ikeda, reported that copper metabolism MURR1 domain-containing 5 (COMMD5) might inhibit malignant phenotypes of RCC, thus inhibiting tumor development and improving patient prognosis.[13] Besides, copper is involved in various physiological and pathological processes in the body.[14] For instance, copper binds to tyrosinase, which is critical for pigment formation.[15] Copper is also a ligand of Cu/Zn superoxide dismutase 1 (SOD1), supporting the antioxidant function.[16] The disorder of copper metabolism, including excessive accumulation or improper transport of copper, can lead to copper-induced apoptosis, known as 'Cuproptosis’.[17],[18] Colossal studies have shown that copper can induce apoptosis by increasing the protein expression levels of caspase-8, caspase-3, caspase-9, and b-cell lymphoma-2 (BCL-2)-related X protein (Bax). Among them, b-cell lymphoma-2 (BCL-2) family proteins, mitochondrial pro-apoptotic proteins, and caspase are indispensable members of the mitochondrial apoptosis pathway, which plays an irreplaceable role in cell death as an in vivo apoptosis pathway.[18],[19] However, the mechanism of cuproptosis in clear-cell renal carcinoma remains unclear and needs further studies.

Long non-coding RNA (lncRNA) refers to transcripts of nucleotide (length > 200 nt); a great number of them lack protein-encoding capacity. LncRNAs are widely expressed in human tissue, participating in the transcriptional regulation of gene expression, consequently conveying its function in cell biology where it plays an essential role in the biological process of cancer cells.[20],[21],[22],[23],[24],[25],[26],[27] Nowadays, an increasing number of studies indicated that an array of lncRNAs may play multiple roles in the initiation and progression of ccRCC.[28],[29] For example, lncRNA MIR4435‐2HG can promote increased KLF6 expression by directly sponging down-regulated miR-513-a-5p in ccRCC and disrupting the interactions between miR-513a-5p and KLF6 in ccRCC, consequently promoting proliferation and invasion of ccRCC cells.[28] What’s more, lncRNA TCL6 can directly interact with miR-155 in ccRCC, and then trigger the Src-Akt pathway to promote ccRCC metastasis. In addition, overexpression of lncRNA TCL6 can inhibit cell growth and epithelial-mesenchymal progression via STAU-1-mediated Src-mRNA decay.[29] Apart from that, lncRNA HCP5 can be combined with microRNA-214, reducing the expression of insulin-like growth factor-1 (IGF-1) receptor and downstream mTORC1 signaling in RCC cells, thereby inhibiting the growth of ccRCC. In addition, lncRNA also plays a specific role in cellular respiration. LncRNA LINC00116 can genetically encode mitochondrial single-pass transmembrane protein MTLN which is closely linked to a unique gene network dedicated to the assembly of mitochondrial protein complexes. Overexpression of MTLN could enhance the mitochondrial function, such as increased membrane potential, respiration, Ca2+ storage, and reduced mitochondrial oxidative stress response, and all of those are consistent with improved recombination, supercomplexation, and coupling of the respiratory chain.[30] LncRNA, nuclear paraspeckle assembly transcript 1 (NEAT1), could induce Copper transporter 1 (CTR1) expression through increasing ROS to enhance cisplatin sensitivity in ovary cancer and non-small-cell lung cancer (NSCLC) cells.[31] LncRNA MALAT1 aggravates the progression of non-small cell lung cancer by stimulating the expression of copper metabolism MURR1 domain-containing 8 (COMMD8) via targeting miR-613.[32] However, the roles and mechanisms of lncRNA in regulating cuproptosis in RCC remain largely unknown.

In this study, the set of cuproptosis-related lncRNAs was established, a novel prognostic model based on cuproptosis-related lncRNAs was constructed, and its relationship between the pathogenesis of ccRCC with immune microenvironment was explored.


Data collection

The relevant RNA sequence data was obtained from The Cancer Genome Atlas (TCGA) database ( Inclusion criteria were as follows: (1) diagnosis of renal cancer; (2) Patients had complete transcriptome expression data and clinical information. According to the inclusion criteria, 539 RCC patients were included. Apart from that, complete patient clinical information can be downloaded from TCGA. When screening clinical information, samples with follow-ups of fewer than 30 days were excluded. As for annotations, we selected the gene with the highest expression as the gene symbol and annotated it through the annotation package human.gtf. Since all the data involved in this study came from the TCGA database, the TCGA publishing guidelines were strictly followed, and ethics committee approval is not necessary (

Cuproptosis-related genes collected

Ten Cuproptosis-related genes were obtained from literature,[10] including FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, MTF1, GLS, and CDKN2A.

Functional enrichment analysis

To further identify the potential functions of potential targets, the data were analyzed via functional enrichment. Gene Ontology (GO) is a widely used tool for annotating functional genes, especially for molecular functions (MF), biological pathways (BP), and cellular components (CC). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis is pragmatic for analyzing gene function and the relevant information of advanced genomic function. To have a better comprehension of the oncogenic role of target genes, the ClusterProfiler package in R language was used to analyze the GO function of potential mRNAs and enrich KEGG pathways.

Distinguish lncRNA from mRNA in the TCGA database

File human.gtf and the matrix file of genes were annotated by the human-related genome, where the matrix file of genes contained lncRNA and mRNA. The annotation file of the human genome includes the corresponding gene attributes, and 14,142 lncRNAs and 19,658 mRNAs were distinguished from the annotation file of the human genome.

Identification of cuproptosis-related lncRNA

Data of ten cuproptosis-related mRNAs were obtained from TCGA, files on cuproptosis-related genes and the expression of lncRNA were read, and the data and samples with average lncRNA expression less than 0.5 were excluded. Then Pearson correlation tests were conducted. The “for” cycle was used to compare the correlation between each cuproptosis-related gene and lncRNA. The correlation coefficient filtering standard is set at 0.4, and the P-value filtering standard is set at 0.001. Finally, 705 cuproptosis-related lncRNAs were collected.

The establishment of a prognostic model

The prognostic value of cuproptosis-related lncRNAs was evaluated by univariate Cox regression. In univariate analysis, cuproptosis-related lncRNAs (P<0.05) were included in the selection factor to establish LASSO regression. Lasso results were then included in a multivariate Cox model to develop risk scores. A linear combination of risk scores β: risk score = ∑i=1nβi(expressionoflncRNAi) was constructed, based on the risk score concerning cuproptosis-related lncRNA expression levels multiplied by regression coefficients. In line with the median risk score, patients were divided into two groups: the high-risk and the low-risk group. The sample data were randomly divided into two categories as the first internal validating set and the second validating set. A logarithmic rank test was utilized to compare survival differences between the two groups. Cox regression was used to establish an independent prognostic model. The survival rate of patients was predicted by nomogram to confirm whether risk score was an independent indicator of prognosis.

Network graph of cuproptosis-related mRNAs and lncRNAs  

According to the corresponding relationship between nodes, R package GGalluvial was used to generate a Sankey plot. In multivariate analysis, lncRNA [HR (hazard ratio)>1] was defined as a risk factor suggesting that increased lncRNA expression may promote tumor development. Whereas lncRNA (HR<1) was defined as a protective factor suggesting that increased lncRNA expression may inhibit tumors from developing. Next, Sankey plots were drawn according to the mRNA-lncRNA risk types. Cytoscape 3.7.2 visual co-expression network was used to describe the lncRNA corresponding to each mRNA.

GSEA functional analysis

Gene Set Enrichment Analysis (GSEA, was used to explore the functional enrichment of the gene expression data. We investigated the functional enrichment of cuproptosis-related lncRNAs with prognostic value and visualized the top 5 cuproptosis-related GO and KEGG pathways.

Cuporptosis-related lncRNA models were evaluated for patients’ prognosis

Patients in the dataset were divided into low- and high-risk groups according to cuproptosis-related lncRNA signature risk scores. Kaplan-Meier survival analysis was used to assess differences in survival between the high- and low-risk groups. The 'survivalROC' package was then used to construct the model, and receiver operating characteristic (ROC) curve analysis was performed to calculate the area under the curve (AUC) for 1-, 3- and 5-year overall survival to determine the prediction accuracy. Principal Component Analysis (PCA) was simultaneously performed to assess the distribution of patients with different risk scores. The R package scatterplot3D was used to generate PCA diagrams. The nomogram was developed and validated by the RMS package in R version 4.0.3 (

Methods of immune infiltration analysis

CIBERSORT was used for immune infiltration analysis, and an immune cell infiltration matrix was obtained via Perl programming language. In this research, 22 kinds of immune cells, including macrophages M2, plasma cells, neutrophils, mast cells activated, T cells CD8, macrophages M1, T cells gamma delta, B cells memory, monocytes, B cells naive, T cells follicular helper, NK cells activated, dendritic cells resting, T cells CD4 memory activated, T cells CD4 naive, NK cells resting, T cells regulatory (Tregs), dendritic cells activated, eosinophils, macrophages M0, T cells CD4 memory resting and mast cells resting. The analysis results were filtered by the cut-off criteria with an adjusted P value <0.05, and each sample was visualized by the "Barplot", "Corrplot", and "ggplot2" packages in R language version 4.1.0. The "Corrplot" package calculated the relationship between the gene expression matrix and immune cells.

Drug sensitivity analysis

For the TCGA database, tumor RNA-SEQ (FPKM) Data (TCGA) can be downloaded from the Genomic Data Commons (GDC) data portal, converting FPKM Data into TPM and normalizing it into log2 (TPM+1). In the meantime, samples with clinical information were retained. We predicted chemotherapy response for each sample according to the most extensive public pharmacogenomics database [cancer drugs sensitivity genomics (GDSC),]. The prognosis process was implemented by R-package pRRophetic, where the 50% maximum inhibitory concentration (IC50) of samples was estimated by ridge regression, all parameters were set to default values, the batch effect of combat and tissue type of all were used, and repeated gene expression was summarized as an average.

Statistical analysis

Kaplan-Meier method was used to generate survival curves and, the logarithmic rank test was used for comparison. Cox and Lasso regressions were used to estimate the prognostic effects of cuproptosis-related lncRNA markers and clinicopathological data. Statistical analysis was performed by R language (version 4.1.0). DEG between the two groups was analyzed by t-test. P<0.05 was considered to be statistically significant.


Identification of cuproptosis-related lncRNAs with prognostic value in RCC

Ten cuproptosis-related genes were obtained from the literature,10 including FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, MTF1, GLS, and CDKN2A, and mRNA data of these genes were obtained from TCGA-KIRC. And then 705 cuproptosis-related lncRNAs (|R2|>0.4, P<0.001) were identified by way of Pearson correlation analysis. Subsequently, 266 lncRNAs whose expression level was associated with prognosis were screened by univariate Cox regression, indicating that they processed prognostic value for RCC (P<0.01). Five lncRNAs (AC121338.2, CD27-AS1, LINC00460, AC026401.3, LINC00944) were identified through LASSO regression analysis. The prognostic cuproptosis-related lncRNA signatures were established by identifying five cuproptosis-related lncRNAs. The expression levels of these five cuproptosis-related lncRNAs are shown in Figure 1A. Cytoscape and GGalluvial R software packages were then used to visualize the co-expression network of lncRNAs further. The expression network includes eight couples of lncRNA - mRNA [Figure 1B], (|R2|>0.4, P<0.001). AC026401.3 was co-expressed with four cuproptosis-related mRNAs (MTF1, DLAT, DLD, and CDKN2A), LINC00460 was co-expressed with one cuproptosis-related mRNA (CDKN2A), LINC00944 was co-expressed with one cuproptosis-related mRNA (CDKN2A). AC121338.2 was co-expressed with one cuproptosis-related mRNA (LIAS), and CD27-AS1 was associated with only one cuproptosis-related mRNA (FDX1). Amongst them, four lncRNAs (AC026401.3, CD27-AS1, LINC00460, and LINC00944) were significant independent unfavorable prognostic factors, and the remaining one lncRNA (AC121338.2) was an independent favorable prognostic factor for OS [Figure 1C]. And survival curves of 5 lncRNAs are shown in Supplementary Figure 1.

Figure 1.
Figure 1. Five cuproptosis-related lncRNA expression levels and lncRNA-mRNA network in prognosis signature. (A) 5 CRLs expression levels in RCC and normal sample; (B) The co-expression network of prognosis-related CRL; (C) The Sankey diagram of cuprotosis-related gene and relevant lncRNA. LncRNA, long non-coding RNA; ccRCC, clear-cell renal cell carcinoma.

Internal validation and independent prognostic risk model established in TCGA cohort

The samples in the TCGA-KIRC dataset were randomly divided into two equivalent cohorts: Training cohorts and validating cohorts. Five previously identified cuproptosis-related lncRNAs were utilized to establish prognostic models. And a risk model consisting of these 5 lncRNAs was established according to the minimum standards [Figure 2A, 2B]. We divided patients of two cohorts into high- and low-risk groups by the median risk score [Figure 2C]. The mortality of the high-risk group was higher and their survival was lower [Figure 2D]. In addition, Kaplan-Meier curves depicted that the overall survival (OS) of high-risk patients was lower than that of low-risk patients (P<0.001, Figure 2E). Subsequent ROC analysis showed that the risk model established by these five lncRNAs could stably assess and predict the survival rate of RCC patients (AUC=0.776, Figure 2F). The heatmap analysis showed that the expression levels of four risk genes (AC026401.3, CD27-AS1, LINC00460, and LINC00944) increased in high-risk patients [Figure 2G].

In addition, univariate and multivariate Cox regression analyses confirmed whether risk scores derived from prognostic risk models could be used as independent prognostic indicators. In univariate Cox regression analysis, risk score (P<0.001, HR=1.025, 95%CI: 1.011-1.038, Figure 2H) was a potential risk factor. Multivariate analysis also showed that risk score could be an independent prognostic factor (P=0.042, HR=1.008, 95%CI: 0.991-1.025, Figure 2I). Finally, the PCA diagram showed that high- and low-risk groups could be effectively separated by these five lncRNAs. All genes, cuproptosis-related genes, and lncRNAs could be used as markers to efficiently distinguish the low-risk group from the high-risk group [Figure 2J-2M]. The model was then testified by an internal validating cohort to assess the capacity of the five lncRNAs risk-score system to predict HCC patient survival. The sample data were randomly divided into the first internal validating set and the second validating set. No significant differences in basic characteristics were observed between the two internal validating sets. The internal validating set was divided into high- and low-risk groups based on risk scores. Consequently, the results of both the internal training group and validating sets consistently showed that the high-risk group was apt to have higher risk gene expression [Figure 3A, 3B], higher mortality [Figure 3C-3F], and lower survival [Figure 3G, 3H]. In addition, both sets of internal validating sets showed that the model has an excellent predictive ability (AUC=0.789, AUC=0.767) [Figure 3I, 3J].

Figure 2.
Figure 2. Construct an independent prognosis model based on CRGs in the TCGA training cohort. (A, B) Construct LASSO regression model based on the minimum standards; (C, D) Distribution of risk score and survival time in line with 5 cuproptosis-related lncRNAs; (E) Kaplan-Meier analysis of OS in high- and low- risk group; (F) ROC analysis applied to assess prognosis efficiency; (G)The heatmap of the expression levels of four risk genes; (H) Forest plots of Univariate Cox regression analysis; (I) Forest plots of multivariate Cox regression analysis; (J-M) PCA analysis in the low-and high-risk group. A distinct separation of red dots and green dots is depicted. ROC, receiver operating characteristics; AUC, area under the curve; OS, overall Survival; TCGA, The Cancer Genome Atlas.

Figure 3.
Figure 3. Internal validation of TCGA database. (A, B) The expression level of 5 cuproptosis-related lncRNAs in prognosis model in HCC and normal tissue in training group and validating group; (C, D) The risk score distribution of HCC patients in training groups and validating groups; (E, F) The mortality and survival with different risk score in training and validating group, where blue refers to survival, yellow refers to death; (G, H) Kaplan-Meier curve of high-and low-risk score in training group and validating group; (I, J) 1,3 and 5-survival ROC curve and AUC in training group and validating group.

Stratified analysis of independent prognostic feathers

The potential of the 5-lncRNA prognostic risk model was further analyzed in the TCGA cohort. We divided patients in the TCGA-KIRC dataset into subgroups based on different clinical parameters. Age (>65 vs ≤65), gender (female vs male), grade (2 vs 3 vs 4), stage (1 vs 2 vs 3 vs 4), T (1 vs  2 vs 3), M (0 vs 1) and N (0 vs 1) was included in the clinical stratification of this study. KM curve showed that the survival of low-risk patients was ubiquitously higher than that of high-risk patients [Supplementary Figure 2].

The correlation between clinicopathological factors and risk score

To further assess the role of the cuproptosis-related lncRNA risk model in tumorigenesis and progression of ccRCC, we assessed the association between risk score and clinicopathological factors [Supplementary Figure 3A-3U], and the significant correlation between risk scores and pathological staging (P<0.01) were observed. Among them, the correlation between risk score and survival (P=0.005, Supplementary Figure 3V), grade (P=6.174e-09, Supplementary Figure 3W), stage (P=1.772e-09, Supplementary 3X), T-stage (P=2.796e-08, Supplementary Figure 3Y) and M-stage (P=0.027, Supplementary Figure 3Z) are especially significant. These results suggest that risk models are closely related to ccRCC progression and prognosis. Given clinical information, the high- and low-risk groups were significantly correlated to immune scores [Supplementary Figure 4].

The construction of prognostic nomogram  

We constructed a clinically adaptive nomogram, and estimated 1-, 3- and 5-year survival of ccRCC patients by the prognostic cuproptosis-related lncRNA signatures combined with other clinicopathological factors [Figure 4A]. The calibration diagrams of 1-, 3- and 5-year nomograms [Figure 4B-4D] revealed that the mortality estimated by the nomogram was approximate to the actual mortality. Generating the time-dependent ROC curve of the 5-year OS, the AUC value of the clinical prognosis nomogram was 0.743, which was significantly higher than the age, gender, tumor stage, T tumor stage, M tumor stage, and N tumor stage [Figure 4E].

Figure 4.
Figure 4. The nomogram and calibration curve for survival prognosis. (A) Construct a nomogram utilized to predict the survival of 1-, 3- and 5-year patients. Depict calibration curve of 1-year (B), 3-year (C), and 5-year (D) survival nomogram; (E) Time-dependent ROC analysis, conduct risk analysis on age, gender, stage, T stage (tumor size), M stage (distant metastasis) and N stage (lymphatic metastasis).

Identification of cuproptosis-related lncRNA biological pathways

GSEA was utilized to further conduct functional annotation. A rich KEGG pathway was identified, among which intestinal-immune-network-for-IgA-production (NES=-2.36, P<0.001), autoimmune-thyroid-disease (NES=-2.36, P<0.001), homologous-recombination (NES=-2.36, P<0.001), systemic-lupus-erythematosus (NES=-2.36, P<0.001) and cytokine-cytokine-receptor-interaction (NES=-2.36, P<0.001) were enriched in ccRCC samples with high-risk score. In contrast, proximal-tubule-bicarbonate-reclamation (NES=-2.36, P<0.001), propanoate-metabolism (NES=-2.29, P<0.001), tight-junction (NES=-2.29, P<0.001), pyruvate-metabolism (NES=-2.20, P<0.001) and insulin-signaling-pathway (NES=-2.20, P<0.001) were enriched in ccRCC samples with low-risk score [Supplementary Figure 5A]. Furthermore, The Gene Ontology (GO) terms: interleukin-4-production (NES=2.43, P<0.001), interleukin-10-production (NES=2.42, P=0.002), interferon-gamma-production (NES=2.42, P=0.002), positive-regulation-of-interleukin-4-production (NES=2.40, P<0.001) and negative-regulation-of -interleukin-10-production (NES=2.37, P<0.001) were enriched in ccRCC samples with high-risk score. While, apical-part-of-cell (NES=-2.54, P<0.001), apical-plasma-membrane (NES=-2.54, P<0.001), ammonium-ion-metabolic-process (NES=-2.54, P<0.001), hormone-mediated-signaling-pathway (NES=-2.54, P<0.001) and PDZ-domain-binding (NES=-2.36, P<0.001) were enriched in ccRCC samples with low-risk score [Supplementary Figure 5B]. These results suggest that lncRNAs in our established prognostic model may be associated with changes in tumor immune microenvironment.

Immune cell infiltration and immune-related pathway

To further explore the correlation between risk score and immune cells and their function, ssGSEA enrichment scores for different immune cell subgroups, related functions, or pathways were quantified. As a result, activated dendritic cells (aDCs), B-cells, CD8+-T-cells, macrophages, mast cells, plasmacytoid dendritic cells (pDCs), T-helper-cells, T follicular helper cells (Tfh), T helper type 1 cells (Th1-cells), T helper type 2 cells (Th2-cells), tumor-infiltrating lymphocytes (TIL), and T regulatory cells (Tregs) were significantly different between the high- and low-risk groups [Figure 5A]. Type-I-IFN-Reponse of Antigen-presenting cell co-inhibition (APC-co-inhibition), APC-co-stimulation, MHC-class-I, CCR, checkpoint, Cytolytic-activity, HLA, Inflammation promoting, Parainflammation, T-cell-co-inhibition, T-cell-CP-stimulation and Type 1 immune function score were higher in the high-risk group than low-risk group [Figure 5B]. Then the correlation between immune cells and the model was analyzed [Figure 5C-5H]. These findings suggest that immune function is more active in high-risk groups. We subsequently utilized CIBERSORT for immune infiltration analysis, filtering with Perl programming language to obtain an immune cell infiltration matrix. Among them, the expression of T-cells CD8, T cells Follicular Helper, Tregs, monocyte, Macrophages M2, and Dendritic cell Resting and Mast cell activated possessed significant differences (P<0.001) among high- and low-risk groups. Among them, Monocyte, Macrophages M2, and Dendritic cell resting were more sharply expressed in the low-risk group, indicating that the occurrence and development of ccRCC may be related to the inhibition of these immune cells [Figure 6A]. Correlation analysis was then conducted on the types of immune infiltration and the model, and the results showing a strong correlation are shown in Figure 6B-6Q.

Figure 5.
Figure 5. Immune cell infiltration score (CIBERSORT algorithm) and immune-related function in high- and low-risk groups. (A) Analysis of 16 immune cells between high-and low-risk score; (B) Analysis of 13 immune functions between high-and low-risk score; (C-H) Analysis of the correlations between this model and immune cells, such as B cell, CD4 T cell, CD8 T cell, Macrophage, Neutrophil, Dendritic cells. ns, non sense; **P<0.01; ***P<0.001.

Figure 6.
Figure 6. Analysis of immune microenvironment between high- and low-risk groups. (A) Violent plot of immune-infiltration lymphocyte in high- and low-risk groups, where red refers to the high-risk sample and blue refers to the low-risk sample; (B-Q) Analysis of the correlation between immune-infiltration lymphocyte and model.

The correlation between prognostic characteristics and ccRCC treatment

The expression level of the immune checkpoint and/or its ligand may constitute prognostic biomarkers of immune-checkpoint blockade therapy. We further studied the relationship between prognostic cuproptosis-related lncRNA signatures and the expression level of the 38 immune-checkpoint suppressor genes between the two risk groups. The expression levels of 3 immune-checkpoint genes, including VTCN1, NRP1, and TNFSF15 were higher in low-risk patients. Other 35 immune-checkpoint genes (CD200R1, BTLA, CD80, LAG3, TNFRSF8, ICOS, CD70, TMIGD2, CD27, TIGIT, IDO2, CD160, TNFSF4, BTNL2, IDO1, LGALS9, TNFRSF18, TNFRSF4, CTLA4, TNFRSF14, CD86, CD40LG, TNFRSF25, LAIR1, PDCD1, CD48, TNFSF9, PDCD1LG2, CD40, TNFRSF9, CD244, CD276, CD44, CD28, and TNFSF14) were highly expressed in high-risk patients. These results suggest that cuproptosis-related lncRNA signatures can be regarded as excellent candidate biomarkers for ICI treatment [Supplementary Figure 6A]. In addition, we assessed the correlation between risk scores and the efficacy of targeted therapies and chemotherapy for ccRCC. Patients with low-risk scores were highly sensitive to chemotherapy drugs such as Mitomycine.C (P=3.9e-14, Supplementary Figure 6B), patients with low-risk scores were highly sensitive to targeted therapies such as Rapamycin (P=9.1e-14), Sunitinib (P=5.1e-13) and Temsirolimus (P=6.8e-12) [Supplementary Figure 6C-6E], indicating that cuproptosis-related lncRNA signatures were potential predictors of drug sensitivity. These results may also explain why high-risk score patients with high prognosis may possess more resistance to chemotherapy and targeted drug therapy.


ccRCC is a kind of highly aggressive renal malignant tumor, notorious for its immune dysfunction characteristics.[33] Existing conventional treatments, such as surgical excision, chemotherapy, and radiation therapy, appeared to be ineffective against aggressive tumors. Compared to a single clinical biomarker, integrating multiple biomarkers into one model can improve prognostic accuracy and avail programming personalized treatment.

In our study, ten cuproptosis-related genes were identified through literature. The expression files and clinical files of RNA were downloaded from the TCGA database. First and foremost, co-expression analysis was conducted to identify cuproptosis-related lncRNAs, and 266 prognostic lncRNAs were identified via univariate regression. Next, we constructed risk models of five cuproptosis-related lncRNAs. Then, patients were divided into low-risk and high-risk groups based on the median risk score. The prognostic accuracy of the risk score was validated by ROC and C index curves. We found that the risk score can be used as a predictor for prognosis. Subsequently, LASSO regression was used to establish a prognostic model, and these 5 prognosis-related lncRNAs were used to establish a prognostic model. ROC curves and internal validation depicted that the prognostic cuproptosis-related lncRNA signatures were highly accurate and reliable. Clinicopathological analysis, survival analysis, and PCA analysis showed that the model provided high sensitivity for survival prognosis. Compared with AUC values of other prognostic models,[34] our lncRNA risk model is more reliable. In addition, our newly developed nomograms possess great potential to improve clinical decisions and guide the establishment of treatment strategies.

Among the identified lncRNAs, 4 were associated with tumor progression, namely CD27−AS1, LINC00460, AC026401.3, and LINC00944, among which lncRNA CD27-AS1 promoted the progression of acute myeloid leukemia through mir-224-5p/PBX3 signaling pathway.[34] Up-regulation of LINC00460 promoted proliferation, migration, and invasion of ccRCC cells and lncRNA LINC00944 plays an oncogenic role in RCC.[35] Knocking down the expression of lncRNA LINC00944 can inhibit cell proliferation and migration,[36] AC026401.3 is the constituent part of the glycolysis-related lncRNA risk model,[37] while the function of the remaining AC121338.2 has not been reported. At present, no studies concerning the role of the 5 lncRNA model in ccRCC or cuproptosis have been reported. Therefore, more studies need to be conducted to explore the role of these lncRNAs in ccRCC and cuproptosis.

The most important aspect of our study is to demonstrate the relationship between cuproptosis-related lncRNA signatures and tumor microenvironment. It is remarkable that not only do complex interactions between tumor cells and tumor microenvironment play a key role in tumor development, but also have a significant impact on immunotherapy efficacy and overall survival.[38],[39] In this study, function enrichment analysis showed that cuproptosis-related lncRNAs were mainly involved in immune pathways. Significant differences existed in an array of immune cells and immune functions between patients in high- and low-risk groups, confirming the role of cuproptosis-related lncRNAs in regulating tumor-infiltrating immune cells. Since our results associated prognostic cuproptosis-related lncRNA signatures with immune infiltration in ccRCC, these cuproptosis-related lncRNAs may be targeted for combination therapy with immune-checkpoint inhibitors.

The immunotherapy (immune-checkpoint blockade combined with inhibitors) is a promising method for the treatment of an array of malignant tumors, and the activated tumor immune microenvironment is relevant to favorable outcomes of immune-checkpoint inhibitors.[40],[41] Interestingly, CD70 was overexpressed in the high-risk group, suggesting that patients with high expression may benefit a lot from immunotherapy against CD70, simultaneously the ligand of CD70 is CD27. Coincidentally, CD27 was also highly expressed in the high-risk group. Therefore, we conclude that activating the co-stimulation of the CD27-CD70 axis may be a potentially effective treatment for ccRCC patients, and activation of CD27-CD70 can serve as a new therapeutic target in the future, and the same viewpoint has been reported in the relevant literature.[42] Apart from that, the molecules CTLA-4, BTLA, and CD28 that are overexpressed in high-risk groups are crucial co-signaling proteins regulating lymphocyte activation.[43],[44],[45] Among them, CD28 is co-stimulatory molecule expressed in CD3+ thymocytes, of CD4+ T cells and CD8+ T cells. After binding with its ligand CD80 or CD86 (both overexpressed in the high-risk group) presented on antigen presenting cells (APCs), the co-stimulatory signal provided by this receptor is of great importance for sustaining cell proliferation, allowing full T cell activation, preventing anergy and/or apoptosis and inducing differentiation to effector and memory status.[46] Previous research revealed that polymorphisms in the CTLA-4, CD28, and BTLA genes increase the likelihood of developing RCC.[44] Other research also found that the presence of the BTLA genotype was related to an increased risk of RCC and high-grade tumors in ccRCC patients.[43] Moreover, LAIR1 was noticeably upregulated in clinical specimens of human RCC tumor tissues compared to that in adjacent non‑cancerous renal tissues. LAIR1 overexpression resulted in soared cell proliferation and tumor growth in RCC cells. RCC patients with high LAIR1 mRNA expression showed poor survival outcomes compared to those with low LAIR1 expression.[47] It is worth noting that, overexpressed LAG3 in the high-risk group was proved to be associated with poor OS in ccRCC.[48] In an oncological context, LAG3 inhibition activates effector T cells and additionally inhibits suppressive Tregs. This collective effect has the potential to enhance the antitumor response of the tumor-infiltrating immune cells.[49],[50] Thus, LAG3 is considered to be a potential target in immunotherapy. In addition, blocking the TIGIT pathway dramatically inhibits the proliferation, migration, and invasion of RCC cells and promotes their apoptosis. TIGIT inhibitor mainly regulates the expression of differently expressed genes to achieve the reconstruction of immune killing and restore the killing effect on the RCC. TIGIT may become a potential target for the immunotherapy of RCC.[51] What’s more, in consideration of the overexpression of CD40 in the high-risk group, another study found that the agonistic anti-CD40 antibody may improve the antitumor response of DC-CIK cells, particularly in RCC, thus we cannot overlook the role of CD40 in the treatment of RCC.[52]

What’s neglected by all but worthy of being mentioned here is that the finding that expression of the immune checkpoint genes: CD44, TNFRSF18, TNFSF14, TNFRSF8, CD276, TNFRSF25, and PDCD1LG2 was upregulated in high-risk ccRCC was supported by other studies,[53],[54] which indicate that the immunotherapy [immune-checkpoint blocking combined with inhibitors (ICI)] is potential to be a very effective method for ccRCC. It also offers new insights into the tumor treatment.

Several shortcomings should be confirmed in our study. First of all, we aspire to meticulously investigate the expression patterns of these cuproptosis-related lncRNAs in clinical ccRCC tissues. However, due to certain limitations on the availability of clinical samples, it is regretful that these insights will not be included in our current study, and the accuracy of real clinical data sets remains to be perfected in our subsequent research.


In summary, the potential relationship between cuproptosis and ccRCC was preliminary explored, and a novel prognostic model related to cuproptosis for predicting ccRCC prognosis was established. In addition, based on this risk model, significant differences in immune status and responses to different therapies such as immunotherapy, chemotherapy, and targeted therapy between the high-risk and low-risk groups were observed. Interestingly, we found that CD27, CD70, CTLA-4, BTLA, CD28, LAIR1, LAG3, TIGIT, CD40, CD44, TNFRSF18, TNFSF14, TNFRSF8, CD276, TNFRSF25, and PDCD1LG2 were highly expressed in the high-risk group by analyzing the differences in expression of immune-checkpoint genes between the high- and low-risk groups. Among them, LAG3, TIGIT, and CD40 may become potential targets for immunotherapy, and activation of CD27-CD70 can serve as a new therapeutic target in the future. It also indicates that this risk model may be involved in the immune microenvironment of ccRCC and provide novel insights into the pathogenesis of ccRCC. However, as research on cuproptosis is limited and the exact mechanism by which the genes in the prognostic model are involved in ccRCC is still unclear, our future work will focus on these scientific questions. Whether the detection of this risk model is effective in diagnosing the early stage of ccRCC will be further studied in our subsequent research.



RCC, renal cell carcinoma; ccRCC, clear-cell renal cell carcinoma; VHL, Von Hippel-Lindau; HIF, hypoxia-inducible factor; SOD1, superoxide dismutase 1; Bax, b-cell lymphoma-2-related X protein; TCA, tricarboxylic acid; lncRNA, Long non-coding RNA; IGF1, insulin-like growth factor-1; TCGA, The Cancer Genome Atlas; GO, Gene Ontology; MF, molecular functions; BP, biological pathways; CC, cellular components; GSEA, Gene Set Enrichment Analysis; ROC, Receiver Operating Characteristic; AUC, area under the curve; PCA, Principal Component Analysis; OS, overall survival; GDC, Genomic Data Commons; ICI, immune-checkpoint blocking combined with inhibitors; DEG, differentially expressed gene.



This work was supported by the Undergraduate Scientific Research and Innovation Experimental Project at Chongqing Medical University to Shukai Zhang (No. 202209).



The authors declare that they have no conflicts of interest.



Shukai Zhang and Jiyao Yu conceived and designed this research. Qingwen Ran collected and downloaded the relavent data. Shukai Zhang analyzed the data and processed them. Jiyao Yu wrote the paper. Xuemei Li revised this manuscript. All authors read and approved the manuscript.



Not applicable.



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Supplementary Figure 1.
Supplementary Figure 1. Survival curve of 5 prognostic cuproptosis-related lncRNA. (A) survival curve of lncRNA AC026401.3; (B) survival curve of lncRNA AC121338.2; (C) survival curve of lncRNA CD27-AS1; (D) survival curve of lncRNA LINC00460; (E) survival curve of lncRNA LINC00944.

Supplementary Figure 2.
Supplementary Figure 2. KM curve between the high-risk group and low-risk group of Patients in TCGA-KIRC based on different clinical parameters. (A, B) Age; (C, D) Gender; (E-G) Grade; (H-K) Stage; (L-N) T stag; (O, P) M stage; (Q, R) N stage.

Supplementary Figure 3.
Supplementary Figure 3. Correlation between risk score and clinicopathological factors. (A-F) stratified lncRNA AC026401.3 expression according to survival status, Grade, M, N, stage and T stage; (G-L) stratified lncRNA AC121338.2 expression according to survival status, grade, M, N, stage and T stage; (M-Q) stratified lncRNA LINC00460 expression according to survival status, grade, M, stage and T stage; (R-U) stratified lncRNA LINC00944 expression according to survival state, M, stage and T stage; (V) cuproptosis-related lncRNA stratified by survival status (P=0.005); (W) cuproptosis-related lncRNA stratified by grade (P<0.001); (X) cuproptosis-related lncRNA stratified by stage (P<0.001); (Y) cuproptosis-related lncRNA stratified by T stage (P<0.001); (Z) cuproptosis-related lncRNA stratified by M stage (P=0.027).

Supplementary Figure 4.
Supplementary Figure 4. Heatmap of correlation between risk score and clinicopathological features.

Supplementary Figure 5.
Supplementary Figure 5. GSEA enrichment of prognostic cuproptosis-related lncRNA. (A) GSEA-KEGG enrichment analyses; (B) GSEA-GO enrichment analyses.

Supplementary Figure 6.
Supplementary Figure 6. Immune checkpoint gene and drug sensitivity analysis. (A) Differential expression of immune-checkpoints in high-and low-risk groups; (B) Sensitivity analyses of Mitomycine.C in low-and high-risk groups; (C) Sensitivity analyses of Rapamycin in low- and high-risk groups; (D) Sensitivity analysis of Temsirolimus in low-and high-risk groups; (E) Sensitivity analysis of Sunitinib in low-and high-risk patients.*P<0.05; **P<0.01; ***P<0.001.

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MicroRNAs are Related to Rituximab in Combination with Cyclophosphamide, Doxorubicin, Vincristine, and Prednisone Resistance in Patients with Diffuse Large B-cell Lymphoma

Haibo Huang1, Junjiao Gu1, Shuna Yao1, Zhihua Yao1, Yan Zhao1, Qingxin Xia2, Jie Ma2, Ling Mai3, Shujun Yang1, Yanyan Liu1

Comparison of Intra-voxel Incoherent Motion Diffusion Magnetic Resonance Imaging and Apparent Diffusion Coefficient in the Evaluation of Focal Malignant Liver Masses

Jinrong Qu1, Xiang Li1, Lei Qin2, Lifeng Wang1, Junpeng Luo1, Jianwei Zhang1, Hongkai Zhang1, Jing Li1, Fei Sun3, Shouning Zhang1, Yanle Li1, Cuicui Liu1, Hailiang Li1

Extracting Breathing Signal Using Fourier Transform from Cine Magnetic Resonance Imaging

Jing Cai1,2, Yilin Liu2, Fangfang Yin1,2

Split End Family RNA Binding Proteins: Novel Tumor Suppressors Coupling Transcriptional Regulation with RNA Processing

Hairui Su1, Yanyan Liu2, Xinyang Zhao1

Thioredoxin-interacting Protein as a Common Regulation Target for Multiple Drugs in Clinical Therapy/Application

Pengxing  Zhang1, Xiaoling Pang2,3, Yanyang Tu1,4

Associations of Age and Chemotherapy with Late Skin and Subcutaneous Tissue Toxicity in a Hypofractionated Adjuvant Radiation Therapy Schedule in Post‑mastectomy Breast Cancer Patients

Mohammad Akram1, Ghufran Nahid1, Shahid Ali Siddiqui1, Ruquiya Afrose2

Monitoring of Disease Activity in Chronic Myeloid Leukemia‑chronic Phase Patients Treated with Indian Generic Veenat (NATCO) Imatinib Mesylate: A Tertiary Care Experience

Khushboo Dewan, Tathagat Chatterjee

Review of Cancer Immunotherapy: Application of Chimeric Antigen Receptor T Cells and Programmed Death 1/Programmed Death‑ligand 1 Antibodies

Tengfei Zhang1,2, Ling Cao1, Zhen Zhang1, Dongli Yue1, Yu Ping1, Hong Li1, Lan Huang1, Yi Zhang1,3,4,5

Systematic Review of MicroRNAs and its Therapeutic Potential in Glioma

Nan Liu1, Yanyang Tu2

The Involvement of p53‑miR‑34a‑CDK4 Signaling During the Development of Cervical Cancer

Huijun Zuo, Jieqi Xiong, Hongwei Chen, Sisun Liu, Qiaoying Gong, Fei Guo

Unusual Clinical Presentation of a Rare Type of Breast Malignancy: A Case Report and a Short Review of Literature

Nadeesha J. Nawarathna1, Navam R. Kumarasinghe1, Palitha Rathnayake2,
Ranjith J. K. Seneviratne1

Sweet’s Syndrome in Acute Lymphoblastic Leukemia with t (9:22)

Khushboo Dewan, Shailaja Shukla

Analysis of the Correlationship between Prostate Specific Antigen Related Variables and Risk Factor in Patients with Prostate Carcinoma

Daoyuan Wang1, Tiejun Yang2, Yongqiang Zou1, Xinqiang Yang1

Recent Progress in Genetic and Epigenetic Profile of Diffuse Gastric Cancer

Zhengxi He1, Bin Li1,2

Strategies for Management of Spinal Metastases: A Comprehensive Review

Zhantao Deng, Bin Xu, Jiewen Jin, Jianning Zhao, Haidong Xu

Application and Perspectives of Traditional Chinese Medicine in the Treatment of Liver Cancer

Xia Mao, Yanqiong Zhang, Na Lin

Primary Hepatic Carcinoid Tumor: A Case Report and Literature Review

Yupeng Lei1, Hongxia Chen2, Pi Liu1, Xiaodong Zhou1

Expression Characteristics of miR‑10b in Nasopharyngeal Carcinoma

Gang Li, Yunteng Zhao, Jianqi Wang, Haoran Huang, Mengwen Zhang

An Update on Immunohistochemistry in Translational Cancer Research

Zonggao Shi, M. Sharon Stack

Promoter Methylated Tumor Suppressor Genes in Glioma

Yingduan Cheng1, Yanyang Tu2, Pei Liang3

Palliative Treatment of Malignant Pleural Effusion

Chenyang Liu1*, Qian Qian2*, Shen Geng1, Wenkui Sun1, Yi Shi1

Functional Perspective and Implications of Gene Expression by Noncoding RNAs

Xiaoshuang Yan1, Huanyu Xu2, Zhonghai Yan3

Expression of E3 Ubiquitin Ligases in Multiple Myeloma Patients after Treatment with the Proteasome Inhibitor Bortezomib

James Joseph Driscoll

miR‑505 Downregulates 6‑Phosphofructo‑2‑Kinase/ Fructose‑2,6‑Biphosphatase 4 to Promote Cell Death in Glioblastoma

Esther H. Chung, Hongwei Yang, Hongyan Xing, Rona S. Carroll, Mark D. Johnson

Utility of Fine Needle Aspiration Cytology in Diagnosing Bone Tumors

Sonal Mahajan1, Akash Arvind Saoji2, Anil Agrawal1

Histone H2A and H2B Deubiquitinase in Developmental Disease and Cancer

Demeng Chen1, Caifeng Dai2, Yizhou Jiang3

Genetic Characteristics of Glioblastoma: Clinical Implications of Heterogeneity

Qian Li1, Yanyang Tu1,2

Acute Lymphoblastic Leukemia with Normal Platelet Count

Khushboo Dewan, Kiran Agarwal

Galanin is a Novel Epigenetic Silenced Functional Tumor Suppressor in Renal Cell Carcinoma

Shengkun Sun1*, Axiang Xu1*, Guoqiang Yang1, Yingduan Cheng2


Selenium Dioxide Induced Apoptosis in Cervical Cancer Cells via Regulating Apoptosis-related Let-7a MicroRNA and Proteins

Sisun Liu1, Jieqi Xiong2, Ling Guo3, Min Xiu1,4, Feng He1,4, Yuanlei Lou5, Fei Guo6,7

Low Expression of Polo‑like Kinase 1 is Associated with Poor Prognosis in Liver Cancer

Weixia Li1, Kunpeng Liu1, Dechen Lin2, Xin Xu2, Haizhen Lu3, Xinyu Bi4, Mingrong Wang2

Extracorporeal Photopheresis for Steroid‑refractory Chronic Graft‑versus‑host Disease After Allogeneic Hematopoietic Stem Cell Transplantation: A Systematic Review and Meta‑Analysis

Runzhe Chen1, Baoan Chen1, Peter Dreger2, Michael Schmitt2, Anita Schmitt2

Glucans and Cancer: Historical Perspective

Petr Sima1, Luca Vannucci1, Vaclav Vetvicka2

Implications of Circadian Rhythm Regulation by microRNAs in Colorectal Cancer

Song Wu1, Andrew Fesler2, Jingfang Ju2

BCL2 Family, Mitochondrial Apoptosis, and Beyond

Haiming Dai1, X. Wei Meng2, Scott H. Kaufmann2

Quantum Dot‑based Immunohistochemistry for Pathological Applications

Li Zhou1, Jingzhe Yan2, Lingxia Tong3, Xuezhe Han4, Xuefeng Wu5, Peng Guo6

CD24 as a Molecular Marker in Ovarian Cancer: A Literature Review

Lu Huang1, Weiguo Lv2, Xiaofeng Zhao1

Etiological Trends in Oral Squamous Cell Carcinoma: A Retrospective Institutional Study

Varsha Salian, Chethana Dinakar, Pushparaja Shetty, Vidya Ajila

Effect of Irinotecan Combined with Cetuximab on Liver Function in Patients with Advanced Colorectal Cancer with Liver Metastases

Yan Liang1, Yang Li2, Xin Li3, Jianfu Zhao4

The Role of Precision Medicine in Pancreatic Cancer: Challenges for Targeted Therapy, Immune Modulating Treatment, Early Detection, and Less Invasive Operations

Khaled Kyle Wong1, Zhirong Qian2, Yi Le3

Targeting Signal Transducer and Activator of Transcription 3 for Colorectal Cancer Prevention and Treatment with Natural Products

Weidong Li1,2*, Cihui Chen3*, Zheng Liu2, Baojin Hua1

The Potential of Wnt Signaling Pathway in Cancer: A Focus on Breast Cancer

Mahnaz M. Kazi, Trupti I. Trivedi, Toral P. Kobawala, Nandita R. Ghosh

Imaging‑driven Digital Biomarkers

Enrico Capobianco

Target‑Matching Accuracy in Stereotactic Body Radiation Therapy of Lung Cancer: An Investigation Based on Four‑Dimensional Digital Human Phantom

Jing Cai1,2, Kate Turner2, Xiao Liang2, W. Paul Segars2,3, Chris R. Kelsey1, David Yoo1, Lei Ren1,2, Fang‑Fang Yin1,2

Downregulation of Death‑associated Protein Kinase 3 and Caspase‑3 Correlate to the Progression and Poor Prognosis of Gliomas

Ye Song, Tianshi Que, Hao Long, Xi’an Zhang, Luxiong Fang, Zhiyong Li, Songtao Qi

Hyaluronic Acid in Normal and Neoplastic Colorectal Tissue: Electrospray Ionization Mass Spectrometric and Fluor Metric Analysis

Ana Paula Cleto Marolla1, Jaques Waisberg2, Gabriela Tognini Saba2, Demétrius Eduardo Germini2, Maria Aparecida da Silva Pinhal1

Melanoma Antigen Gene Family in the Cancer Immunotherapy

Fengyu Zhu1, Yu Liang1, Demeng Chen2, Yang Li1

Combined Chronic Lymphocytic Leukemia and Pancreatic Neuroendocrine Carcinoma: A Collision Tumor Variation

Kaijun Huang1, Panagiotis J. Vlachostergios1, Wanhua Yang2, Rajeev L. Balmiki3

Antiproliferative and Apoptotic Effect of Pleurotus ostreatus on Human Mammary Carcinoma Cell Line (Michigan Cancer Foundation‑7)

Krishnamoorthy Deepalakshmi, Sankaran Mirunalini

Impact of Age on the Biochemical Failure and Androgen Suppression after Radical Prostatectomy for Prostate Cancer in Chilean Men

Nigel P. Murray1,2, Eduardo Reyes1,3, Nelson Orellana1, Cynthia Fuentealba1, Omar Jacob1

Carcinoma of Unknown Primary: 35 Years of a Single Institution’s Experience

Rana I. Mahmood1,2, Mohammed Aldehaim1,3, Fazal Hussain4, Tusneem A. Elhassan4,
Zubeir A. Khan5, Muhammad A. Memon6

Metformin in Ovarian Cancer Therapy: A Discussion

Yeling Ouyang1, Xi Chen2, Chunyun Zhang1, Vichitra Bunyamanop1, Jianfeng Guo3

The Progress in Molecular Biomarkers of Gliomas

Jing Qi1, Hongwei Yang2, Xin Wang2, Yanyang Tu1

Correlation between Paclitaxel Tc > 0.05 and its Therapeutic Efficacy and Severe Toxicities in Ovarian Cancer Patients

Shuyao Zhang1*, Muyin Sun2*, Yun Yuan3*, Miaojun Wang4*, Yuqi She1*, Li Zhou5, Congzhu Li5, Chen Chen1, Shengqi Zhang4

Identifying Gaps and Relative Opportunities for Discovering Membrane Proteomic Biomarkers of Triple‑negative Breast Cancer as a Translational Priority

Bhooma Venkatraman

The Molecular Mechanism and Regulatory Pathways of Cancer Stem Cells

Zhen Wang1, Hongwei Yang2, Xin Wang2, Liang Wang3, Yingduan Cheng4, Yongsheng Zhang5, Yanyang Tu1,2

Nanoparticle Drug Delivery Systems and Three‑dimensional Cell Cultures in Cancer Treatments and Research

Wenjin Shi1, Ding Weng2,3, Wanting Niu2,3

Choline Kinase Inhibitors Synergize with TRAIL in the Treatment of Colorectal Tumors and Overcomes TRAIL Resistance

Juan Carlos Lacal1, Ladislav Andera2

MicroRNA Regulating Metabolic Reprogramming in Tumor Cells: New Tumor Markers

Daniel Otero‑Albiol, Blanca Felipe‑Abrio

Biomarkers of Colorectal Cancer: A Genome‑wide Perspective

José M. Santos‑Pereira1, Sandra Muñoz‑Galván2

Nicotinamide Adenine Dinucleotide+ Metabolism Biomarkers in Malignant Gliomas

Manuel P. Jiménez‑García, Eva M. Verdugo‑Sivianes, Antonio Lucena‑Cacace

Patient-derived Xenografts as Models for Personalized Medicine Research in Cancer

Marco Perez, Lola Navas, Amancio Carnero

Genome‑wide Transcriptome Analysis of Prostate Cancer Tissue Identified Overexpression of Specific Members of the Human Endogenous Retrovirus‑K Family

Behnam Sayanjali1,2

Clinical Utility of Interleukin‑18 in Breast Cancer Patients: A Pilot Study

Reecha A. Parikh, Toral P. Kobawala, Trupti I. Trivedi, Mahnaz M. Kazi, Nandita R. Ghosh

Current and Future Systemic Treatment Options for Advanced Soft‑tissue Sarcoma beyond Anthracyclines and Ifosfamide

Nadia Hindi1,2, Javier Martin‑Broto1,2

The Genomic Organization and Function of IRX1 in Tumorigenesis and Development

Pengxing Zhang1, Hongwei Yang2, Xin Wang2, Liang Wang3, Yingduan Cheng4, Yongsheng Zhang5, Yanyang Tu1,2

Stem Cell‑based Approach in Diabetes and Pancreatic Cancer Management

Yi‑Zhou Jiang1, Demeng Chen2

Mutation Detection with a Liquid Biopsy 96 Mutation Assay in Cancer Patients and Healthy Donors

Aaron Yun Chen, Glenn D. Braunstein, Megan S. Anselmo, Jair A. Jaboni, Fernando Troy Viloria, Julie A. Neidich, Xiang Li, Anja Kammesheidt

The Application of Estrogen Receptor‑1 Mutations’ Detection through Circulating Tumor DNA in Breast Cancer

Binliang Liu, Yalan Yang, Zongbi Yi, Xiuwen Guan, Fei Ma

Circulating MicroRNAs and Long Noncoding RNAs: Liquid Biomarkers in Thoracic Cancers

Pablo Reclusa1, Anna Valentino1, Rafael Sirera1,2, Martin Frederik Dietrich3, Luis Estuardo Raez3, Christian Rolfo1

Exosomes Biology: Function and Clinical Implications in Lung Cancer

Martin Frederik Dietrich1, Christian Rolfo2, Pablo Reclusa2, Marco Giallombardo2, Anna Valentino2, Luis E. Raez1

Circulating Tumor DNA: A Potential Biomarker from Solid Tumors’ Monitor to Anticancer Therapies

Ting Chen1,2, Rongzhang He1,3, Xinglin Hu1,3,4, Weihao Luo1, Zheng Hu1,3, Jia Li1, Lili Duan1, Yali Xie1,2, Wenna Luo1,2, Tan Tan1,2, Di‑Xian Luo1,2

Novel Molecular Multilevel Targeted Antitumor Agents

Poonam Sonawane1, Young A. Choi1, Hetal Pandya2, Denise M. Herpai1, Izabela Fokt3,
Waldemar Priebe3, Waldemar Debinski1

Fish Oil and Prostate Cancer: Effects and Clinical Relevance

Pei Liang, Michael Gao Jr.

Stemness‑related Markers in Cancer

Wenxiu Zhao1, Yvonne Li2, Xun Zhang1

Autophagy Regulated by miRNAs in Colorectal Cancer Progression and Resistance

Andrew Fesler1, Hua Liu1, Ning Wu1,2, Fei Liu3, Peixue Ling3, Jingfang Ju1,3

Gastric Metastases Mimicking Primary Gastric Cancer: A Brief Literature Review

Simona Gurzu1,2,3, Marius Alexandru Beleaua1, Laura Banias2, Ioan Jung1

Possibility of Specific Expression of the Protein Toxins at the Tumor Site with Tumor‑specialized Promoter

Liyuan Zhou1,2, Yujun Li1,2, Changchen Hu3, Binquan Wang1,2

SKI‑178: A Multitargeted Inhibitor of Sphingosine Kinase and Microtubule Dynamics Demonstrating Therapeutic Efficacy in Acute Myeloid Leukemia Models

Jeremy A. Hengst1,2, Taryn E. Dick1,2, Arati Sharma1, Kenichiro Doi3, Shailaja Hegde4, Su‑Fern Tan5, Laura M. Geffert1,2, Todd E. Fox5, Arun K. Sharma1, Dhimant Desai1, Shantu Amin1, Mark Kester5, Thomas P. Loughran5, Robert F. Paulson4, David F. Claxton6, Hong‑Gang Wang3, Jong K. Yun1,2

A T‑cell Engager‑armed Oncolytic Vaccinia Virus to Target the Tumor Stroma

Feng Yu1, Bangxing Hong1, Xiao‑Tong Song1,2,3

Real‑world Experience with Abiraterone in Metastatic Castration‑resistant Prostate Cancer

Yasar Ahmed1, Nemer Osman1, Rizwan Sheikh2, Sarah Picardo1, Geoffrey Watson1

Combination of Interleukin‑11Rα Chimeric Antigen Receptor T‑cells and Programmed Death‑1 Blockade as an Approach to Targeting Osteosarcoma Cells In vitro

Hatel Rana Moonat, Gangxiong Huang, Pooja Dhupkar, Keri Schadler, Nancy Gordon,
Eugenie Kleinerman

Efficacy and Safety of Paclitaxel‑based Therapy and Nonpaclitaxel‑based Therapy in Advanced Gastric Cancer

Tongwei Wu, Xiao Yang, Min An, Wenqin Luo, Danxian Cai, Xiaolong Qi

Motion Estimation of the Liver Based on Deformable Image Registration: A Comparison Between Four‑Dimensional‑Computed Tomography and Four‑Dimensional-Magnetic Resonance Imaging

Xiao Liang1, Fang‑Fang Yin1,2, Yilin Liu1, Brian Czito2, Manisha Palta2, Mustafa Bashir3, Jing Cai1,2

A Feasibility Study of Applying Thermal Imaging to Assist Quality Assurance of High‑Dose Rate Brachytherapy

Xiaofeng Zhu1, Yu Lei1, Dandan Zheng1, Sicong Li1, Vivek Verma1, Mutian Zhang1, Qinghui Zhang1, Xiaoli Tang2, Jun Lian2, Sha X. Chang2, Haijun Song3, Sumin Zhou1, Charles A. Enke1

Role of Exosome microRNA in Breast Cancer

Wang Qu, Ma Fei, Binghe Xu

Recent Progress in Technological Improvement and Biomedical Applications of the Clustered Regularly Interspaced Short Palindromic Repeats/Cas System

Yanlan Li1,2*, Zheng Hu1*, Yufang Yin3, Rongzhang He1, Jian Hu1, Weihao Luo1, Jia Li1, Gebo Wen2, Li Xiao1, Kai Li1, Duanfang Liao4, Di-Xian Luo1,5

The Significance of Nuclear Factor‑Kappa B Signaling Pathway in Glioma: A Review

Xiaoshan Xu1, Hongwei Yang2, Xin Wang2, Yanyang Tu1

Markerless Four‑Dimensional‑Cone Beam Computed Tomography Projection‑Phase Sorting Using Prior Knowledge and Patient Motion Modeling: A Feasibility Study

Lei Zhang1,2, Yawei Zhang2, You Zhang1,2,3, Wendy B. Harris1,2, Fang‑Fang Yin1,2,4, Jing Cai1,4,5, Lei Ren1,2

The Producing Capabilities of Interferon‑g and Interleukin‑10 of Spleen Cells in Primary and Metastasized Oral Squamous Cell Carcinoma Cells-implanted Mice

Yasuka Azuma1,2, Masako Mizuno‑Kamiya3, Eiji Takayama1, Harumi Kawaki1, Toshihiro Inagaki4, Eiichi Chihara2, Yasunori Muramatsu5, Nobuo Kondoh1

“Eating” Cancer Cells by Blocking CD47 Signaling: Cancer Therapy by Targeting the Innate Immune Checkpoint

Yi‑Rong Xiang, Li Liu

Glycosylation is Involved in Malignant Properties of Cancer Cells

Kazunori Hamamura1, Koichi Furukawa2

Biomarkers in Molecular Epidemiology Study of Oral Squamous Cell Carcinoma in the Era of Precision Medicine

Qing‑Hao Zhu1*, Qing‑Chao Shang1*, Zhi‑Hao Hu1*, Yuan Liu2, Bo Li1, Bo Wang1, An‑Hui Wang1

I‑Kappa‑B Kinase‑epsilon Activates Nuclear Factor‑kappa B and STAT5B and Supports Glioblastoma Growth but Amlexanox Shows Little Therapeutic Potential in These Tumors

Nadège Dubois1, Sharon Berendsen2, Aurélie Henry1,2, Minh Nguyen1, Vincent Bours1,
Pierre Alain Robe1,2

Suppressive Effect of Mesenchymal Stromal Cells on Interferon‑g‑Producing Capability of Spleen Cells was Specifically Enhanced through Humoral Mediator(s) from Mouse Oral Squamous Cell Carcinoma Sq‑1979 Cells In Vitro

Toshihiro Inagaki1,2, Masako Mizuno‑Kamiya3, Eiji Takayama1, Harumi Kawaki1, Eiichi Chihara4, Yasunori Muramatsu5, Shinichiro Sumitomo5, Nobuo Kondoh1

An Interplay Between MicroRNA and SOX4 in the Regulation of Epithelial–Mesenchymal Transition and Cancer Progression

Anjali Geethadevi1, Ansul Sharma2, Manish Kumar Sharma3, Deepak Parashar1

MicroRNAs Differentially Expressed in Prostate Cancer of African‑American and European‑American Men

Ernest K. Amankwah

The Role of Reactive Oxygen Species in Screening Anticancer Agents

Xiaohui Xu1, Zilong Dang2, Taoli Sun3, Shengping Zhang1, Hongyan Zhang1

Panobinostat and Its Combination with 3‑Deazaneplanocin‑A Induce Apoptosis and Inhibit In vitro Tumorigenesis and Metastasis in GOS‑3 Glioblastoma Cell Lines

Javier de la Rosa*, Alejandro Urdiciain*, Juan Jesús Aznar‑Morales, Bárbara Meléndez1,
Juan A. Rey2, Miguel A. Idoate3, Javier S. Castresana

Cancer Stem‑Like Cells Have Cisplatin Resistance and miR‑93 Regulate p21 Expression in Breast Cancer

Akiko Sasaki1, Yuko Tsunoda2, Kanji Furuya3, Hideto Oyamada1, Mayumi Tsuji1, Yuko Udaka1, Masahiro Hosonuma1, Haruna Shirako1, Nana Ichimura1, Yuji Kiuchi1

The Contribution of Hexokinase 2 in Glioma

Hui Liu1, Hongwei Yang2, Xin Wang3, Yanyang Tu1

The Mechanism of BMI1 in Regulating Cancer Stemness Maintenance, Metastasis, Chemo‑ and Radiation Resistance

Xiaoshan Xu, Zhen Wang, Nan Liu, Pengxing Zhang, Hui Liu, Jing Qi, Yanyang Tu

A Multisource Adaptive Magnetic Resonance Image Fusion Technique for Versatile Contrast Magnetic Resonance Imaging

Lei Zhang1,2, Fang‑Fang Yin1,2,3, Brittany Moore1,2, Silu Han1,2, Jing Cai1,2,4

Senescence and Cancer

Sulin Zeng1,2, Wen H. Shen2, Li Liu1

The “Wild”‑type Gastrointestinal Stromal Tumors: Heterogeneity on Molecule Characteristics and Clinical Features

Yanhua Mou1, Quan Wang1, Bin Li1,2

Retreatment with Cabazitaxel in a Long‑Surviving Patient with Castration‑Resistant Prostate Cancer and Visceral Metastasis

Raquel Luque Caro, Carmen Sánchez Toro, Lucia Ochoa Vallejo

Therapy‑Induced Histopathological Changes in Breast Cancers: The Changing Role of Pathology in Breast Cancer Diagnosis and Treatment

Shazima Sheereen1, Flora D. Lobo1, Waseemoddin Patel2, Shamama Sheereen3,
Abhishek Singh Nayyar4, Mubeen Khan5

Glioma Research in the Era of Medical Big Data

Feiyifan Wang1, Christopher J. Pirozzi2, Xuejun Li1

Transarterial Embolization for Hepatocellular Adenomas: Case Report and Literature Review

Jian‑Hong Zhong1,2, Kang Chen1, Bhavesh K. Ahir3, Qi Huang4, Ye Wu4, Cheng‑Cheng Liao1, Rong‑Rong Jia1, Bang‑De Xiang1,2, Le‑Qun Li1,2

Nicotinamide Phosphoribosyltransferase: Biology, Role in Cancer, and Novel Drug Target

Antonio Lucena‑Cacace1,2,3, Amancio Carnero1,2

Enhanced Anticancer Effect by Combination of Proteoglucan and Vitamin K3 on Bladder Cancer Cells

Michael Zhang, Kelvin Zheng, Muhammad Choudhury, John Phillips, Sensuke Konno

Molecular Insights Turning Game for Management of Ependymoma: A Review of Literature

Ajay Sasidharan, Rahul Krishnatry

IDH Gene Mutation in Glioma

Leping Liu1, Xuejun Li1,2

Challenges and Advances in the Management of Pediatric Intracranial Germ Cell Tumors: A Case Report and Literature Review

Gerard Cathal Millen1, Karen A. Manias1,2, Andrew C. Peet1,2, Jenny K. Adamski1

Assessing the Feasibility of Using Deformable Registration for Onboard Multimodality‑Based Target Localization in Radiation Therapy

Ge Ren1,2,3, Yawei Zhang1,2, Lei Ren1,2

Research Advancement in the Tumor Biomarker of Hepatocellular Carcinoma

Qing Du1, Xiaoying Ji2, Guangjing Yin3, Dengxian Wei3, Pengcheng Lin1, Yongchang Lu1,
Yugui Li3, Qiaohong Yang4, Shizhu Liu5, Jinliang Ku5, Wenbin Guan6, Yuanzhi Lu7

Novel Insights into the Role of Bacterial Gut Microbiota in Hepatocellular Carcinoma

Lei Zhang1, Guoyu Qiu2, Xiaohui Xu2, Yufeng Zhou3, Ruiming Chang4

Central Odontogenic Fibroma with Unusual Presenting Symptoms

Aanchal Tandon, Bharadwaj Bordoloi, Safia Siddiqui, Rohit Jaiswal

The Prognostic Role of Lactate in Patients Who Achieved Return of Spontaneous Circulation after Cardiac Arrest: A Systematic Review and Meta‑analysis

Dongni Ren1, Xin Wang2, Yanyang Tu1,2

Inhibitory Effect of Hyaluronidase‑4 in a Rat Spinal Cord Hemisection Model

Xipeng Wang1,2, Mitsuteru Yokoyama2, Ping Liu3

Research and Development of Anticancer Agents under the Guidance of Biomarkers

Xiaohui Xu1, Guoyu Qiu1, Lupeng Ji2, Ruiping Ma3, Zilong Dang4, Ruling Jia1, Bo Zhao1

Idiopathic Hypereosinophilic Syndrome and Disseminated Intravascular Coagulation

Mansoor C. Abdulla

Phosphorylation of BRCA1‑Associated Protein 1 as an Important Mechanism in the Evasion of Tumorigenesis: A Perspective

Guru Prasad Sharma1, Anjali Geethadevi2, Jyotsna Mishra3, G. Anupa4, Kapilesh Jadhav5,
K. S. Vikramdeo6, Deepak Parashar2

Progress in Diagnosis and Treatment of Mixed Adenoneuroendocrine Carcinoma of Biliary‑Pancreatic System

Ge Zengzheng1, Huang-Sheng Ling2, Ming-Feng Li2, Xu Xiaoyan1, Yao Kai1, Xu Tongzhen3,
Ge Zengyu4, Li Zhou5

Surface-Enhanced Raman Spectroscopy to Study the Biological Activity of Anticancer Agent

Guoyu Qiu1, Xiaohui Xu1, Lupeng Ji2, Ruiping Ma3, Zilong Dang4, Huan Yang5

Alzheimer’s Disease Susceptibility Genes in Malignant Breast Tumors

Steven Lehrer1, Peter H. Rheinstein2

OSMCC: An Online Survival Analysis Tool for Merkel Cell Carcinoma

Umair Ali Khan Saddozai1, Qiang Wang1, Xiaoxiao Sun1, Yifang Dang1, JiaJia Lv1,2, Junfang Xin1, Wan Zhu3, Yongqiang Li1, Xinying Ji1, Xiangqian Guo1

Protective Activity of Selenium against 5‑Fluorouracil‑Induced Nephrotoxicity in Rats

Elias Adikwu, Nelson Clemente Ebinyo, Beauty Tokoni Amgbare

Advances on the Components of Fibrinolytic System in Malignant Tumors

Zengzheng Ge1, Xiaoyan Xu1, Zengyu Ge2, Shaopeng Zhou3, Xiulin Li1, Kai Yao1, Lan Deng4

A Patient with Persistent Foot Swelling after Ankle Sprain: B‑Cell Lymphoblastic Lymphoma Mimicking Soft‑tissue Sarcoma

Crystal R. Montgomery‑Goecker1, Andrew A. Martin2, Charles F. Timmons3, Dinesh Rakheja3, Veena Rajaram3, Hung S. Luu3

Coenzyme Q10 and Resveratrol Abrogate Paclitaxel‑Induced Hepatotoxicity in Rats

Elias Adikwu, Nelson Clemente Ebinyo, Loritta Wasini Harris

Progress in Clinical Follow‑up Study of Dendritic Cells Combined with Cytokine‑Induced Killer for Stomach Cancer

Ling Wang1,2, Run Wan1,2, Cong Chen1,2, Ruiliang Su1,2, Yumin Li1,2

Supraclavicular Lymphadenopathy as the Initial Manifestation in Carcinoma of Cervix

Priyanka Priyaarshini1, Tapan Kumar Sahoo2

ABO Typing Error Resolution and Transfusion Support in a Case of an Acute Leukemia Patient Showing Loss of Antigen Expression

Debasish Mishra1, Gopal Krushna Ray1, Smita Mahapatra2, Pankaj Parida2

Protein Disulfide Isomerase A3: A Potential Regulatory Factor of Colon Epithelial Cells

Yang Li1, Zhenfan Huang2, Haiping Jiang3

Clinicopathological Association of p16 and its Impact on Outcome of Chemoradiation in Head‑and‑Neck Squamous Cell Cancer Patients in North‑East India

Srigopal Mohanty1, Yumkhaibam Sobita Devi2, Nithin Raj Daniel3, Dulasi Raman Ponna4,
Ph. Madhubala Devi5, Laishram Jaichand Singh2

Potential Inhibitor for 2019‑Novel Coronaviruses in Drug Development

Xiaohui Xu1, Zilong Dang2, Lei Zhang3, Lingxue Zhuang4, Wutang Jing5, Lupeng Ji6, Guoyu Qiu1

Best‑Match Blood Transfusion in Pediatric Patients with Mixed Autoantibodies

Debasish Mishra1, Dibyajyoti Sahoo1, Smita Mahapatra2, Ashutosh Panigrahi3

Characteristics and Outcome of Patients with Pheochromocytoma

Nadeema Rafiq1, Tauseef Nabi2, Sajad Ahmad Dar3, Shahnawaz Rasool4

Comparison of Histopathological Grading and Staging of Breast Cancer with p53‑Positive and Transforming Growth Factor‑Beta Receptor 2‑Negative Immunohistochemical Marker Expression Cases

Palash Kumar Mandal1, Anindya Adhikari2, Subir Biswas3, Amita Giri4, Arnab Gupta5,
Arindam Bhattacharya6

Chemical Compositions and Antiproliferative Effect of Essential Oil of Asafoetida on MCF7 Human Breast Cancer Cell Line and Female Wistar Rats

Seyyed Majid Bagheri1,2, Davood Javidmehr3, Mohammad Ghaffari1, Ehsan Ghoderti‑Shatori4

Cyclooxygenase‑2 Contributes to Mutant Epidermal Growth Factor Receptor Lung Tumorigenesis by Promoting an Immunosuppressive Environment

Mun Kyoung Kim1, Aidin Iravani2, Matthew K. Topham2,3

Potential role of CircMET as A Novel Diagnostic Biomarker of Papillary Thyroid Cancer

Yan Liu1,2,3,4#, Chen Cui1,2,3,4#, Jidong Liu1,2,3,4, Peng Lin1,2,3,4,Kai Liang1,2,3,4, Peng Su5, Xinguo Hou1,2,3,4, Chuan Wang1,2,3,4, Jinbo Liu1,2,3,4, Bo Chen6, Hong Lai1,2,3,4, Yujing Sun1,2,3,4* and Li Chen 1,2,3,4*

Cuproptosis-related Genes in Glioblastoma as Potential Therapeutic Targets

Zhiyu Xia1,2, Haotian Tian1, Lei Shu1,2, Guozhang Tang3, Zhenyu Han4, Yangchun Hu1*, Xingliang Dai1*

Cancer Diagnosis and Treatments by Porous Inorganic Nanocarriers

Jianfeng Xu1,2, Hanwen Zhang1,2, Xiaohui Song1,2, Yangong Zheng3, Qingning Li1,2,4*

Delayed (20 Years) post-surgical Esophageal Metastasis of Breast Cancer - A Case Report

Bowen Hu1#, Lingyu Du2#, Hongya Xie1, Jun Ma1, Yong Yang1*, Jie Tan2*

Subtyping of Undifferentiated Pleomorphic Sarcoma and Its Clinical Meaning

Umair Ali Khan Saddozai, Zhendong Lu, Fengling Wang, Muhammad Usman Akbar, Saadullah Khattak, Muhammad Badar, Nazeer Hussain Khan, Longxiang Xie, Yongqiang Li, Xinying Ji, Xiangqian Guo

Construction of Glioma Prognosis Model and Exploration of Related Regulatory Mechanism of Model Gene

Suxia Hu, Abdusemer Reyimu, Wubi Zhou, Xiang Wang, Ying Zheng, Xia Chen, Weiqiang Li, Jingjing Dai

ESRP2 as a Non-independent Potential Biomarker-Current Progress in Tumors

Yuting Chen, Yuzhen Rao, Zhiyu Zeng, Jiajie Luo, Chengkuan Zhao, Shuyao Zhang

Resection of Bladder Tumors at the Ureteral Orifice Using a Hook Plasma Electrode: A Case Report

Jun Li, Ziyong Wang, Qilin Wang

Structural Characterization and Bioactivity for Lycium Barbarum Polysaccharides

Jinghua Qi1,2,  Hangping Chen3,Huaqing Lin2,4,Hongyuan Chen1,2,5* and Wen Rui2,3,5,6*

The Role of IL-22 in the Prevention of Inflammatory Bowel Disease and Liver Injury

Xingli Qi1,2, Huaqing Lin2,3, Wen Rui2,3,4,5 and Hongyuan Chen1,2,3

RBM15 and YTHDF3 as Positive Prognostic Predictors in ESCC: A Bioinformatic Analysis Based on The Cancer Genome Atlas (TCGA)

Yulou Luo1, Lan Chen2, Ximing Qu3, Na Yi3, Jihua Ran4, Yan Chen3,5*

Mining and Analysis of Adverse Drug Reaction Signals Induced by Anaplastic Lymphoma Kinase-Tyrosine Kinase Inhibitors Based on the FAERS Database

Xiumin Zhang1,2#, Xinyue Lin1,3#, Siman Su1,3#, Wei He3, Yuying Huang4, Chengkuan Zhao3, Xiaoshan Chen3, Jialin Zhong3, Chong Liu3, Wang Chen3, Chengcheng Xu3, Ping Yang5, Man Zhang5, Yanli Lei5*, Shuyao Zhang1,3*

Advancements in Immunotherapy for Advanced Gastric Cancer

Min Jiang1#, Rui Zheng1#, Ling Shao1, Ning Yao2, Zhengmao Lu1*

Tumor Regression after COVID-19 Infection in Metastatic Adrenocortical Carcinoma Treated with Immune Checkpoint Blockade: A Case Report

Qiaoxin Lin1, Bin Liang1, Yangyang Li2, Ling Tian3*, Dianna Gu1*

Mining and Analysis of Adverse Events of BRAF Inhibitors Based on FDA Reporting System

Silan Peng1,2#, Danling Zheng1,3#, Yanli Lei4#, Wang Chen3, Chengkuan Zhao3, Xinyue Lin1, Xiaoshan Chen3, Wei He3, Li Li3, Qiuzhen Zhang5*, Shuyao Zhang1,3*

Malignant Phyllodes Tumor with Fever, Anemia, Hypoproteinemia: A Rare and Strange Case Report and Literature Review

Zhenghang Li1, Yuxian Wei1*

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