Identification of CXCL13 as a potential biomarker in clear cell renal cell carcinoma via comprehensive bioinformatics analysis

Screening of differential gene expression patterns through survival analysis for diagnosis, prognosis and therapies of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of kidney cancer. Although there is increasing evidence linking ccRCC to genetic alterations, the exact molecular mechanism behind this relationship is not yet completely known to the researchers. Though drug therapies are the best choice after the metastasis, unfortunately, the majority of the patients progressively develop resistance against the therapeutic drugs after receiving it for almost 2 years. In this case, multi-targeted different variants of therapeutic drugs are essential for effective treatment against ccRCC. To understand molecular mechanisms of ccRCC development and progression, and explore multi-targeted different variants of therapeutic drugs, it is essential to identify ccRCC-causing key genes (KGs). In order to obtain ccRCC-causing KGs, at first, we detected 133 common differentially expressed genes (cDEGs) between ccRCC and control samples based on nine (9) microarray gene-expression datasets with NCBI accession IDs GSE16441, GSE53757, GSE66270, GSE66272, GSE16449, GSE76351, GSE66271, GSE71963, and GSE36895. Then, we filtered these cDEGs through survival analysis with the independent TCGA and GTEx database and obtained 54 scDEGs having significant prognostic power. Next, we used protein-protein interaction (PPI) network analysis with the reduced set of 54 scDEGs to identify ccRCC-causing top-ranked eight KGs (PLG, ENO2, ALDOB, UMOD, ALDH6A1, SLC12A3, SLC12A1, SERPINA5). The pan-cancer analysis with KGs based on TCGA database showed the significant association with different subtypes of kidney cancers including ccRCC. The gene regulatory network (GRN) analysis revealed some crucial transcriptional and post-transcriptional regulators of KGs. The scDEGs-set enrichment analysis significantly identified some crucial ccRCC-causing molecular functions, biological processes, cellular components, and pathways that are linked to the KGs. The results of DNA methylation study indicated the hypomethylation and hyper-methylation of KGs, which may lead the development of ccRCC. The immune infiltrating cell types (CD8+ T and CD4+ T cell, B cell, neutrophil, dendritic cell and macrophage) analysis with KGs indicated their significant association in ccRCC, where KGs are positively correlated with CD4+ T cells, but negatively correlated with the majority of other immune cells, which is supported by the literature review also. Then we detected 10 repurposable drug molecules (Irinotecan, Imatinib, Telaglenastat, Olaparib, RG-4733, Sorafenib, Sitravatinib, Cabozantinib, Abemaciclib, and Dovitinib.) by molecular docking with KGs-mediated receptor proteins. Their ADME/T analysis and cross-validation with the independent receptors, also supported their potent against ccRCC. Therefore, these outputs might be useful inputs/resources to the wet-lab researchers and clinicians for considering an effective treatment strategy against ccRCC.

ATAD2 promotes glycolysis and tumor progression in clear cell renal cell carcinoma by regulating the transcriptional activity of c-Myc

Clear cell renal cell carcinoma (ccRCC) is a common malignant tumor of the urogenital tract. Given that ccRCC is often resistant to radiotherapy and traditional chemotherapy, the clinical treatment of patients with ccRCC remains a challenge. The present study found that ATAD2 was significantly upregulated in ccRCC tissues. In vitro and in vivo experiments showed that the inhibition of ATAD2 expression mitigated the aggressive phenotype of ccRCC. ATAD2 was also associated with glycolysis in ccRCC. Interestingly, we found that ATAD2 could physically interact with c-Myc and promote the expression of its downstream target gene, thereby enhancing the Warburg effect of ccRCC. Overall, our study emphasizes the role of ATAD2 in ccRCC. The targeted expression or functional regulation of ATAD2 could be a promising method to reduce the proliferation and progression of ccRCC.

Bioinformatic analysis identifies GPR91 as a potential key gene in brain injury after deep hypothermic low flow

Purpose

Explore the transcription change of brain ischemia and reperfusion injury after deep hypothermic low flow.

Method

The data from PRJNA739516 and GSE104036 were obtained for the differentially expressed genes identification, functional enrichment analysis, gene set enrichment analysis, protein-protein interaction construction and hub gene identification. Oxygen and glucose deprivation model was set to validate the hub gene and explore the detailed brain injury mechanism.

Result

Interleukin, immunological response, NF-κB signaling pathway, G protein-coupled receptor signaling pathway and NLRP inflammatory are functional pathway were enriched in differentially expressed genes analysis. Sucnr1, Casr, Cxcr4, C5ar1, Tas2r41, Tas2r60 and Hcar2 were identified and verified in the OGD model. Knocking down GPR91 reduces the inflammatory response after OGD and GPR91 may be involved in the inflammatory pre-reaction through the synergistic activation of NF-κB, NLRP3, and IL-1β respectively.

Conclusion

Our study found that Interleukin, immunological response, NF-κB signaling pathway, G protein-coupled receptor signaling pathway and NLRP inflammatory are all associated with brain ischemia and reperfusion injury after deep hypothermic low flow and GPR91 can activate NF-κB/NLRP3 pathway and trigger the release of IL-1β in this progress.

Low expression of SLC34A1 is associated with poor prognosis in clear cell renal cell carcinoma

OBJECTIVE

Clear cell renal cell carcinoma (ccRCC) is a malignant renal tumor that is highly prone to metastasis and recurrence. The exact pathogenesis of this cancer is still not well understood. This study aimed to identify novel hub genes in renal clear cell carcinoma and determine their diagnostic and prognostic value.

METHODS

Intersection genes were obtained from multiple databases, and protein-protein interaction analysis and functional enrichment analysis were performed to identify key pathways related to the intersection genes. Hub genes were identified using the cytoHubba plugin in Cytoscape. GEPIA and UALCAN were utilized to observe differences in mRNA and protein expression of hub genes between KIRC and adjacent normal tissues. The Wilcoxon rank sum test was used to analyze hub gene levels between paired KIRC and matched non-cancer samples. IHC results were obtained from the HPA online database, and according to the median gene expression level, they were divided into a high-expression group and a low-expression group. The correlation of these groups with the prognosis of KIRC patients was analyzed. Logistic regression and the Wilcoxon rank sum test were used to test the relationship between SLC34A1 level and clinicopathological features. The diagnostic value of SLC34A1 was evaluated by drawing the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). Cox regression analysis was used to analyze the relationship between clinicopathological features, SLC34A1 expression, and KIRC survival rate. LinkedOmics was used to obtain the genes most related to SLC34A1 and their functional enrichment. Genetic mutations and methylation levels of SLC34A1 in KIRC were obtained from the cBioPortal website and the MethSurv website, respectively.

RESULTS

Fifty-eight ccRCC differential genes were identified from six datasets, and they were mainly enriched in 10 functional items and 4 pathways. A total of 5 hub genes were identified. According to the GEPIA database analysis, low expression of SLC34A1, CASR, and ALDOB in tumors led to poor prognosis. Low expression of SLC34A1 mRNA was found to be related to clinicopathological features of patients. SLC34A1 expression in normal tissues could accurately identify tumors (AUC 0.776). SLC34A1 was also found to be an independent predictor of ccRCC in univariate and multivariate Cox analyses. The mutation rate of the SLC34A1 gene was 13%. Eight of the 10 DNA methylated CpG sites were associated with the prognosis of ccRCC. SLC34A1 expression in ccRCC was positively correlated with B cells, eosinophils, neutrophils, T cells, TFH, and Th17 cells, and negatively correlated with Tem, Tgd, and Th2 cells.

CONCLUSION

The expression level of SLC34A1 in KIRC samples was found to be decreased, which predicted a decreased survival rate of KIRC. SLC34A1 may serve as a molecular prognostic marker and therapeutic target for KIRC patients.

Prognostic Significance of the CXCLs and Its Impact on the Immune Microenvironment in Ovarian Cancer

The chemokine (C-X-C motif) ligand (CXCL) family in tumor tissue is closely related to tumor growth, metastasis, and survival. However, the differential expression profile and prognostic value of the CXCLs in ovarian cancer (OC) have not been elucidated. Therefore, we studied the expression levels and mutations of CXCLs in OC patient in TCGA and various public databases. The expression differences of CXCLs in OC cancer tissues and normal tissues were compared through the Gene Expression Profiling Interactive Analysis (GEPIA) database. The effect of CXCLs on OC prognosis was analyzed using the Kaplan-Meier curves in GEPIA database. The impact of CXCLs on immune infiltration and clinicopathological outcomes in OC was assessed using the TIMER algorithm. Compared with normal tissues, we found that eight CXCLs were significantly differentially expressed in OC. The expression levels of CXCL9 (P = 0.0201), CXCL11 (P = 0.0385), and CXCL13 (P = 0.0288) were significantly associated with tumor stage. CXCL13 was the only gene that significantly affected both disease-free survival (DFS) and overall survival (OS) in OC, and higher CXCL13 transcript levels implied longer DFS and OS. Although there was no significant impact on DFS, CXCL10 (P = 0.0079) and CXCL11 (P = 0.0011) expression levels had a significant effect on OS in OC. At the same time, CXCLs were significantly associated with several immune-infiltrating cells in OC tissues. The CXCLs were significantly associated with one or more immune-infiltrating cells in OC tissue. CXCL13 was differentially expressed in OC and significantly affected the prognosis of patients and was a potential marker of OC prognosis.

Cellular and soluble immune checkpoint signaling forms PD-L1 and PD-1 in renal tumor tissue and in blood

Immune checkpoint blockade therapy is a treatment option of various metastatic cancer diseases including renal cell carcinoma (RCC). Approved antibody drugs target the co-inhibitory signaling of Programmed Cell Death Ligand-1 (PD-L1) and its receptor Programmed Cell Death-1 (PD-1). The combined evaluation of PD-L1 and PD-1 at the mRNA and protein levels in tumor tissue with differentiation of tumor and immune cells as well as of soluble forms (sPD-L1) and (sPD-1) in blood is of basic interest in assessing biomarker surrogates. Here, we demonstrate that PD-L1 determined as fraction of stained tumor cells (TPS-score) correlates with PD-L1-mRNA in tumor tissue, reflecting the predominant expression of PD-L1 in tumor cells. Conversely, PD-1 in immune cells of tumor tissue (IC-score) correlated with PD-1-mRNA tissue levels reflecting the typical PD-1 expression in immune cells. Of note, sPD-L1 in blood did not correlate with either the TPS-score of PD-L1 or with PD-L1-mRNA in tumor tissue. sPD-L1 released into the supernatant of cultured RCC cells closely followed the cellular PD-L1 expression as tested by interferon γ (IFNG) induction and siRNA knockdown of PD-L1. Further analysis in patients revealed that sPD-L1 significantly increased in blood following renal tumor resection. In addition, sPD-L1 correlated significantly with inflammation marker C-reactive protein (CRP) and with PD-L1 mRNA level in whole blood. These results indicate that the major source of sPD-L1 in blood may be peripheral blood cells and not primarily tumor tissue PD-L1.

Potential Role of CXCL13/CXCR5 Signaling in Immune Checkpoint Inhibitor Treatment in Cancer

Simple Summary

Immunotherapy is currently the backbone of new drug treatments for many cancer patients. CXC chemokine ligand 13 (CXCL13) is an important factor involved in recruiting immune cells that express CXC chemokine receptor type 5 (CXCR5) in the tumor microenvironment and serves as a key molecular determinant of tertiary lymphoid structure (TLS) formation. An increasing number of studies have identified the influence of CXCL13 on prognosis in patients with cancer, regardless of the use of immunotherapy treatment. However, no comprehensive reviews of the role of CXCL13 in cancer immunotherapy have been published to date. This review aims to provide an overview of the CXCL13/CXCR5 signaling axis to summarize its mechanisms of action in cancer cells and lymphocytes, in addition to effects on immunity and cancer pathobiology, and its potential as a biomarker for the response to cancer immunotherapy.

Abstract

Immune checkpoint inhibitors (ICIs), including antibodies that target programmed cell death protein 1 (PD-1), programmed death-ligand 1 (PD-L1), or cytotoxic T lymphocyte antigen 4 (CTLA4), represent some of the most important breakthroughs in new drug development for oncology therapy from the past decade. CXC chemokine ligand 13 (CXCL13) exclusively binds CXC chemokine receptor type 5 (CXCR5), which plays a critical role in immune cell recruitment and activation and the regulation of the adaptive immune response. CXCL13 is a key molecular determinant of the formation of tertiary lymphoid structures (TLSs), which are organized aggregates of T, B, and dendritic cells that participate in the adaptive antitumor immune response. CXCL13 may also serve as a prognostic and predictive factor, and the role played by CXCL13 in some ICI-responsive tumor types has gained intense interest. This review discusses how CXCL13/CXCR5 signaling modulates cancer and immune cells to promote lymphocyte infiltration, activation by tumor antigens, and differentiation to increase the antitumor immune response. We also summarize recent preclinical and clinical evidence regarding the ICI-therapeutic implications of targeting the CXCL13/CXCR5 axis and discuss the potential role of this signaling pathway in cancer immunotherapy.

Identification of Hub Genes Correlated With Poor Prognosis for Patients With Uterine Corpus Endometrial Carcinoma by Integrated Bioinformatics Analysis and Experimental Validation

Uterine Corpus Endometrial Carcinoma (UCEC) is one of the most common malignancies of the female genital tract and there remains a major public health problem. Although significant progress has been made in explaining the progression of UCEC, it is still warranted that molecular mechanisms underlying the tumorigenesis of UCEC are to be elucidated. The aim of the current study was to investigate key modules and hub genes related to UCEC pathogenesis, and to explore potential biomarkers and therapeutic targets for UCEC. The RNA-seq dataset and corresponding clinical information for UCEC patients were obtained from the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened between 23 paired UCEC tissues and adjacent non-cancerous tissues. Subsequently, the co-expression network of DEGs was determined via weighted gene co-expression network analysis (WGCNA). The Blue and Brown modules were identified to be significantly positively associated with neoplasm histologic grade. The highly connected genes of the two modules were then investigated as potential key factors related to tumor differentiation. Additionally, a protein-protein interaction (PPI) network for all genes in the two modules was constructed to obtain key modules and nodes. 10 genes were identified by both WGCNA and PPI analyses, and it was shown by Kaplan-Meier curve analysis that 6 out of the 10 genes were significantly negatively related to the 5-year overall survival (OS) in patients (AURKA, BUB1, CDCA8, DLGAP5, KIF2C, TPX2). Besides, according to the DEGs from the two modules, lncRNA-miRNA-mRNA and lncRNA-TF-mRNA networks were constructed to explore the molecular mechanism of UCEC-related lncRNAs. 3 lncRNAs were identified as being significantly negatively related to the 5-year OS (AC015849.16, DUXAP8 and DGCR5), with higher expression in UCEC tissues compared to non-tumor tissues. Finally, quantitative Real-time PCR was applied to validate the expression patterns of hub genes. Cell proliferation and colony formation assays, as well as cell cycle distribution and apoptosis analysis, were performed to test the effects of representative hub genes. Altogether, this study not only promotes our understanding of the molecular mechanisms for the pathogenesis of UCEC but also identifies several promising biomarkers in UCEC development, providing potential therapeutic targets for UCEC.

Microenvironment-associated gene HSD11B1 may serve as a prognostic biomarker in clear cell renal cell carcinoma: a study based on TCGA, RT‑qPCR, Western blotting, and immunohistochemistry

Clear cell renal cell carcinoma (ccRCC) is one of the most common malignant tumors worldwide. The clinical treatment of ccRCC is strongly associated with the tumor microenvironment (TME). Identifying potential markers of ccRCC is important to improve prognosis. Therefore, in the present study, the levels of immune/stromal components and the proportion of tumor-infiltrating immune cells (TIICs) were determined in 611 ccRCC samples using the ESTIMATE and CIBERSORT analytical tools. Subsequently, hydroxysteroid 11-beta dehydrogenase-1 (HSD11B1) was identified by univariate Cox regression analysis, protein-protein interaction (PPI) networks and clinical survival analysis to be associated with ccRCC prognosis. At the same time, the abundance of HSD11B1 increased significantly in ccRCC was verified by western blotting, RT‑qPCR and immunostaining analysis. Furthermore, Gene Set Enrichment Analysis (GSEA) and TME suggested that HSD11B1 was involved in TME immune-related status. Taken together, the results of the present study demonstrated that HSD11B1 is a potential prognostic biomarker associated with immune cell infiltration in ccRCC.

Integration of scRNA-Seq and Bulk RNA-Seq to Analyse the Heterogeneity of Ovarian Cancer Immune Cells and Establish a Molecular Risk Model

Background

Considerable evidence suggests that the heterogeneity of ovarian cancer (OC) is a major cause of treatment failure. Single-cell RNA sequencing (scRNA-seq) is a powerful tool to analyse the heterogeneity of the tumour at the single-cell level, leading to a better understanding of cell function at the genetic and cellular levels.

Methods

OC scRNA-seq data were extracted from the Gene Expression Omnibus (GEO) database and the FindCluster () package used for cell cluster analysis. The GSVA package was used for single-sample gene set enrichment analysis (ssGSEA) analysis to obtain a Hallmark gene set score and bulk RNA-seq data were used to analyse the key genes of OC-associated immune cell subsets. CIBERSORT was used to identify immune scores of cells and the “WGCNA” package for the weighted correlation network analysis (WGCNA). KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) analyses of subtype groups were performed by GSEA. Then, univariate Cox and lasso regression were performed to further establish a signature. Finally, qPCR and immunohistochemistry staining were used to evaluate the expression of signature genes in OC.

Results

Two scRNA-seq (GSE154600 and GES158937) datasets were integrated to obtain 20 cell clusters. T cells or NK cells (cluster 5, 6, 7, 11), B cells (cluster 16, 19, 20) and myeloid cells (cluster 4, 9, 10) were clustered according to immune cell markers. The ssGSEA revealed that M1- and M2-like myeloid cell-related genes were significantly upregulated in P3 and P4 patients in the GSE154600 data. Immune cell analysis in TCGA-OC showed that a high abundance of M1-like tumour-associated macrophages (TAMS) predicts better survival. WGCNA, univariate Cox and lasso Cox regression established a two-gene signature (RiskScore=-0.059*CXCL13-0.034*IL26). Next, the TCGA-test and TCGA-OC were used to test the risk prediction ability of the signature, showing a good effect in the datasets. Moreover, the qPCR and immunohistochemistry staining revealed that the expression of CXCL13 and IL26 was reduced in OC tissues.

Conclusion

A two-gene signature prognostic stratification system (CXCL13 and IL26) was developed based on the heterogeneity of OC immune cells to accurately evaluate the prognostic risk.

Identification of Prognostic Metabolism-Related Genes in Clear Cell Renal Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is a cancer with abnormal metabolism. The purpose of this study was to investigate the effect of metabolism-related genes on the prognosis of ccRCC patients.

Methods

The data of ccRCC patients were downloaded from the TCGA and the GEO databases and clustered using the nonnegative matrix factorization method. The limma software package was used to analyze differences in gene expression. A random forest model was used to screen for important genes. A novel Riskscore model was established using multivariate regression. The model was evaluated based on the metabolic pathway, immune infiltration, immune checkpoint, and clinical characteristics.

Results

According to metabolism-related genes, kidney clear cell carcinoma (KIRC) datasets downloaded from TCGA were clustered into two groups and showed significant differences in prognosis and immune infiltration. There were 667 differentially expressed genes between the two clusters, of which 408 were screened by univariate analysis. Finally, 12 differentially expressed genes (MDK, SLC1A1, SGCB, C4orf3, MALAT1, PILRB, IGHG1, FZD1, IFITM1, MUC20, KRT80, and SALL1) were filtered out using the random forest model. The model of Riskscore was obtained by multiplying the expression levels of these 12 genes with the corresponding coefficients of the multivariate regression. We found that the Riskscore correlated with the expression of these 12 genes; the high Riskscore matched the low survival rate verified in the verification set. The analysis found that the Riskscore model was associated with most of the metabolic processes, immune infiltration of cells such as plasma cells, immune checkpoints such as PD-1, and clinical characteristics such as M stage.

Conclusion

We established a new Riskscore model for the prognosis of ccRCC based on metabolism. The genes in the model provided several novel targets for the study of ccRCC.

Expression of AOX1 Predicts Prognosis of Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most prevalent kidney cancer worldwide, and appropriate cancer biomarkers facilitate early diagnosis, treatment, and prognosis prediction in cancer management. However, an accurate biomarker for ccRCC is lacking. This study identified 356 differentially expressed genes in ccRCC tissues compared with normal kidney tissues by integrative analysis of eight ccRCC datasets. Enrichment analysis of the differentially expressed genes unveiled improved adaptation to hypoxia and metabolic reprogramming of the tumor cells. Aldehyde oxidase 1 (AOX1) gene was identified as a biomarker for ccRCC among all the differentially expressed genes. ccRCC tissues expressed significantly lower AOX1 than normal kidney tissues, which was further validated by immunohistochemistry at the protein level and The Cancer Genome Atlas (TCGA) data mining at the mRNA level. Higher AOX1 expression predicted better overall survival in ccRCC patients. Furthermore, AOX1 DNA copy number deletion and hypermethylation were negatively correlated with AOX1 expression, which might be the potential mechanism for its dysregulation in ccRCC. Finally, we illustrated that the effect of AOX1 as a tumor suppressor gene is not restricted to ccRCC but universally exists in many other cancer types. Hence, AOX1 may act as a potential prognostic biomarker and therapeutic target for ccRCC.

Multidiscipline Immunotherapy-Based Rational Combinations for Robust and Durable Efficacy in Brain Metastases from Renal Cell Carcinoma

Advanced imaging techniques for diagnosis have increased awareness on the benefits of brain screening, facilitated effective control of extracranial disease, and prolonged life expectancy of metastatic renal cell carcinoma (mRCC) patients. Brain metastasis (BM) in patients with mRCC (RCC-BM) is associated with grave prognoses, a high degree of morbidity, dedicated assessment, and unresponsiveness to conventional systemic therapeutics. The therapeutic landscape of RCC-BM is rapidly changing; however, survival outcomes remain poor despite standard surgery and radiation, highlighting the unmet medical needs and the requisite for advancement in systemic therapies. Immune checkpoint inhibitors (ICIs) are one of the most promising strategies to treat RCC-BM. Understanding the role of brain-specific tumor immune microenvironment (TIME) is important for developing rationale-driven ICI-based combination strategies that circumvent tumor intrinsic and extrinsic factors and complex positive feedback loops associated with resistance to ICIs in RCC-BM via combination with ICIs involving other immunological pathways, anti-antiangiogenic multiple tyrosine kinase inhibitors, and radiotherapy; therefore, novel combination approaches are being developed for synergistic potential against RCC-BM; however, further prospective investigations with longer follow-up periods are required to improve the efficacy and safety of combination treatments and to elucidate dynamic predictive biomarkers depending on the interactions in the brain TIME.

High expression of TRAF4 predicts poor prognosis in tamoxifen-treated breast cancer and promotes tamoxifen resistance

Tamoxifen is the main adjuvant endocrine therapeutic agent for patients with estrogen receptor positive breast cancer. However, the resistance to tamoxifen has become a serious clinical challenge and the underlying mechanisms are still poorly understood. TRAF4 is a member of tumor necrosis factor receptor-associated factor family and its role in tamoxifen resistance has not been found. In this study, we aimed to explore the roles of TRAF4 in tamoxifen-treated breast cancer and tamoxifen resistance. Through high-throughput sequencing and differential gene expression analyses, TRAF4 was identified as the research object in this study. The prognosis significance of TRAF4 was studied based on 155 tamoxifen-treated breast cancer patients obtained from Gene Expression Omnibus (GEO) database. We then investigated the TRAF4 expression level in tamoxifen-resistant and the tamoxifen-sensitive breast cancer cell lines with western blot and real-time quantitative PCR. The loss- and gain-of-function assay of TRAF4 in a tamoxifen-resistant cell line was evaluated using colony formation experiments and cell count kit-8 assay. We identified that TRAF4 was overexpressed in tamoxifen-resistant breast cancer cell line and TRAF4 overexpression was associated with worse overall survival (hazard ratio = 2.538, P = 0.017) and cancer-specific survival (hazard ratio = 2.713, P = 0.036) in tamoxifen-treated patients. Knockdown of TRAF4 reversed tamoxifen resistance, while overexpression of TRAF4 increased tamoxifen resistance, which confirmed the role of TRAF4 in tamoxifen resistance. Taken together, our study demonstrated that TRAF4 could be a novel prognostic biomarker for tamoxifen-treated breast cancer patients and a potential therapeutic target for tamoxifen resistance.

Development of an Autophagy-Related Gene Prognostic Model and Nomogram for Estimating Renal Clear Cell Carcinoma Survival

Background

Kidney renal clear cell carcinoma (KIRC) is a fatal malignancy of the urinary system. Autophagy is implicated in KIRC occurrence and development. Here, we evaluated the prognostic value of autophagy-related genes (ARGs) in kidney renal clear cell carcinoma.

Materials and Methods

We analyzed RNA sequencing and clinical KIRC patient data obtained from TCGA and ICGC to develop an ARG prognostic signature. Differentially expressed ARGs were further evaluated by functional assessment and bioinformatic analysis. Next, ARG score was determined in 215 KIRC patients using univariable Cox and LASSO regression analyses. An ARG nomogram was built based on multivariable Cox analysis. The prognosis nomogram model based on the ARG signatures and clinicopathological information was evaluated for discrimination, calibration, and clinical usefulness.

Results

A total of 47 differentially expressed ARGs were identified. Of these, 8 candidates that significantly correlated with KIRC overall survival were subjected to LASSO analysis and an ARG score built. Functional enrichment and bioinformatic analysis were used to reveal the differentially expressed ARGs in cancer-related biological processes and pathways. Multivariate Cox analysis was used to integrate the ARG nomogram with the ARG signature and clinicopathological information. The nomogram exhibited proper calibration and discrimination (C-index = 0.75, AUC = >0.7). Decision curve analysis also showed that the nomogram was clinically useful.

Conclusions

KIRC patients and doctors could benefit from ARG nomogram use in clinical practice.

Intratumoral CXCL13+CD8+T cell infiltration determines poor clinical outcomes and immunoevasive contexture in patients with clear cell renal cell carcinoma

Background

Chemokine (C-X-C motif) ligand 13 (CXCL13) was known as a selective chemotaxis for B cells, a product of follicular helper CD4⁺T cells (T_FH) and a contributor to tertiary lymphoid structures (TLS). Although secretion and function of CXCL13 produced by T_FH have been deeply explored, the immune function and prognostic significance of CXCL13 secreted by CD8⁺T cells still remain unrevealed. This study aims to investigate the clinical merit of CXCL13⁺CD8⁺T cells in clear cell renal cell carcinoma (ccRCC).

Methods

We analyzed prognostic value and immune contexture that associated with CXCL13⁺CD8⁺T cells infiltration level in a total of 755 patients from Zhongshan Hospital cohort (n=223) and The Cancer Genome Atlas cohort (n=532). In vitro analyses were conducted on 42 samples of resected tumor tissue from Zhongshan Hospital in order to detect the immune status of CXCL13⁺CD8⁺T cells and total CD8⁺T cells. Immunohistochemistry (IHC) and flow cytometry were applied to characterize immune cells and portray the tumor microenvironment (TME) in ccRCC.

Results

Intratumoral CXCL13⁺CD8⁺T cells abundance was associated with inferior overall survival and disease-free survival. CXCL13⁺CD8⁺T cells possessed higher level of immune checkpoints like programmed cell-death protein 1 (PD-1), T-cell immunoglobulin mucin 3 (Tim-3), T cell immunoreceptor with Ig and ITIM domains (TIGIT) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), higher Ki-67 expression and lower tumor necrosis factor α (TNF-α), interferon γ (IFN-γ) expression. Total CD8⁺T cells in high-level CXCL13⁺CD8⁺T cells infiltration subgroup exhibited elevated exhausted markers (PD-1, Tim-3, TIGIT) and descended activated markers (TNF-α, IFN-γ) without quantity variance. Furthermore, the abundance of intratumoral CXCL13⁺CD8⁺T cell was correlated with immunoevasive TME accompanied by increased T helper 2 cells, tumor-associated macrophages, Foxp3⁺ regulatory T cells, TLS and decreased natural killer cells, GZMB⁺ cells.

Conclusions

Intratumoral CXCL13⁺CD8⁺T cells infiltration indicated inferior clinical outcome in patients with ccRCC. CXCL13⁺CD8⁺T cells possessed increased exhausted markers, decreased effector molecules and better proliferation ability. CXCL13⁺CD8⁺T cells abundance impaired total CD8⁺T cells’ immune function. Intratumoral CXCL13⁺CD8⁺T cells abundance was associated with immunoevasive contexture. The abundance of CXCL13⁺CD8⁺T cells was an independent prognosticator and a potential immunotherapeutic target marker for ccRCC treatment.

Glycolysis-Related Genes Serve as Potential Prognostic Biomarkers in Clear Cell Renal Cell Carcinoma

Metabolic rearrangement is a marker of cancer that has been widely studied in recent years. One of the major metabolic characteristics of tumor cells is the high levels of glycolysis, even under aerobic conditions, a phenomenon that is called the "Warburg effect." We investigated the expression and copy number variation (CNV) frequency of all glycolysis-related genes in multiple cancer types and found many differentially expressed genes, particularly in clear cell renal cell carcinoma (ccRCC). Single nucleotide variants (SNVs) showed that the overall average mutation frequency for all genes was low. The purpose of this study was to establish a predictive model by studying glycolysis-related genes in ccRCC. We compared the expression of glycolysis-related genes in 539 ccRCC tissues and 72 normal renal tissues from The Cancer Genome Atlas dataset and identified 17 upregulated and 26 downregulated genes. Pathway analysis revealed that PSAT1 and SDHB could activate the cell cycle, RPIA could activate the DNA damage response, and HK3 could activate apoptosis and EMT signaling, while PDK2 could inhibit apoptosis. The results of the drug sensitivity analysis suggested that some of these differentially expressed genes were positively correlated with drug sensitivity. Thirteen genes were selected from the gene coexpression network and the LASSO regression analysis. The Kaplan-Meier overall survival curves showed that the expression of upregulated genes in ccRCC patients was associated with lower overall survival. We established a predictive model consisting of 13 genes (RPIA, G6PD, PSAT1, ENO2, HK3, IDH1, PDK4, PGM2, PGK1, FBP1, OGDH, SUCLA2, and SUCLG2). This predictive model correlated well with the development and progression of ccRCC. Thus, it is of great value in the diagnosis and prognostic evaluation of ccRCC and may aid the identification of potential prognostic biomarkers and drug targets.

Identification of biomarkers and construction of a microRNA‑mRNA regulatory network for clear cell renal cell carcinoma using integrated bioinformatics analysis

With the recent research development, the importance of microRNAs (miRNAs) in renal clear cell carcinoma (CCRCC) has become widely known. The purpose of this study is to screen out the potential biomarkers of renal clear cell carcinoma (CCRCC) by microarray analysis. The miRNA chip (GSE16441) and mRNA chip (GSE66270) related to CCRCC were downloaded from the Gene Expression Omnibus (GEO) database. After data filtering and pretreating, R platform and a series of analysis tools (funrich3.1.3, string, Cytoscape_ 3.2.1, David, etc.) were used to analyze chip data and identify the specific and highly sensitive biomarkers. Finally, by constructing the miRNA -mRNA interaction network, it was determined that five miRNAs (hsa-mir-199a-5p, hsa-mir-199b-5p, hsa-mir-532-3p and hsa-mir-429) and two key genes (ETS1 and hapln1) are significantly related to the overall survival rate of patients.

Identification of RNA Transcript Makers Associated With Prognosis of Kidney Renal Clear Cell Carcinoma by a Competing Endogenous RNA Network Analysis

Objective

This study aims to identify several RNA transcripts associated with the prognosis of kidney renal clear cell carcinoma (KIRC).

Methods

The differentially expressed mRNAs, lncRNAs, and miRNAs (DEmRNAs, DElncRNAs, and DEmiRNAs) between KIRC cases and controls were screened based on an RNA-seq dataset from The Cancer Genome Atlas (TCGA) database. Subsequently, miRcode, miRDB, and TargetScan database were used to predict interactions between lncRNAs, miRNAs and target mRNAs. Then, a ceRNA network was built using miRNAs-mRNAs and lncRNAs-miRNAs pairs. Functional analysis of mRNAs in ceRNA was performed. Finally, the survival analysis of RNA transcripts in ceRNA network and correlation analysis for key RNA regulators were carried out.

Results

There were 1527 DElncRNAs, 54 DEmiRNAs, and 2321 DEmRNAs. A ceRNA network was constructed among 81 lncRNAs, 9 miRNAs, and 197 mRNAs. Functional analysis showed that numerous mRNAs were significantly associated with regulation of cellular glucuronidation. In addition, 35 lncRNAs, 84 mRNAs and two miRNAs were significantly corelated to the survival of patients with KIRC (P < 0.05). Among them, miRNA-21 and miRNA-155 were negatively related to three lncRNAs (LINC00472, SLC25A5.AS1, and TCL6). Seven mRNA targets of miRNA-21 (FASLG, FGF1, TGFBI, ALX1, SLC30A10, ADCY2, and ABAT) and 12 mRNAs targets of miRNA-155 (STXBP5L, SCG2, SPI1, C12orf40, TYRP1, CTHRC1, TDO2, PTPRQ, TRPM8, ERMP1, CD36, and ST9SIA4) also acted as prognostic biomarkers for KIRC patients.

Conclusion

We screened numerous novel prognosis-related RNA markers for KIRC patients by a ceRNA network analysis, providing deeper understandings of prognostic values of RNA transcripts for KIRC.

CircHIPK3 Promotes Clear Cell Renal Cell Carcinoma (ccRCC) Cells Proliferation and Metastasis via Altering of miR-508-3p/CXCL13 Signal

Introduction

Accumulating evidence has demonstrated that circular RNAs (circRNAs) play a key role in the tumorigenesis of various types of cancers, including clear cell renal cell carcinoma (ccRCC).

Materials and Methods

Reverse transcription-quantitative polymerase chain reaction was used to detect the expression of circRNA homeodomain interacting protein kinase 3 (circHIPK3) and microRNAs (miRNAs), including miR-508-3p. The clinical measurement of circHIPK3 was evaluated by Kaplan-Meier survival analysis and receiver operating characteristic analysis. Cell Counting Kit-8 and Transwell chamber assays were performed to determine the changes in the proliferative and metastatic ability of A498 and 786-O cells. C-X-C motif chemokine ligand 13 (CXCL13) protein expression was detected by Western blot analysis. The targeted binding effect between miR-508-3p and circHIPK3 or CXCL13 was confirmed by constructed luciferase and RNA immunoprecipitation (RIP) assays, respectively. Fluorescence in situ hybridization (FISH) assay was used to measure the subcellular localization of circHIPK3 and miR-508-3p.

Results

It was found that circHIPK3 was markedly upregulated in ccRCC tissue and cell lines, and circHIPK3-upregulation was closely correlated with poor clinicopathological features in patients with ccRCC. It was found that both miR-508-3p and circHIPK3 were localized in the cytoplasm of ccRCC cells. The up- and downregulation of circHIPK3 positively regulated ccRCC cell proliferation and metastasis, and this regulatory effect was reversed by miR-508-3p. Through luciferase and RIP assays, it was confirmed that circHIPK3 could interacted with miR-508-3p. Furthermore, it was revealed that CXCL13, which was negatively correlated with miR-508-3p, was upregulated in ccRCC. It was also shown that CXCL13 was a downstream target of miR-508-3p. miR-508-3p suppressed ccRCC cell proliferation and metastasis by targeting CXCL13. Lastly, it was demonstrated that circHIPK3 promoted CXCL13 to facilitate ccRCC cell proliferation and metastasis by decoying miR-508-3p.

Conclusion

In brief, the results of the present study showed that circHIPK3 promoted ccRCC cell proliferation and metastasis by altering miR-5083p/CXCL13 signaling. The present findings might provide a novel target for the molecular treatment of ccRCC.

Identification of biomarkers of clear cell renal cell carcinoma by bioinformatics analysis

Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cancer, and more and more researches find that the occurrence of ccRCC is associated with genetic changes, but the molecular mechanism still remains unclear. The present study aimed to identify aggregation trend of differentially expressed genes (DEGs) in ccRCC, which would be beneficial to the treatment of ccRCC and provide research ideas using a series of bioinformatics approach. Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) analysis were used to get the enrichment trend of DEGs of GSE53757 and GSE16449. Draw Venn Diagram was applied for co-expression of DEGs. Cytoscape with the Retrieval of Interacting Gene (STRING) datasets and Molecular Complex Detection (MCODE) were performed protein-protein interaction (PPI) of DEGs. The Kaplan-Meier Plotter analysis of top 15 upregulated and top 15 downregulated were selected in Gene Expression Profiling Interactive Analysis (GEPIA). Then, the expression level of hub genes between normal renal tissue and different pathological stages of ccRCC tissue, which significantly correlated with overall survival in ccRCC patients, were also analyzed by Ualcan based on The Cancer Genome Atlas (TCGA) database. In this study, we got 167 co-expression DEGs, including 72 upregulated DEGs and 95 downregulated DEGs. We identified 11 hub genes had significantly correlated with overall survival in ccRCC patients. Among them, KIF23, APLN, ADCY1, GREB1, TLR4, IRF8, CXCL1, CXCL2, deserved our attention.

Identification of significant genes with prognostic influence in clear cell renal cell carcinoma via bioinformatics analysis

Background

Clear cell renal cell carcinoma (ccRCC) is the most common malignant tumor of kidney with high mortality. The pathogenesis of ccRCC is complicated and effective prognostic predictors for clinical practice are still limited. This study aimed to identify significant genes with prognostic influence in ccRCC via bioinformatics analysis.

Methods

Four gene expression profiles were acquired from the Gene Expression Omnibus (GEO) database, including 168 ccRCC tissues and 143 normal tissues. Common differentially expressed genes (DEGs) between ccRCC tissues and normal kidney tissues were screened out. Then gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were investigated. Protein-protein interaction (PPI) network of the common DEGs was diagrammed and analyzed. Kaplan–Meier analysis was conducted to identify genes with prognostic influence in ccRCC. Gene Expression Profiling Interactive Analysis (GEPIA) was finally applied to validating differential expression of genes.

Results

Ninety-nine common DEGs between ccRCC tissues and normal kidney tissues were eventually screened out (P<0.05, |log FC| >2). GO functional analysis showed that the down-regulated genes were enriched in excretion, negative regulation of cell proliferation, heparin binding and cellular response to BMP stimulus, etc. KEGG pathway analysis indicated that the common DEGs were particularly enriched in HIF-1 signaling pathway and aldosterone-regulated sodium reabsorption. Seven core DEGs were distinguished through PPI network analysis, of which 6 core genes ANGPTL4, CA9, CXCR4, LOX, EGF and HRG showed significantly prognostic difference in patients with ccRCC by Kaplan–Meier analysis (P<0.05). And GEPIA confirmed these genes were expressed differentially between tumor and normal tissues (P<0.05). High expression of HRG was correlated with good OS in ccRCC patients. Specifically, HRG was commonly down-regulated in ccRCC tissues compared with normal tissues according to GEPIA.

Conclusions

Our study shows that high expression of HRG denotes a better prognosis in ccRCC patients. HRG is down-regulated in ccRCC tissues compared with normal kidney tissues. The selective expression pattern suggests that HRG could be a novel prognostic predictor and potential therapeutic target for ccRCC patients.

Association of CXCL13 and Immune Cell Infiltration Signature in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most commonly diagnosed kidney tumors and is often accompanied by immune cell infiltration. In this study, we attempted to identify microenvironment-associated genes and explore the correlation between CXCL13 and tumor-infiltrating immune cells (TIICs). Gene expression profiles and their corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. The ESTIMATE (Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data) algorithm was used to calculate immune cell and stromal cell scores, according to which patients were divided into high- and low-score groups, allowing differentially expressed genes (DEGs) to be identified. Functional enrichment and PPI network analysis were used to identify the functions of the DEGs. CIBERSORT algorithm and TIMER analysis were used to evaluate the immune score. Oncomine and TCGA database were used to explore CXCL13 mRNA expression level in ccRCC. High ESTIMATE score was significantly associated with prognosis. Functional enrichment analysis clarified that DEGs were associated with T cell activation, immune response-regulating cell surface receptor signaling pathway, and positive regulation of cytokine production. PPI network was used to identify CXCL13 as a hub gene. And CIBERSORT algorithm and TIMER analysis showed that strong correlation between CXCL13 expression level and TIICs. Oncomine database was used to validate high CXCL13 expression level in ccRCC tissue, compared to normal tissues. In conclusion, we obtained a list of tumor microenvironment-related genes and identified CXCL13 as an immune response biomarker in patients with ccRCC, GSEA analysis, wound healing and transwell assays showed CXCL13 played a role in tumor migration.