Identification of a Novel Glycolysis-Related Gene Signature Correlates With the Prognosis and Therapeutic Responses in Patients With Clear Cell Renal Cell Carcinoma

Development of a prognostic model for personalized prediction of colon adenocarcinoma (COAD) patient outcomes using methylation-driven genes

The objective of this study was to identify methylation-driven genes and explore their prognostic value in colon adenocarcinoma (COAD). The Cancer Genome Atlas (TCGA) database was used to acquire collated COAD transcriptome gene expression matrix (containing 59,427 transcripts), transcriptome gene methylation level matrix (containing 29,602 methylated modified genes), which included 517 samples containing 41 samples of normal tissue (NT) & 476 samples of COAD, and patient clinical information files (including patient survival time, survival status, age, gender and tumor stage, etc.), for all COAD samples. A total of 9807 differentially expressed genes (DEGs) were obtained by DEG analysis of the COAD transcriptional expression matrix, of which 5874 were up-regulated and 3933 were down-regulated. And 46 methylation-driven DEGs (MD-DEGs) in COAD were obtained by DEG analysis, differential analysis of gene methylation levels, and correlation analysis between them. Next, three prognostic associated MD-DEGs (PMD-DEGs) (IDUA, ZBTB18 and C5orf38) were identified by Cox regression analysis, and a prognostic model composed of the three PMD-DEGs was constructed by least absolute shrinkage and selection operator (LASSO) regression analysis and cross-validation analysis. In addition, survival analysis, the receiver operating characteristics (ROC) curve analysis and independent prognostic analysis were used to evaluate and verify that the prognostic model we constructed could accurately and independently predict the prognosis of COAD patients. Finally, we constructed a nomogram based on the prognosis model to accurately and personalized predict the survival prognosis of COAD patients. In conclusion, we identified the methylation driver gene of COAD and constructed a prognostic model and nomogram to personalized predict the prognosis of patients, which opened a new prospect for accurate diagnosis and treatment in clinical practice.

Endoplasmic Reticulum Stress in Renal Cell Carcinoma

The endoplasmic reticulum is an organelle exerting crucial functions in protein production, metabolism homeostasis and cell signaling. Endoplasmic reticulum stress occurs when cells are damaged and the capacity of this organelle to perform its normal functions is reduced. Subsequently, specific signaling cascades, together forming the so-called unfolded protein response, are activated and deeply impact cell fate. In normal renal cells, these molecular pathways strive to either resolve cell injury or activate cell death, depending on the extent of cell damage. Therefore, the activation of the endoplasmic reticulum stress pathway was suggested as an interesting therapeutic strategy for pathologies such as cancer. However, renal cancer cells are known to hijack these stress mechanisms and exploit them to their advantage in order to promote their survival through rewiring of their metabolism, activation of oxidative stress responses, autophagy, inhibition of apoptosis and senescence. Recent data strongly suggest that a certain threshold of endoplasmic reticulum stress activation needs to be attained in cancer cells in order to shift endoplasmic reticulum stress responses from a pro-survival to a pro-apoptotic outcome. Several endoplasmic reticulum stress pharmacological modulators of interest for therapeutic purposes are already available, but only a handful were tested in the case of renal carcinoma, and their effects in an in vivo setting remain poorly known. This review discusses the relevance of endoplasmic reticulum stress activation or suppression in renal cancer cell progression and the therapeutic potential of targeting this cellular process for this cancer.

The lncRNA XIST/miR-29b-3p/COL3A1 axis regulates central carbon metabolism in head and neck squamous cell carcinoma and is associated with poor tumor prognosis

Background

Recent evidence shows that COL3A1 promotes the progression of many types of cancer. The purpose of our study is to explore the correlation between COL3A1 and the prognosis of patients with head and neck squamous cell carcinoma (HNSCC) and its potential mechanism.

Methods

We initially screened the differentially expressed gene COL3A1 in The Cancer Genome Atlas (TCGA) database, and the association between the expression level of COL3A1, prognosis, and the clinical parameters of HNSCC patients was verified. A nomogram was constructed according to the multivariate analysis results. Next, a heatmap of COL3A1 co-expressed genes was constructed in TCGA database. The TargetScan database is used to explore the microRNAs (miRNA) related to COL3A1. The starBase database was used to explore and predict the long non-coding RNAs (lncRNAs) that the candidate miRNAs might bind to. Finally, the potential mechanism of action was investigated using Gene Set Enrichment Analysis (GSEA).

Results

COL3A1 expression is elevated in HNSCC tumor tissues, and HNSCC patients with high COL3A1 expression have worse prognostic factors. COL3A1 was positively correlated with the central carbon metabolism-related proteins: epidermal growth factor receptor (EGFR), phosphoglycerate mutase 1 (PGAM1), hexokinase 3 (HK3), and phosphofructokinase, platelet (PFKP). The TargetScan database showed that the best candidate miRNA for binding to the three prime untranslated region (3'UTR) end of COL3A1 mRNA was hsa-miR-29b-3p, which was negatively correlated with COL3A1. The starBase database showed that the lncRNA X Inactive Specific Transcript (lncRNA XIST) was the best candidate upstream non-coding RNA for regulating hsa-miR-29b-3p. GSEA showed that COL3A1 may be involved in the poor prognosis of HNSCC by participating in carbon metabolism, glucose metabolism, oxidative stress, and the Wingless-Type MMTV Integration Site Family (Wnt) and vascular endothelial growth factor A-vascular endothelial growth factor receptor 2 (VEGFA-VEGFR2) pathways.

Conclusions

Low COL3A1 expression can be employed as a new HNSCC predictive biomarker, and the prognosis of HNSCC patients with lower COL3A1 expression can be greatly improved. At the same time, we found that the lncRNA XIST/miR-29b-3p/COL3A1 axis may regulate the central carbon metabolism of HNSCC and is associated with poor prognosis. These findings point to a potential target for developing HNSCC anticancer therapies.

Special issue "The advance of solid tumor research in China": Multi-omics analysis based on 1311 clear cell renal cell carcinoma samples identifies a glycolysis signature associated with prognosis and treatment response

In clear cell renal cell carcinoma (ccRCC), glycolysis is enhanced mainly because of the increased expression of key enzymes in glycolysis. Hence, the discovery of new molecular biomarkers for glycolysis may help guide and establish a precise system of diagnosis and treatment for ccRCC. Expression profiles of 1079 tumor samples of ccRCC patients (including 311 patients treated with everolimus or nivolumab) were downloaded from public databases. Proteomic profiles of 232 ccRCC samples were obtained from Fudan University Shanghai Cancer Center (FUSCC). Biological changes, tumor microenvironment and prognostic differences were explored between samples with various glycolysis characteristics. There were significant differences in CD8⁺ effector T cells, epithelial-to-mesenchymal transition and pan-fibroblast TGFb between the Low and High glyScore groups. The tumor mutation burden of the Low glyScore group was lower than that of the High glyScore group. And higher glyScore was significantly associated with worse overall survival (OS) in 768 ccRCC patients (P < .0001). External validation in FUSCC cohort also indicated that glyScore was of strong ability for predicting OS (P < .05). GlyScore may serve as a biomarker for predicting everolimus response in ccRCC patients due to its significant associations with progression-free survival (PFS). And glyScore may also predict overall survival in patients treated with nivolumab. We calculated the glyScore in ccRCC and the defined glyScore was of strong ability for predicting OS. In addition, glyScore may also serve as a biomarker for predicting PFS in patients treated with everolimus and could predict OS in patients treated with nivolumab.

Identification of a claudin-low subtype in clear cell renal cell carcinoma with implications for the evaluation of clinical outcomes and treatment efficacy

Background

In bladder and breast cancer, the claudin-low subtype is widely identified, revealing a distinct tumor microenvironment (TME) and immunological feature. Although we have previously identified individual claudin members as prognostic biomarkers in clear cell renal cell carcinoma (ccRCC), the existence of an intrinsic claudin-low subtype and its interplay with TME and clinical outcomes remains unclear.

Methods

Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA)- kidney clear cell carcinoma (KIRC) cohort and E-MTAB-1980 were derived as the training and validation cohorts, respectively. In addition, GSE40435, GSE53757, International Cancer Genome Consortium (ICGC) datasets, and RNA-sequencing data from local ccRCC patients were utilized as validation cohorts for claudin clustering based on silhouette scores. Using weighted correlation network analysis (WGCNA) and multiple machine learning algorithms, including least absolute shrinkage and selection operator (LASSO), CoxBoost, and random forest, we constructed a claudin-TME related (CTR) risk signature. Furthermore, the CTR associated genomic characteristics, immunity, and treatment sensitivity were evaluated.

Results

A claudin-low phenotype was identified and associated with an inferior survival and distinct TME and cancer immunity characteristics. Based on its interaction with TME, a risk signature was developed with robust prognostic prediction accuracy. Moreover, we found its association with a claudin-low, stem-like phenotype and advanced clinicopathological features. Intriguingly, it was also effective in kidney chromophobe and renal papillary cell carcinoma. The high CTR group exhibited genomic characteristics similar to those of claudin-low phenotype, including increased chromosomal instability (such as deletions at 9p) and risk genomic alterations (especially BAP1 and SETD2). In addition, a higher abundance of CD8 T cells and overexpression of immune checkpoints, such as LAG3, CTLA4 and PDCD1, were identified in the high CTR group. Notably, ccRCC patients with high CTR were potentially more sensitive to immune checkpoint inhibitors; their counterparts could have more clinical benefits when treated with antiangiogenic drugs, mTOR, or HIF inhibitors.

Conclusion

We comprehensively evaluated the expression features of claudin genes and identified a claudin-low phenotype in ccRCC. In addition, its related signature could robustly predict the prognosis and provide guide for personalizing management strategies.

Development of a Phagocytosis-Dependent Gene Signature to Predict Prognosis and Response to Checkpoint Inhibition in Clear-Cell Renal Cell Carcinoma

Aim

The action of immune checkpoint inhibition (ICI) largely depends on antibody-dependent cellular phagocytosis (ADCP). We thus aim to develop ADCP-based ccRCC risk stratification as both prognostic and therapeutic markers of ICI.

Method

Genomic data from multiple public datasets (TCGA, etc.) were integrated. A cancer-intrinsic ADCP gene set for ccRCC tailored from a recent report was constructed based on the association with prognosis, immune infiltrates, and response to ICI. Therapeutic potential was profiled using genome-drug sensitivity datasets.

Results

ADCP genes were selected from a recent CRISPR/Cas9 screen report. Following a four-module panel based on clinical traits, we generated a six-gene signature (ARPC3, PHF19, FKBP11, MS4A14, KDELR3, and CD1C), which showed a strong correlation with advanced grade and stage and worsened prognosis, with a nomogram showing predictive efficacies of 0.911, 0.845, and 0.867 (AUC) at 1, 3, and 5 years, respectively. Signatures were further dichotomized, and groups with a higher risk score showed a positive correlation with tumor mutation burden, higher expressions of inhibitory checkpoint molecules, and increased antitumor immune infiltrates and were enriched for antitumor immune pathways. The high risk-score group showed better response to ICI and could benefit from TKIs of axitinib, tivozanib, or sorafenib, preferentially in combination, whereas sunitinib and pazopanib would better fit the low risk-score group.

Conclusion

Here we showed a six-gene ADCP signature that correlated with prognosis and immune modulation in ccRCC. The signature-based risk stratification was associated with response to both ICI and tyrosine kinase inhibition in ccRCC.

A Novel Gene Signature of Tripartite Motif Family for Predicting the Prognosis in Kidney Renal Clear Cell Carcinoma and Its Association With Immune Cell Infiltration

Given the importance of tripartite motif (TRIM) proteins in diverse cellular biological processes and that their dysregulation contributes to cancer progression, we constructed a robust TRIM family signature to stratify patients with kidney renal clear cell carcinoma (KIRC). Transcriptomic profiles and corresponding clinical information of KIRC patients were obtained from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. Prognosis-related TRIM family genes were screened and used to construct a novel TRIM family-based signature for the training cohort. The accuracy and generalizability of the prognostic signature were assessed in testing, entire, and external ICGC cohorts. We analyzed correlations among prognostic signatures, tumor immune microenvironment, and immune cell infiltration. The results of univariate Cox regression and Kaplan-Meier survival analyses revealed 27 TRIMs that were robustly associated with the prognosis of patients with KIRC. We applied Lasso regression and multivariate Cox regression analyses to develop a prognostic signature containing the TRIM1, 13, 35, 26, 55, 2, 47, and 27 genes to predict the survival of patients with KIRC. The accuracy and generalizability of this signature were confirmed in internal and external validation cohorts. We also constructed a predictive nomogram based on the signature and the clinicopathological characteristics of sex, age, and T and M status to aid clinical decision-making. We analyzed immune cell infiltration analysis and found that CD8 T cells, memory resting CD4 T cells, and M2 macrophages were the most enriched components in the KIRC tumor immune microenvironment. A higher level of immune infiltration by plasma cells, follicular helper T cells, and activated NK cells, and a lower level of immune infiltration by memory resting CD4 T cells, M1 and M2 macrophages, and resting dendritic cells were associated with higher risk scores. Overall, our eight-gene TRIM family signature has sufficient accuracy and generalizability for predicting the overall survival of patients with KIRC. Furthermore, this prognostic signature is associated with tumor immune status and distinct immune cell infiltrates in the tumor microenvironment.

A combined signature of glycolysis and immune landscape predicts prognosis and therapeutic response in prostate cancer

Prostate cancer (PCa) is a common malignancy that poses a major threat to the health of men. Prostate-specific antigen (PSA) and its derivatives, as FDA-approved detection assays, are insufficient to serve as optimal markers for patient prognosis and clinical decision-making. It is widely acknowledged that aberrant glycolytic metabolism in PCa is related to tumor progression and acidifies the tumor microenvironment (TME). Considering the non-negligible impacts of glycolysis and immune functions on PCa, we developed a combined classifier in prostate cancer. The Glycolysis Score containing 19 genes and TME Score including three immune cells were created, using the univariate and multivariate Cox proportional hazards model, log-rank test, least absolute shrinkage and selection operator (LASSO) regression analysis and the bootstrap approach. Combining the glycolysis and immunological landscape, the Glycolysis-TME Classifier was then constructed. It was observed that the classifier was more accurate in predicting the prognosis of patients than the current biomarkers. Notably, there were significant differences in metabolic activity, signaling pathways, mutational landscape, immunotherapeutic response, and drug sensitivity among the Glycolysis^high/TME^low, Mixed group and Glycolysis^low/TME^high identified by this classifier. Overall, due to the significant prognostic value and potential therapeutic guidance of the Glycolysis-TME Classifier, we anticipate that this classifier will be clinically beneficial in the management of patients with PCa.

Gene signatures with therapeutic value: emerging perspective for personalized immunotherapy in renal cancer

Renal cancer is one of the deadliest urogenital diseases. In recent years, the advent of immunotherapy has led to significant improvement in the management of patients with renal cancer. Although cancer immunotherapy and its combinations had benefited numerous patients, several challenges need to be addressed. Apart from the high costs of treatment, the lack of predictive biomarkers and toxic side-effects have impeded its wider applicability. To address these issues, new biomarkers are required to predict responsiveness and design personalized treatment strategies. Recent advances in the field of single-cell sequencing and multi-dimensional spatial transcriptomics have identified clinically relevant subtypes of renal cancer. Furthermore, there is emerging potential for gene signatures based on immune cells, non-coding RNAs, and pathways such as metabolism and RNA modification. In this review article, we have discussed recent progress in the identification of gene signatures with predictive and prognostic potential in renal cancer.

Angiogenesis-Related Molecular Subtypes and a Novel Prognostic Signature in Clear Cell Renal Cell Carcinoma Patients

Background

This study aimed to develop and validate a novel angiogenesis-related gene (ARG) signature and molecular subtypes by bioinformatics analysis.

Materials and Methods

The transcriptome data and clinical data were obtained from TCGA and ICGC database. We performed consensus clustering analysis to identify angiogenesis molecular subtypes for ccRCC. Univariate and multivariate Cox regression analyses were used to develop a novel ARG-related signature as a prognostic biomarker for ccRCC. Internal and external validation were then performed in TCGA and ICGC cohort, respectively.

Results

We identified a total of two angiogenesis molecular subtypes of ccRCC. The overall survival (OS) of subtype 1 ccRCC was significantly decreased compared with that of subtype 2 ccRCC (P=0.001). These two molecular subtypes have significantly different tumor microenvironment and immune checkpoint inhibitor sensitivities (P<0.05). Besides, we developed a novel signature based on three ARGs (including MSX1, TIMP1 and JAG2) for subtype 1 ccRCC. The difference in OS between high- and low-risk group was statistically significant in training cohort (P=0.009), test cohort (P=0.024), the whole type 1 cohort (P<0.001), and validation cohort (P=0.041). The AUC for one-year OS prediction was 0.732, 0.710, 0.725, and 0.645 in training cohort, test cohort, the whole type 1 cohort, and validation cohort, respectively. Independent prognostic analysis showed that this signature was an independent predictor for OS of subtype 1 ccRCC (P=0.028914). The power of this prognostic signature was superior to other signatures reported in previous studies.

Conclusion

We developed and successfully validated a novel ARG signature for predicting prognosis of subtype 1 ccRCC, which was superior to several previous signatures.

Altered proTGFα/cleaved TGFα ratios offer new therapeutic strategies in renal carcinoma

BACKGROUND

Treatment of renal cancer has significantly improved with the arrival to the clinic of kinase inhibitors and immunotherapies. However, the disease is still incurable in advanced stages. The fact that several approved inhibitors for kidney cancer target receptor tyrosine kinases (RTKs) suggests that these proteins play a critical role in the pathophysiology of the disease. Based on these precedents, we decided to explore whether RTKs other than those targeted by approved drugs, contribute to the development of kidney cancer.

METHODS

The activation status of 49 RTKs in 44 paired samples of normal and tumor kidney tissue was explored using antibody arrays, with validation by western blotting. Genetic and pharmacologic approaches were followed to study the biological implications of targeting the epidermal growth factor receptor (EGFR) and its ligand Transforming Growth Factor-α (TGFα).

RESULTS

Activation of the EGFR was found in a substantial number of tumors. Moreover, kidney tumors expressed elevated levels of TGFα. Down-regulation of EGFR or TGFα using RNAi or their pharmacological targeting with blocking antibodies resulted in inhibition of the proliferation of in vitro cellular models of renal cancer. Importantly, differences in the molecular forms of TGFα expressed by tumors and normal tissues were found. In fact, tumor TGFα was membrane anchored, while that expressed by normal kidney tissue was proteolytically processed.

CONCLUSIONS

The EGFR-TGFα axis plays a relevant role in the pathophysiology of kidney cancer. This study unveils a distinctive feature in renal cell carcinomas, which is the presence of membrane-anchored TGFα. That characteristic could be exploited therapeutically to act on tumors expressing transmembrane TGFα, for example, with antibody drug conjugates that could recognize the extracellular region of that protein.