Development of a novel gene signature to predict prognosis and response to PD-1 blockade in clear cell renal cell carcinoma

Network analysis of histopathological image features and genomics data improving prognosis performance in clear cell renal cell carcinoma

INTRODUCTION

Clear cell renal cell carcinoma is the most common type of kidney cancer, but the prediction of prognosis remains a challenge.

METHODS

We collected whole-slide histopathological images, corresponding clinical and genetic information from the The Cancer Imaging Archive and The Cancer Genome Atlas databases and randomly divided patients into training (n = 197) and validation (n = 84) cohorts. After feature extraction by CellProfiler, we used 2 different machine learning techniques (Least Absolute Shrinkage and Selector Operation-regularized Cox and Support Vector Machine-Recursive Feature Elimination) and weighted gene co-expression network analysis to select prognosis-related image features and genes, respectively. These features and genes were integrated into a joint model using random forest and used to create a nomogram that combines other predictive indicators.

RESULTS

A total of 4 overlapped features were identified, represented by the computed histopathological risk score in the random forest model, and showed predictive value for overall survival (test set: 1-year area under the curves (AUC) = 0.726, 3-year AUC = 0.727, and 5-year AUC = 0.764). The histopathological-genetic risk score (HGRS) integrating the genetic information computed performed better than the model that used image features only (test set: 1-year AUC = 0.682, 3-year AUC = 0.734, and 5-year AUC = 0.78). The nomogram (gender, stage, and HGRS) achieved the highest net benefit according to decision curve analysis compared to HGRS or clinical model.

CONCLUSION

This study developed a histopathological-genetic-related nomogram by combining histopathological features and clinical predictors, providing a more comprehensive prognostic assessment for clear cell renal cell carcinoma patients.

Assessing the role of programmed cell death signatures and related gene TOP2A in progression and prognostic prediction of clear cell renal cell carcinoma

Kidney Clear Cell Carcinoma (KIRC), the predominant form of kidney cancer, exhibits a diverse therapeutic response to Immune Checkpoint Inhibitors (ICIs), highlighting the need for predictive models of ICI efficacy. Our study has constructed a prognostic model based on 13 types of Programmed Cell Death (PCD), which are intertwined with tumor progression and the immune microenvironment. Validated by analyses of comprehensive datasets, this model identifies seven key PCD genes that delineate two subtypes with distinct immune profiles and sensitivities to anti-PD-1 therapy. The high-PCD group demonstrates a more immune-suppressive environment, while the low-PCD group shows better responses to PD-1 treatment. In particular, TOP2A emerged as crucial, with its inhibition markedly reducing KIRC cell growth and mobility. These findings underscore the relevance of PCDs in predicting KIRC outcomes and immunotherapy response, with implications for enhancing clinical decision-making.

Machine learning-based integration develops a stress response stated T cell (Tstr)-related score for predicting outcomes in clear cell renal cell carcinoma

BACKGROUND

Establishment of a reliable prognostic model and identification of novel biomarkers are urgently needed to develop precise therapy strategies for clear cell renal cell carcinoma (ccRCC). Stress response stated T cells (Tstr) are a new T-cell subtype, which are related to poor disease stage and immunotherapy response in various cancers.

METHODS

10 machine-learning algorithms and their combinations were applied in this work. A stable Tstr-related score (TCs) was constructed to predict the outcomes and PD-1 blockade treatment response in ccRCC patients. A nomogram based on TCs for personalized prediction of patient prognosis was constructed. Functional enrichment analysis and TimiGP algorithm were used to explore the underlying role of Tstr in ccRCC. The key TCs-related gene was identified by comprehensive analysis, and the bioinformatics results were verified by immunohistochemistry using a tissue microarray.

RESULTS

A robust TCs was constructed and validated in four independent cohorts. TCs accurately predicted the prognosis and PD-1 blockade treatment response in ccRCC patients. The novel nomogram was able to precisely predict the outcomes of ccRCC patients. The underlying biological process of Tstr was related to acute inflammatory response and acute-phase response. Mast cells were identified to be involved in the role of Tstr as a protective factor in ccRCC. TNFS13B was shown to be the key TCs-related gene, which was an independent predictor of unfavorable prognosis. The protein expression analysis of TNFSF13B was consistent with the mRNA analysis results. High expression of TNFSF13B was associated with poor response to PD-1 blockade treatment.

CONCLUSIONS

This study provides a Tstr cell-related score for predicting outcomes and PD-1 blockade therapy response in ccRCC. Tstr cells may exert their pro-tumoral role in ccRCC, acting against mast cells, in the acute inflammatory tumor microenvironment. TNFSF13B could serve as a key biomarker related to TCs.

Efficacy and toxicity of immune checkpoint inhibitors combination therapy for advanced renal cell carcinoma: a systematic review and network meta-analysis

Background

Although immune checkpoint inhibitors (ICIs) show a significant overall survival advantage over standard advanced renal cell carcinoma (aRCC) therapies, tumor response to these agents remains poor. Some studies have shown that combination therapy including an ICI appears to be the best treatment; however, the overall benefit in terms of efficacy and toxicity still needs to be assessed. Thus, we performed a network meta-analysis to evaluate the differences in the efficacy of several combinations that include an ICI to provide a basis for clinical treatment selection.

Methods

We conducted a thorough search of PubMed, EMBASE, and the Cochrane Library for articles from January 2010 to June 2023. R 4.4.2 and STATA 16.0 were used to analyze data; hazard ratio (HR) and odds ratio (OR) with 95% confidence intervals (CI) were used to assess the results.

Results

An indirect comparison showed that nivolumab plus cabozantinib and pembrolizumab plus lenvatinib were the most effective treatments for progression-free survival (PFS), with no significant differences between the two interventions (HR, 1.31; 95% CI, 0.96-1.78; P=0.08); rank probability showed that pembrolizumab plus lenvatinib had a 57.1% chance of being the preferred treatment. In the absence of indirect comparisons between pembrolizumab plus axitinib, nivolumab plus ipilimumab, avelumab plus axitinib, nivolumab plus cabozantinib, and pembrolizumab plus lenvatinib, pembrolizumab plus axitinib (40.2%) was the best treatment option for overall survival (OS). Compared to pembrolizumab plus lenvatinib, nivolumab plus ipilimumab (OR, 0.07; 95% CI, 0.01-0.65; P=0.02) and pembrolizumab plus axitinib (OR, 0.05; 95% CI, 0.00-0.78; P<0.001) had a lower incidence of overall adverse events (AEs).

Conclusion

Pembrolizumab plus lenvatinib and pembrolizumab plus axitinib resulted in the highest PFS and OS rates, respectively. Pembrolizumab plus axitinib may be the best option when AEs are a concern.

Systematic review registration

https://inplasy.com/, identifier INPLASY202410078.

PBRM1 mutation and WDR72 expression as potential combinatorial biomarker for predicting the response to Nivolumab in patients with ccRCC: a tumor marker prognostic study

PURPOSE

Immune checkpoint therapy (ICT) provides a new idea for the treatment of advanced clear cell renal cell carcinoma (ccRCC), which can bring significant benefits to patients. However, the clinical application of ICT is limited because of the lack of predictive biomarkers to select potential responders. This study aims to propose a new biomarker to predict the response to Nivolumab in patients with ccRCC.

MATERIALS AND METHODS

The genes that significantly improve the prognosis of ccRCC were retrieved from The Cancer Genome Atlas (TCGA) database. The genomic and clinical data were from patients that had been registered in prospective clinical trials (CheckMate 009, CheckMate 010 and CheckMate 025). TCGA, Gene Expression Omnibus (GEO), and The Human Protein Atlas database were used to analyze the gene and protein expression of WD repeat-containing protein 72 (WDR72) in ccRCC. Gene Ontology (GO) & The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) were performed to dig relevant mechanisms of WDR72. Single sample gene set enrichment analysis (ssGSEA) was conducted to evaluate the role of WDR72 in immune infiltration. Cell proliferation assay, FAO and ATP quantification were used to explore and verify the molecular mechanisms. The expression of WDR72, FOXP3, CD8, and CPT1A was examined by IHC in 20 advanced ccRCC tissue samples at the Urology Department of our hospital. The MethSurv was used to identify PBRM1 and WDR72 gene methylation and its effect on prognosis of ccRCC.

RESULTS

WDR72 is the most significant gene for improving overall survival (OS) in ccRCC. In all three checkmates, OS and progression free survival (PFS) were found to be significantly higher in WDR72 high expression group than that in WDR72 low expression group (P=0.040 and P=0.012, respectively), and similar conclusions could be drawn from the PBRM1-mutation (MUT) compared with the PBRM1-wildtype (WT) (P=0.007 and P=0.006, respectively). What's more, high expression of WDR72 plus PBRM1-MUT as a combinatorial biomarker showed improved OS (HR=0.388, P=0.0026) and PFS (HR=0.39, P=0.0066) compared to low expression of WDR72 plus PBRM1-WT. Functional enrichment analysis showed that WDR72 was closely positively related to fatty acid degradation and fatty acid beta oxidation pathway in ccRCC. In vitro experiments showed that high expression of WDR72 can promote fatty acids oxidation and inhibit the proliferation of ccRCC cells. Immune analysis revealed that WDR72 high expression was associated with decreased infiltration of Treg cells and low ssGSEA score of check-point. IHC results showed that WDR72 was negatively correlated with FOXP3 expression (r=-0.506, P=0.023) and positively correlated with CPT1A expression (r=0.529, P=0.017).

CONCLUSIONS

The present study indicated that high expression of WDR72 may indicate a good prognosis of patients treated with Nivolumab and WDR72 expression combined with PBRM1 mutation could be more persuasive to predict the response for ICT in ccRCC patients.

A novel immune-related gene signature for predicting immunotherapy outcomes and survival in clear cell renal cell carcinoma

Clear cell renal carcinoma (ccRCC) is one of the most common cancers worldwide. In this study, a new model of immune-related genes was developed to predict the overall survival and immunotherapy efficacy in patients with ccRCC. Immune-related genes were obtained from the ImmPort database. Clinical data and transcriptomics of ccRCC samples were downloaded from GSE29609 and The Cancer Genome Atlas. An immune-related gene-based prognostic model (IRGPM) was developed using the least absolute shrinkage and selection operator regression algorithm and multivariate Cox regression. The reliability of the developed models was evaluated by Kaplan-Meier survival curves and time-dependent receiver operating characteristic curves. Furthermore, we constructed a nomogram based on the IRGPM and multiple clinicopathological factors, along with a calibration curve to examine the predictive power of the nomogram. Overall, this study investigated the association of IRGPM with immunotherapeutic efficacy, immune checkpoints, and immune cell infiltration. Eleven IRGs based on 528 ccRCC samples significantly associated with survival were used to construct the IRGPM. Remarkably, the IRGPM, which consists of 11 hub genes (SAA1, IL4, PLAUR, PLXNB3, ANGPTL3, AMH, KLRC2, NR3C2, KL, CSF2, and SEMA3G), was found to predict the survival of ccRCC patients accurately. The calibration curve revealed that the nomogram developed with the IRGPM showed high predictive performance for the survival probability of ccRCC patients. Moreover, the IRGPM subgroups showed different levels of immune checkpoints and immune cell infiltration in patients with ccRCC. IRGPM might be a promising biomarker of immunotherapeutic responses in patients with ccRCC. Overall, the established IRGPM was valuable for predicting survival, reflecting the immunotherapy response and immune microenvironment in patients with ccRCC.

Immune-related gene prognostic index (IRGPI) for lung adenocarcinoma predicts patient prognosis and immunotherapy response

OBJECTIVE

We searched for a predictive biomarker that also predicts whether patients would benefit from immune checkpoint blockade (ICB) treatment from a few angles, because existing biomarkers no longer wholly replicate the interconnections of distinctive elements in the tumor microenvironment (TME).

METHODS

We identified 55 pivotal IRGs by performing a WGCNA and univariate Cox regression analysis on a lung adenocarcinoma dataset from the TCGA database. The IRGPI model was then constructed using multivariate Cox regression analysis, which identified 16 genes and verified the use of the GSE68465 database. The AUC of the IRGPI was compared to those of the current biomarkers to determine its predictive potential. Then we examined the molecular and immunological properties of ICB and assessed its effectiveness using CTLA4 expression and TIDE.

RESULTS

Patients with a high IRGPI had a later clinical stage, more severe symptoms, and a worse prognosis. Patients with a low IRGPI had a higher immune escape potential and were less responsive to immunotherapy.

CONCLUSION

The IRGPI may be a biomarker for determining the prognosis of patients and whether they respond favorably to ICB therapy.

Identification and validation of a genetic risk signature associated with prognosis in clear-cell renal cell carcinoma patients

Clear-cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma (RCC), which exhibits great variability in the prognosis of patients. Endoplasmic reticulum stress (ERS) is a persistent state triggered by disruption of endoplasmic reticulum (ER) homeostasis, which has been shown to control multiple pro-tumor-associated pathways in malignant cells while dynamically reprogramming immune cell function. This study aimed to identify ERS-related genetic risk signatures (ERSGRS) to ameliorate survival prediction in ccRCC patients. In this study, we adopted differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) and constructed ERSGRS with independent prognostic significance by least absolute shrinkage and selection operator (LASSO) regression. After separation of patients based on risk score, survival analysis showed that low-risk patients had longer overall survival (OS) than high-risk patients, and receiver operating characteristic (ROC) curve analysis confirmed the strong predictive ability of ERSGRS. Meanwhile, the tumor microenvironment (TME) of the high-risk group demonstrated an immunosuppressive phenotype, with more infiltration of regulatory T cells (Tregs) and macrophages. The TME in the low-risk group had a stronger potential for anti-tumor immunity. Overall, the ERSGRS could be a valuable predictive tool for ccRCC prognosis.

Integrative analysis of multi-omics data for discovery of ferroptosis-related gene signature predicting immune activity in neuroblastoma

Immunotherapy for neuroblastoma remains unsatisfactory due to heterogeneity and weak immunogenicity. Exploring powerful signatures for the evaluation of immunotherapy outcomes remain the primary purpose. We constructed a ferroptosis-related gene (FRG) signature by least absolute shrinkage and selection operator and Cox regression, identified 10 independent prognostic FRGs in a training cohort (GSE62564), and then verified them in an external validation cohort (TCGA). Associated with clinical factors, the signature accurately predicts overall survival of 3, 5, and 10 years. An independent prognostic nomogram, which included FRG risk, age, stage of the International Neuroblastoma Staging System, and an MYCN status, was constructed. The area under the curves showed satisfactory prognostic predicting performance. Through bulk RNA-seq and proteomics data, we revealed the relationship between hub genes and the key onco-promoter MYCN gene and then validated the results in MYCN-amplified and MYCN-non-amplified cell lines with qRT-PCR. The FRG signature significantly divided patients into high- and low-risk groups, and the differentially expressed genes between the two groups were enriched in immune actions, autophagy, and carcinogenesis behaviors. The low-risk group embodied higher positive immune component infiltration and a higher expression of immune checkpoints with a more favorable immune cytolytic activity (CYT). We verified the predictive power of this signature with data from melanoma patients undergoing immunotherapy, and the predictive power was satisfactory. Gene mutations were closely related to the signature and prognosis. AURKA and PRKAA2 were revealed to be nodal hub FRGs in the signature, and both were shown to have significantly different expressions between the INSS stage IV and other stages after immunohistochemical validation. With single-cell RNA-seq analysis, we found that genes related to T cells were enriched in TNFA signaling and interferon-γ hallmark. In conclusion, we constructed a ferroptosis-related gene signature that can predict the outcomes and work in evaluating the effects of immunotherapy.

Prognostic prediction and immunotherapy response analysis of the fatty acid metabolism-related genes in clear cell renal cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is a common urinary cancer. Although diagnostic and therapeutic approaches for ccRCC have been improved, the survival outcomes of patients with advanced ccRCC remain unsatisfactory. Fatty acid metabolism (FAM) has been increasingly recognized as a critical modulator of cancer development. However, the significance of the FAM in ccRCC remains unclear. Herein, we explored the function of a FAM-related risk score in the stratification and prediction of treatment responses in patients with ccRCC.

Methods

First, we applied an unsupervised clustering method to categorize patients from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets into subtypes and retrieved FAM-related genes from the MSigDB database. We discern differentially expressed genes (DEGs) among different subtypes. Then, we applied univariate Cox regression analysis followed by least absolute shrinkage and selection operator (LASSO) linear regression based on DEGs expression to establish a FAM-related risk score for ccRCC.

Results

We stratified the three ccRCC subtypes based on FAM-related genes with distinct overall survival (OS), clinical features, immune infiltration patterns, and treatment sensitivities. We screened nine genes from the FAM-related DEGs in the three subtypes to establish a risk prediction model for ccRCC. Nine FAM-related genes were differentially expressed in the ccRCC cell line ACHN compared to the normal kidney cell line HK2. High-risk patients had worse OS, higher genomic heterogeneity, a more complex tumor microenvironment (TME), and elevated expression of immune checkpoints. This phenomenon was validated in the ICGC cohort.

Conclusion

We constructed a FAM-related risk score that predicts the prognosis and therapeutic response of ccRCC. The close association between FAM and ccRCC progression lays a foundation for further exploring FAM-related functions in ccRCC.

Cancer immunotherapy with immune checkpoint inhibitors (ICIs): potential, mechanisms of resistance, and strategies for reinvigorating T cell responsiveness when resistance is acquired

Cancer is still the leading cause of death globally. The approval of the therapeutic use of monoclonal antibodies against immune checkpoint molecules, notably those that target the proteins PD-1 and PD-L1, has changed the landscape of cancer treatment. In particular, first-line PD-1/PD-L1 inhibitor drugs are increasingly common for the treatment of metastatic cancer, significantly prolonging patient survival. Despite the benefits brought by immune checkpoint inhibitors (ICIs)-based therapy, the majority of patients had their diseases worsen following a promising initial response. To increase the effectiveness of ICIs and advance our understanding of the mechanisms causing cancer resistance, it is crucial to find new, effective, and tolerable combination treatments. In this article, we addressed the potential of ICIs for the treatment of solid tumors and offer some insight into the molecular pathways behind therapeutic resistance to ICIs. We also discuss cutting-edge therapeutic methods for reactivating T-cell responsiveness after resistance has been established.

Solute carrier-related signature for assessing prognosis and immunity in patients with clear-cell renal cell carcinoma

Background

Clear-cell renal cell carcinoma (ccRCC) is the most common malignant kidney cancer. However, the tumor microenvironment and crosstalk involved in metabolic reprogramming in ccRCC are not well-understood.

Methods

We used The Cancer Genome Atlas to obtain ccRCC transcriptome data and clinical information. The E-MTAB-1980 cohort was used for external validation. The GENECARDS database contains the first 100 solute carrier (SLC)-related genes. The predictive value of SLC-related genes for ccRCC prognosis and treatment was assessed using univariate Cox regression analysis. An SLC-related predictive signature was developed through Lasso regression analysis and used to determine the risk profiles of patients with ccRCC. Patients in each cohort were separated into high- and low-risk groups based on their risk scores. The clinical importance of the signature was assessed through survival, immune microenvironment, drug sensitivity, and nomogram analyses using R software.

Results

SLC25A23, SLC25A42, SLC5A1, SLC3A1, SLC25A37, SLC5A6, SLCO5A1, and SCP2 comprised the signatures of the eight SLC-related genes. Patients with ccRCC were separated into high- and low-risk groups based on the risk value in the training and validation cohorts; the high-risk group had a significantly worse prognosis (p < 0.001). The risk score was an independent predictive indicator of ccRCC in the two cohorts according to univariate and multivariate Cox regression (p < 0.05). Analysis of the immune microenvironment showed that immune cell infiltration and immune checkpoint gene expression differed between the two groups (p < 0.05). Drug sensitivity analysis showed that compared to the low-risk group, the high-risk group was more sensitive to sunitinib, nilotinib, JNK-inhibitor-VIII, dasatinib, bosutinib, and bortezomib (p < 0.001). Survival analysis and receiver operating characteristic curves were validated using the E-MTAB-1980 cohort.

Conclusions

SLC-related genes have predictive relevance in ccRCC and play roles in the immunological milieu. Our results provide insight into metabolic reprogramming in ccRCC and identify promising treatment targets for ccRCC.

A novel gene signature related to oxidative stress predicts the prognosis in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is considered to be related to the worse prognosis, which might in part be attributed to the early recurrence and metastasis, compared with other type of kidney cancer. Oxidative stress refers to an imbalance between production of oxidants and antioxidant defense. Accumulative studies have indicated that oxidative stress genes contribute to the tumor invasion, metastasis and drug sensitivity. However, the biological functions of oxidative stress genes in ccRCC remain largely unknown. In this study, we identified 1,399 oxidative stress genes from GeneCards with a relevance score ≥7. Data for analysis were accessed from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) database, and were utilized as training set and validation set respectively. Univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox were employed to construct a prognostic signature in ccRCC. Finally, a prognostic signature including four different oxidative stress genes was constructed from 1,399 genes, and its predictive performance was verified through Kaplan-Meier survival analysis and the receiver operating characteristic (ROC) curve. Interestingly, we found that there was significant correlation between the expression of oxidative stress genes and the immune infiltration and the sensitivity of tumor cells to chemotherapeutics. Moreover, the highest hazard ratio gene urocortin (UCN) was chosen for further study; some necessary vitro experiments proved that the UCN could promote the ability of ccRCC proliferation and migration and contribute to the degree of oxidative stress. In conclusion, it was promising to predict the prognosis of ccRCC through the four oxidative stress genes signature. UCN played oncogenic roles in ccRCC by influencing proliferation and oxidative stress pathway, which was expected to be the novel therapeutic target for ccRCC.

Prognostic prediction of clear cell renal cell carcinoma based on lipid metabolism-related lncRNA risk coefficient model

Objective: In order to predict the prognosis in patients with clear cell renal cell carcinoma (ccRCC) so as to understand cancer lipid metabolism and sensitivity to immune-targeting drugs, model algorithms were used to establish a risk coefficient model of long non-coding RNAs (lncRNAs) associated with lipid metabolism. Methods: The transcriptome data were retrieved from TCGA, and lncRNAs associated with lipid metabolism were obtained through Pearson correlation and differential expression analyses. Differentially expressed lipid metabolism-related lncRNAs and lipid metabolism-related lncRNA pairs were obtained using the R language software. The minimum absolute shrinkage method and the selector operation regression method were used to construct the model and draw the receiver operator characteristic curve. High-risk patients were differentiated from low-risk patients through the cut-off value, and the correlation analyses of the high-risk subgroup and low-risk subgroup were performed. Results: This research discovered that 25 pairs of lncRNAs were associated with the lipid metabolism of ccRCC, and 12 of these pairs were utilized to build the model. In combination with clinical data, the areas under the 1-, 3- and 5-year survival curves of ccRCC patients were 0.809, 0.764 and 0.792, separately. The cut-off value was used to perform subgroup analysis. The results showed that high-risk patients had poor prognosis. The results of Cox multivariate regressive analyses revealed that age and risk score were independent prediction factors of ccRCC prognosis. In addition, immune cell infiltration, the levels of gene expression at immune checkpoints, and high-risk patients more susceptible to sunitinib-targeted treatment were assessed by the risk model. Conclusion: Our team identified new prognostic markers of ccRCC and established risk models that could assess the prognosis of ccRCC patients and help determine which type of patients were more susceptible to sunitinib. These discoveries are vital for the optimization of risk stratification and personalized management.

Prognostic value of programmed cell death ligand 1 expression in patients with intrahepatic cholangiocarcinoma: a meta-analysis

Background

Programmed cell death ligand 1 (PD-L1) is highly expressed in intrahepatic cholangiocarcinoma (ICC) tissues. But there is still a dispute over the prognostic value of PD-L1 in patients with ICC. This study aimed to evaluate the prognostic value of PD-L1 expression in patients with ICC.

Methods

We performed a meta-analysis based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines. We searched the literature from PubMed, Embase, Web of Science, and the Cochrane Library up to December 5, 2022. Hazard ratios (HR) and their 95% confidence intervals (95% CI) were calculated to analyze the overall survival (OS), recurrence-free survival (RFS), and time to relapse. The quality of the studies was assessed using the Newcastle-Ottawa scale. Publication bias was assessed using a funnel plot and Egger's test.

Results

Ten trials with 1944 cases were included in this meta-analysis. The results showed that the low-PD-L1 group had a statistically significant advantage in OS (HR, 1.57; 95% CI, 1.38-1.79, P <0.00001), RFS (HR, 1.62; 95% CI, 1.34-1.97, P <0.00001), and time to relapse (HR, 1.60; 95% CI, 1.25-2.05, P = 0.0002) compared with the high-PD-L1 group. High programmed cell death (PD1)levels, on the other hand, were correlated with poorer OS (HR, 1.96; 95% CI, 1.43-2.70; P <0.0001) and RFS (HR, 1.87; 95% CI, 1.21-2.91; P = 0.005). Multivariate analysis showed that PD-L1 could act as an independent predictor for OS (HR, 1.48; 95% CI, 1.14-1.91; P = 0.003) and RFS (HR, 1.74; 95% CI, 1.22-2.47; P = 0.002), and PD1 acted as an independent predictor for OS (HR, 1.66; 95% CI, 1.15-2.38; P = 0.006).

Conclusion

This meta-analysis demonstrated that high PD-L1/PD1 expression is associated with poor survival in ICC. PD-L1/PD1 may be a valuable prognostic and predictive biomarker and potential therapeutic target in ICC.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022380093.

Prognostic and immunological significance of calcium-related gene signatures in renal clear cell carcinoma

Background: Calcium signaling is implicated in multiple processes including immune response that important in tumor progression. Kidney renal clear cell carcinoma (KIRC) is the most frequent histological type of renal cell carcinoma with up to a third of cases develop metastases. As a result of a lack of in-depth understanding of the mechanisms underlying KIRC, treatment options have been limited. Here, we aim to comprehensively investigate the landscape of Ca²⁺ channels, pumps and exchangers in KIRC patients. Methods: The mRNA expression profiles and gene variations of 58 calcium-related genes (CRGs) in KIRC patients and normal control cases were downloaded from TCGA database. CRGs-related risk score was constructed to quantify calcium patterns by using least absolute shrinkage and selection operator (LASSO) regression. The prognostic value, biological functions, immune landscape and therapeutic sensitivities based on CRGs-related risk score were then evaluated using multiple methods. Finally, key gene of CRGs was identified by weighted gene co-expression network analysis (WGCNA). TCGA-CPTAC, GSE53757 datasets, as well as human tissues were used for validation. Results: KIRC patients had significant differences in CRG expression, prognosis, and biological functions between two CRG clusters. CRGs-related risk score was then determined. The prognosis, tumor mutation burden, immune cell infiltration, immune checkpoints, and the response of targeted inhibitors were remarkably different between high and low CRGs-related risk subtypes. CRGs-related high-risk subtype was characterized by immunosuppressive microenvironment with poor prognosis. Meanwhile, several targeted drugs showed distinct sensitivity between CRGs-related risk subtypes. Finally, TRPM3 was identified as a key CRG based on risk score in KIRC patients. TRPM3 mRNA and protein expression were significantly lower in KIRC tumors than in normal controls. Low TRPM3 expression was associated with poor prognosis in KIRC patients. Conclusion: Our study highlighted the promising prognostic value of CRGs in KIRC tumors. The evaluation of CRGs-related risk score will contribute to predicting prognosis and clinical therapy in KIRC patients.

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.

Construction and validation of a novel ten miRNA-pair based signature for the prognosis of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most predominate pathological subtype of renal cell carcinoma, causing a recurrence or metastasis rate as high as 20% to 40% after operation, for which effective prognostic signature is urgently needed.

METHODS

The mRNA and miRNA profiles of ccRCC specimens were collected from the Cancer Genome Atlas. MiRNA-pair risk score (miPRS) for each miRNA pair was generated as a signature and validated by univariate and multivariate Cox proportional hazards regression analysis. Functional enrichment was performed, and immune cells infiltration, as well as tumor mutation burden (TMB), and immunophenoscore (IPS) were evaluated between high and low miPRS groups. Target gene-prediction and differentially expressed gene-analysis were performed based on databases of miRDB, miRTarBase, and TargetScan. Multivariate Cox proportional hazards regression analysis was adopted to establish the prognostic model and Kaplan-Meier survival analysis was performed.

FINDINGS

A novel 10 miRNA-pair based signature was established. Area under the time-dependent receiver operating curve proved the performance of the signature in the training, validation, and testing cohorts. Higher TMB, as well as the higher CTLA4-negative PD1-negative IPS, were discovered in high miPRS patients. A prognostic model was built based on miPRS (1 year-, 5 year-, 10 year- ROC-AUC=0.92, 0.84, 0.82, respectively).

INTERPRETATION

The model based on miPRS is a novel and valid tool for predicting the prognosis of ccRCC.

FUNDING

This study was supported by research grants from the China National Natural Scientific Foundation (81903972, 82002018, and 82170752) and Shanghai Sailing Program (19YF1406700 and 20YF1406000).

Aging-based molecular classification and score system in ccRCC uncovers distinct prognosis, tumor immunogenicity, and treatment sensitivity

Objective

Aging is a complex biological process and a major risk factor for cancer development. This study was conducted to develop a novel aging-based molecular classification and score system in clear cell renal cell carcinoma (ccRCC).

Methods

Integrative analysis of aging-associated genes was performed among ccRCC patients in the TCGA and E-MTAB-1980 cohorts. In accordance with the transcriptional expression matrix of 173 prognostic aging-associated genes, aging phenotypes were clustered with the consensus clustering approach. The agingScore was generated to quantify aging phenotypes with principal component analysis. Tumor-infiltrating immune cells and the cancer immunity cycle were quantified with the ssGSEA approach. Immunotherapy response was estimated through the TIDE algorithm, and a series of tumor immunogenicity indicators were computed. Drug sensitivity analysis was separately conducted based on the GDSC, CTRP, and PRISM analyses.

Results

Three aging phenotypes were established for ccRCC, with diverse prognosis, clinical features, immune cell infiltration, tumor immunogenicity, immunotherapeutic response, and sensitivity to targeted drugs. The agingScore was developed, which enabled to reliably and independently predict ccRCC prognosis. Low agingScore patients presented more undesirable survival outcomes. Several small molecular compounds and three therapeutic targets, namely, CYP11A1, SAA1, and GRIK4, were determined for the low agingScore patients. Additionally, the high agingScore patients were more likely to respond to immunotherapy.

Conclusion

Overall, our findings introduced an aging-based molecular classification and agingScore system into the risk stratification and treatment decision-making in ccRCC.

An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer

Simple Summary

Bladder cancer is evidently a challenge as far as its prognosis and treatment are concerned. The investigation of potential biomarkers and therapeutic targets is indispensable and still in progress. Most studies attempt to identify differential signatures between distinct molecular tumor subtypes. Therefore, keeping in mind the heterogeneity of urinary bladder tumors, we attempted to identify a consensus gene-related signature between the common expression profile of bladder cancer and control samples. In the quest for substantive features, we were able to identify key hub genes, whose signatures could hold diagnostic, prognostic, or therapeutic significance, but, primarily, could contribute to a better understanding of urinary bladder cancer biology.

Abstract

Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high morbidity and mortality. This study intended to elucidate potential key biomarkers related to the occurrence, development, and prognosis of BCa through an integrated bioinformatics analysis. In this context, a systematic meta-analysis, integrating 18 microarray gene expression datasets from the GEO repository into a merged meta-dataset, identified 815 robust differentially expressed genes (DEGs). The key hub genes resulted from DEG-based protein–protein interaction and weighted gene co-expression network analyses were screened for their differential expression in urine and blood plasma samples of BCa patients. Subsequently, they were tested for their prognostic value, and a three-gene signature model, including COL3A1, FOXM1, and PLK4, was built. In addition, they were tested for their predictive value regarding muscle-invasive BCa patients’ response to neoadjuvant chemotherapy. A six-gene signature model, including ANXA5, CD44, NCAM1, SPP1, CDCA8, and KIF14, was developed. In conclusion, this study identified nine key biomarker genes, namely ANXA5, CDT1, COL3A1, SPP1, VEGFA, CDCA8, HJURP, TOP2A, and COL6A1, which were differentially expressed in urine or blood of BCa patients, held a prognostic or predictive value, and were immunohistochemically validated. These biomarkers may be of significance as prognostic and therapeutic targets for BCa.

A bioinformatics-based immune-related prognostic index for lung adenocarcinoma that predicts patient response to immunotherapy and common treatments

Background

There is increasing evidence of the effectiveness of immune checkpoint blockade (ICB) therapy for the treatment of lung adenocarcinoma (LUAD). However, the benefits of ICB therapy vary among LUAD patients. Due to the research dimension, existing biomarkers, such as programmed death-ligand 1 (PD-L1) expression and tumor mutation burden (TMB), could not reflect the complex tumor environment, and had low prediction accuracy of ICB. Therefore, we aimed to uncover a prognostic biomarker that could also predict whether a patient would benefit from ICB therapy and other common treatments from multiple dimensions, so as to improve the prediction accuracy of pre-treatment patients.

Methods

Based on the LUAD dataset retrieved from The Cancer Genome Atlas (TCGA) database, 50 immune-related hub genes were identified using weighted gene co-expression network analysis and univariate Cox regression analyses. An immune-related gene prognostic index (IRGPI) was constructed using a Cox proportional-hazards model based on 15 genes and validated using GSE72094 dataset. We tested its prognostic accuracy by Kaplan-Meier (K-M) survival curves of the two datasets and assessed its predictive power by comparing area under curve (AUC) of IRGPI with existing biomarkers. Subsequently, we analyzed the molecular and immune characteristics, and evaluated the benefits of ICB by PD-L1 expression and Tumor Immune Dysfunction and Exclusion (TIDE) analysis, predicted the inhibitory concentration 50 of common treatments drugs for two IRGPI score-related subgroups.

Results

Patients in the IRGPI-high subgroup had lower overall survival (OS) than patients in the IRGPI-low subgroup in K-M survival curve in two cohorts. And IRGPI has AUC values of 0.715, 0.724, and 0.743 in 1, 2, and 3 years, respectively. A higher tumor mutation burden and PD-L1 expression and the tumor microenvironment (TME) landscape demonstrated that IRGPI-high subgroup patients may respond better to ICB therapy. Genomics of Drug Sensitivity in Cancer (GDSC) analysis indicated that the IRGPI-high subgroup showed greater sensitivity to chemotherapy.

Conclusions

IRGPI is a prospective biomarker for evaluating whether a patient will benefit from ICB therapy and other treatments, and distinguishing patients with different molecular and immune characteristics.

An immune gene signature to predict prognosis and immunotherapeutic response in lung adenocarcinoma

Lung adenocarcinoma is one of the most common malignant tumors worldwide. The purpose of this study was to construct a stable immune gene signature for prediction of prognosis (IGSPP) and response to immune checkpoint inhibitors (ICIs) therapy in LUAD patients. Five genes were screened by weighted gene coexpression network analysis, Cox regression and LASSO regression analyses and were used to construct the IGSPP. The survival rate of the IGSPP low-risk group was higher than that of the IGSPP high-risk group. Multivariate Cox regression analysis showed that IGSPP could be used as an independent prognostic factor for the overall survival of LUAD patients. IGSPP genes were enriched in cell cycle pathways. IGSPP gene mutation rates were higher in the high-risk group. CD4 memory-activated T cells, M0 and M1 macrophages had higher infiltration abundance in the high-risk group, which was associated with poor overall survival. In contrast, the abundance of resting CD4 memory T cells, monocytes, resting dendritic cells and resting mast cells associated with a better prognosis was higher in the low-risk group. TIDE scores and the expressions of different immune checkpoints showed that patients in the high-risk IGSPP group benefited more from ICIs treatment. In short, an IGSPP of LUAD was constructed and characterized. It could be used to predict the prognosis and benefits of ICIs treatment in LUAD patients.

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.

Crosstalk Between Metabolism and Immune Activity Reveals Four Subtypes With Therapeutic Implications in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is characterized by metabolic dysregulation and distinct immunological signatures. The interplay between metabolic and immune processes in the tumor microenvironment (TME) causes the complexity and heterogeneity of immunotherapy responses observed during ccRCC treatment. Herein, we initially identified two distinct metabolic subtypes (C1 and C2 subtypes) and immune subtypes (I1 and I2 subtypes) based on the occurrence of differentially expressed metabolism-related prognostic genes and immune-related components. Notably, we observed that immune regulators with upregulated expression actively participated in multiple metabolic pathways. Therefore, we further delineated four immunometabolism-based ccRCC subtypes (M1, M2, M3, and M4 subtypes) according to the results of the above classification. Generally, we found that high metabolic activity could suppress immune infiltration. Immunometabolism subtype classification was associated with immunotherapy response, with patients possessing the immune-inflamed, metabolic-desert subtype (M3 subtype) that benefits the most from immunotherapy. Moreover, differences in the shifts in the immunometabolism subtype after immunotherapy were observed in the responder and non-responder groups, with patients from the responder group transferring to subtypes with immune-inflamed characteristics and less active metabolic activity (M3 or M4 subtype). Immunometabolism subtypes could also serve as biomarkers for predicting immunotherapy response. To decipher the genomic and epigenomic features of the four subtypes, we analyzed multiomics data, including miRNA expression, DNA methylation status, copy number variations occurrence, and somatic mutation profiles. Patients with the M2 subtype possessed the highest VHL gene mutation rates and were more likely to be sensitive to sunitinib therapy. Moreover, we developed non-invasive radiomic models to reveal the status of immune activity and metabolism. In addition, we constructed a radiomic prognostic score (PRS) for predicting ccRCC survival based on the seven radiomic features. PRS was further demonstrated to be closely linked to immunometabolism subtype classification, immune score, and tumor mutation burden. The prognostic value of the PRS and the association of the PRS with immune activity and metabolism were validated in our cohort. Overall, our study established four immunometabolism subtypes, thereby revealing the crosstalk between immune and metabolic activities and providing new insights into personal therapy selection.