Identification and validation of a hypoxia-related prognostic signature in clear cell renal cell carcinoma patients

HDAC8 Enhances the Function of HIF-2α by Deacetylating ETS1 to Decrease the Sensitivity of TKIs in ccRCC

Drug resistance after long-term use of Tyrosine kinase inhibitors (TKIs) has become an obstacle for prolonging the survival time of patients with clear cell renal cell carcinoma (ccRCC). Here, genome-wide CRISPR-based screening to reveal that HDAC8 is involved in decreasing the sensitivity of ccRCC cells to sunitinib is applied. Mechanically, HDAC8 deacetylated ETS1 at the K245 site to promote the interaction between ETS1 and HIF-2α and enhance the transcriptional activity of the ETS1/HIF-2α complex. However, the antitumor effect of inhibiting HDAC8 on sensitized TKI is not very satisfactory. Subsequently, inhibition of HDAC8 increased the expression of NEK1, and up-regulated NEK1 phosphorylated ETS1 at the T241 site to promote the interaction between ETS1 and HIF-2α by impeded acetylation at ETS1-K245 site is showed. Moreover, TKI treatment increased the expression of HDAC8 by inhibiting STAT3 phosphorylation in ccRCC cells is also found. These 2 findings highlight a potential mechanism of acquired resistance to TKIs and HDAC8 inhibitors in ccRCC. Finally, HDAC8-in-PROTACs to optimize the effects of HDAC8 inhibitors through degrading HDAC8 and overcoming the resistance of ccRCC to TKIs are synthesized. Collectively, the results revealed HDAC8 as a potential therapeutic candidate for resistance to ccRCC-targeted therapies.

Bridging the gap between clear cell renal cell carcinoma and cutaneous melanoma: the role of SCARB1 in dysregulated cholesterol metabolism

OBJECTIVE

The metabolism of cholesterol has been found to be closely related to the proliferation, invasion, and metastasis of tumors. The purpose of this study was to investigate the correlation between cholesterol metabolic genes and the prognosis of clear cell renal cell carcinoma (ccRCC).

METHODS

Gene expression profiles and clinical information of individuals diagnosed with prevalent malignant tumors were obtained from the TCGA database. For survival analysis, Kaplan-Meier curves were used. Consensus clustering was utilized to identify distinct molecular clusters. LASSO regression analysis was utilized to construct a novel prognostic signature. Differential analysis was used to analyze the differences in gene expression and various evaluation indicators between different subgroups. RT-qPCR and Immunohistochemistry were performed to examine the gene expression. Small interfering RNA transfection, CCK-8, and clone formation assays were conducted to verify the function of the target gene in ccRCC cell lines.

RESULTS

Based on genes involved in cholesterol metabolism related to survival, two molecular ccRCC subtypes were identified with distinct clinical, immune, and biological features. A molecular signature which would be utilized to evaluate the prognosis and the immune status of the tumor microenvironment of ccRCC patients was also established. The SCARB1-mediated cholesterol-dependent metabolism occurred both in ccRCC and skin cutaneous melanoma.

CONCLUSION

A gene signature related to cholesterol metabolism was developed and validated to forecast the prognosis of ccRCC, demonstrating a correlation with immune infiltration. Cholesterol metabolic genes such as SCARB1, were expected to contribute to the diagnosis and precision treatment of both ccRCC and skin cutaneous melanoma.

In Vitro and In Silico Analysis of Epithelial-Mesenchymal Transition and Cancer Stemness as Prognostic Markers of Clear Cell Renal Cell Carcinoma

The process of epithelial-mesenchymal transition (EMT) involves the phenotypic transformation of cells from epithelial to mesenchymal status. The cells exhibiting EMT contain features of cancer stem cells (CSC), and the dual processes are responsible for progressive cancers. Activation of hypoxia-inducible factors (HIF) is fundamental to the pathogenesis of clear cell renal cell carcinoma (ccRCC), and their role in promoting EMT and CSCs is crucial for ccRCC tumour cell survival, disease progression, and metastatic spread. In this study, we explored the status of HIF genes and their downstream targets, EMT and CSC markers, by immunohistochemistry on in-house accrued ccRCC biopsies and adjacent non-tumorous tissues from patients undergoing partial or radical nephrectomy. In combination, we comprehensively analysed the expression of HIF genes and its downstream EMT and CSC-associated targets relevant to ccRCC by using publicly available datasets, the cancer genome atlas (TCGA) and the clinical proteome tumour analysis consortium (CPTAC). The aim was to search for novel biological prognostic markers that can stratify high-risk patients likely to experience metastatic disease. Using the above two approaches, we report the development of novel gene signatures that may help to identify patients at a high risk of developing metastatic and progressive disease.

SLC9A1 Binding mTOR Signaling Pathway-Derived Risk Score Predicting Survival and Immune in Clear Cell Renal Cell Carcinoma

Objective. Clear cell renal cell carcinoma (ccRCC) is one of the common renal cell carcinomas (RCC) with a high risk of recurrence. Considering that SLC9A1 is involved in various cellular physiological processes and probably mediates the course of mTOR signaling in tumors, this study constructed a risk model for SLC9A1 combined with mTOR signaling in ccRCC, aiming at better predicting the prognosis of patients. Methods. ccRCC expression matrices were downloaded from TCGA and ICGC databases to compare the expression of SLC9A1 in TCGA, and qRT-PCR was adopted to validate the SLC9A1 expression in different RCC cells and normal kidney cells. The CIBERSORT and ESTIMATE algorithms were used to assess samples for immunity. mTOR signaling-associated genes were downloaded from the KEGG website, and then the genes were adopted to screen genes associated with SLC9A1 expression and mTOR signaling pathway colleagues, based on which univariate COX regression and lasso regression Cox analyses were conducted to construct a ccRCC prognostic risk model. ROC curves and nomograms were used to assess the validity of the models. Results. ccRCC tumor samples showed lower SLC9A1 expression than normal samples, as also evidenced by qRT-PCR. The SLC9A1 expression was highly correlated with tumor immunity. Totally, 564 key genes associated with both SLC9A1 expression and mTOR signaling were screened out, and the risk model consisting of 11 gene signatures was constructed in ccRCC based on the 564 genes. Since patients at a high risk had poorer survival outcomes, the high-risk group presented poorer immunotherapy outcomes. Moreover, a higher clinical grade of patients suggested a higher risk score. The risk score can serve as one independent prognostic factor for the prognosis prediction of ccRCC patients. Conclusion. An extremely promising prognostic indicator for ccRCC based on SLCA9A1 and mTOR signaling has been constructed to provide reference for clinical treatment.

Cuproptosis-related lncRNA: Prediction of prognosis and subtype determination in clear cell renal cell carcinoma

Background: Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma, accounting for approximately 70% of all RCC cases. Cuproptosis, a novel mechanism of cell death, may be a potential target for intervention in tumor development.

Methods: Cuproptosis-related prognostic lncRNAs were identified by co-expression analysis and univariable Cox regression. Five lncRNA profiles were obtained by LASSO regression analysis, and a model with high accuracy was constructed to assess the prognosis of ccRCC patients based on these cuproptosis-related lncRNAs. Survival analysis and time-dependent ROC curves were performed for the α and β groups, and the results confirmed the high accuracy of the model in predicting the prognosis of ccRCC patients. Immunoassay, principal component analysis (PCA), and drug sensitivity analysis were also performed for different risk categories. Finally, we classified ccRCC patients into two different subtypes by consistent class clustering, and performed immune checkpoint activation, tumor microenvironment analysis, PCA, and drug sensitivity analysis for different subtypes.

Results: We developed a prognostic model using five cuproptosis-associated lncRNAs, which was found to be highly accurate in predicting ccRCC patients’ prognosis. Immunotherapy may be more beneficial to the hyper-risk category and the C2 subtype.

Conclusion: The results of this study confirm that five cuproptosis-associated lncRNAs can be used as potential prognostic markers for ccRCC.

An inflammation-related signature could predict the prognosis of patients with kidney renal clear cell carcinoma

Background: Kidney renal clear cell carcinoma (KIRC) is an inflammation-related carcinoma, and inflammation has been recognized as an important factor in inducing carcinogenesis. To further explore the role of inflammation in KIRC, we developed an inflammation-related signature and verified its correlation with the tumor micro-environment.

Methods: After the differential inflammation-related prognostic genes were screened by Lasso regression, the inflammation-related signature (IRS) was constructed based on the risk score of multivariate Cox regression. Then, the prognostic value of the IRS was evaluated by Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis and multivariate Cox regression. Gene set variation analysis (GSVA) was applied to screen out enriched signaling pathways. Infiltrated immune cells, tumor mutational burden (TMB) and immune checkpoints were explored by CIBERSORTx and maftool.

Results: Four genes (TIMP1, PLAUR, CCL22, and IL15RA) were used to construct the IRS in patients with KIRC. Kaplan-Meier analysis and multivariate Cox regression identified that the IRS could independently predict the prognosis of patients with KIRC in the training and validation groups. The diagnostic value of the nomogram increased from 0.811 to 0.845 after adding the IRS to the multiparameter ROC analysis. The GSVA results indicated that IRS was closely related to primary immunodeficiency and antigen processing and presentation. The immune checkpoint LAG3 was highly expressed in patients with high-risk score (p < 0.05), while CD274 (PD-L1) and HAVCR2 were highly expressed in patients with low-risk score (p < 0.001). There was a significant positive correlation between the high-risk score group and CD8⁺ T, activated CD4⁺ memory T, gamma and delta regulatory T and M0 macrophage cells, while the low-risk score group was negatively associated with B memory, plasma, resting CD4⁺ memory T, activated NK, M1 macrophages and resting mast cells.

Conclusion: We found that the IRS might serve as a biomarker to predict the survival of KIRC. Moreover, patients with high or low-risk score might be sensitive to immune drugs at different immune checkpoints.

Cancer-Associated Fibroblasts: Mechanisms of Tumor Progression and Novel Therapeutic Targets

Simple Summary

The tumor microenvironment plays an important role in determining the biological behavior of several of the more aggressive malignancies. Among the various cell types evident in the tumor “field”, cancer-associated fibroblasts (CAFs) are a heterogenous collection of activated fibroblasts secreting a wide repertoire of factors that regulate tumor development and progression, inflammation, drug resistance, metastasis and recurrence. Insensitivity to chemotherapeutics and metastatic spread are the major contributors to cancer patient mortality. This review discusses the complex interactions between CAFs and the various populations of normal and neoplastic cells that interact within the dynamic confines of the tumor microenvironment with a focus on the involved pathways and genes.

Abstract

Cancer-associated fibroblasts (CAFs) are a heterogenous population of stromal cells found in solid malignancies that coexist with the growing tumor mass and other immune/nonimmune cellular elements. In certain neoplasms (e.g., desmoplastic tumors), CAFs are the prominent mesenchymal cell type in the tumor microenvironment, where their presence and abundance signal a poor prognosis in multiple cancers. CAFs play a major role in the progression of various malignancies by remodeling the supporting stromal matrix into a dense, fibrotic structure while secreting factors that lead to the acquisition of cancer stem-like characteristics and promoting tumor cell survival, reduced sensitivity to chemotherapeutics, aggressive growth and metastasis. Tumors with high stromal fibrotic signatures are more likely to be associated with drug resistance and eventual relapse. Clarifying the molecular basis for such multidirectional crosstalk among the various normal and neoplastic cell types present in the tumor microenvironment may yield novel targets and new opportunities for therapeutic intervention. This review highlights the most recent concepts regarding the complexity of CAF biology including CAF heterogeneity, functionality in drug resistance, contribution to a progressively fibrotic tumor stroma, the involved signaling pathways and the participating genes.

Identification of a Hypoxia-Related Gene Model for Predicting the Prognosis and Formulating the Treatment Strategies in Kidney Renal Clear Cell Carcinoma

Purpose

The present study aimed to establish a hypoxia related genes model to predict the prognosis of kidney clear cell carcinoma (KIRC) patients using data accessed from The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database.

Methods

Patients’ data were downloaded from the TCGA and ICGC databases, and hypoxia related genes were accessed from the Molecular Signatures Database. The differentially expressed genes were evaluated and then the differential expressions hypoxia genes were screened. The TCGA cohort was randomly divided into a discovery TCGA cohort and a validation TCGA cohort. The discovery TCGA cohort was used for constructing the hypoxia genes risk model through Lasso regression, univariate and multivariate Cox regression analysis. Receiver operating characteristic (ROC) curves were used to assess the reliability and sensitivity of our model. Then, we established a nomogram to predict the probable one-, three-, and five-year overall survival rates. Lastly, the Tumor Immune Dysfunction and Exclusion (TIDE) score of patients was calculated.

Results

We established a six hypoxia-related gene prognostic model of KIRC patients in the TCGA database and validated in the ICGC database. The patients with high riskscore present poorer prognosis than those with low riskscore in the three TCGA cohorts and ICGC cohort. ROC curves show our six-gene model with a robust predictive capability in these four cohorts. In addition, we constructed a nomogram for KIRC patients in the TCGA database. Finally, the high risk-group had a high TIDE score than the patients with low riskscore.

Conclusions

We established a six hypoxia-related gene risk model for independent prediction of the prognosis of KIRC patients was established and constructed a robust nomogram. The different riskscores might be a biomarker for immunotherapy strategy.