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

ERH Impacts Patient Prognosis and Tumor Immune Microenvironment: A Pan-Cancer Analysis

BACKGROUND

The enhancer of rudimentary homolog (ERH) has been shown to play significant roles in tumorigenesis and progression. However, few systematic pan-cancer analyses about ERH have been described.

METHODS

From the tumor immune estimation resource web server2.0 (TIMER2.0), the Genotype- Tissue Expression database (GTEx) and the Gene Expression Profile Interactive Analysis version 2 (GEPIA2) databases, we explored the expression profiles and prognostic significance of ERH in 33 cancers. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Human Protein Atlas (HPA) databases were further used to examine the differential expression of ERH at the protein level. The genetic alteration profile was obtained from the cBioPortal. The correlation between ERH expression and the quantities of immune infiltrating cells was examined by the TIMER tool. Spearman's correlation test was conducted to analyze the association between ERH expression status and a number of prognostic indicators, including immune checkpoints, TMB, MSI, immune neoantigen, MMR genes, and DNA methyltransferases. Protein- Protein Interaction analyses were performed in the String and GeneMANIA databases, and enrichment analysis and predicted signaling pathways were identified through GO and KEGG. To make our results convincing, we validated them in six datasets in the Gene Expression Omnibus (GEO) database. In addition, we verified the expression of ERH between gastric cancer tissues and adjacent normal tissues by RT-qPCR.

RESULTS

ERH expression was elevated in numerous tumors, and it was associated with the patient's prognosis. Furthermore, the quantities of immune infiltrating cells and immune checkpoint genes were remarkably associated with ERH. TMB and MSI were related to ERH expression in 14 and 15 cancer types, respectively. Moreover, the expression of ERH was strongly associated with MMR defects in multiple cancer types, and almost all tumors showed co-expression of ERH and four DNA methyltransferases. The results of GO and KEGG analysis confirmed that ERH potentially impacts several important signaling pathways. Both the GEO datasets and the RTqPCR experiment validated our previous analysis.

CONCLUSION

Our pan-cancer analysis demonstrated the characterization of ERH in multiple tumors. ERH may be a valuable novel biological indicator for assessing immunotherapy efficacy and prognosis in various malignancies.

A SRC-slug-TGFβ2 signaling axis drives poor outcomes in triple-negative breast cancers

BACKGROUND

Treatment options for the Triple-Negative Breast Cancer (TNBC) subtype remain limited and the outcome for patients with advanced TNBC is very poor. The standard of care is chemotherapy, but approximately 50% of tumors develop resistance.

METHODS

We performed gene expression profiling of 58 TNBC tumor samples by microarray, comparing chemosensitive with chemoresistant tumors, which revealed that one of the top upregulated genes was TGFβ2. A connectivity mapping bioinformatics analysis predicted that the SRC inhibitor Dasatinib was a potential pharmacological inhibitor of chemoresistant TNBCs. Claudin-low TNBC cell lines were selected to represent poor-outcome, chemoresistant TNBC, for in vitro experiments and in vivo models.

RESULTS

In vitro, we identified a signaling axis linking SRC, AKT and ERK2, which in turn upregulated the stability of the transcription factors, Slug and Snail. Slug was shown to repress TGFβ2-antisense 1 to promote TGFβ2 signaling, upregulating cell survival via apoptosis and DNA-damage responses. Additionally, an orthotopic allograft in vivo model demonstrated that the SRC inhibitor Dasatinib reduced tumor growth as a single agent, and enhanced responses to the TNBC mainstay drug, Epirubicin.

CONCLUSION

Targeting the SRC-Slug-TGFβ2 axis may therefore lead to better treatment options and improve patient outcomes in this highly aggressive subpopulation of TNBCs.

Identification and validation of a glycolysis-related taxonomy for improving outcomes in glioma

BACKGROUND

Reprogramming of glucose metabolism is a prominent abnormal energy metabolism in glioma. However, the efficacy of treatments targeting glycolysis varies among patients. The present study aimed to classify distinct glycolysis subtypes (GS) of glioma, which may help to improve the therapy response.

METHODS

The expression profiles of glioma were downloaded from public datasets to perform an enhanced clustering analysis to determine the GS. A total of 101 combinations based on 10 machine learning algorithms were performed to screen out the most valuable glycolysis-related glioma signature (GGS). Through RSF and plsRcox algorithms, adrenomedullin (ADM) was eventually obtained as the most significant glycolysis-related gene for prognostic prediction in glioma. Furthermore, drug sensitivity analysis, molecular docking, and in vitro experiments were utilized to verify the efficacy of ADM and ingenol mebutate (IM).

RESULTS

Glioma patients were classified into five distinct GS (GS1-GS5), characterized by varying glycolytic metabolism levels, molecular expression, immune cell infiltration, immunogenic modulators, and clinical features. Anti-CTLA4 and anti-PD-L1 antibodies significantly improved the prognosis for GS2 and GS5, respectively. ADM has been identified as a potential biomarker for targeted glycolytic therapy in glioma patients. In vitro experiments demonstrated that IM inhibited glioma cell progression by inhibiting ADM.

CONCLUSION

This study elucidates that evaluating GS is essential for comprehending the heterogeneity of glioma, which is pivotal for predicting immune cell infiltration (ICI) characterization, prognosis, and personalized immunotherapy regimens. We also explored the glycolysis-related genes ADM and IM to develop a theoretical framework for anti-tumor strategies targeting glycolysis.

The discovery of promising candidate biomarkers in kidney renal clear cell carcinoma: evidence from the in-depth analysis of high-throughput data

Kidney renal clear cell carcinoma (KIRC) is the most prevalent subtype of renal tumor. The underlying mechanisms governing KIRC initiation and progression are less known. The present study aimed to reveal novel hub genes associated with the initiation and progression of KIRC, which may be utilized as novel molecular biomarkers and therapeutic targets for the treatment of KIRC. The GSE6344 dataset from the Gene Expression Omnibus (GEO) database was integrated to identify differentially expressed genes (DEGs) using the limma package. Then, hub genes were identified and UALCAN, GEPIA, OncoDB, DriverDBv3, GENT2, and HPA databases were employed for the expression, survival, and methylation analyses. cBioPortal tool was used to investigate the genetic alterations, while CancerSEA, TIMER, DAVID, ENCORI, DrugBank, and GSCAlite were utilized to explore a few more hub gene-associated parameters. Finally, targeted bisulfite sequencing (bisulfite-seq), and RT-qPCR techniques were used to validate the expression and methylation level of the hub genes using Human RCC cell line 786-O, A-498, and normal renal tubular epithelial cell line HK-2. In total, 7299 DEGs were found between KIRC and normal samples in the GSE6344 dataset. Using STRING and Cytohubba analysis, four hub genes including VEGFA (vascular endothelial growth factor), ALB (Albumin), ENO2 (enolase 2), and CAVI1 (Caveolin 1) were selected as the hub genes. Further, it was validated through extensive analysis of TCGA datasets that these VEGA, ENO2, and CAV1 hub genes were significantly up-regulated, while ALB was significantly down-regulated in KIRC samples compared to controls. The dysregulation of these genes was found to be associated with the overall survival (OS) of the KIRC patients. Moreover, this study also revealed some novel links between VEGA, ALB, ENO2, and CAV1 expression and genetic alterations, promoter methylation status, immune cell infiltration, miRNAs, gene enrichment terms, and various chemotherapeutic drugs. The present study revealed a panel of four hub genes, which contributed to improving our understanding of the underlying molecular mechanisms of KIRC development and can be utilized as promising novel biomarkers for KIRC diagnosis, prognosis, and treatment.

The role of claudins in homeostasis

Sequential expression of claudins, a family of tight junction proteins, along the nephron mirrors the sequential expression of ion channels and transporters. Only by the interplay of transcellular and paracellular transport can the kidney efficiently maintain electrolyte and water homeostasis in an organism. Although channel and transporter defects have long been known to perturb homeostasis, the contribution of individual tight junction proteins has been less clear. Over the past two decades, the regulation and dysregulation of claudins have been intensively studied in the gastrointestinal tract. Claudin expression patterns have, for instance, been found to be affected in infection and inflammation, or in cancer. In the kidney, a deeper understanding of the causes as well as the effects of claudin expression alterations is only just emerging. Little is known about hormonal control of the paracellular pathway along the nephron, effects of cytokines on renal claudin expression or relevance of changes in paracellular permeability to the outcome in any of the major kidney diseases. By summarizing current findings on the role of specific claudins in maintaining electrolyte and water homeostasis, this Review aims to stimulate investigations on claudins as prognostic markers or as druggable targets in kidney disease.

Identification of exosomes-related lncRNAs in clear cell renal cell carcinoma based on Bayesian spike-and-slab lasso approach

Exosomes-related long non-coding RNAs (lncRNAs) have been reported to play significant roles in clear cell renal cell carcinoma (ccRCC). However, there is little known about the relationship between exosomes-related lncRNAs and ccRCC. This study aimed to select optimal prognostic model based on exosomes-related lncRNAs to provide a methodological reference for high-dimensional data. Based on the Cancer Genome Atlas (TCGA) database of 515 ccRCC patients, two risk score models were generated underlying Bayesian spike-and-slab lasso and lasso regression. The optimal model was determined by calculating the area of time-dependent receiver-operating characteristic (ROC) curves in the TCGA and ArrayExpress databases. The immune patterns and sensitivity of immunotherapy between the high and low groups were further explored. Initially, we constructed two risk score models containing 11 and 7 exosomes-related lncRNAs according to Bayesian spike-and-slab lasso and lasso regression respectively. ROC curves revealed that the model constructed by Bayesian spike-and-slab lasso regression was more reliable in predicting survival at 1, 3, and 5 years, yielding an area under the curves (AUCs) of 0.796, 0.732, and 0.742, respectively. Kaplan-Meier (K-M) curves presented that prognosis was poorer in the high-risk score group (P < 0.001). Additionally, the high-risk score group patients were enriched in immune-activating phenotypes and more sensitive to immunotherapy. The exosomes-related lncRNAs model constructed with Bayesian spike-and-slab lasso regression has higher predictive power for ccRCC patients' prognosis, which provides methodological reference for the analysis of high-dimensional data in bioinformatics and guides the tailored treatment of ccRCC patients.

Identification and Validation of the Prognostic Panel in Clear Cell Renal Cell Carcinoma Based on Resting Mast Cells for Prediction of Distant Metastasis and Immunotherapy Response

Clear cell renal cell carcinoma (ccRCC) has a high metastatic rate, and its incidence and mortality are still rising. The aim of this study was to identify the key tumor-infiltrating immune cells (TIICs) affecting the distant metastasis and prognosis of patients with ccRCC and to construct a relevant prognostic panel to predict immunotherapy response. Based on ccRCC bulk RNA sequencing data, resting mast cells (RMCs) were screened and verified using the CIBERSORT algorithm, survival analysis, and expression analysis. Distant metastasis-associated genes were identified using single-cell RNA sequencing data. Subsequently, a three-gene (CFB, PPP1R18, and TOM1L1) panel with superior distant metastatic and prognostic performance was established and validated, which stratified patients into high- and low-risk groups. The high-risk group exhibited lower infiltration of RMCs, higher tumor mutation burden (TMB), and worse prognosis. Therapeutically, the high-risk group was more sensitive to anti-PD-1 and anti-CTLA-4 immunotherapy, whereas the low-risk group displayed a better response to anti-PD-L1 immunotherapy. Furthermore, two immune clusters revealing distinct immune, clinical, and prognosis heterogeneity were distinguished. Immunohistochemistry of ccRCC samples verified the expression patterns of the three key genes. Collectively, the prognostic panel based on RMCs is able to predict distant metastasis and immunotherapy response in patients with ccRCC, providing new insight for the treatment of advanced ccRCC.