Patient Stratification of Clear Cell Renal Cell Carcinoma Using the Global Transcription Factor Activity Landscape Derived From RNA-Seq Data

Pan-cancer analysis reveals TRA16 as a master regulator of human carcinogenesis

Introduction

The nuclear receptor TR4 binding protein, TRA16, has been implicated in lung carcinogenesis; however, its broader role across diverse human cancers remains poorly understood. Understanding TRA16's involvement in cancer biology could uncover novel regulatory mechanisms and potential therapeutic targets.

Methods

We conducted a comprehensive pan-cancer analysis of TRA16 expression and function across multiple human malignancies. Gene co-expression networks, pathway enrichment, transcription factor analysis, organoid modeling, and intercellular communication profiling were employed. Tumor mutation burden (TMB) and microenvironmental features were also assessed in relation to TRA16 expression, stratified by TP53 mutation status.

Results

Correlation analysis identified the cell cycle as the top enriched pathway among TRA16-associated genes, with key transcription factors, including RB-E2F, MYC, and TP53, regulating genes co-expressed with TRA16. In liver cancer organoid models, TRA16 and its co-expressed genes were significantly upregulated. Intercellular communication analysis showed that TRA16-positive cells exhibited increased autocrine signaling and overall signaling activity. Importantly, patients with high TRA16 expression demonstrated elevated TMB and decreased stromal and immune features.

Discussion

These findings highlight TRA16 as a potential master regulator of oncogenic processes, contributing to tumor progression through coordinated regulation of cell cycle genes, intercellular signaling, and genomic instability. Our results provide new insights into TRA16's role across cancers and support its potential as a novel oncogene.

DUSP5 regulated by YTHDF1-mediated m6A modification promotes epithelial-mesenchymal transition and EGFR-TKI resistance via the TGF-β/Smad signaling pathway in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) patients have a dismal survival rate because of cancer metastasis and drug resistance. The study aims to identify the genes that concurrently modulate EMT, metastasis and EGFR-TKI resistance, and to investigate the underlying regulatory mechanisms.

METHODS

Cox regression and Kaplan-Meier analyses were applied to identify prognostic oncogenes in LUAD. Gene set enrichment analysis (GSEA) was used to indicate the biological functions of the gene. Wound-healing and Transwell assays were used to detect migratory and invasive ability. EGFR-TKI sensitivity was evaluated by assessing the proliferation, clonogenic survival and metastatic capability of cancer cells with treatment with gefitinib. Methylated RNA immunoprecipitation (MeRIP) and RNA immunoprecipitation (RIP) analyses established the level of m6A modification present on the target gene and the protein's capability to interact with RNA, respectively. Single-sample gene set enrichment (ssGSEA) algorithm used to investigate levels of immune cell infiltration.

RESULTS

Our study identified dual-specificity phosphatase 5 (DUSP5) as a novel and powerful predictor of adverse outcomes for LUAD by using public datasets. Functional enrichment analysis found that DUSP5 was positively enriched in EMT and transforming growth factor-beta (TGF-β) signaling pathway, a prevailing pathway involved in the induction of EMT. As expected, DUSP5 knockdown suppressed EMT via inhibiting the canonical TGF-β/Smad signaling pathway in in vitro experiments. Consistently, knockdown of DUSP5 was first found to inhibit migratory ability and invasiveness of LUAD cells in in vitro and prevent lung metastasis in in vivo. DUSP5 knockdown re-sensitized gefitinib-resistant LUAD cells to gefitinib, accompanying reversion of EMT progress. In LUAD tissue samples, we found 14 cytosine-phosphate-guanine (CpG) sites of DUSP5 that were negatively associated with DUSP5 gene expression. Importantly, 5'Azacytidine (AZA), an FDA-approved DNA methyltransferase inhibitor, restored DUSP5 expression. Moreover, RIP experiments confirmed that YTH N6-methyladenosine RNA binding protein 1 (YTHDF1), a m6A reader protein, could bind DUSP5 mRNA. YTHDF1 promoted DUSP5 expression and the malignant phenotype of LUAD cells. In addition, the DUSP5-derived genomic model revealed the two clusters with distinguishable immune features and tumor mutational burden (TMB).

CONCLUSIONS

Briefly, our study discovered DUSP5 which was regulated by epigenetic modification, might be a potential therapeutic target, especially in LUAD patients with acquired EGFR-TKI resistance.

Applications of artificial intelligence in urologic oncology

PURPOSE

With the recent rising interest in artificial intelligence (AI) in medicine, many studies have explored the potential and usefulness of AI in urological diseases. This study aimed to comprehensively review recent applications of AI in urologic oncology.

MATERIALS AND METHODS

We searched the PubMed-MEDLINE databases for articles in English on machine learning (ML) and deep learning (DL) models related to general surgery and prostate, bladder, and kidney cancer. The search terms were a combination of keywords, including both "urology" and "artificial intelligence" with one of the following: "machine learning," "deep learning," "neural network," "renal cell carcinoma," "kidney cancer," "urothelial carcinoma," "bladder cancer," "prostate cancer," and "robotic surgery."

RESULTS

A total of 58 articles were included. The studies on prostate cancer were related to grade prediction, improved diagnosis, and predicting outcomes and recurrence. The studies on bladder cancer mainly used radiomics to identify aggressive tumors and predict treatment outcomes, recurrence, and survival rates. Most studies on the application of ML and DL in kidney cancer were focused on the differentiation of benign and malignant tumors as well as prediction of their grade and subtype. Most studies suggested that methods using AI may be better than or similar to existing traditional methods.

CONCLUSIONS

AI technology is actively being investigated in the field of urological cancers as a tool for diagnosis, prediction of prognosis, and decision-making and is expected to be applied in additional clinical areas soon. Despite technological, legal, and ethical concerns, AI will change the landscape of urological cancer management.

Prognostic value and model construction of preoperative inflammatory markers in patients with metastatic renal cell carcinoma

BACKGROUND

Inflammation is considered to be one of the driving factors of cancer, and chronic inflammation plays a crucial role in tumor growth and metastasis. The aim of this study was to examine the predictive value of preoperative inflammatory biomarkers for overall survival (OS) in patients with metastatic renal cell carcinoma (mRCC), including preoperative neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and aspartate aminotransferase-to-lymphocyte ratio (ALR), a novel inflammatory biomarker.

METHOD

This study included 198 patients with mRCC from a single center from 2006 to 2022. The optimal cut-off levels for the three biomarkers were derived using the receiver operating characteristic curve (ROC). Cox univariate and multivariate analyses were used to assess independent prognostic inflammatory biomarkers. Finally, independent prognostic inflammatory biomarkers were incorporated into the prognostic model to establish a nomogram to predict the postoperative survival of patients with mRCC.

RESULT

The area under the ROC curve for NLR, LMR, and ALR, respectively, is 0.71 (CI: 0.635-0.784), 0.68 (CI: 0.604-0.755), and 0.75 (CI: 0.680-0.819). The optimal LMR, NLR, and ALR cut-off levels as evaluated by the ROC curve were 3.836, 3.106, and 68.056, respectively. Patients with NLR and ALR higher than the cut-off level and LMR lower than the cut-off level had a significant relationship with OS. Multivariate analysis revealed that tumor necrosis, lower LMR, and higher ALR were independent risk factors for OS. In addition, a nomogram that includes independent prognostic inflammatory biomarkers can accurately predict the OS in patients with mRCC.

CONCLUSION

ALR and LMR are independent risk factors for the prognosis of individuals with mRCC. By monitoring ALR and LMR postoperatively, the prognosis of patients with mRCC can be better evaluated.

Identification of key genes of the ccRCC subtype with poor prognosis

Clear cell renal carcinoma has been reported in many research studies as a rather heterogeneous disease. Identification of different subtypes and their molecular characteristics can help in choosing a more effective treatment and predicting a response to it. In this study, using multi-omics clustering of RNA-Seq data of patients with clear cell renal carcinoma from TCGA. Specific genes were identified for the most aggressive ccRCC subtype associated with metastasis and a subtype associated with a more favorable course of the disease. Among them were genes associated with blood clotting (FGA, FGG) and genes associated with changes in the immune characteristics of a tumor (ENAM, IGFBP1, IL6). In addition, an association of hub genes of poor survival ccRCC subtype with the levels of infiltration of endothelial cells, hematopoietic stem cells, T cells NK and mast cells was revealed. It was shown that MFI2, CP, FGA, and FGG expression can predict the response to sunitinib, while the APOB, ENAM, IGFBP1, and MFI2 expression predict the response to nivolumab. The results obtained provide insight into the genetic characteristics underlying the aggressive subtype of ccRCC and may help develop new approaches to the treatment of this disease.

BNIP3 Upregulation Characterizes Cancer Cell Subpopulation With Increased Fitness and Proliferation

BNIP3 is a BH3-only protein with both pro-apoptotic and pro-survival roles depending on the cellular context. It remains unclear how BNIP3 RNA level dictates cell fate decisions of cancer cells. Here, we undertook a quantitative analysis of BNIP3 expression and functions in single-cell datasets of various epithelial malignancies. Our results demonstrated that BNIP3 upregulation characterizes cancer cell subpopulations with increased fitness and proliferation. We further validated the upregulation of BNIP3 in liver cancer 3D organoid cultures compared with 2D culture. Taken together, the combination of in silico perturbations using public single-cell datasets and experimental cancer modeling using organoids ushered in a new approach to address cancer heterogeneity.

Survival Prognosis, Tumor Immune Landscape, and Immune Responses of ADAMTS14 in Clear Cell Renal Cell Carcinoma and Its Potential Mechanisms

Background

ADAMTS14 played a crucial role in the formation and development of various cancers. Currently, no associations had been revealed between ADAMTS14 and clear cell renal cell carcinoma (ccRCC). Hence, this study was designed to assess the prognostic values and immunological roles of ADAMTS14 in ccRCC and to reveal its potential mechanisms.

Methods

ADAMTS14-related expression profiles and related clinical data were downloaded from The Cancer Genome Atlas (TCGA) dataset, validated by the ICGC dataset, qRT-PCR, and immunohistochemistry. We utilized gene set enrichment analysis (GSEA) to find potentially ADAMTS14-related pathways and applied univariate/multivariate Cox regression analyses to identify independent factors significantly related to overall survival (OS) for ccRCC. A nomogram consisted of independent prognostic factors was also conducted. We further explored the associations between ADAMTS14 with immunity and revealed its potential mechanisms.

Results

ADAMTS14 displayed a higher expression in ccRCC tumor than in adjacent normal tissues, and further validated results of the ICGC dataset; qRT-PCR and immunohistochemistry remained consistent (all p < 0.05). Moreover, elevated ADAMTS14 expression was significantly associated with poor OS (p < 0.001). Through univariate/multivariate Cox regression analyses, ADAMTS14 was found to be an independent prognostic factor for ccRCC (both p < 0.05) and GSEA identified several signaling pathways including INSULIN, MTOR, and PPAR pathways. The nomogram based on independent prognostic factors was successfully established and well evaluated. Moreover, the expression of ADAMTS14 was remarkably associated with immune checkpoint molecules, tumor mutational burden (TMB), immune cells, and tumor immune microenvironment (all p < 0.05). Results from TIDE and TCIA showed that highly expressed ADAMTS14 could predict worse efficacy of immunotherapy (all p < 0.05). As for its potential mechanisms, we also revealed several LncRNA/RNA binding protein (RBP)/ADAMTS14 mRNA networks.

Conclusions

ADAMTS14 was found to play oncogenic roles in ccRCC and to be significantly associated with immunity. Several LncRNA/RBP/ADAMTS14 mRNA networks were also identified for its potential mechanisms.

Single-Cell Sequencing to Identify Six Heat Shock Protein (HSP) Genes-Mediated Progression Subtypes of Clear Cell Renal Cell Carcinoma

Background

Heat shock proteins (HSPs) are widely involved in tumor occurrence and development and are prognostic markers for multiple tumors. However, the role of HSPs in clear cell renal cell carcinoma (ccRCC) remains unclear.

Methods

We used Cytoscape to identify hub genes in the ccRCC single-cell sequencing data set from the Gene Expression Omnibus (GEO) data repository. We identified subtypes, C1 and C2, of The Cancer Genome Atlas (TCGA) patients based on the expression of hub genes using unsupervised consensus clustering. Principal component analysis (PCA) was used to verify the clustering differences, and Kaplan-Meier (K-M) estimate was used to verify the survival differences between C1 and C2 patients. We used TIMER 2.0 and CIBERSORT to evaluate the immune cell infiltration of HSP genes and C1 and C2 patients. The R package "pRRophetic" was used to evaluate the sensitivity in C1 and C2 patients to the four first-line treatment drugs.

Results

We identified six hub genes (HSP90AA1, HSPH1, HSPA1B, HSPA8, and HSPA1A) encoding HSP, five of which were significantly downregulated in TCGA group, and four had a protective effect on prognosis (p <0.05). Survival analysis showed that C1 patients had a better overall survival (p <0.001). TIMER 2.0 analysis showed that three HSP genes were significantly correlated with the infiltration of CD4+ T cells and CD4+ Th1 cells (|cor|>0.5, p<0.001). CIBERSORT showed significant differences in multiple infiltrating immune cells between C1 and C2 patients. Meanwhile, the expression of PD1 was significantly lower in C1 patients than in C2 patients, and the expression of PDL1 is the another way around. Drug sensitivity analysis showed that C1 patients were more sensitive to sorafenib, pazopanib, and axitinib (p <0.001).

Conclusion

Our research revealed two molecular subtypes of ccRCC based on 6 HSP genes, and revealed significant differences between the two subtypes in terms of clinical prognosis, immune infiltration, and drug sensitivity.