Identification of CWH43 as a novel prognostic biomarker and therapeutic target in clear cell renal cell carcinoma by a multi-omics approach and correlation with autophagy progression

Abstract

Background: Clear cell renal cell carcinoma (ccRCC) poses significant challenges due to its asymptomatic nature and poor prognosis at advanced stages. Identifying novel biomarkers is essential for enhancing prognostic accuracy and therapeutic strategies. This study explores the CWH43 gene, utilizing multi-omics data to determine its role in ccRCC.

Methods: Genomic, transcriptomic, and methylation data from TCGA-KIRC and GEO databases were analyzed to evaluate CWH43 expression and clinical impact. Bioinformatics tools assessed correlations with patient outcomes and pathway involvement.

Results: CWH43 expression was significantly reduced in ccRCC tissues and correlated with advanced disease stages and poor patient survival. Enrichment analyses revealed CWH43's involvement in critical cancer pathways, such as autophagy and immune response modulation, suggesting its significant role in ccRCC pathophysiology. Lower CWH43 levels were associated with increased tumor progression and immune evasion, impacting the tumor microenvironment.

Conclusion: This study highlights the utility of multi-omics data in identifying CWH43 as a novel prognostic biomarker for ccRCC. Integrating CWH43 into clinical practice could refine prognostic assessments and guide personalized therapy strategies, aligning with advancements in modern oncology. Further research is warranted to explore CWH43's mechanisms and therapeutic potential.

Keywords: CWH43; Cancer immunology; CcRCC; Immunotherapy response biomarkers; Next-generation sequencing technologies; Therapeutic targets in immunotherapy; Tumor microenvironment; Tumor-immune interactions.

Fig. 1

Differential Expression of CWH43 in ccRCC and Normal Tissues. A A Wilcoxon rank sum test assessed the variation in CWH43 expression between ccRCC and adjacent normal tissues. B This statistical test also evaluated CWH43 levels across normal adjacent tissues from the GTEx and ccRCC tissues from the TCGA databases. C Comparison of CWH43 expression in 72 paired ccRCC and adjacent normal samples. D, E ROC curves illustrating the diagnostic ability of CWH43 expression to differentiate between ccRCC and non-tumor tissue; the false positive rate is plotted on the X-axis against the true positive rate on the Y-axis. F Ualcan database analysis of CWH43 protein levels in ccRCC versus normal tissues. G, H Comparative levels of CWH43 protein in ccRCC and normal tissues, as indicated in the Human Protein Atlas (Antibody HPA042814, magnification 10X)

Fig. 2

Correlations Between CWH43 Expression and Clinical Variables in ccRCC. A Age, (B) Gender, (C) Tumor (T) stage, (D) Metastasis (M) stage, (E) Pathological stage, (F) Histological grade

Fig. 3

Kaplan–Meier Survival Curves Based on CWH43 Expression Levels in KIRC from the TCGA Dataset. A Overall survival, (B) Disease-specific survival, (C) Progression-free interval, (D) Disease-free survival

Fig. 4

Prognostic Nomogram and Calibration for ccRCC. A Nomogram predicting 1, 3, and 5 year overall survival probabilities for ccRCC patients. B Calibration plots verifying the accuracy of the nomogram predictions for 1, 3, and 5 year overall survival in ccRCC patients

Fig. 5

Analysis of CWH43 Methylation and Its Association with Clinical Characteristics in ccRCC Patients. Methylation levels of CWH43 are analyzed by (A) type of sample, (B) age of patients, (C) stage of individual tumors, (D) grade of tumors, (E) race of patients, (F) gender of patients, and (G) status of nodal metastasis

Fig. 6

Survival Outcomes Based on CWH43 Promoter Methylation at Specific CpG Sites in ccRCC. Kaplan–Meier curves illustrating overall survival (OS) for low and high methylation at CpG sites: (A) cg03170472, (B) cg04005707, (C) cg08529049, (D) cg18280362, (E) cg24534566, (F) cg25316310, (G) cg25484904, (H) cg11935592, (I) cg22826333, (J) cg22930650, (K) cg24060908, and (L) cg13693941

Fig. 7

Relationship Between Genetic Alterations in CWH43 and Prognosis in ccRCC. A OncoPrint visualizing CWH43 alterations. Various genetic alterations are depicted in distinct colors. B Analysis of mutation frequency and (C) mutation sites via cBioPortal. D Expression levels across different CWH43 copy number variations (CNV) groups, noting a significant expression increase in the CWH43 gain group. E Kaplan–Meier plots assessing the impact of CWH43 gene alterations on overall survival

Fig. 8

Correlation Between CWH43 Expression and Drug Sensitivity in ccRCC. This figure plots CWH43 gene expression against the sensitivity to various anticancer drugs

Fig. 9

Influence of CWH43 Expression on Tumor Microenvironment (TME) in ccRCC. A Expression differences in 122 immunomodulators between high and low CWH43 expression groups. B Variations in cancer-immune cycle steps between groups. Differences in (C) immune score, (D) stromal score, (E) ESTIMATE score, (F) tumor purity, (G) interactions with infiltrating immune cells, and (H) expression of immune checkpoints between groups. Statistical significance is noted as *p < 0.05; **p < 0.01; ***p < 0.001

Fig. 10

CWH43’s Role in Immunotherapy for ccRCC. A Correlation of CWH43 with clinical immunotherapy response. B Association of CWH43 with stages of the cancer-immune cycle. C Links between CWH43 and enriched pathways predictive of immunotherapy outcomes

Fig. 11

Gene Expression Profiling Linked to CWH43 in ccRCC. A Heat map of 2776 up-regulated and 2211 down-regulated genes. B Focused heat map of 10 most significantly altered RNAs. C GO analysis of biological processes associated with CWH43. D KEGG pathway annotations

Fig. 12

CWH43 and Autophagy in ccRCC. A Correlations between CWH43 expression and autophagy-related gene expression. B-I Scatter plots for the top 8 autophagy-related genes correlated with CWH43. J Comparative expression of autophagy-related genes between high and low CWH43 expression groups. Significance levels are marked accordingly