Translational Oncology (Sep 2024)

Integrated analysis of histone modification features in clear cell renal cancer for risk stratification and therapeutic prediction

  • Wenming Ma,
  • Qintao Ge,
  • Yu Guan,
  • Li Zhang,
  • Liqun Huang,
  • Lei Chen,
  • Wenlong Xu,
  • Jialin Meng,
  • Guosheng Yang,
  • Chaozhao Liang

Journal volume & issue
Vol. 47
p. 102042

Abstract

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Clear cell renal cell carcinoma (ccRCC) is a common urological malignancy that is involved in tumor genesis and development. However, few studies have focused on the predictive role of the global histone modification status in ccRCC. A total of 621 patients with complete transcript information and corresponding clinical profiles were obtained from TCGA-KIRC, GSE22541, and EMTAB3267 cohorts. A total of 122 histone modification relevant pathways were derived from MSigDB, and their activation status was quantified using GSVA. Differentially expressed genes (DEGs) were identified and filtrated using univariate Cox regression analysis. The signature was built relied on the least absolute shrinkage and selection operator (LASSO) regression analysis, and evaluated from survival difference, chemotherapy response, and activated pathways. A novel nomogram was established to quantify the probability of death in different patients. Seven risky and fifty-eight protective genes were used in LASSO analysis, and six genes were used to build the histone modification gene (HiMG) signature, which showed significant independent prognostic potential in all three cohorts. The nomogram showed acceptable incremental predictions. CKS2 (p = 0.004) and PD1 (p = 0.002) expression were significantly higher in grade 3 ccRCC than in grades 1–2. CKS2 siRNA in renal cancer cells caused reductions in cellular proliferation, migration, and invasion. Patients with low HiMG may be potential responders to rapamycin, erlotinib and FH535, while AZD6482 and CHIR-99,021 may be more suitable for patients with high HiMG levels. ccRCC histone modification distribution and a clinical signature for prognosis prediction, clinical decision making, and molecular mechanism exploration, were established for risk stratification and personalized treatments.

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