Identifying molecular subtypes and tumor microenvironment infiltration signatures in kidney renal clear cell carcinoma based on stemness-associated disulfidptosis genes by integrating machine learning, single-cell analyses and experimental validation

Hongquan Liu; Xiaoqing Liang; Gonglin Tang; Xiaofeng Wang; Zhen Wang; Leijie Tong; Qiancheng Mao; Jian Ma; Jitao Wu

doi:10.1016/j.heliyon.2024.e26094

Identifying molecular subtypes and tumor microenvironment infiltration signatures in kidney renal clear cell carcinoma based on stemness-associated disulfidptosis genes by integrating machine learning, single-cell analyses and experimental validation

Heliyon. 2024 Feb 13;10(4):e26094. doi: 10.1016/j.heliyon.2024.e26094. eCollection 2024 Feb 29.

Authors

Hongquan Liu¹, Xiaoqing Liang², Gonglin Tang¹, Xiaofeng Wang¹, Zhen Wang³, Leijie Tong⁴, Qiancheng Mao¹, Jian Ma¹, Jitao Wu¹

Affiliations

¹ Department of Urology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, NO. 20 East Yuhuangding Road, Yantai, 264000, Shandong, China.
² Department of Oncology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
³ Department of Prosthodontics, Affiliated Stomatological Hospital of Zunyi Medical University, Zunyi Medical University, Zunyi, China.
⁴ Department of Immunology, China Medical University, Shenyang, China.

Abstract

Clear cell renal cell carcinoma (ccRCC) is an aggressive malignant tumor. Disulfidptosis is a new programmed cell death mechanism, which is characterized by the abnormal accumulation of intracellular disulfides that are highly toxic to cells. However, the contribution of disulfidptosis to ccRCC progression has not been fully clarified. In this study, two different molecular subtypes related to disulfidptosis were identified in ccRCC patients by the non-negative matrix factorization (NMF) algorithm. The cluster 1 was characterized by a worse prognosis and higher mRNAsi levels. Then, difference analysis and weighted gene co-expression network analysis (WGCNA) were conducted to search modular genes that are highly associated with tumor stemness and tumor microenvironment. Subsequently, a SADG signature containing nine genes was constructed stepwise by WGCNA and least absolute shrinkage and selection operator (LASSO) Cox regression analysis. The high-risk score group had a worse outcome, and immune regulation and metabolic signatures might be responsible for cancer progression in the high-risk group. After that, a predictive nomogram was constructed, and the predicting power of the risk model was verified using inter and three independent external validation datasets. Nine SADGs were shown to significantly correlate with immune infiltration, tumor mutation burden (TMB), microsatellite instability (MSI) and immune checkpoint. In addition, based on the single-cell RNA sequencing dataset (GSE139555), the distribution and expression of nine hub genes in various types of immune cells were analyzed. Finally, the expression level of the nine genes was verified in clinical samples by qRT-PCR.

Keywords: Clear cell renal cell carcinoma (ccRCC); Disulfidptosis; Machine learning and biomarker; Molecular subtype; Targeted therapy and immunotherapy; Tumor microenvironment.