Elucidating the Influence of MPT-driven necrosis-linked LncRNAs on immunotherapy outcomes, sensitivity to chemotherapy, and mechanisms of cell death in clear cell renal carcinoma

Abstract

BackgroundClear cell renal carcinoma (ccRCC) stands as the prevailing subtype among kidney cancers, making it one of the most prevalent malignancies characterized by significant mortality rates. Notably,mitochondrial permeability transition drives necrosis (MPT-Driven Necrosis) emerges as a form of cell death triggered by alterations in the intracellular microenvironment. MPT-Driven Necrosis, recognized as a distinctive type of programmed cell death. Despite the association of MPT-Driven Necrosis programmed-cell-death-related lncRNAs (MPTDNLs) with ccRCC, their precise functions within the tumor microenvironment and prognostic implications remain poorly understood. Therefore, this study aimed to develop a novel prognostic model that enhances prognostic predictions for ccRCC.MethodsEmploying both univariate Cox proportional hazards and Lasso regression methodologies, this investigation distinguished genes with differential expression that are intimately linked to prognosis.Furthermore, a comprehensive prognostic risk assessment model was established using multiple Cox proportional hazards regression. Additionally, a thorough evaluation was conducted to explore the associations between the characteristics of MPTDNLs and clinicopathological features, tumor microenvironment, and chemotherapy sensitivity, thereby providing insights into their interconnectedness.The model constructed based on the signatures of MPTDNLs was verified to exhibit excellent prediction performance by Cell Culture and Transient Transfection, Transwell and other experiments.ResultsBy analyzing relevant studies, we identified risk scores derived from MPTDNLs as an independent prognostic determinant for ccRCC, and subsequently we developed a Nomogram prediction model that combines clinical features and associated risk assessment. Finally, the application of experimental techniques such as qRT-PCR helped to compare the expression of MPTDNLs in healthy tissues and tumor samples, as well as their role in the proliferation and migration of renal clear cell carcinoma cells. It was found that there was a significant correlation between CDK6-AS1 and ccRCC results, and CDK6-AS1 plays a key role in the proliferation and migration of ccRCC cells. Impressive predictive results were generated using marker constructs based on these MPTDNLs.ConclusionsIn this research, we formulated a new prognostic framework for ccRCC, integrating mitochondrial permeability transition-induced necrosis. This model holds significant potential for enhancing prognostic predictions in ccRCC patients and establishing a foundation for optimizing therapeutic strategies.

Deciphering the tumour microenvironment of clear cell renal cell carcinoma: Prognostic insights from programmed death genes using machine learning

“…It represents a predominant subtype of kidney cancer, accounting for 85%–90% of malignant renal tumours. 24 , 25 If not promptly managed, it can exhibit aggressive growth, infiltrating adjacent tissues and organs, and even disseminating to distant sites such as the lungs, bones, and brain. In its advanced stages, ccRCC poses a substantial threat to overall health.…”

Unravelling infiltrating T‐cell heterogeneity in kidney renal clear cell carcinoma: Integrative single‐cell and spatial transcriptomic profiling

“…The progression of renal clear cell carcinoma (RCCC) is intricately linked to the immune system, with a particular emphasis on the distribution and function of T cells within the tumour microenvironment, exerting a direct influence on RCCC's growth and dissemination. 9 , 10 , 11 However, the heterogeneity within these T‐cell populations remains a subject warranting more comprehensive exploration.…”

Precision prognostication in breast cancer: unveiling a long non-coding RNA-based model linked to disulfidptosis for tailored immunotherapeutic strategies

“…Long non-coding RNAs (lncRNAs), distinguished by their lack of protein-coding capability and ranging in size from 200 nt to 100 kb, have emerged as crucial players in cancer diagnosis, straddling roles as both oncogenic factors and cancer suppressors [14][15][16][17]. The compelling association between lncRNA expression levels and breast cancer subtype classification positions these molecules as promising biomarkers with transformative potential [18][19][20][21][22].…”