A novel marker based on necroptosis-related long non-coding RNA for forecasting prognostic in patients with clear cell renal cell carcinoma

Lv, Jinxing; Xu, Qinghui; Wu, Guoqing; Hou, Jian; Yang, Guang; Tang, Chaowei; Qu, Genyi; Xu, Yong

doi:10.3389/fgene.2022.948254

Cited by 3 publications

(1 citation statement)

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“…In clinical practice, the tumor stage is determined by the tumor size, node, and metastasis (TNM) method, which is generally used for evaluating the prognosis of tumor patients. With the advent of precision medicine, an increasing number of studies have suggested that lncRNAs could have some predictive significance for tumor prognosis (Bhan et al 2017 ; Lv et al 2022 ; Yan et al 2021 ). Therefore, the findings revealed that the prognostic model constructed by combining lncRNAs with tumor stage showed a higher predictive accuracy compared to the currently available methods for predicting prognosis.…”

Section: Discussionmentioning

confidence: 99%

Mitochondria-related lncRNAs: predicting prognosis, tumor microenvironment and treatment response in lung adenocarcinoma

Zhou,

Xiong,

Gao

et al. 2023

Funct Integr Genomics

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Lung cancer is the most common type of malignant tumor that affects people in China and even across the globe, as it exhibits the highest rates of morbidity and mortality. Lung adenocarcinoma (LUAD) is a type of lung cancer with a very high incidence. The purpose of this study was to identify potential biomarkers that could be used to forecast the prognosis and improve the existing therapy options for treating LUAD. Clinical and RNA sequencing data of LUAD patients were retrieved from the TCGA database, while the mitochondria-associated gene sets were acquired from the MITOMAP database. Thereafter, Pearson correlation analysis was carried out to screen mitochondria-associated lncRNAs. Furthermore, univariate Cox and Lasso regression analyses were used for the initial screening of the target lncRNAs for prognostic lncRNAs before they could be incorporated into a multivariate Cox Hazard ratio model. Then, the clinical data, concordance index, Kaplan–Meier (K-M) curves, and the clinically-relevant subjects that were approved by the Characteristic Curves (ROC) were employed for assessing the model's predictive value. Additionally, the differences in immune-related functions and biological pathway enrichment between high- and low-risk LUAD groups were examined. Nomograms were developed to anticipate the OS rates of the patients within 1-, 3-, and 5 years, and the differences in drug sensitivity and immunological checkpoints were compared. In this study, 2175 mitochondria-associated lncRNAs were screened. Univariate, multivariate, and Lasso Cox regression analyses were carried out to select 13 lncRNAs with an independent prognostic significance, and a prognostic model was developed. The OS analysis of the established prognostic prediction model revealed significant variations between the high- and low-risk patients. The AUC-ROC values after 1, 3, and 5 years were seen to be 0.746, 0.692, and 0.726, respectively. The results suggested that the prognostic model riskscore could be used as an independent prognostic factor that differed from the other clinical characteristics. After analyzing the findings of the study, it was noted that both the risk groups showed significant differences in their immune functioning, immunological checkpoint genes, and drug sensitivity. The prognosis of patients with LUAD could be accurately and independently predicted using a risk prediction model that included 13 mitochondria-associated lncRNAs.

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Section: Discussionmentioning

confidence: 99%