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. 2022 Sep 14:9:974722.
doi: 10.3389/fmolb.2022.974722. eCollection 2022.

A cuproptosis-related lncRNA signature identified prognosis and tumour immune microenvironment in kidney renal clear cell carcinoma

Sheng Xin  1   2 , Jiaquan Mao  1   2 , Kai Cui  1   2 , Qian Li  3 , Liang Chen  1   2 , Qinyu Li  1   2 , Bocheng Tu  1   2 , Xiaming Liu  1   2 , Tao Wang  1   2 , Shaogang Wang  1   2 , Jihong Liu  1   2 , Xiaodong Song  1   2 , Wen Song  1   2
Affiliations

A cuproptosis-related lncRNA signature identified prognosis and tumour immune microenvironment in kidney renal clear cell carcinoma

Sheng Xin et al. Front Mol Biosci. .

Abstract

Kidney renal clear cell carcinoma (KIRC) is a heterogeneous malignant tumor with high incidence, metastasis, and mortality. The imbalance of copper homeostasis can produce cytotoxicity and cause cell damage. At the same time, copper can also induce tumor cell death and inhibit tumor transformation. The latest research found that this copper-induced cell death is different from the known cell death pathway, so it is defined as cuproptosis. We included 539 KIRC samples and 72 normal tissues from the Cancer Genome Atlas (TCGA) in our study. After identifying long non-coding RNAs (lncRNAs) significantly associated with cuproptosis, we clustered 526 KIRC samples based on the prognostic lncRNAs and obtained two different patterns (Cuproptosis.C1 and C2). C1 indicated an obviously worse prognostic outcome and possessed a higher immune score and immune cell infiltration level. Moreover, a prognosis signature (CRGscore) was constructed to effectively and accurately evaluate the overall survival (OS) of KIRC patients. There were significant differences in tumor immune microenvironment (TIME) and tumor mutation burden (TMB) between CRGscore-defined groups. CRGscore also has the potential to predict medicine efficacy.

Keywords: cuproptosis; kidney renal clear cell carcinoma; lncRNAs; prognostic signature; tumor immune microenvironment.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Research flow chart.
FIGURE 2
FIGURE 2
Selection of the cuproptosis-related prognostic lncRNAs differentially expressed in the KIRC. (A) Network of cuproptosis-related mRNAs and lncRNAs. (B) Volcano plot to identify differentially expressed lncRNAs. (C) Expression profile of the differentially expressed lncRNAs. (D) Univariate COX regression analysis to select prognostic lncRNAs. (E) Sankey diagram to visualize the relevance between mRNAs and lncRNAs.
FIGURE 3
FIGURE 3
NMF clustering of cuproptosis phenotypes. (A) Consensus matrix heatmap. (B) Survival analysis of cuproptosis-related patterns. (C) PCA analysis. (D) Expression profile of prognosis-related lncRNAs. (E) Sankey diagram to reveal the distribution of samples. (F) Clinical relevance of cuproptosis-related patterns.
FIGURE 4
FIGURE 4
Tumor immune microenvironment between cuproptosis-related patterns. (A) The composition of TME in cuproptosis-related patterns. (B) Difference analysis of immune checkpoints expression. (C,D) TIICs-infiltrated phenotype (C) and enriched immune functions (D) of cuproptosis-related patterns.
FIGURE 5
FIGURE 5
Construction of the prognostic signature. (A) Survival analysis of the CRGscore-defined training set. (B)Time-dependent ROC curves. (C–E) Distribution of the CRGscore (C), OS outcomes (D), and hub lncRNAs expression (E) in the training cohort.
FIGURE 6
FIGURE 6
Verification of the prognostic signature. (A) Survival analysis of the CRGscore-defined test cohort. (B)Time-dependent ROC curves. (C–E) Distribution of the CRGscore (C), OS outcomes (D), and hub lncRNAs expression (E) in the test cohort. (F) Survival analysis of the CRGscore-defined overall cohort. (G)Time-dependent ROC curves. (H–J) Distribution of the CRGscore (H), OS outcomes (I), and hub lncRNAs expression (J) in the overall cohort.
FIGURE 7
FIGURE 7
Clinical significance of the prognostic signature. (A–D) Clinic relevance of CRGscore, including pathologic stage (A), histologic grade (B), gender (C), and age (D). (E,F) Evaluation of the prognostic value with the univariate (E) and multivariate (F) Cox regression analyses.
FIGURE 8
FIGURE 8
Nomogram to predict overall survival rate at 1, 3, and 5 years.
FIGURE 9
FIGURE 9
Evaluation of the nomogram. Confirmation of the nomogram coincidence at 1, 3, and 5 years with calibration curves in the training cohort (A–C), test cohort (F–H), and overall cohort (K–M). Evaluation and verification of the prognostic value with survival analysis and ROC curves in the training cohort (D,E), test cohort (I,J), and overall cohort (N,O).
FIGURE 10
FIGURE 10
Functional enrichment analysis with GSEA. (A) Gene Ontology Biological Process (GOBP), (B) Kyoto Encyclopedia of Genes and Genomes (KEGG), (C) Hallmark, (D) Reactome, (E) BioCarta, and (F) PID.
FIGURE 11
FIGURE 11
Immune cell-infiltrated phenotype of the CRGscore-defined groups. (A) The composition of TME was calculated through the ESTIMATE algorithm. (B) The correlation between CRGscore and the expression of immune checkpoints. (C,D) TIICs infiltration (C) and enriched immune functions (D) were calculated with ssGSEA.
FIGURE 12
FIGURE 12
Somatic variants of the CRGscore-defined groups. (A,B) Mutation spectrum of the top 20 mutated genes. (C) Survival analysis of the TMB-defined groups. (D) The relevance of TMB with CRGscore was revealed through the correlation analysis.
FIGURE 13
FIGURE 13
Predict potential drug treatment options. (A–I) The IC50 of chemotherapy and targeted drugs based on the TCIA database, including, Axitinib (A), Gefitinib (B), and Sorafenib (C).
FIGURE 14
FIGURE 14
RT-qPCR of the four hub lncRNAs. (A) LINC01605, (B) AGAP2-AS1, (C) FOXD2-AS1, and (D) LINC02195.
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