Lactate Metabolism-Associated lncRNA Pairs: A Prognostic Signature to Reveal the Immunological Landscape and Mediate Therapeutic Response in Patients With Colon Adenocarcinoma

Abstract

Background: Lactate metabolism is critically involved in the tumor microenvironment (TME), as well as cancer progression. It is important to note, however, that lactate metabolism-related long non-coding RNAs (laRlncRNAs) remain incredibly understudied in colon adenocarcinoma (COAD).

Methods: A gene expression profile was obtained from the Cancer Genome Atlas (TCGA) database to identify laRlncRNA expression in COAD patients. A risk signature with prognostic value was identified from TCGA and Gene Expression Omnibus (GEO) cohort based on laRlncRNA pairs by the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. Quantitative real-time polymerase chain reaction (qRT-PCR) and functional experiments were carried out to verify the expression of laRlncRNAs in COAD. The relationship of laRlncRNA pairs with immune landscape as well as the sensitivity of different therapies was explored.

Results: In total, 2378 laRlncRNAs were identified, 1,120 pairs of which were studied to determine their prognostic validity, followed by a risk signature established based on the screened 5 laRlncRNA pairs. The laRlncRNA pairs-based signature provided a better overall survival (OS) prediction than other published signatures and functioned as a prognostic marker for COAD patients. According to the calculated optimal cut-off point, patients were divided into high- and low-risk groups. The OS of COAD patients in the high-risk group were significantly shorter than that of those in the low-risk group (P=4.252e-14 in the TCGA cohort and P=2.865-02 in the GEO cohort). Furthermore, it remained an effective predictor of survival in strata of gender, age, TNM stage, and its significance persisted after univariate and multivariate Cox regressions. Additionally, the risk signature was significantly correlated with immune cells infiltration, tumor mutation burden (TMB), microsatellite instability (MSI) as well as immunotherapeutic efficacy and chemotherapy sensitivity. Finally, one of the laRlncRNA, LINC01315, promotes proliferation and migration capacities of colon cancer cells.

Conclusion: The newly identified laRlncRNAs pairs-based signature exhibits potential effects in predicting prognosis, deciphering patients' immune landscape, and mediating sensitivity to immunotherapy and chemotherapy. Findings in our study may provide evidence for the role of laRlncRNAs pairs as novel prognostic biomarkers and potentially individualized therapy targets for COAD patients.

Keywords: colon adenocarcinoma; lactate metabolism-related lncRNAs; prognosis; therapy response; tumor immune cell infiltration.

Figure 1

(A) Flowchart of our study, (B, C) The volcano plot and heatmap of lactate metabolism-related DEGs between tumor and normal samples, (D, E) The GO and KEGG circle plot of functional enrichment analysis.

Figure 2

(A) Consensus clustering CDF of k=2 to 6; Delta area under the CDF curve; Consensus clustering matrix of k=4. (B) Heatmap illustrating the relationships of four clusters and different immnue features (C) Immune, stromal, estimate scores and tumor purity using ESTIMATE (D) The expression of immune checkpoint genes in different clusters (E) The correlations of GZMA, PRFI gene expressions and CYT scores with clusters.

Figure 3

(A) LASSO Cox regression analysis. Kaplan—Meier (KM) curve for overall survival (OS) of COAD patients in different risk group, risk survival status plot in TCGA cohort (B, C) and GEO cohort (D, E).

Figure 4

Cox analysis in TCGA (A univariate B multivariate) and GEO (C univariate D multivariate) showed that the signature was an independent risk factor for COAD patients. (E) A nomogram established regarding the risk score and clinicopathological charateristics. (F) ROC curves of laRlncRNA pairs-based signature at 1, 3, 5-year compared with three other lncRNA-based signature studies on COAD patients. (G) The AUC values of the risk score and clinicopathological features. (H) Calibration plot to depict the consistence between the predicted and the actual OS at l, 3, 5 years.

Figure 5

The survival curves of the lalncRNA pairs signature concerning each strata of age, gender, TNM stage. (A) ≥65 years, <65years (B) Female, male (C) stage I, II, III, IV (D) T1-2, T3-4 (E) M0,M1 (F) N0,N1-2.

Figure 6

(A) GSVA analysis (B) Spearman correlation analysis between the signature and six immune cells (B cell, Macrophage, Myeloid dendritic cell, Neutrophil cell, T cell CD4+ T cell CD8+) ; (C) The comparison of PDCDl, PDCD1LG2, LAG3, HAVCR2, CD274,IDO1 and CTLA-4 expression levels between high-risk and low-groups; (D) Relationships between risk score and MSI. (E) Relationships between risk score and TMB. (F, G) The waterfall plot of somatic mutation landscape between two risk groups, ranked by top 20 frequently mutated genes.

Figure 7

Based on CIBERSORT, CIBERSORT-ABS, EPIC, ESTIMATE, MCP counters, QUANTISEQ, TIMER and ssGSEA algorithms, heatmap of immune infiltration in the high- and low-risk groups was showed.

Figure 8

(A–F) Chemosensitivity between different risk groups (Camptothecin, Doxorubicin, Erlotinib, Gemcitabine, Paclitaxel, Rapamycin).

Figure 9

qRT-PCR validation of lncRNA expression levels in different tissues and cell lines. The expression levels of (A) CEBPA-DTM|R210HG, (B) GABPB1-AS1|LINC00513, (C) LINC00513|MIR181A2HG, (D). LINC01315|VPS9D1-AS1 and (E) PVT1|LINC00261 in different cell lines (NCM460, SW480, Caco2, HCT15, HCT116, and HT29) were measured. (F–N) The expression levels of these lncRNAs in patients of colon cancer and their adjacent normal tissues (N=4) were measured. Results were normalized to reference gene GAPDH. (*P < 0.05, **P < 0.01, ***P < 0.001, ****p < 0.0001; ns, not significant).

Figure 10

Functional validations of one candidate lncRNA: LINC01315 promotes proliferation and migration of colon cancer cells in vitro. SW480 and HT29 cell were selected and transfected with siLINC01315 and overexpression vector. Evaluation of migration and proliferation capacity by wound healing assay (A), transwell assay (B) and CCK-8 assay (C). (*P < 0.05, **P < 0.01, ***P < 0.001).