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. 2022 Apr 12;20(1):118.
doi: 10.1186/s12957-022-02572-8.

Identification of a pyroptosis-related lncRNA risk model for predicting prognosis and immune response in colon adenocarcinoma

Yuying Tan #  1 , Liqing Lu #  1 , Xujun Liang  2 , Yongheng Chen  3   4
Affiliations

Identification of a pyroptosis-related lncRNA risk model for predicting prognosis and immune response in colon adenocarcinoma

Yuying Tan et al. World J Surg Oncol. .

Abstract

Background: Colon adenocarcinoma (COAD) is one of the most common malignant tumors and is diagnosed at an advanced stage with a poor prognosis worldwide. Pyroptosis is involved in the initiation and progression of tumors. This research focused on constructing a pyroptosis-related ceRNA network to generate a reliable risk model for risk prediction and immune infiltration analysis of COAD.

Methods: Transcriptome data, miRNA-sequencing data, and clinical information were downloaded from the TCGA database. First, differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs), and lncRNAs (DElncRNAs) were identified to construct a pyroptosis-related ceRNA network. Second, a pyroptosis-related lncRNA risk model was developed applying univariate Cox regression analysis and least absolute shrinkage and selection operator method (LASSO) regression analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were utilized to functionally annotate RNAs contained in the ceRNA network. In addition, Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, univariate and multivariate Cox regression, and nomogram were applied to validate this risk model. Finally, the relationship of this risk model with immune cells and immune checkpoint blockade (ICB)-related genes was analyzed.

Results: A total of 5373 DEmRNAs, 1159 DElncRNAs, and 355 DEmiRNAs were identified. A pyroptosis-related ceRNA regulatory network containing 132 lncRNAs, 7 miRNAs, and 5 mRNAs was constructed, and a ceRNA-based pyroptosis-related risk model including 11 lncRNAs was built. The tumor tissues were classified into high- and low-risk groups according to the median risk score. Kaplan-Meier analysis showed that the high-risk group had a shorter survival time; ROC analysis, independent prognostic analysis, and nomogram further indicated the risk model was a significant independent prognostic factor what had an excellent ability to predict patients' risk. Moreover, immune infiltration analysis indicated that the risk model was related to immune infiltration cells (i.e., B cell naïve, T cell follicular helper, macrophage M1) and ICB-related genes (i.e., PD-1, CTLA4, HAVCR2).

Conclusions: This pyroptosis-related lncRNA risk model possessed good prognostic value, and the ability to predict the outcome of ICB immunotherapy in COAD.

Keywords: Colon adenocarcinoma; Immunotherapy; Pyroptosis; Tumor immune microenvironment; ceRNA network.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The analysis process of the study. TCGA, The Cancer Genome Atlas; DElncRNAs, differentially expressed lncRNAs; DEmRNAs, differentially expressed mRNAs, DEmiRNAs, differentially expressed miRNAs; ceRNA, competitive endogenous RNA; CIBERSORT, cell-type identification by estimating relative subsets of RNA transcripts
Fig. 2
Fig. 2
Differentially expressed genes. Volcano plots of A DEmRNAs, B DEmiRNAs, and C DElncRNAs. Heatmaps of D DEmRNAs, E DEmiRNAs, and F DElncRNAs
Fig. 3
Fig. 3
The pyroptosis-related ceRNA network. A The intersection of DEmRNAs, miTGs, and PRGs. B A pyroptosis-related ceRNA network. Lavender circles indicate pyroptosis-related DEmRNAs, green circles indicate DEmiRNAs, and purple circles indicate DElncRNAs
Fig. 4
Fig. 4
The PRlncRNA risk model. A The univariate Cox regression of selected lncRNAs (criterion: P value < 0.05). B, C The LASSO Cox regression analysis of lncRNAs with P<0.05. D A ceRNA network of 11 lncRNAs with 7 miRNAs and 5 mRNAs based on the prognostic PRlncRNA risk model. E, F GO and KEGG enrichment analyses of genes included in the above ceRNA network
Fig. 5
Fig. 5
Validation of the PRlncRNA risk model. A Heatmap of 11 prognostic lncRNAs between the two risk groups where red presents high expression level and green presents low expression level. B K-M analysis. C Distribution of the risk score. D The survival status of COAD patients. E ROC curves of 1-, 3-, and 5-year OS time
Fig. 6
Fig. 6
Independent prognostic analysis. A Univariate Cox analysis. B Multivariate Cox analysis. C Heatmap of the correlation of the risk model and clinical characteristics (P value** < 0.01, P value* < 0.05)
Fig. 7
Fig. 7
Validation of predictive ability of the risk model. A The nomogram contained risk values, age, and stage to predict survival. B One-year nomogram calibration curve. C Three-year nomogram calibration curve. D Five-year nomogram calibration curve
Fig. 8
Fig. 8
Immune cell infiltration analysis. A Heatmap of the composition of the immune cells was calculated based on the CIBERSORT algorithm. B The proportion of immune cells in the two risk groups (P<0.05). C Correlation analysis between the risk score and 8 immune cells (P<0.05)
Fig. 9
Fig. 9
The connection between the prognostic risk score and ICB-related genes. A Heatmap of the correlation of the risk score and 11 ICB-related genes. C The connection between the risk score and 11 ICB-related genes, respectively (P<0.05)
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