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. 2024;27(8):1161-1174.
doi: 10.2174/1386207326666230823104952.

Machine Learning-derived Multi-omics Prognostic Signature of Pyroptosis-related lncRNA with Regard to ZKSCAN2-DT and Tumor Immune Infiltration in Colorectal Cancer

Affiliations

Machine Learning-derived Multi-omics Prognostic Signature of Pyroptosis-related lncRNA with Regard to ZKSCAN2-DT and Tumor Immune Infiltration in Colorectal Cancer

Jiamin Chen et al. Comb Chem High Throughput Screen. 2024.

Abstract

Background: Colorectal cancer (CRC) has become the most prevalent gastrointestinal malignant tumor, ranking third (10.2%) in incidence and second (9.2%) in death among all malignancies globally. The most common histological subtype of CRC is colon adenocarcinoma (COAD), although the cause of CRC remains unknown, as there are no valid biomarkers.

Methods: A thorough investigation was used to build a credible biomolecular risk model based on the pyroptosis-associated lncRNAs discovered for COAD prediction. Furthermore, Cibersort and Tumor Immune Dysfunction and Exclusion (TIDE), the methods of exploring tumor immune infiltration, were adopted in our paper to detect the effects of differential lncRNAs on the tumor microenvironment. Finally, quantitative real-time polymerase chain reaction (qPCR), as the approach of exploring expressions, was utilized on four different cell lines.

Results: Seven pyroptosis-related lncRNAs have been identified as COAD predictive risk factors. Cox analysis, both univariate and multivariate, revealed that the established signature might serve as a novel independent factor with prognostic meaning in COAD patients. ZKSCAN2-DT was shown to be considerably overexpressed in the COAD cell line when compared to normal human colonic epithelial cells. Furthermore, ssGSEA analysis results revealed that the immune infiltration percentage of most immune cells dropped considerably as ZKSCAN2-DT expression increased, implying that ZKSCAN2-DT may play an important role in COAD immunotherapy.

Conclusion: Our research is the first to identify pyroptosis-related lncRNAs connected with COAD patient prognosis and to construct a predictive prognosis signature, directing COAD patient prognosis in therapeutic interventions.

Keywords: Colon adenocarcinoma; biomarkers; colorectal cancer (CRC).; immune infiltration; prognosis; pyroptosis-related lncRNA.

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Conflict of interest statement

The authors declared no conflict of interest, financial or otherwise.

Figures

Fig. (1)
Fig. (1)
The results of 7 pyroptosis-related lncRNAs based in the multivariate Cox regression analysis. (A) The validation via LASSO regression. (B) LASSO regression coefficient of 7 pyroptosis-related lncRNAs. (C) The forest plot shown 7 independent prognostic pyroptosis-related lncRNAs in TCGA-COAD. (D) ZKSCAN2-DT expression in TCGA-COAD (P<0.001). (E) AC007128.1 expression in TCGA-COAD (P<0.001). (F) AC099850.4 expression in TCGA-COAD (P<0.001). (G) AL161729.4 expression in TCGA-COAD (P<0.001). (H) AL137782.1 expression in TCGA-COAD (P<0.001) (I) LINC02381 expression in TCGA-COAD (P<0.001). (J) AC016394.3 expression in TCGA-COAD (P<0.001).
Fig. (2)
Fig. (2)
Confirmation of the pyroptosis-related lncRNA signature. (A) Survival analysis indicated that subgroup with high-risk score subpopulation possessed a miserable OS. (B) The scatter plot of risk scores. (C) The scatter plot of survival status. (D) The optimal cutoff value as the threshold in ROC curve analysis. (E) ROC analysis for predicting 1/3/5-year OS was performed using risk scores. (F) Univariate Cox analysis combined with other clinical features. (G) Multivariate Cox analysis combined with other clinical features.
Fig. (3)
Fig. (3)
Predictive analysis of the pyroptosis-related lncRNA signature with ROC curve and nomogram. (A) ROC curve for 1-year OS with different clinicopathological features. (B) ROC curve for 3-year OS with different clinicopathological features. (C) ROC curve for 5-year OS with different clinicopathological features. (D) Nomogram quantitatively shown the predictive accuracy of our model among age, gender, stage, and TNM. (E) Calibration curve for 1-year OS in nomogram. (F) Calibration curve for 3-year OS in nomogram. (G) Calibration curve for 5-year OS in nomogram.
Fig. (4)
Fig. (4)
Association with other clinicopathological features. (A) Association between risk score and age. (B) Association between risk score and gender. (C) Association between risk score and pathological stage. (D-F) Association between risk score and AJCC-TNM. (G-R) Kaplan–Meier survival analysis of COAD patients with high-risk score in various subgroups.
Fig. (5)
Fig. (5)
The TME features and immune infiltration in TCGA-COAD. (A) Association between risk score and Immune score, Stromal score and ESTIMATE score. (B) Association between risk score and Tumor purity. (C) Association between risk score, and B cells naïve. (D) Association between risk score and T cells regulatory. (E) Association between risk score and Neutrophils. (F) Association between risk score and Mast cells resting. (G) Association between risk score and Mast cells activated. (H) Association between risk score and Eosinophils. (I) Association between risk score and T cells CD4 memory activated. (J) Cibersort shown the tumor immune infiltration fraction of multiple immune cells between high-risk score subgroup and low-risk score subgroup. (K) ssGSEA exhibited differential significance of 29 immune cell functions in two groups.
Fig. (6)
Fig. (6)
Association between the pyroptosis-related lncRNA signature and all crucial immune checkpoint inhibitors. (A) Association between CD274 and risk score. (B) Association between IDO1 and risk score. (C) Association between MSI and risk score. (D) Association between TIDE and risk score. (E) Correlation Diagram between risk score and ICI related genes: PDCD1LG2, PDCD1, HAVCR2, CTLA4, CD274 and IDO1. (F) Comparison of all crucial immune checkpoint inhibitors related genes in two groups.
Fig. (7)
Fig. (7)
The clinical significance of ZKSCN2-DT in vitro and COAD study. (A) ZKSCAN2-DT was overexpressed in three COAD cell lines and (B) TGCA-COAD cohort. (C) Higher ZKSCAN2-DT expression possessed poor prognosis. (D) The ZKSCAN2-DT expression was significantly different in different stage. (E) The correlation between ZKSCAN2-DT and 49 ICI-related genes.
Fig. (8)
Fig. (8)
Role of ZKSCAN2-DT in immune status and TME. Association between ZKSCAN2-DT expression and immune status of Immune score (A). Association between ZKSCAN2-DT expression and immune status of Stromal score (B). Association between ZKSCAN2-DT expression and immune status of ESTIMATE score (C). Association between ZKSCAN2-DT expression and immune status of Tumor purity (D). CIBERSORT results revealed that some immune cells showed a significantly higher level in low-ZKSCAN2-DT subtype, including T cells, B cells, as well as M1 macrophages (E). ssGSEA analysis results shown that the immune infiltration fraction of most immune cells decreased significantly with the increase of ZKSCAN2-DT expression in TCGA-COAD (F).
Fig. (9)
Fig. (9)
The cell type distribution of pyroptosis-related lncRNA using scRNA seq database. The cell types and their distribution in GSE166555 (A). Distribution of AL161729.4 in different cells in GSE166555 (B). Distribution of LINC02381 in different cells in GSE166555 (C). Distribution of AC007128.1 in different cells in GSE166555 (D).

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