Identification and validation of a new pyroptosis-associated lncRNA signature to predict survival outcomes, immunological responses and drug sensitivity in patients with gastric cancer

Jinsong Liu; Yuyang Dai; Yueyao Lu; Xiuling Liu; Jianzhong Deng; Wenbin Lu; Qian Liu; Jinsong Liu; Yuyang Dai; Yueyao Lu; Xiuling Liu; Jianzhong Deng; Wenbin Lu; Qian Liu

doi:10.3934/mbe.2023085

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 2: 1856-1881. doi: 10.3934/mbe.2023085

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Research article

Identification and validation of a new pyroptosis-associated lncRNA signature to predict survival outcomes, immunological responses and drug sensitivity in patients with gastric cancer

1.
Department of Oncology, Wujin Hospital Affiliated with Jiangsu University, Changzhou 213017, China
2.
Changzhou Key Laboratory of Molecular Diagnostics and Precision Cancer Medicine, Changzhou 213017, China
3.
Department of Radiology, Wujin Hospital Affiliated with Jiangsu University, Changzhou 213017, China
4.
Department of Oncology, The Changzhou Clinical School of Nanjing Medical University, Changzhou 213017, China
5.
Department of Oncology, The Wujin Clinical College of Xuzhou Medical University, Changzhou 213017, China
^†These authors contributed equally to this work.
Academic editor: Hao Wang

Received: 27 August 2022 Revised: 10 October 2022 Accepted: 18 October 2022 Published: 08 November 2022

Background
Gastric cancer (GC) ranks fifth in prevalence among carcinomas worldwide. Both pyroptosis and long noncoding RNAs (lncRNAs) play crucial roles in the occurrence and development of gastric cancer. Therefore, we aimed to construct a pyroptosis-associated lncRNA model to predict the outcomes of patients with gastric cancer.

Methods
Pyroptosis-associated lncRNAs were identified through co-expression analysis. Univariate and multivariate Cox regression analyses were performed using the least absolute shrinkage and selection operator (LASSO). Prognostic values were tested through principal component analysis, a predictive nomogram, functional analysis and Kaplan‒Meier analysis. Finally, immunotherapy and drug susceptibility predictions and hub lncRNA validation were performed.

Results
Using the risk model, GC individuals were classified into two groups: low-risk and high-risk groups. The prognostic signature could distinguish the different risk groups based on principal component analysis. The area under the curve and the conformance index suggested that this risk model was capable of correctly predicting GC patient outcomes. The predicted incidences of the one-, three-, and five-year overall survivals exhibited perfect conformance. Distinct changes in immunological markers were noted between the two risk groups. Finally, greater levels of appropriate chemotherapies were required in the high-risk group. AC005332.1, AC009812.4 and AP000695.1 levels were significantly increased in gastric tumor tissue compared with normal tissue.

Conclusions
We created a predictive model based on 10 pyroptosis-associated lncRNAs that could accurately predict the outcomes of GC patients and provide a promising treatment option in the future.
- gastric cancer,
- pyroptosis-associated genes,
- lncRNA,
- survival outcome,
- immunological response,
- drug sensitivity
Citation: Jinsong Liu, Yuyang Dai, Yueyao Lu, Xiuling Liu, Jianzhong Deng, Wenbin Lu, Qian Liu. Identification and validation of a new pyroptosis-associated lncRNA signature to predict survival outcomes, immunological responses and drug sensitivity in patients with gastric cancer[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 1856-1881. doi: 10.3934/mbe.2023085

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Abstract

Background

Gastric cancer (GC) ranks fifth in prevalence among carcinomas worldwide. Both pyroptosis and long noncoding RNAs (lncRNAs) play crucial roles in the occurrence and development of gastric cancer. Therefore, we aimed to construct a pyroptosis-associated lncRNA model to predict the outcomes of patients with gastric cancer.

Methods

Pyroptosis-associated lncRNAs were identified through co-expression analysis. Univariate and multivariate Cox regression analyses were performed using the least absolute shrinkage and selection operator (LASSO). Prognostic values were tested through principal component analysis, a predictive nomogram, functional analysis and Kaplan‒Meier analysis. Finally, immunotherapy and drug susceptibility predictions and hub lncRNA validation were performed.

Results

Using the risk model, GC individuals were classified into two groups: low-risk and high-risk groups. The prognostic signature could distinguish the different risk groups based on principal component analysis. The area under the curve and the conformance index suggested that this risk model was capable of correctly predicting GC patient outcomes. The predicted incidences of the one-, three-, and five-year overall survivals exhibited perfect conformance. Distinct changes in immunological markers were noted between the two risk groups. Finally, greater levels of appropriate chemotherapies were required in the high-risk group. AC005332.1, AC009812.4 and AP000695.1 levels were significantly increased in gastric tumor tissue compared with normal tissue.

Conclusions

We created a predictive model based on 10 pyroptosis-associated lncRNAs that could accurately predict the outcomes of GC patients and provide a promising treatment option in the future.

References

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