
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

39029079
MD-D-24-05455
00081
10.1097/MD.0000000000038979
3
4500
Research Article
Observational Study
Construction and validation of a TTN mutation associated immune prognostic model for evaluating immune microenvironment and outcomes of gastric cancer: An observational study
Chen Ruyue MM ruyuechen985@163.com
ab
Yao Zengwu MM yzw1986yzw@126.com
bc
https://orcid.org/0009-0005-1229-4784
Jiang Lixin MD abcd*
a Medical College, Qingdao University, Qingdao, Shandong Province, China
b Department of Gastrointestinal Surgery, Yantai Yuhuangding Hospital, Yantai, Shandong Province, China
c Department of Gastrointestinal Surgery, Yantai Yuhuangding Hospital, Shandong University, Jinan, Shandong Province, China
d Department of General Surgery, Yantai Yeda Hospital, Yantai, Shandong Province, China.
* Correspondence: Lixin Jiang, Qingdao University, Qingdao, Shandong Province 266071, China (e-mail: jianglixin_1969@163.com).
19 7 2024
19 7 2024
103 29 e3897916 5 2024
24 6 2024
27 6 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

Gastric cancer (GC) is a prevalent form of cancer worldwide, and TTN (titin) mutations are frequently observed in GC. However, the association between TTN mutations and immunotherapy for GC remains unclear, necessitating the development of novel prognostic models. The prognostic value and potential mechanisms of TTN in stomach adenocarcinoma were evaluated by TCGA (The Cancer Genome Atlas)-stomach adenocarcinoma cohort analysis, and an immune prognostic model was constructed based on TTN status. We validated it using the GSE84433 dataset. We performed Gene Set Enrichment Analysis and screened for differentially expressed genes, and used lasso (least absolute shrinkage and selection operator) regression analysis to screen for survival genes to construct a multifactorial survival model. In addition, we evaluated the relative proportions of 22 immune cells using the CIBERSORT algorithm for immunogenicity analysis. Finally, we constructed the nomogram integrating immune prognostic model and other clinical factors. GESA showed enrichment of immune-related phenotypes in patients with TTN mutations. We constructed an immune prognostic model based on 16 genes could identify gastric cancer patients with higher risk of poor prognosis. Immuno-microenvironmental analysis showed increased infiltration of naive B cells, plasma cells, and monocyte in high-risk patients. In addition, Nomo plots predicted the probability of 1-year, 3-year, and 5-year OS (overall survival) in GC patients, showing good predictive performance. In this study, we identified that TTN gene may be a potential clinical biomarker for GC and TTN mutations may be a predictor of immunotherapy in patients. We constructed and validated a new model for prognosis of GC patients based on immune characteristics associated with TTN mutations. This study may provide potential therapeutic strategies for gastric cancer.

gastric cancer
immune prognostic model
mutations
TTN gene
tumor microenvironment
OPEN-ACCESSTRUE
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pmc1. Introduction

Gastric cancer (GC) is the fifth most common cancer worldwide, and its early stages often lack obvious symptoms, leading to advanced stages at the time of diagnosis. This contributes to the high mortality rate of gastric cancer.[1,2] The current treatment for GC involves surgery combined with adjuvant therapy, such as radiotherapy and chemotherapy. However, the prognosis for advanced patients remains poor.[3–5] The etiology and pathogenesis of gastric cancer are still under investigation.[3] Genome sequencing has provided valuable insights into the heterogeneity of gastric cancer, offering useful tools for identifying new predictive biomarkers and enabling personalized treatment.[6]

Several genes and biomarkers related to GC have been identified, including TP53, MUC16, LRP1B, and ARID1A.[7–10] TTN is a common oncogene in human tumors and its mutation is a high-frequency somatic mutation. TTN is responsible for expressing myosin, which maintains muscle tone.[11] Studies have shown that TTN gene mutations are associated with various cancers, such as colorectal cancer, lung adenocarcinoma, and squamous lung cancer.[12–15] However, limited research has been conducted on the association between TTN gene mutations and GC.

The objective of this study was to examine the association between TTN gene mutations and GC, and to assess the potential of the TTN gene as a prognostic indicator for GC. We conducted a thorough analysis of TTN mutation status and RNA expression in GC, with the aim of investigating the relationship between TTN mutations and immune response. Notably, our study developed an immune prognostic model (IPM) that incorporates immune genes affected by TTN mutations, which could be utilized for patient management. The genes included in the IPM hold promise as potential therapeutic biomarkers for GC.

In this study, we developed a prognostic marker consisting of 16 genes associated with TTN mutations to predict the survival of patients with gastric cancer. Additionally, we assessed the tumor immune properties of high-risk and low-risk groups of gastric cancer patients. Finally, we created a prognostic nomogram by integrating the characteristics of gastric cancer patients and clinical factors.

2. Materials and methods

2.1. Data sources

Somatic mutations, gene expression, and corresponding clinical data for stomach adenocarcinoma (STAD) samples were downloaded from The Cancer Genome Atlas (TCGA) project (https://portal.gdc.cancer.gov/). We excluded samples without survival information and incomplete TNM staging data, resulting in a final dataset of 341 STAD samples with complete mutation, expression, and clinical data for further analysis. For the validation set, the GSE84433 microarray gene chip from the Gene Expression Omnibus database (GEO, www.ncbi.nlm.nih.gov/geo/, GEO accession: GSE84433, Platforms: GPL6947) was used, which comprised 357 patients with complete clinical information.

2.2. Biological pathway assessment by gene set enrichment analysis (GSEA)

We obtained version 3.0 of the GSEA software from the GSEA website (DOI:10.1073/pnas.0506580102, http://software.broadinstitute.org/gsea/index.jsp). The samples were divided into 2 groups based on TTN gene mutations: the mutant (MUT) group (n = 177) and the wild type (WT) group (n = 164). To assess relevant pathways and molecular mechanisms, we downloaded the c7.immunesigdb.v7.4.symbols.gmt subset from the Molecular Signatures Database (DOI:10.1093/bioinformatics/btr260, http://www.gsea-msigdb.org/gsea/downloads.jsp). P value of <.05 and a false discovery rate (FDR) of <0.25 were considered statistically significant based on gene expression profiles and phenotypic grouping.

2.3. Differentially expressed gene (DEG) and enrichment analysis

We assessed the significance of the difference between the TTN-MUT group and the TTN-WT group for each gene using the t.test function from the R package. In addition, we calculated the difference for each gene using the p.adjust function. We selected adjusted P < .05 and |log2fold change (FC)| > 1.

Then, Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out to compare the differential signal pathway and biological effects among the different cohorts. KEGG enrichment analyses were premised on the q value and P value thresholds of <.05.

2.4. Construction and validation of the IPM

Least absolute shrinkage and selection operator (LASSO) is a commonly used method of regression analysis. It combines variable selection and regularization to improve the predictive performance and interpretability of the resulting statistical model. We utilized the R packages survival and glmnet to integrate survival time, survival status, and gene expression data. Finally, we assessed performed regression analyses using the lasso-cox method. Besides, we used the R software package pROC (version 1.17.0.1) to perform ROC (receiver operating characteristic) analysis and obtain the AUC (area under the curve).

2.5. Mutation landscape analysis

We obtained somatic mutation data from TCGA for STAD patients and used waterfall plots to visualize mutated genes.

2.6. Analysis of immune cell infiltration

The immunogenicity analysis was conducted by utilizing the CIBERSORT algorithm to determine the relative proportions of 22 immune cells. We utilized the KEGG rest API (https://www.kegg.jp/kegg/rest/keggapi.html) to retrieve the most up-to-date gene annotations for the KEGG Pathway. We employed the R package clusterProfiler (version 3.14.3).Statistical significance was determined by a P value of <.05 and a FDR of < 0.05.

2.7. Construction of the clinical prediction model

In this study, we utilized the R package rms to evaluate the prognostic significance of specific features in 341 samples from the TCGA-STAD dataset. We create a nomogram using the cox method.

3. Result

3.1. Association between immune phenotype and TTN mutations in STAD

We analyzed the somatic mutation profiles of 437 samples, of which 405 (92.7%) were from STAD patients. Among these patients, TTN mutations were the most common (57.5%), followed by TP53 mutations (49.4%) and MUC16 mutations (34.1%) (Fig. 1A). Our objective was to investigate the relationship between TTN mutations and immune response using the TCGA-STAD dataset. To accomplish this, we performed GSEA on 341 STAD patients, dividing them into 2 groups based on TTN status: 177 samples with TTN mutations and 164 samples without TTN mutations. Figure 1B displays a subset of the TTN-WT group that exhibits significantly modified immune-related phenotypes.

Figure 1. Somatic mutation profiles and gene set enrichment analysis (GSEA) of STAD (stomach adenocarcinoma) samples based on TNN status. (A) Panorama of mutations in STAD patients. (B) Significant enrichment of immune-related phenotypes in TTN-MUT STAD patients compared with TTN-WT STAD patients. STAD = stomach adenocarcinoma.

3.2. Survival analysis for the prognostic signature according to TTN status

Differential expression analyses were conducted for both groups, revealing significant up-regulation of 567 genes in the MUT group and significant down-regulation of 986 genes in the MUT group (Fig. 2A and B). Furthermore, we conducted KEGG pathway analysis on the collected related genes (Fig. 3C). Finally, the prognostic significance of each gene was evaluated using the cox method, with regression analyses performed using the lasso-cox method. The Lambda value was set to 0.0661869813654359, resulting in the identification of 16 genes. (Fig. 2D and E).

Figure 2. Differentially expressed gene (DEG) analysis. (A, B) Differential information volcano plot and heat map for each gene in TTN-MUT group and TTN-WT group; (C) circular plot of gene-enriched biological processes; (D, E) multifactorial survival model constructed by screening survival genes using lasso (least absolute shrinkage and selection operator) regression analysis.

Figure 3. Predictive analysis of immune prognostic model (IPM). (A) Kaplan–Meier survival for IPM in the TGCA (The Cancer Genome Atlas)-STAD cohort; (B) Kaplan–Meier survival for IPM in the GSE84433 cohort; (C) risk scores in relation to patient survival time, status, and changes in expression of individual genes; (D) time-dependent ROC (receiver operating characteristic) curves. ROC = receiver operating characteristic.

Based on our findings, we observed that the genes associated with TTN risk scores in the TCGA-STAD dataset are mainly enriched in pathways such as Hedgehog signaling pathway, cAMP signaling pathway, and Pathways in cancer. Additionally, we found that the relevant signaling molecules include ABC transporters, leukocyte transendothelial migration, and Cell adhesion molecules (CAMs).

We then analyzed the prognostic difference between these 2 groups using the survfit function from the R package survival. The significance of the prognostic difference between the samples of the different groups was evaluated using the logrank test method. Our analysis revealed a significant prognostic difference (P = 7.2e − 18) between the different groups of samples (Fig. 3A). We also obtained significant differences in the validation dataset (GSE84433) (Fig. 3B).

We conducted an analysis to examine the association between various risk scores and patients’ follow-up time, events, and changes in the expression of specific genes. Our findings revealed a significant decrease in patient survival as risk scores increased. Consistent with our expectations, the genes KRTAP8-1, AC097625.1, VWFP1, UPK1B, and LRRC55 were identified as risk factors, exhibiting an up-regulated trend in expression as risk scores increased (Fig. 3C).

We used the R software package pROC (version 1.17.0.1) to perform ROC analysis and obtain the AUC. Specifically, we collected the follow-up time and risk scores of the patients and conducted ROC analysis at the time points of 365, 1095, and 1825 using the roc function in pROC. We also used the ci function to assess the AUC and confidence intervals. The AUC results were 0.75, 0.74, and 0.74, respectively (Fig. 3D).

3.3. The association of immune-related prognostic signature with tumor microenvironment

We used the CIBERSORT method, in conjunction with the LM22 signaling matrix, to evaluate the disparities in immune infiltration among 22 immune cell types in low-risk and high-risk GC patients. The findings from 341 patients are summarized in Figure 4A. The proportion of immune cells in STAD exhibited variability both within and between groups, as depicted in Figure 4B.

Figure 4. Immune infiltration in high-risk and low-risk gastric cancer (GC) patients. (A) Differences in immune infiltration of 22 immune cell types in GC patients; (B) correlation matrix of all 22 immune cell proportions; (C) violin plots of the differences in immune cell proportions of patients according to their high-risk and low-risk types.

Furthermore, there were variations in the proportions of different subpopulations of tumor-infiltrating immune cells. High-risk patients exhibited significantly higher proportions of naive B cells, plasma cells, and Monocytes, while showing significantly lower proportions of T cells CD4 memory activated, Macrophages M0, and Macrophages M1, among other subpopulations, when compared to low-risk patients (Fig. 4C).

3.4. Nomogram construction based on IPM

In this study, we utilized the R package rms to analyze data on survival time, survival status, and 8 characteristics. We employed the cox method to construct a nomogram and evaluated the prognostic significance of these characteristics in a sample of 341 participants (Fig. 5A).

Figure 5. Prognostic efficacy of assessing immune-related prognostic genes for patient survival. (A) Nomograms used to predict the probability of OS (overall survival) at 1, 3, and 5 years in GC patients; (B) calibration plots of nomograms used to predict the probability of OS at 1, 3, and 5 years; (C) time-dependent ROC curve analysis of the immune prognostic model nomograms. ROC = receiver operating characteristic.

In order to evaluate the predictive accuracy of the nomogram, calibration curves (Fig. 5B), and ROC analysis (Fig. 5C) were conducted, demonstrating favorable performance in predicting patient survival. The calibration curves indicated strong concordance between the predicted and observed GC survival groups. The AUC values obtained from the ROC analysis were 0.80, 0.83, and 0.87 for 1, 3, and 5 years, respectively. Consequently, these findings suggest that the nomogram serves as a reliable model for predicting survival in GC patients.

4. Discussion

GC remains a significant global health issue, and there is an urgent need for new prognostic models to enhance patient prognosis. The TTN gene, specifically its tumor immune activation, plays a crucial role in this context. TTN mutations have the potential to promote the development of gastric cancer by influencing the immune characteristics of patients. In our study, we constructed an IPM based on the TTN mutation status, utilizing data from the TCGA-STAD cohort. This 16-gene-based IPM successfully identified gastric cancer patients at a higher risk of experiencing a poor prognosis. The findings from this study may provide insights into a potential therapeutic approach involving the modulation of the tumor microenvironment.

Our prognostic marker consists of 16 genes, namely KRTAP8-1, AC069287.1, AC124854.1, VWFP1, APOA5, LRRC55, ANKRD1, AC097625.1, UPK1B, AP000676.1, AC008635.1, GDF6, AC138982.1, HMGB1P17, Y10196.1, and AL050303.3. These genes have been implicated in cancer or inflammation, as they are known to influence the immune response.

The APOA5 gene, also known as apolipoprotein A5, plays a crucial role in regulating triglyceride levels. Polymorphisms in the APOA5 gene have been identified as powerful genetic factors contributing to elevated plasma triglyceride levels.[16] Furthermore, APOA5 copy number deletions are more commonly observed in East Asian women with breast tumors, and decreased expression of APOA5 is associated with higher ESTIMATE immunity scores.[17] ANKRD1 plays a crucial role in maintaining the equilibrium between normal and abnormal inflammatory responses in skeletal muscle. It exerts its anti-inflammatory effects by inhibiting the transcriptional activity of NF-κB through a feedback mechanism.[18] Furthermore, ANKRD1 serves as a diagnostic marker for biliary atresia and is also linked to immune infiltration.[19] GDF6 has been shown to improve the structure of the intervertebral disc (IVD) and inhibit the expression of inflammation-related and pain-related factors. Specifically, GDF6 decreases the expression of cytokines such as IL-6, ICAM1, MMP-13, IL-1β, and TNF-α, while increasing the expression of TIMP1, TGF-β2, IL-10, and resistin.[20]

In recent years, numerous studies have demonstrated a strong correlation between immune infiltration and cancer.[21–23] As a result, there has been a rapid development of new targeted drugs and immunotherapies, which offer promising treatments for patients with malignant tumors.[24,25] Consequently, we conducted a further analysis to examine the differences in immune characteristics in low-risk and high-risk groups. Our analysis revealed that high-risk GC patients exhibited higher levels of naive B cells, plasma cells, and Monocytes, while showing lower levels of CD4 memory activated T cells, Macrophages M0, and Macrophages M1. These differential findings suggest that the poor prognosis observed in high-risk GC patients may be attributed to lower immune reactivity within the tumor microenvironment, thereby promoting tumor growth, progression, invasion, and metastasis. Given these disparities, it is plausible that high-risk patients may derive greater benefits from immunotherapy.

Additionally, the role of this signaling in immunomodulation of the tumor microenvironment was further validated through KEGG analysis. The genes associated with our risk score were primarily enriched for pathways such as the Hedgehog signaling pathway, cAMP signaling pathway, ABC transporters, and other related signaling molecules. Dysregulation of the Hedgehog signaling pathway has been linked to developmental abnormalities and cancers, including colon cancer and head and neck squamous cell carcinoma (HNSCC).[26–28] In the immune system, cyclic adenosine monophosphate (cAMP) is recognized as a potent regulator of innate and adaptive immune cell function. Therapeutic strategies that disrupt or enhance the cAMP signaling pathway have shown immunomodulatory potential in autoimmune and inflammatory diseases.[29] In ovarian cancer, growth and metabolism are largely dependent on changes in cAMP-PKA-CREB axis signaling. The transduction of the cAMP signaling pathway has been implicated in carcinogenesis, proliferation, metastasis, and survival of cancer cells.[30] Targeted inhibition of ABC transporters’ expression or function can enhance the effectiveness of immune checkpoint inhibitors by promoting an anticancer immune microenvironment.[31] Infiltrative immune cells and peritumour stromal cells support tumor growth, angiogenesis, metastasis, and immunosuppression through communication with inflammatory cytokines and Cell adhesion molecules (CAMs).[32,33]

We developed a prognostic nomogram by integrating prognostic features with TNM staging. The calibration curves demonstrated a high level of concordance between the actual and predicted survival rates at 3 and 5 years. Additionally, the AUC-ROC analysis indicated that the nomogram effectively predicted the 1-year, 3-year, and 5-year survival rates in patients with GC. These findings strongly suggest that the nomogram holds significant potential as a robust tool for prognosticating the survival of individuals with gastric cancer.

However, we also acknowledge the limitations of this study. First, while bioinformatics analysis is a widely used tool that can provide high-precision data analysis and prediction, it is important to note that conclusive evidence in life sciences must be obtained through molecular biology experiments. In future studies, we aim to validate our findings using experimental methods such as real-time PCR (polymerase chain reaction), western blot, and immunohistochemistry in cell and animal experiments. Second, it is crucial to validate the results in more other GC cohorts to ensure the generalizability of our findings. Lastly, it is important to acknowledge that this study is retrospective in nature and has a small sample size. Therefore, further prospective studies are needed to validate our results.

In conclusion, this study investigated the prognostic value of TTN gene mutation in GC. Additionally, the findings of this study may contribute to the identification of potential therapeutic approaches for the treatment of gastric cancer.

5. Conclusion

In this study, we discovered a strong association between TTN gene mutations and the development of GC. We further investigated the relationship between TTN mutations and immune response. The findings revealed that the TTN gene has the potential to serve as a clinical biomarker for GC. Additionally, we developed and validated a novel model based on immune characteristics associated with TTN mutations.

Author contributions

Conceptualization: Ruyue Chen.

Data curation: Ruyue Chen.

Methodology: Ruyue Chen.

Software: Ruyue Chen.

Writing – original draft: Ruyue Chen, Zengwu Yao.

Writing – review & editing: Ruyue Chen, Zengwu Yao, Lixin Jiang.

Formal analysis: Zengwu Yao.

Investigation: Zengwu Yao.

Validation: Zengwu Yao.

Visualization: Zengwu Yao.

Project administration: Lixin Jiang.

Supervision: Lixin Jiang.

Abbreviation:

AUC area under the curve

DEGs differentially expressed genes

GC gastric cancer

GESA gene set enrichment analysis

IPM immune prognostic model

KEGG Kyoto Encyclopedia of Genes and Genomes

LASSO least absolute shrinkage and selection operator

OS overall survival

ROC receiver operating characteristic

STAD stomach adenocarcinoma

TCGA The Cancer Genome Atlas

TME tumor microenvironment.

This research is supported by the Shandong University Cooperation Project (3460019005), Shandong Province Medical and Health Technology Project (202304010040), and Yantai Science and Technology Plan (2023YD049).

Ethical approval is not applicable for this study. All the data involved in this study were obtained from an open platform, no ethical permission was required. It is complying with the specific requirements of country.

The authors have no conflicts of interest to study.

The datasets generated during and/or analyzed during the current study are publicly available.

How to cite this article: Chen R, Yao Z, Jiang L. Construction and validation of a TTN mutation associated immune prognostic model for evaluating immune microenvironment and outcomes of gastric cancer: An observational study. Medicine 2024;103:29(e38979).

RC and ZY contributed equally to this work.
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