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. 2022 Dec 6;13(12):2295.
doi: 10.3390/genes13122295.

Identification of an Amino Acid Metabolism-Related Gene Signature for Predicting Prognosis in Lung Adenocarcinoma

Wuguang Chang  1 , Hongmu Li  1 , Chun Wu  1 , Leqi Zhong  1 , Tengfei Zhu  1 , Zenghao Chang  1 , Wei Ou  1 , Siyu Wang  1
Affiliations

Identification of an Amino Acid Metabolism-Related Gene Signature for Predicting Prognosis in Lung Adenocarcinoma

Wuguang Chang et al. Genes (Basel). .

Abstract

Dysregulation of amino acid metabolism (AAM) is an important factor in cancer progression. This study intended to study the prognostic value of AAM-related genes in lung adenocarcinoma (LUAD). Methods: The mRNA expression profiles of LUAD datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were applied as the training and validation sets. After identifying the differentially expressed AAM-related genes, an AAM-related gene signature (AAMRGS) was constructed and validated. Additionally, we systematically analyzed the differences in immune cell infiltration, biological pathways, immunotherapy response, and drug sensitivity between the two AAMRGS subgroups. Results: The prognosis-related signature was constructed on the grounds of key AAM-related genes. LUAD patients were divided into AAMRGS-high and -low groups. Patients in the two subgroups differed in prognosis, tumor microenvironment (TME), biological pathways, and sensitivity to chemotherapy and immunotherapy. The area under the receiver operating characteristics (ROC) and calibration curves showed good predictive ability for the nomogram. Analysis of immune cell infiltration revealed that the TME of the AAMRGS-low group was in a state of immune activation. Conclusion: We constructed an AAMRGS that could effectively predict prognosis and guide treatment strategies for patients with LUAD.

Keywords: amino acid metabolism; immunotherapy; lung adenocarcinoma; prognosis; tumor microenvironment.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Identification and enrichment analysis of AAM-related genes in LUAD. (A) Heatmap showing 64 differentially expressed AAM-related genes in LUAD and normal tissues. (B) Volcano plot exhibiting 25 downregulated and 39 upregulated genes. (C) GO enrichment analysis. (D) KEGG pathway enrichment analysis.
Figure 2
Figure 2
Construction of AAM-related gene signature for LUAD. (A) Univariate Cox regression analysis identifying prognostic-related genes. (B) Tenfold cross-validation in LASSO model. (C) LASSO coefficients of 17 prognostic-related genes.
Figure 3
Figure 3
Evaluation and validation of the prognostic value of AAMRGS. (A) Kaplan‒Meier survival analysis in the TCGA cohort. (B) Distribution of risk scores and OS status in the TCGA cohort. (C) Heatmap displaying six AAM-related genes in the TCGA cohort. (D) Time-dependent ROC analysis in the TCGA cohort.
Figure 4
Figure 4
Validation of AAMRGS in GEO datasets. Kaplan‒Meier survival analysis in GSE72094 (A) and GSE31210 (B). The distribution of risk scores and survival status in GSE72094 (C) and GSE31210 (E). Heatmap displaying six AAM-related genes in GSE72094 (D) and GSE31210 (F). Time-dependent ROC analysis in GSE72094 (G) and GSE31210 (H).
Figure 5
Figure 5
Establishment and assessment of a nomogram in the TCGA cohort. Univariate (A) and multivariate (B) analyses of risk score and clinicopathological features. (C) Nomogram for predicting 1-, 3-, and 5-year survival probability. (D) AUC values derived from time-dependent ROC of the nomogram. (E) Calibration curves for evaluating the compatibility between the predicted and actual OS.
Figure 6
Figure 6
Gene set enrichment analysis between the two AAMRGS subgroups based on the AAMRGS. GO enrichment in the AAMRGS-high group (A) and AAMRGS-low group (B). KEGG enrichment in the AAMRGS-high group (C) and AAMRGS-low group (D).
Figure 7
Figure 7
Analysis of immune cell infiltration and prediction of immunotherapy response. (A) The differences in the proportions of 28 immune cells between the two subgroups. (B) CTLA4negative/PD-1negative. (C) CTLA4positive/PD-1negative. (D) CTLA4negative/PD-1positive. (E) CTLA4positive/PD-1positive. (F) KM survival analysis in the anti-PD-L1 immunotherapy cohort. (G) Evaluation of response to anti-PD-L1 immunotherapy. CR, Complete Response; PR, Partial Response; SD, Stable Disease; and PD, Progressive Disease. P values were shown as ns: p > 0.05; * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001.
Figure 8
Figure 8
Sensitivity to chemotherapeutic drugs in the two AAMRGS subgroups. (A) Cisplatin. (B) Docetaxel. (C) Doxorubicin. (D) Etoposide. (E) Gemcitabine. (F) Paclitaxel. (G) Vinblastine. (H) Vinorelbine. P values are shown as * p < 0.05; *** p < 0.001; **** p < 0.0001.
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