Integrative bioinformatics approaches to establish potential prognostic immune-related genes signature and drugs in the non-small cell lung cancer microenvironment
Introduction: Research has revealed that the tumor microenvironment (TME) is associated with the progression of malignancy. The combination of meaningful prognostic biomarkers related to the TME is expected to be a reliable direction for improving the diagnosis and treatment of non-small cell lung c...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-04-01
|
| Series: | Frontiers in Pharmacology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2023.1153565/full |
| _version_ | 1797854097734369280 |
|---|---|
| author | Jiao Zhou Jiao Zhou Shan Shi Shan Shi Yeqing Qiu Yeqing Qiu Zhongwen Jin Zhongwen Jin Wenyan Yu Wenyan Yu Rongzhi Xie Rongzhi Xie Hongyu Zhang Hongyu Zhang |
| author_facet | Jiao Zhou Jiao Zhou Shan Shi Shan Shi Yeqing Qiu Yeqing Qiu Zhongwen Jin Zhongwen Jin Wenyan Yu Wenyan Yu Rongzhi Xie Rongzhi Xie Hongyu Zhang Hongyu Zhang |
| author_sort | Jiao Zhou |
| collection | DOAJ |
| description | Introduction: Research has revealed that the tumor microenvironment (TME) is associated with the progression of malignancy. The combination of meaningful prognostic biomarkers related to the TME is expected to be a reliable direction for improving the diagnosis and treatment of non-small cell lung cancer (NSCLC).Method and Result: Therefore, to better understand the connection between the TME and survival outcomes of NSCLC, we used the “DESeq2” R package to mine the differentially expressed genes (DEGs) of two groups of NSCLC samples according to the optimal cutoff value of the immune score through the ESTIMATE algorithm. A total of 978 up-DEGs and 828 down-DEGs were eventually identified. A fifteen-gene prognostic signature was established via LASSO and Cox regression analysis and further divided the patients into two risk sets. The survival outcome of high-risk patients was significantly worse than that of low-risk patients in both the TCGA and two external validation sets (p-value < 0.05). The gene signature showed high predictive accuracy in TCGA (1-year area under the time-dependent ROC curve (AUC) = 0.722, 2-year AUC = 0.708, 3-year AUC = 0.686). The nomogram comprised of the risk score and related clinicopathological information was constructed, and calibration plots and ROC curves were applied, KEGG and GSEA analyses showed that the epithelial-mesenchymal transition (EMT) pathway, E2F target pathway and immune-associated pathway were mainly involved in the high-risk group. Further somatic mutation and immune analyses were conducted to compare the differences between the two groups. Drug sensitivity provides a potential treatment basis for clinical treatment. Finally, EREG and ADH1C were selected as the key prognostic genes of the two overlapping results from PPI and multiple Cox analyses. They were verified by comparing the mRNA expression in cell lines and protein expression in the HPA database, and clinical validation further confirmed the effectiveness of key genes.Conclusion: In conclusion, we obtained an immune-related fifteen-gene prognostic signature and potential mechanism and sensitive drugs underling the prognosis model, which may provide accurate prognosis prediction and available strategies for NSCLC. |
| first_indexed | 2024-04-09T20:00:25Z |
| format | Article |
| id | doaj.art-05b8ffcad8b04780aa91439939adba9b |
| institution | Directory Open Access Journal |
| issn | 1663-9812 |
| language | English |
| last_indexed | 2024-04-09T20:00:25Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj.art-05b8ffcad8b04780aa91439939adba9b2023-04-03T04:37:23ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122023-04-011410.3389/fphar.2023.11535651153565Integrative bioinformatics approaches to establish potential prognostic immune-related genes signature and drugs in the non-small cell lung cancer microenvironmentJiao Zhou0Jiao Zhou1Shan Shi2Shan Shi3Yeqing Qiu4Yeqing Qiu5Zhongwen Jin6Zhongwen Jin7Wenyan Yu8Wenyan Yu9Rongzhi Xie10Rongzhi Xie11Hongyu Zhang12Hongyu Zhang13The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, ChinaZhongshan School of Medicine, Sun Yat-sen University, Guangzhou, ChinaThe Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, ChinaZhongshan School of Medicine, Sun Yat-sen University, Guangzhou, ChinaThe Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, ChinaZhongshan School of Medicine, Sun Yat-sen University, Guangzhou, ChinaThe Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, ChinaZhongshan School of Medicine, Sun Yat-sen University, Guangzhou, ChinaThe Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, ChinaZhongshan School of Medicine, Sun Yat-sen University, Guangzhou, ChinaThe Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, ChinaCancer Center, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, ChinaThe Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, ChinaCancer Center, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, ChinaIntroduction: Research has revealed that the tumor microenvironment (TME) is associated with the progression of malignancy. The combination of meaningful prognostic biomarkers related to the TME is expected to be a reliable direction for improving the diagnosis and treatment of non-small cell lung cancer (NSCLC).Method and Result: Therefore, to better understand the connection between the TME and survival outcomes of NSCLC, we used the “DESeq2” R package to mine the differentially expressed genes (DEGs) of two groups of NSCLC samples according to the optimal cutoff value of the immune score through the ESTIMATE algorithm. A total of 978 up-DEGs and 828 down-DEGs were eventually identified. A fifteen-gene prognostic signature was established via LASSO and Cox regression analysis and further divided the patients into two risk sets. The survival outcome of high-risk patients was significantly worse than that of low-risk patients in both the TCGA and two external validation sets (p-value < 0.05). The gene signature showed high predictive accuracy in TCGA (1-year area under the time-dependent ROC curve (AUC) = 0.722, 2-year AUC = 0.708, 3-year AUC = 0.686). The nomogram comprised of the risk score and related clinicopathological information was constructed, and calibration plots and ROC curves were applied, KEGG and GSEA analyses showed that the epithelial-mesenchymal transition (EMT) pathway, E2F target pathway and immune-associated pathway were mainly involved in the high-risk group. Further somatic mutation and immune analyses were conducted to compare the differences between the two groups. Drug sensitivity provides a potential treatment basis for clinical treatment. Finally, EREG and ADH1C were selected as the key prognostic genes of the two overlapping results from PPI and multiple Cox analyses. They were verified by comparing the mRNA expression in cell lines and protein expression in the HPA database, and clinical validation further confirmed the effectiveness of key genes.Conclusion: In conclusion, we obtained an immune-related fifteen-gene prognostic signature and potential mechanism and sensitive drugs underling the prognosis model, which may provide accurate prognosis prediction and available strategies for NSCLC.https://www.frontiersin.org/articles/10.3389/fphar.2023.1153565/fullnon-small cell lung cancertumor microenvironmentestimateprognostic gene signaturedrug sensitivity |
| spellingShingle | Jiao Zhou Jiao Zhou Shan Shi Shan Shi Yeqing Qiu Yeqing Qiu Zhongwen Jin Zhongwen Jin Wenyan Yu Wenyan Yu Rongzhi Xie Rongzhi Xie Hongyu Zhang Hongyu Zhang Integrative bioinformatics approaches to establish potential prognostic immune-related genes signature and drugs in the non-small cell lung cancer microenvironment Frontiers in Pharmacology non-small cell lung cancer tumor microenvironment estimate prognostic gene signature drug sensitivity |
| title | Integrative bioinformatics approaches to establish potential prognostic immune-related genes signature and drugs in the non-small cell lung cancer microenvironment |
| title_full | Integrative bioinformatics approaches to establish potential prognostic immune-related genes signature and drugs in the non-small cell lung cancer microenvironment |
| title_fullStr | Integrative bioinformatics approaches to establish potential prognostic immune-related genes signature and drugs in the non-small cell lung cancer microenvironment |
| title_full_unstemmed | Integrative bioinformatics approaches to establish potential prognostic immune-related genes signature and drugs in the non-small cell lung cancer microenvironment |
| title_short | Integrative bioinformatics approaches to establish potential prognostic immune-related genes signature and drugs in the non-small cell lung cancer microenvironment |
| title_sort | integrative bioinformatics approaches to establish potential prognostic immune related genes signature and drugs in the non small cell lung cancer microenvironment |
| topic | non-small cell lung cancer tumor microenvironment estimate prognostic gene signature drug sensitivity |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2023.1153565/full |
| work_keys_str_mv | AT jiaozhou integrativebioinformaticsapproachestoestablishpotentialprognosticimmunerelatedgenessignatureanddrugsinthenonsmallcelllungcancermicroenvironment AT jiaozhou integrativebioinformaticsapproachestoestablishpotentialprognosticimmunerelatedgenessignatureanddrugsinthenonsmallcelllungcancermicroenvironment AT shanshi integrativebioinformaticsapproachestoestablishpotentialprognosticimmunerelatedgenessignatureanddrugsinthenonsmallcelllungcancermicroenvironment AT shanshi integrativebioinformaticsapproachestoestablishpotentialprognosticimmunerelatedgenessignatureanddrugsinthenonsmallcelllungcancermicroenvironment AT yeqingqiu integrativebioinformaticsapproachestoestablishpotentialprognosticimmunerelatedgenessignatureanddrugsinthenonsmallcelllungcancermicroenvironment AT yeqingqiu integrativebioinformaticsapproachestoestablishpotentialprognosticimmunerelatedgenessignatureanddrugsinthenonsmallcelllungcancermicroenvironment AT zhongwenjin integrativebioinformaticsapproachestoestablishpotentialprognosticimmunerelatedgenessignatureanddrugsinthenonsmallcelllungcancermicroenvironment AT zhongwenjin integrativebioinformaticsapproachestoestablishpotentialprognosticimmunerelatedgenessignatureanddrugsinthenonsmallcelllungcancermicroenvironment AT wenyanyu integrativebioinformaticsapproachestoestablishpotentialprognosticimmunerelatedgenessignatureanddrugsinthenonsmallcelllungcancermicroenvironment AT wenyanyu integrativebioinformaticsapproachestoestablishpotentialprognosticimmunerelatedgenessignatureanddrugsinthenonsmallcelllungcancermicroenvironment AT rongzhixie integrativebioinformaticsapproachestoestablishpotentialprognosticimmunerelatedgenessignatureanddrugsinthenonsmallcelllungcancermicroenvironment AT rongzhixie integrativebioinformaticsapproachestoestablishpotentialprognosticimmunerelatedgenessignatureanddrugsinthenonsmallcelllungcancermicroenvironment AT hongyuzhang integrativebioinformaticsapproachestoestablishpotentialprognosticimmunerelatedgenessignatureanddrugsinthenonsmallcelllungcancermicroenvironment AT hongyuzhang integrativebioinformaticsapproachestoestablishpotentialprognosticimmunerelatedgenessignatureanddrugsinthenonsmallcelllungcancermicroenvironment |