Characterization of the Immune Cell Infiltration Landscape Uncovers Prognostic and Immunogenic Characteristics in Lung Adenocarcinoma

Wang, Xin; Xu, Zhenyi; Liu, Zhilin; Lin, Wen‐Cheng; Cao, Zheng; Feng, Xiaoli; Gao, Yibo

doi:10.3389/fgene.2022.902577

Cited by 4 publications

(3 citation statements)

References 62 publications

(69 reference statements)

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“…Compared to previous studies (42)(43)(44), this study identified the prognostic value of the TMEscore in CRC and the predictive value of TMEscore-related genes in the response to fluoropyrimidine-based chemotherapy, which has not previously been reported. Moreover, our results were derived from a gene dataset with large cohorts of public databases, and validated in several independent cohorts, thereby guaranteeing the robustness of the results.…”

Section: Discussionmentioning

confidence: 77%

Tumor-infiltrating immune cells based TMEscore and related gene signature is associated with the survival of CRC patients and response to fluoropyrimidine-based chemotherapy

Guo

Lei

et al. 2022

Front. Oncol.

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BackgroundTumor-infiltrating immune cells (TIICs) are associated with chemotherapy response. This study aimed to explore the prognostic value of a TIIC-related tumor microenvironment score (TMEscore) in patients with colorectal cancer (CRC) who underwent chemotherapy and construct a TMEscore-related gene signature to determine its predictive value.MethodsGene profiles of patients who underwent fluoropyrimidine-based chemotherapy were collected, and their TIIC fractions were calculated and clustered. Differentially expressed genes (DEGs) between clusters were used to calculate the TMEscore. The association between the TMEscore, chemotherapy response, and survival rate was analyzed. Machine learning methods were used to identify key TMEscore-related genes, and a gene signature was constructed to verify the predictive value.ResultsTwo clusters based on the TIIC fraction were identified, and the TMEscore was calculated based on the DEGs of the two clusters. The TMEscore was higher in patients who responded to chemotherapy than in those who did not, and was associated with the survival rate of patients who underwent chemotherapy. Three machine learning methods, support vector machine (SVM), decision tree (DT), and Extreme Gradient Boosting (XGBoost), identified three TMEscore-related genes (ADH1C, SLC26A2, and NANS) associated with the response to chemotherapy. A TMEscore-related gene signature was constructed, and three external cohorts validated that the gene signature could predict the response to chemotherapy. Five datasets and clinical samples showed that the expression of the three TMEscore-related genes was increased in tumor tissues compared to those in control tissues.ConclusionsThe TIIC-based TMEscore was associated with the survival of CRC patients who underwent fluoropyrimidine-based chemotherapy, and predicted the response to chemotherapy. The TMEscore-related gene signature had a better predictive value for response to chemotherapy than for survival.

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Section: Discussionmentioning

confidence: 77%

Tumor-infiltrating immune cells based TMEscore and related gene signature is associated with the survival of CRC patients and response to fluoropyrimidine-based chemotherapy

Guo

Lei

et al. 2022

Front. Oncol.

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“…Xin Wang.etc had been reported that the immune cells infiltration in TME might be associated with immunotherapy efficiency and prognosis in LC.They nd that the survival time of patients with high TME scrore was poor and that of patients with low TME scrore was better,that result indicated that TME classi cation may help to distinguish immune and molecular characteristics and predict the clinical outcome of LUAD patients. These ndings will further improve the implementation and effectiveness of precise personalized immunotherapy strategies in LUAD [30].…”

Section: Discussionmentioning

confidence: 85%

Identification of MCM4 as an independent prognostic factor and correlate with immune infiltration in Lung Adenocarcinoma through bioinformatics analysis

Wei

Lei

Zhao

et al. 2022

Preprint

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Background: Lung cancer is the most common malignant tumor with the highest mortality all over the world. Among it, Lung Adenocarcinoma owns the highest proportion of pathological classification.Yet, the fuction of MCM4 in the occurrence of Lung Adenocarcinoma is still unknown.In this research, we attempted to disscuss the potential role of MCM4 in LUAD through bioinformatics analysis and hoped to provide a new idea for clinical treatment.Methods: The data of Lung Adenocarcinoma in TCGA and GEO were downloaded.The correlation between MCM4 and immune infiltration were performed by R software packages .Enrichment analysis were underwent to analyze the functions of MCM4. Univariate and multivariate Cox regression analysis were utilized to determine whether MCM4 could be act as an independent prognostic factor for Lung Adenocarcinoma.Lastly,the expression of MCM4 was validated in clinical tissues by immunohistochemistry (IHC), real-time quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), and western blotting (WB).Results: The expression of MCM4 was up-regulated in 13 tumors and was significantly correlated with age, gender, stage, T, N and smoke history. Furthermore, the Kaplan-Meier survival curves pointed that LUAD samples with lower MCM4 expression had longer Overall survival (OS) and the Disease-free survival (DFS) in 4 datasets. Univariate Cox regression analysis showed that the p value of MCM4 expression was less than 0.05(P＜0.05) and it appeared in the results of multivariate Cox regression analysis(HR≥1), which indicated that MCM4 can be used as an independent prognostic factor for LUAD.Enrichment Analysis suggested that MCM4 was correlated with the multiple biological functions and involved in vital pathways in GO and KEGG. Furthermore, the MCM4 expression was correlated with immune cells infiltration.Finally,the experimental results were consistent with the bioinformatic analysis.Conclusion: MCM4 might serve as a prognostic biomarker and correlate with immune infiltration in LUAD.

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“…Using the bioinformatics method, genome analysis for distinguishing pathological states in LUAD is a current developing trend (19)(20)(21). Biomedical research has led to demands for tools and strategies so that information can be obtained from genome-wide expression data.…”

Section: Introductionmentioning

confidence: 99%

Construction of a multiple-class classifier based on mRNAs and lncRNA FAM66A and PSORS1C3 for predicting distant metastasis in lung adenocarcinoma

Lin¹,

Zhi²,

Zheng³

et al. 2022

Ann Transl Med

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Background: There are several mechanisms believed to be essential for the development of distant metastasis in lung adenocarcinoma (LUAD), but the prediction of distant metastasis is still a challenge. The purpose of the present study was to examine the specific changes in RNA expression, including long noncoding RNAs (lncRNAs) in distant metastasis patients.Methods: We compared differentially expressed genes involved in distant metastasis from otherwise nonmetastasis and healthy adults using a gene expression profile. We first ranked gene sets (or gene signatures) that identify each class. An advanced multiple-class classifier was built based on the gene sets. Our classifier consisted of 282 genes and could predict cancer and distant metastasis with error rates of approximately 0.01 and 0.2, respectively. Then, gene networks were built to undermine gene relations to each class.Results: Cytochrome P450 family 4 subfamily F member 12 (CYP4F12) was the first gene in the ranking of the distant metastasis case. Down syndrome cell adhesion molecule (DSCAM) was the top gene in the rank list of the non-metastasis case. Solute carrier family 6 member 4 (SLC6A4) was associated with normal tissues. LncRNA family with sequence similarity 66 member A (FAM66A) and lncRNA PSORS1C3 were found to be associated with tumor metastasis.Conclusions: Our classifier could successfully predict distant metastasis in LUAD patients. LncRNA FAM66A and lncRNA PSORS1C3 in our model could play a role in cancer development.

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Characterization of the Immune Cell Infiltration Landscape Uncovers Prognostic and Immunogenic Characteristics in Lung Adenocarcinoma

Cited by 4 publications

References 62 publications

Tumor-infiltrating immune cells based TMEscore and related gene signature is associated with the survival of CRC patients and response to fluoropyrimidine-based chemotherapy

Tumor-infiltrating immune cells based TMEscore and related gene signature is associated with the survival of CRC patients and response to fluoropyrimidine-based chemotherapy

Identification of MCM4 as an independent prognostic factor and correlate with immune infiltration in Lung Adenocarcinoma through bioinformatics analysis

Construction of a multiple-class classifier based on mRNAs and lncRNA FAM66A and PSORS1C3 for predicting distant metastasis in lung adenocarcinoma

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