Novel immune subtypes of lung adenocarcinoma identified through bioinformatic analysis

Qin, Fanglu; Xu, Zhanyu; Yuan, Liqiang; Chen, Wenjie; Wei, Jiangbo; Sun, Yu; Li, Shikang

doi:10.1002/2211-5463.12934

Cited by 7 publications

(5 citation statements)

References 45 publications

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“…Current studies have found that the development of cancer are not only dependent on tumor cell characteristics but are also affected by the interaction with infiltrated immunocytes [ 30 , 31 ]. The tumors with higher immune cells and mediators proportion were proved to be more effective to the immune treatment [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

Construction of the optimization prognostic model based on differentially expressed immune genes of lung adenocarcinoma

Zhai

Zhao

Wang³

et al. 2021

BMC Cancer

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Background Lung adenocarcinoma (LUAD) is the most common pathology subtype of lung cancer. In recent years, immunotherapy, targeted therapy and chemotherapeutics conferred a certain curative effects. However, the effect and prognosis of LUAD patients are different, and the efficacy of existing LUAD risk prediction models is unsatisfactory. Methods The Cancer Genome Atlas (TCGA) LUAD dataset was downloaded. The differentially expressed immune genes (DEIGs) were analyzed with edgeR and DESeq2. The prognostic DEIGs were identified by COX regression. Protein-protein interaction (PPI) network was inferred by STRING using prognostic DEIGs with p value< 0.05. The prognostic model based on DEIGs was established using Lasso regression. Immunohistochemistry was used to assess the expression of FERMT2, FKBP3, SMAD9, GATA2, and ITIH4 in 30 cases of LUAD tissues. Results In total,1654 DEIGs were identified, of which 436 genes were prognostic. Gene functional enrichment analysis indicated that the DEIGs were involved in inflammatory pathways. We constructed 4 models using DEIGs. Finally, model 4, which was constructed using the 436 DEIGs performed the best in prognostic predictions, the receiver operating characteristic curve (ROC) was 0.824 for 3 years, 0.838 for 5 years, 0.834 for 10 years. High levels of FERMT2, FKBP3 and low levels of SMAD9, GATA2, ITIH4 expression are related to the poor overall survival in LUAD (p < 0.05). The prognostic model based on DEIGs reflected infiltration by immune cells. Conclusions In our study, we built an optimal prognostic signature for LUAD using DEIGs and verified the expression of selected genes in LUAD. Our result suggests immune signature can be harnessed to obtain prognostic insights.

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Section: Discussionmentioning

confidence: 99%

Construction of the optimization prognostic model based on differentially expressed immune genes of lung adenocarcinoma

Zhai

Zhao

Wang³

et al. 2021

BMC Cancer

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“…For breast cancer, Zheng et al developed an immune phenotype classifier for predicting both immune activity within the tumor microenvironment and prognoses for patients with triple-negative breast cancer [24]. In addition, numerous investigations have been conducted using immune subtypes to predict clinical prognoses and treatment guidance for bladder cancer [25], ovarian carcinoma [26], uveal melanoma [27], lung cancer [28,29], and head and neck cancers [30]. Here, we have identified two immune system-related PC phenotypes with differing transcription level scores among immune-cell subsets using a CIBERSORT analysis and further characterized their differences using a multiomic approach.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics Analysis of GFAP as a Potential Key Regulator in Different Immune Phenotypes of Prostate Cancer

Yao

Jia

et al. 2021

BioMed Research International

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Tumor immune escape plays an essential role in both cancer progression and immunotherapy responses. For prostate cancer (PC), however, the molecular mechanisms that drive its different immune phenotypes have yet to be fully elucidated. Patient gene expression data were analyzed from The Cancer Genome Atlas-prostate adenocarcinoma (TCGA-PRAD) and the International Cancer Genome Consortium (ICGC) databases. We used a Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) analysis and an unsupervised clustering analysis to identify patient subgroups with distinct immune phenotypes. These distinct phenotypes were then explored for associations for differentially expressed genes (DEGs) and both epigenetic and genetic landscapes. Finally, we used a protein-protein interaction analysis to identify key hub genes. We identified two patient subgroups with independent immune phenotypes associated with the expression of Programmed death-ligand 1 (PD-L1). Patient samples in Cluster 1 ( C 1 ) had higher scores for immune-cell subsets compared to Cluster 2 ( C 2 ), and C 2 samples had higher specific somatic mutations, MHC mutations, and genomic copy number variations compared to C 1 . We also found additional cluster phenotype differences for DNA methylation, microRNA (miRNA) expression, and long noncoding RNA (lncRNA) expression. Furthermore, we established a 4-gene model to distinguish between clusters by integrating analyses for DEGs, lncRNAs, miRNAs, and methylation. Notably, we found that glial fibrillary acidic protein (GFAP) might serve as a key hub gene within the genetic and epigenetic regulatory networks. These results improve our understanding of the molecular mechanisms underlying tumor immune phenotypes that are associated with tumor immune escape. In addition, GFAP may be a potential biomarker for both PC diagnosis and prognosis.

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“…For instance, a study used immunogenomic profiling of 29 immune signatures to identify three distinct LUAD subtypes (Immunity High, Immunity Medium, and Immunity Low) 15 . Similarly, Qin et al utilized critical immune-prognosis genes for patients with LUAD and stratified the patients into low-immunity and high-immunity subtypes 16 . In another study, researchers performed consensus clustering on differentially expressed aging-related genes in the Cancer Genome Atlas (TCGA) database.…”

Section: Introductionmentioning

confidence: 99%

Investigating subtypes of lung adenocarcinoma by oxidative stress and immunotherapy related genes

Duan,

Huang,

Zhao

et al. 2023

Sci Rep

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Lung adenocarcinoma (LUAD) is one of the most widespread and fatal types of lung cancer. Oxidative stress, resulting from an imbalance in the production and accumulation of reactive oxygen species (ROS), is considered a promising therapeutic target for cancer treatment. Currently, immune checkpoint blockade (ICB) therapy is being explored as a potentially effective treatment for early-stage LUAD. In this research, we aim to identify distinct subtypes of LUAD patients by investigating genes associated with oxidative stress and immunotherapy. Additionally, we aim to propose subtype-specific therapeutic strategies. We conducted a thorough search of the Gene Expression Omnibus (GEO) datasets. From this search, we pinpointed datasets that contained both expression data and survival information. We selected genes associated with oxidative stress and immunotherapy using keyword searches on GeneCards. We then combined expression data of LUAD samples from both The Cancer Genome Atlas (TCGA) and 11 GEO datasets, forming a unified dataset. This dataset was subsequently divided into two subsets, Dataset_Training and Dataset_Testing, using a random bifurcation method, with each subset containing 50% of the data. We applied consensus clustering (CC) analysis to identify distinct LUAD subtypes within the Dataset_Training. Molecular variances associated with oxidative stress levels, the tumor microenvironment (TME), and immune checkpoint genes (ICGs) were then investigated among these subtypes. Employing feature selection combined with machine learning techniques, we constructed models that achieved the highest accuracy levels. We validated the identified subtypes and models from Dataset_Training using Dataset_Testing. A hub gene with the highest importance values in the machine learning model was identified. We then utilized virtual screening to discover potential compounds targeting this hub gene. In the unified dataset, we integrated 2,154 LUAD samples from TCGA-LUAD and 11 GEO datasets. We specifically selected 1,311 genes associated with immune and oxidative stress processes. The expression data of these genes were then employed for subtype identification through CC analysis. Within Dataset_Training, two distinct subtypes emerged, each marked by different levels of immune and oxidative stress pathway values. Consequently, we named these as the OX+ and IM+ subtypes. Notably, the OX+ subtype showed increased oxidative stress levels, correlating with a worse prognosis than the IM+ subtype. Conversely, the IM+ subtype demonstrated enhanced levels of immune pathways, immune cells, and ICGs compared to the OX+ subtype. We reconfirmed these findings in Dataset_Testing. Through gene selection, we identified an optimal combination of 12 genes for predicting LUAD subtypes: ACP1, AURKA, BIRC5, CYC1, GSTP1, HSPD1, HSPE1, MDH2, MRPL13, NDUFS1, SNRPD1, and SORD. Out of the four machine learning models we tested, the support vector machine (SVM) stood out, achieving the highest area under the curve (AUC) of 0.86 and an accuracy of 0.78 on Dataset_Testing. We focused on HSPE1, which was designated as the hub gene due to its paramount importance in the SVM model, and computed the docking structures for four compounds: ZINC3978005 (Dihydroergotamine), ZINC52955754 (Ergotamine), ZINC150588351 (Elbasvir), and ZINC242548690 (Digoxin). Our study identified two subtypes of LUAD patients based on oxidative stress and immunotherapy-related genes. Our findings provided subtype-specific therapeutic strategies.

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Novel immune subtypes of lung adenocarcinoma identified through bioinformatic analysis

Cited by 7 publications

References 45 publications

Construction of the optimization prognostic model based on differentially expressed immune genes of lung adenocarcinoma

Construction of the optimization prognostic model based on differentially expressed immune genes of lung adenocarcinoma

Bioinformatics Analysis of GFAP as a Potential Key Regulator in Different Immune Phenotypes of Prostate Cancer

Investigating subtypes of lung adenocarcinoma by oxidative stress and immunotherapy related genes

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