Classification of Lung Adenocarcinoma Based on Immune Checkpoint and Screening of Related Genes

Aims. Lung adenocarcinoma (LUAD) cells could escape from the monitoring of immune cells and metastasize rapidly through immune escape. Therefore, we aimed to develop a method to predict the prognosis of LUAD patients based on immune checkpoints and their associated genes, thus providing guidance for LUAD treatment. Methods. Gene sequencing data were downloaded from the Cancer Genome Atlas (TCGA) and analyzed by R software and R Bioconductor software package. Based on immune checkpoint genes, kmdist clustering in ConsensusClusterPlus R software package was utilized to classify LUAD. CIBERSORT was used to quantify the abundance of immune cells in LUAD samples. LM22 signature was performed to distinguish 22 phenotypes of human infiltrating immune cells. Gene set variation analysis (GSVA) was performed on immune checkpoint cluster and immune checkpoint score using GSVA R software package. The risk score was calculated by LASSO regression coefficient. Gene Ontology (GO), Hallmark, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed. PROC was performed to generate the ROC curve and calculate the area under the curve (AUC). Results. According to the immune checkpoint, LUAD was classified into clusters 1 and 2. Survival rate, immune infiltration patterns, TMB, and immune score were significantly different between the two clusters. Functional prediction showed that the functions of cluster 1 focused on apoptosis, JAK/STAT signaling pathway, TNF-α/NFκB signaling pathway, and STAT5 signaling pathway. The risk score model was constructed based on nine genes associated with immune checkpoints. Survival analysis and ROC analysis showed that patients with high-risk score had poor prognosis. The risk score was significantly correlated with cancer status (with tumor), male proportion, status, tobacco intake, and cancer stage. With the increase of the risk score, the enrichment of 22 biological functions increased, such as p53 signaling pathway. The signature was verified in IMvigor immunotherapy dataset with excellent diagnostic accuracy. Conclusion. We established a nine-gene signature based on immune checkpoints, which may contribute to the diagnosis, prognosis, and clinical treatment of LUAD.

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Comprehensive Profiling Reveals Distinct Microenvironment and Metabolism Characterization of Lung Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.619821 ◽

2021 ◽

Vol 12 ◽

Author(s):

Chang Li ◽

Chen Tian ◽

Yangyang Liu ◽

Jinyan Liang ◽

Yulan Zeng ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Immune Cells ◽

Immune Checkpoint ◽

Immune Escape ◽

Cholesterol Biosynthesis ◽

Bioinformatic Analysis ◽

Gene Set Enrichment Analysis ◽

Cell Infiltration ◽

Metabolic State ◽

Metabolic States

Lung adenocarcinoma has entered into an era of immunotherapy with the development of immune checkpoint inhibitors (ICIs). The identification of immune subtype is crucial to prolonging survival in patients. The tumor microenvironment (TME) and metabolism have a profound impact on prognosis and therapy. The majority of previous studies focused on only one aspect, while both of them are essential to the understanding of tumorigenesis and development. We hypothesized that lung adenocarcinoma can be stratified into immune subgroups with alterations in the TME infiltration. We aimed to explore the “TME-Metabolism-Risk” patterns in each subtypes and the mechanism behind. Glycolysis and cholesterol were selected for the analysis of metabolic states based on the first half of the study. Bioinformatic analysis was performed to investigate the transcriptomic and clinical data integrated by three lung adenocarcinoma cohorts (GSE30219, GSE31210, GSE37745, N = 415). The results were validated in an independent cohort (GSE50081, N = 127). In total, 415 lung adenocarcinoma samples were integrated and analyzed. Four major immune subtypes were indentified using bioinformatic analysis. Subtype NC1, characterized by a high level of glycolysis, with extremely low microenvironment cell infiltration. Subtype NC2, characterized by the “Silence” and “Cholesterol biosynthesis Predominant” metabolic states, with a middle degree infiltration of microenvironment cell. Subtype NC3, characterized by the lack of “Cholesterol biosynthesis Predominant” metabolic state, with abundant microenvironment cell infiltration. Subtype NC4, characterized by “Mixed” metabolic state, with a relatively low microenvironment cell infiltration. Least absolute shrinkage and selection operator (LASSO) regression and multivariate analyses were performed to calculate the risk of each sample, and we attempted to find out the potential immune escape mechanism in different subtypes. The result revealed that the lack of immune cells infiltration might contribute to the immune escape in subtypes NC1 and NC4. NC3 was characterized by the high expression of immune checkpoint molecules and fibroblasts. NC2 had defects in activation of innate immune cells. There existed an obviously survival advantage in subtype NC2. Gene set enrichment analysis (GSEA) and Gene Ontology analysis indicated that the PI3K-AKT-mTOR, TGF-β, MYC-related pathways might be correlated with this phenomenon. In addition, some differentially expressed genes (DEGs) were indentified in subtype NC3, which might be potential targets for survival phenotype transformation.

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Identification of an immune-related signature indicating the dedifferentiation of thyroid cells

Cancer Cell International ◽

10.1186/s12935-021-01939-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xuemin Wang ◽

Wen Peng ◽

Chunyan Li ◽

Rujia Qin ◽

Zhaoming Zhong ◽

...

Keyword(s):

Risk Score ◽

Immune Cells ◽

Immune Checkpoint ◽

Tumour Microenvironment ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Thyroid Cells ◽

Normal Thyroid ◽

Thyroid Carcinomas ◽

Immune Checkpoint Molecules

Abstract Background Immune cells account for a large proportion of the tumour microenvironment in anaplastic thyroid carcinomas (ATCs). However, the expression pattern of immune-related genes (IRGs) in ATCs is unclear. Our study aimed to identify an immune-related signature indicating the dedifferentiation of thyroid cells. Methods We compared the differences in thyroid differentiation score (TDS), infiltration of immune cells and enriched pathways between ATCs and papillary thyroid carcinomas (PTCs) or normal thyroid tissues in the Gene Expression Omnibus database. Univariate and multivariable Cox analyses were used to screen prognosis-associated IRGs in The Cancer Genome Atlas database. After constructing a risk score, we investigated its predictive value for differentiation and survival by applying receiver operating characteristic and Kaplan–Meier curves. We further explored its associations with important immune checkpoint molecules, infiltrating immune cells and response to immunotherapy. Results Compared with PTCs or normal thyroid tissues, ATCs exhibited lower TDS values and higher enrichment of immune cells and activation of the inflammatory response. The quantitative analyses and immunohistochemical staining validated that most ATC cell lines and ATC tissues had higher expression of MMP9 and lower expression of SDC2 than normal thyroid samples and PTC. Higher risk scores indicates dedifferentiation and a worse prognosis. Additionally, the risk score was positively correlated with the immune checkpoint molecules PDL1, CTLA4, IDO1, and HAVCR2 and infiltration of multiple immune cells. Importantly, we found that the samples with higher risk scores tended to have a better response to immunotherapy than those with lower scores. Conclusion Our findings indicate that the risk score may not only contribute to the determination of differentiation and prognosis of thyroid carcinomas but also help the prediction of immune cells infiltration and immunotherapy response.

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Identification of an immune-related signature indicating the dedifferentiation of thyroid cells

10.21203/rs.3.rs-84430/v2 ◽

2021 ◽

Author(s):

Xuemin Wang ◽

Wen Peng ◽

Chunyan Li ◽

Rujia Qin ◽

Zhaoming Zhong ◽

...

Keyword(s):

Risk Score ◽

Immune Cells ◽

Immune Checkpoint ◽

Tumour Microenvironment ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Thyroid Cells ◽

Normal Thyroid ◽

Thyroid Carcinomas ◽

Immune Checkpoint Molecules

Abstract Background: Immune cells account for a large proportion of the tumour microenvironment in anaplastic thyroid carcinomas (ATCs). However, the expression pattern of immune-related genes (IRGs) in ATCs is unclear. Our study aimed to identify an immune-related signature indicating the dedifferentiation of thyroid cells.Methods: We compared the differences in thyroid differentiation score (TDS), infiltration of immune cells and enriched pathways between ATCs and papillary thyroid carcinomas (PTCs) or normal thyroid tissues in the Gene Expression Omnibus database. Univariate and multivariable Cox analyses were used to screen prognosis-associated IRGs in The Cancer Genome Atlas database. After constructing a risk score, we investigated its predictive value for differentiation and survival by applying receiver operating characteristic and Kaplan-Meier curves. We further explored its associations with important immune checkpoint molecules, infiltrating immune cells and response to immunotherapy. Results: Compared with PTCs or normal thyroid tissues, ATCs exhibited lower TDS values and higher enrichment of immune cells and activation of the inflammatory response. The quantitative analyses and immunohistochemical staining validated that most ATC cell lines and ATC tissues had higher expression of MMP9 and lower expression of SDC2 than normal thyroid samples and PTC. Higher risk scores indicates dedifferentiation and a worse prognosis. Additionally, the risk score was positively correlated with the immune checkpoint molecules PDL1, CTLA4, IDO1, and HAVCR2 and infiltration of multiple immune cells. Importantly, we found that the samples with higher risk scores tended to have a better response to immunotherapy than those with lower scores. Conclusion: Our findings indicate that the risk score may not only contribute to the determination of differentiation and prognosis of thyroid carcinomas but also help the prediction of immune cells infiltration and immunotherapy response.

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Identification of an Immune-related Signature That Indicates the Dedifferentiation of Thyroid Cells

10.21203/rs.3.rs-84430/v1 ◽

2020 ◽

Author(s):

Xuemin Wang ◽

Chunyan Li ◽

Rujia Qin ◽

Zhaoming Zhong ◽

Chuan-Zheng Sun

Keyword(s):

Thyroid Carcinoma ◽

Risk Score ◽

Immune Cells ◽

Immune Checkpoint ◽

Predictive Value ◽

The Cancer Genome Atlas ◽

Term Survival ◽

Thyroid Cells ◽

Normal Thyroid ◽

Immune Checkpoint Molecules

Abstract Background: Patients with well-differentiated thyroid carcinoma can achieve long-term survival after reasonable treatments, but there is no standard treatment mode for poorly or undifferentiated thyroid carcinoma and its prognosis is very poor. Immune cells, especially tumor-associated macrophages, account for a large proportion of the tumor microenvironment of anaplastic thyroid carcinomas (ATCs). However, whether immune-related genes can mediate the dedifferentiation of thyroid cells is unclear.Methods: We initially compared the differences of thyroid differentiation score, infitration of immune cells and enriched pathways between ATCs and papillary thyroid carcionma (PTCs) or normal thyroid tissues in Gene Expression Omnibus database. Then, The Cancer Genome Atlas database was used to screen out the prognosis associated IRGs. A risk score was constructed and we next investigated its predictive value for differentiation by applying receiver operating characteristic (ROC) curves and correlation analyses. Kaplan-Meier curves were used to evaluated its prognostic value. We further explored the associations of the risk score with important immune checkpoint molecules, infiltrating immune cells and response to immunotherapy.Results: Compared with PTCs or normal thyroid tissues, ATCs exhibited lower thyroid differentiation scores, higher infiltration of most immune cells and higher activation of inflammatory response. The risk score composed of MMP9 and SDC2 was significantly increased in ATCs and low differentiated PTCs. Moreover, it showed favorable predictive value for differentiation and survival. Higher risk score displayed dedifferentiation status and a worse prognosis. Additionly, the risk score was positively correlated with immune checkpoint molecules PDL1, CTLA4, IDO1, HAVCR2 and infiltration of multiple immune cells. Importantly, we found that samples with higher risk score tend to have a better response to immune checkpoint agents than lower ones.Conclusion: Our findings indicate that the risk score may not only contribute to the judgement of differentiation and prognosis of thyroid cancer, but also help to the prediction of immune cell infiltration and immune checkpoint inhibitor response.

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MicroRNA-326 attenuates immune escape and prevents metastasis in lung adenocarcinoma by targeting PD-L1 and B7-H3

Cell Death Discovery ◽

10.1038/s41420-021-00527-8 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Lijuan Shao ◽

Qian He ◽

Jingbo Wang ◽

Fei He ◽

Shengcheng Lin ◽

...

Keyword(s):

T Cells ◽

T Cell ◽

Lung Adenocarcinoma ◽

Tumor Progression ◽

Tumor Cells ◽

Immune Checkpoint ◽

Immune Escape ◽

Immune Checkpoints ◽

Tumor Cell Migration ◽

Immune Checkpoint Molecules

AbstractTumor-infiltrating T cells are highly expressive of inhibitory receptor/immune checkpoint molecules that bind to ligand expressed by tumor cells and antigen-presenting cells, and eventually lead to T cell dysfunction. It is a hot topic to restore T cell function by targeting immune checkpoint. In recent years, immunotherapy of blocking immune checkpoint and its receptor, such as PD-L1/PD-1 targeted therapy, has made effective progress, which brings hope for patients with advanced malignant tumor. However, only a few patients benefit from directly targeting these checkpoints or their receptors by small compounds or antibodies. Since the complexity of the regulation of immune checkpoints in tumor cells, further research is needed to identify the novel endogenous regulators of immune checkpoints which can help for developing effective drug target to improve the effect of immunotherapy. Here, we verified that microRNA-326 (miR-326) repressed the gene expression of immune checkpoint molecules PD-L1 and B7-H3 in lung adenocarcinoma (LUAD). We detected that the expression of miR-326 in LUAD tissue was negatively correlated with PD-L1/B7-H3. The repression of PD-L1 and B7-H3 expression through miR-326 overexpression leads to the modification the cytokine profile of CD8+ T cells and decreased migration capability of tumor cells. Meanwhile, the downregulation of miR-326 promoted tumor cell migration. Moreover, blocking PD-L1 and B7-H3 attenuated the tumor-promoting effect induced by miR-326 inhibitor. In tumor-bearing mice, the infiltration of CD8+ T cells was significantly increased and the expression of TNF-α, and IFN-γ was significantly enhanced which contributed to tumor progression after miR-326 overexpression. Collectively, miR-326 restrained tumor progression by downregulating PD-L1 and B7-H3 expression and increasing T cell cytotoxic function in LUAD. Our findings revealed a novel perspective on the complex regulation of immune checkpoint molecules. A new strategy of using miR-326 in tumor immunotherapy is proposed.

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Identification and Validation of a Novel Six-lncRNA-Based Prognostic Model for Lung Adenocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.775583 ◽

2022 ◽

Vol 11 ◽

Author(s):

Lingge Yang ◽

Yuan Wu ◽

Huan Xu ◽

Jingnan Zhang ◽

Xinjie Zheng ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Risk Score ◽

Prognostic Model ◽

Roc Curves ◽

The Cancer Genome Atlas ◽

Immune Checkpoints ◽

Lasso Regression ◽

Predictive Values ◽

Angiogenic Therapy ◽

Cox Analysis

ObjectiveThis study was conducted in order to establish a long non-coding RNA (lncRNA)-based model for predicting overall survival (OS) in patients with lung adenocarcinoma (LUAD).MethodsOriginal RNA-seq data of LUAD samples were extracted from The Cancer Genome Atlas (TCGA) database. Univariate Cox survival analysis was performed to select lncRNAs associated with OS. The least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox analysis were performed for building an OS-associated lncRNA prognostic model. Moreover, receiver operating characteristic (ROC) curves were generated to assess predictive values of the hub lncRNAs. Consequently, qRT-PCR was conducted to validate its prognostic value. The potential roles of these lncRNAs in immunotherapy and anti-angiogenic therapy were also investigated.ResultsThe lncRNA-associated risk score of OS (LARSO) was established based on the LASSO coefficient of six individual lncRNAs, including CTD-2124B20.2, CTD-2168K21.1, DEPDC1-AS1, RP1-290I10.3, RP11-454K7.3, and RP11-95M5.1. Kaplan–Meier analysis revealed that LUAD patients with higher LARSO values had a shorter OS. Furthermore, a new risk score (NRS), including LARSO, stage, and N stage, could better predict the prognosis of LUAD patients compared with LARSO alone. Evaluation of the prognostic model in our cohort demonstrated that patients with higher scores had a worse prognosis. In addition, correlation analysis between these six lncRNAs and immune checkpoints or anti-angiogenic targets suggested that LUAD patients with high LARSO might not be sensitive to immunotherapy or anti-angiogenic therapy.ConclusionsThis robust six-lncRNA prognostic signature may be used as a novel and powerful prognostic biomarker for lung adenocarcinoma.

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From Oncogenic Signaling Pathways to Single-Cell Sequencing of Immune Cells: Changing the Landscape of Cancer Immunotherapy

Molecules ◽

10.3390/molecules26082278 ◽

2021 ◽

Vol 26 (8) ◽

pp. 2278

Author(s):

Afshin Derakhshani ◽

Zeinab Rostami ◽

Hossein Safarpour ◽

Mahdi Abdoli Shadbad ◽

Niloufar Sadat Nourbakhsh ◽

...

Keyword(s):

Single Cell ◽

Signaling Pathways ◽

Immune Cells ◽

Immune Checkpoint ◽

Immune Checkpoint Inhibitors ◽

Checkpoint Inhibitors ◽

Cancer Development ◽

Immune Checkpoints ◽

Single Cell Sequencing ◽

Increased Risk

Over the past decade, there have been remarkable advances in understanding the signaling pathways involved in cancer development. It is well-established that cancer is caused by the dysregulation of cellular pathways involved in proliferation, cell cycle, apoptosis, cell metabolism, migration, cell polarity, and differentiation. Besides, growing evidence indicates that extracellular matrix signaling, cell surface proteoglycans, and angiogenesis can contribute to cancer development. Given the genetic instability and vast intra-tumoral heterogeneity revealed by the single-cell sequencing of tumoral cells, the current approaches cannot eliminate the mutating cancer cells. Besides, the polyclonal expansion of tumor-infiltrated lymphocytes in response to tumoral neoantigens cannot elicit anti-tumoral immune responses due to the immunosuppressive tumor microenvironment. Nevertheless, the data from the single-cell sequencing of immune cells can provide valuable insights regarding the expression of inhibitory immune checkpoints/related signaling factors in immune cells, which can be used to select immune checkpoint inhibitors and adjust their dosage. Indeed, the integration of the data obtained from the single-cell sequencing of immune cells with immune checkpoint inhibitors can increase the response rate of immune checkpoint inhibitors, decrease the immune-related adverse events, and facilitate tumoral cell elimination. This study aims to review key pathways involved in tumor development and shed light on single-cell sequencing. It also intends to address the shortcomings of immune checkpoint inhibitors, i.e., their varied response rates among cancer patients and increased risk of autoimmunity development, via applying the data from the single-cell sequencing of immune cells.

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DNASE1L3 as a Novel Diagnostic and Prognostic Biomarker for Lung Adenocarcinoma Based on Data Mining

Frontiers in Genetics ◽

10.3389/fgene.2021.699242 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jianlin Chen ◽

Junping Ding ◽

Wenjie Huang ◽

Lin Sun ◽

Jinping Chen ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Prognostic Biomarker ◽

Function Analysis ◽

Mrna Level ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Immune Checkpoints ◽

Diagnostic Efficiency ◽

Protein Levels ◽

Normal Tissues

Previous researches have highlighted that low-expressing deoxyribonuclease1-like 3 (DNASE1L3) may play a role as a potential prognostic biomarker in several cancers. However, the diagnosis and prognosis roles of DNASE1L3 gene in lung adenocarcinoma (LUAD) remain largely unknown. This research aimed to explore the diagnosis value, prognostic value, and potential oncogenic roles of DNASE1L3 in LUAD. We performed bioinformatics analysis on LUAD datasets downloaded from TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus), and jointly analyzed with various online databases. We found that both the mRNA and protein levels of DNASE1L3 in patients with LUAD were noticeably lower than that in normal tissues. Low DNASE1L3 expression was significantly associated with higher pathological stages, T stages, and poor prognosis in LUAD cohorts. Multivariate analysis revealed that DNASE1L3 was an independent factor affecting overall survival (HR = 0.680, p = 0.027). Moreover, decreased DNASE1L3 showed strong diagnostic efficiency for LUAD. Results indicated that the mRNA level of DNASE1L3 was positively correlated with the infiltration of various immune cells, immune checkpoints in LUAD, especially with some m6A methylation regulators. In addition, enrichment function analysis revealed that the co-expressed genes may participate in the process of intercellular signal transduction and transmission. GSEA indicated that DNASE1L3 was positively related to G protein-coupled receptor ligand biding (NES = 1.738; P adjust = 0.044; FDR = 0.033) and G alpha (i) signaling events (NES = 1.635; P adjust = 0.044; FDR = 0.033). Our results demonstrated that decreased DNASE1L3 may serve as a novel diagnostic and prognostic biomarker associating with immune infiltrates in lung adenocarcinoma.

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Identification and Validation of an Immune-Related eRNA Prognostic Signature for Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.657051 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shenglan Cai ◽

Xingwang Hu ◽

Ruochan Chen ◽

Yiya Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Immune Cells ◽

Cox Regression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Roc Curves ◽

The Cancer Genome Atlas ◽

Immune Checkpoints ◽

Immune Genes ◽

Normal Tissues

BackgroundEnhancer RNAs (eRNAs) are intergenic long non-coding RNAs (lncRNAs) that participate in the progression of malignancies by targeting tumor-related genes and immune checkpoints. However, the potential role of eRNAs in hepatocellular carcinoma (HCC) is unclear. In this study, we aimed to construct an immune-related eRNA prognostic model that could be used to prospectively assess the prognosis of patients with HCC.MethodsGene expression profiles of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). The eRNAs co-expressed from immune genes were identified as immune-related eRNAs. Cox regression analyses were applied in a training cohort to construct an immune-related eRNA signature (IReRS), that was subsequently used to analyze a testing cohort and combination of the two cohorts. Kaplan-Meier and receiver operating characteristic (ROC) curves were used to validate the predictive effect in the three cohorts. Gene Set Enrishment Analysis (GSEA) computation was used to identify an IReRS-related signaling pathway. A web-based cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) computation was used to evaluate the relationship between the IReRS and infiltrating immune cells.ResultsA total of sixty-four immune-related eRNAs (IReRNAs) was identified in HCC, and 14 IReRNAs were associated with overall survival (OS). Five IReRNAs were used for constructing an immune-related eRNA signature (IReRS), which was shown to correlate with poor survival and to be an independent prognostic biomarker for HCC. The GSEA results showed that the IReRS was correlated to cancer-related and immune-related pathways. Moreover, we found that IReRS was correlated to infiltrating immune cells, including CD8+ T cells and M0 macrophages. Finally, differential expressions of the five risk IReRNAs in tumor tissues vs. adjacent normal tissues and their prognostic values were verified, in which the AL445524.1 may function as an oncogene that affects prognosis partly by regulating CD4-CLTA4 related genes.ConclusionOur results suggest that the IReRS could serve as a biomarker for predicting prognosis in patients with HCC. Additionally, it may be correlated to the tumor immune microenvironment and could also be used as a biomarker in immunotherapy for HCC.

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Identification of Prognostic Signature and Gliclazide as Candidate Drugs in Lung Adenocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.665276 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yang Cheng ◽

Kezuo Hou ◽

Yizhe Wang ◽

Yang Chen ◽

Xueying Zheng ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Risk Score ◽

Clinical Pathology ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Rank Test ◽

Therapeutic Drug ◽

Induce Cell Cycle Arrest ◽

Survival Difference ◽

Incidence And Mortality

BackgroundLung adenocarcinoma (LUAD) is the most common pathological type of lung cancer, with high incidence and mortality. To improve the curative effect and prolong the survival of patients, it is necessary to find new biomarkers to accurately predict the prognosis of patients and explore new strategy to treat high-risk LUAD.MethodsA comprehensive genome-wide profiling analysis was conducted using a retrospective pool of LUAD patient data from the previous datasets of Gene Expression Omnibus (GEO) including GSE18842, GSE19188, GSE40791 and GSE50081 and The Cancer Genome Atlas (TCGA). Differential gene analysis and Cox proportional hazard model were used to identify differentially expressed genes with survival significance as candidate prognostic genes. The Kaplan–Meier with log-rank test was used to assess survival difference. A risk score model was developed and validated using TCGA-LUAD and GSE50081. Additionally, The Connectivity Map (CMAP) was used to predict drugs for the treatment of LUAD. The anti-cancer effect and mechanism of its candidate drugs were studied in LUAD cell lines.ResultsWe identified a 5-gene signature (KIF20A, KLF4, KRT6A, LIFR and RGS13). Risk Score (RS) based on 5-gene signature was significantly associated with overall survival (OS). Nomogram combining RS with clinical pathology parameters could potently predict the prognosis of patients with LUAD. Moreover, gliclazide was identified as a candidate drug for the treatment of high-RS LUAD. Finally, gliclazide was shown to induce cell cycle arrest and apoptosis in LUAD cells possibly by targeting CCNB1, CCNB2, CDK1 and AURKA.ConclusionThis study identified a 5-gene signature that can predict the prognosis of patients with LUAD, and Gliclazide as a potential therapeutic drug for LUAD. It provides a new direction for the prognosis and treatment of patients with LUAD.

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