Prognostic Factor For Lung Adenocarcinoma Patients Research Articles

NME6 as a potential biomarker and therapeutic target involved in immune infiltration for lung adenocarcinoma.

Lung adenocarcinoma (LUAD), a prevalent form of lung cancer, is characterized by its high global mortality rate. Previous studies have demonstrated the significance of Nucleoside diphosphate kinase (NME) in various cancers; however, the specific role of NME6 in LUAD remains inadequately understood. This research aims to enhance our understanding of LUAD by investigating the expression level, epigenetic mechanism, signaling activities, and immune infiltrating characteristic immune cells of NME6 in patients. The NME6 expression was explored between LUAD and normal tissue samples using GEPIA, UALCAN and HPA databases. The survival analysis was performed by Kaplan-Meier plotter. The Shiny Methylation Analysis Resource Tool was employed to examine the methylation characteristics of NME6. The Tumor Immune Single-cell Hub (TISCH) and CIBERSORT algorithm were utilized to analyze immune infiltrating characteristic immune cells between NME6 high- and low-expression group in LUAD. According to GEPIA, UALCAN, and HPA databases, NME6 is highly expressed in LUAD compared to normal tissues. At the same time, elevated levels of NME6 were found to be significantly correlated with inferior overall survival outcomes in LUAD patients. Subsequently, the top 10 genes interacted with NME6 were mainly involved in seven pathways, such as p53 signaling pathway, glutathione metabolism, thiamine metabolism, metabolic pathways, and drug metabolism. Notably, NME6 methylation in LUAD samples was lower than in normal samples. The methylation of cg04625862 has a significant impact on the regulation of NME6 expression in LUAD. Furthermore, high NME6 expression in LUAD was associated with tumor stages and relative abundance of tumor infiltrating immune cells, such as Macrophage M2, activated mast cell, and neutrophil. Moreover, NME6 regulated the expression of m6A modification of genes related to LUAD, including METTL3, WTAP, RBM15B, METTL14, RBMX, VIRMA, YTHDC1, RBM15, ZC3H13, YTHDF1, YTHDC2, IGF2BP2, YTHDF3, HNRNPA2B1, YTHDF2, HNRNPC, FTO, and ALKBH5. The analysis showed that NME6 is a crucial prognostic factor for LUAD patients. NME6 regulates genes related to m6A modification and immune cells infiltration. Furthermore, NME6 could sever as a potential therapeutic target for LUAD.

Prognostic value of genes related to cancer-associated fibroblasts in lung adenocarcinoma.

Although it has been established that cancer-associated fibroblasts (CAFs) facilitate tumor development, the relationship between CAFs and the prognosis of patients with lung adenocarcinoma (LUAD) has not been extensively explored. This study was formulated to investigate the prognostic value of CAF-related genes in LUAD. Differential analysis was carried out with TCGA-LUAD dataset as the training set. By overlapping differentially expressed genes (DEGs) with genes associated with CAF, CAF-related DEGs specific to LUAD were obtained. A prognostic risk model was constructed by Lasso and Cox regression analysis, and samples were grouped according to median risk score. The efficacy of the model was accessed through survival curve and receiver operating characteristic curve (ROC) analyses, with the validation set for verification. Risk score combined with clinical factors was utilized for Cox analysis to verify the independence of the model, and a nomogram was drawn. GSEA was performed on different risk groups. Immunologic infiltration and tumor mutational burden were assessed in different risk groups. Eleven feature genes including DLGAP5, KCNE2, UPK2, NPAS2, ARHGAP11A, ANGPTL4, ANLN, DKK1, SMUG1, C16orf74, and ACAD8 were identified, based on which a prognostic model was constructed. Risk score could predict the prognosis of LUAD patients and could be an independent prognostic factor for LUAD patients. GSEA outcomes displayed significant enrichment of genes in the high-risk group in the P53 SIGNALING PATHWAY. In comparison to the low-risk group, the high-risk group exhibited a decreased degree of immune infiltration and an elevated level of tumor mutational burden. An 11-gene model was constructed based on CAF-related genes to predict LUAD prognosis. This model represented an independent prognostic factor for LUAD.

A novel anoikis-related gene signature predicts in lung adenocarcinoma

ObjectiveTo generate a prognostic model prognosis based on anoikis-related genes (ANRGs) expression for Lung adenocarcinoma (LUAD), an exploration of the prognostic value of ANRGs in LUAD was conducted. MethodsBased on the expression matrix of genes from the TCGA database, we built the co-expressed modules by weighted gene co-expression network analysis (WGCNA). Then we identified the differentially expressed ANRGs (DE-ANGs) between LUAD and normal samples by the WGCNA results, DEGs, and the 345 ANRGs. The biofunction of the DE-ANRGs was interpreted using the GO and KEGG databases. Univariate and multivariate regression models were used to verify whether the risk model could serve as an independent prognostic factor. A nomogram was utilized to predict overall survival (OS) in LUAD. ResultsThe expression of 56 DE-ANRGs was significantly different in tumor tissues. We established a 4-ANRG prognostic signature. In the TCGA cohort and the external GSE31210 validation cohort, the OS of the high-risk group was lower than that of the low-risk group significantly. Moreover, the prediction performance of the risk model was excellently verified by the ROC curve. In addition, both univariate COX and multivariate Cox analyses indicated that risk score could act as an independent prognostic factor for LUAD patients. The calibration curve and C-index demonstrated that the nomogram was satisfactory in predicting 1, 3- and 5-year survival in LUAD patients. ConclusionsOur study developed a novel prognostic signature of 4 ANRGs with Excellent prognostic performance for LUAD patients.

Pyrimidinergic receptor P2Y6 expression is elevated in lung adenocarcinoma and is associated with poor prognosis.

Previous in vitro studies have indicated that pyrimidinergic receptor P2Y6 (P2RY6, P2Y6 receptor) may function as a cancer-promoting factor in lung adenocarcinoma (LUAD). However, the prognostic significance of P2RY6 expression in LUAD has not been investigated. This study aimed to assess the impact of P2RY6 expression on the survival of patients with LUAD. First, we assessed P2RY6 mRNA and protein expression in LUAD and non-cancerous lung tissues using the online bioinformatics analysis tool GEPIA, fresh LUAD tissues, and LUAD tissue microarrays (TMAs). Second, we investigated the correlation between P2RY6 expression and clinicopathological parameters of LUAD patients based on data from The Cancer Genome Atlas (TCGA) database and TMAs. Finally, we analyzed the prognostic significance of P2RY6 expression in LUAD using the online survival analysis tool Kaplan-Meier Plotter and data from TMAs. We demonstrated that P2RY6 mRNA and protein expression levels in LUAD tissues were significantly higher than those in non-cancerous lung tissues. The expression of P2RY6 in LUAD was positively correlated with poor differentiation, more lymph node metastasis, and more advanced clinical stage. Higher P2RY6 expression level was correlated with shorter survival of the LUAD patients. Univariate and multivariate Cox regression analyses indicated that higher P2RY6 tumor expression was an independent unfavorable prognostic factor for LUAD patients. P2RY6 expression was elevated in LUAD and correlated with poor prognosis.

Intrinsic Immunogenic Tumor Cell Death Subtypes Delineate Prognosis and Responsiveness to Immunotherapy in Lung Adenocarcinoma.

Recent studies have highlighted the combination of activation of host immunogenic cell death (ICD) and tumor-directed cytotoxic strategies. However, overall multiomic analysis of the intrinsic ICD property in lung adenocarcinoma (LUAD) has not been performed. Therefore, the aim of this study was to develop an ICD-based risk scoring system to predict overall survival (OS) and immunotherapeutic efficacy in patients. In our study, both weighted gene co-expression network analysis (WGCNA) and LASSO-Cox analysis were utilized to identify ICDrisk subtypes (ICDrisk). Moreover, we identify genomic alterations and differences in biological processes, analyze the immune microenvironment, and predict the response to immunotherapy in patients with pan-cancer. Importantly, immunogenicity subgroup typing was performed based on the immune score (IS) and microenvironmental tumor neoantigens (meTNAs). Our results demonstrate that ICDrisk subtypes were identified based on 16 genes. Furthermore, high ICDrisk was proved to be a poor prognostic factor in LUAD patients and indicated poor efficacy of immune checkpoint inhibitor (ICI) treatment in patients with pan-cancer. The two ICDrisk subtypes displayed distinct clinicopathologic features, tumor-infiltrating immune cell patterns, and biological processes. The ISlowmeTNAhigh subtype showed low intratumoral heterogeneity (ITH) and immune-activated phenotypes and correlated with better survival than the other subtypes within the high ICDrisk group. This study suggests effective biomarkers for the prediction of OS in LUAD patients and immunotherapeutic response across Pan-cancer and contributes to enhancing our understanding of intrinsic immunogenic tumor cell death.

High Expression of COA6 Is Related to Unfavorable Prognosis and Enhanced Oxidative Phosphorylation in Lung Adenocarcinoma.

Mitochondrial metabolism plays an important role in the occurrence and development of cancers. Cytochrome C oxidase assembly factor six (COA6) is essential in mitochondrial metabolism. However, the role of COA6 in lung adenocarcinoma (LUAD) remains unknown. Here we report that the expression of COA6 mRNA and protein were upregulated in LUAD tissues compared with lung normal tissues. We found that COA6 had high sensitivity and specificity to distinguish LUAD tissues from normal lung tissues shown by a receiver operating characteristic (ROC) curve. In addition, our univariate and multivariate Cox regression analysis indicated that COA6 was an independent unfavorable prognostic factor for LUAD patients. Furthermore, our survival analysis and nomogram showed that a high expression of COA6 mRNA was related to the short overall survival (OS) of LUAD patients. Notably, our weighted correlation network analysis (WGCNA) and functional enrichment analysis revealed that COA6 may participate in the development of LUAD by affecting mitochondrial oxidative phosphorylation (OXPHOS). Importantly, we demonstrated that depletion of COA6 could decrease the mitochondrial membrane potential (MMP), nicotinamide adenine dinucleotide (NAD) + hydrogen (H) (NADH), and adenosine triphosphate (ATP) production in LUAD cells (A549 and H1975), hence inhibiting the proliferation of these cells in vitro. Together, our study strongly suggests that COA6 is significantly associated with the prognosis and OXPHOS in LUAD. Hence, COA6 is highly likely a novel prognostic biomarker and therapeutic target of LUAD.

MRPL12 Acts as A Novel Prognostic Biomarker Involved in Immune Cell Infiltration and Tumor Progression of Lung Adenocarcinoma.

Mitochondrial ribosomal protein L7/L12 (MRPL12) is a member of the mitochondrial ribosomal proteins (MRPs). However, the biological function of MRPL12 in lung adenocarcinoma (LUAD) remains unclear. The expression and prognostic value of MRPL12 in LUAD were systematically analyzed using UALCAN, TIMER, HPA, Kaplan-Meier plotter, and GEPIA databases. The relationship between MRPL12 and immune infiltrates was investigated using TIMER and TISIDB databases. The clinical significance of MRPL12 in LUAD patients was validated using a tissue microarray (TMA). Cellular functional experiments were carried out to examine the influences of MRPL12 knockdown on cell proliferation, migration, and invasion. MRPL12 was significantly upregulated in LUAD samples, and high MRPL12 expression was correlated with worse prognosis. MRPL12 expression was markedly associated with immunomodulators, chemokines, and infiltration levels of multiple immune cells. Furthermore, TMA results confirm the upregulation of MRPL12 expression in LUAD, and MRPL12 was identified as an independent prognostic factor in LUAD patients. MRPL12 knockdown inhibited proliferation, migration, and invasion of LUAD cells. These data indicate that MRPL12 is a prognostic biomarker and correlated with immune infiltrates in LUAD. Therefore, MRPL12 shows potential as a therapeutic target for LUAD.

Development and validation of a combined metabolism and immune prognostic model in lung adenocarcinoma.

Tumor metabolism and immune response can affect the biological behavior of tumor cells. There is an obvious relationship between glycolysis and immune response. However, the association between metabolism and immune response and prognosis in lung adenocarcinoma (LUAD) has not yet been examined in a comprehensive and detailed manner. The establishment of reliable models for predicting the prognosis of LUAD based on glycolysis ability and immune status is still highly anticipated. The expression of genes were obtained from online databases, and the differentially expressed genes in LUAD tissues and adjacent tissues were identified. We used LUAD samples in The Cancer Genome Atlas (TCGA) database as training set and the Gene Expression Omnibus (GEO) databases as validation sets. The best predictive model was constructed using least absolute selection and shrinkage operator (LASSO) regression and Cox regression. The receiver operator characteristic (ROC) curve is used to verify the accuracy of the model. The expression status of the Glycolysis-related genes (GRGs) and the status of the immune cells in LUCD patients were further analyzed. The protein levels of the 3 identified genes were then tested in LUAD patients. We identified 3 GRGs and immune-related genes (i.e., fibroblast growth factor 2, hyaluronan-mediated motor receptor, and nuclear receptor 0B2) and constructed a stable comprehensive index of glycolysis and immunity (CIGI) prediction model. The validation results for this CIGI model were quite stable across different datasets and patient subgroups and the CIGI score can be included as an independent prognostic factor for LUAD patients. The area under the curve (AUC) values of 1-, 3- and 5-year of the finally established nomogram model are 0.767, 0.735 and 0.769. Further analysis showed that LUAD patients in the low-risk group had lower levels of glycolytic gene expression than those in the high-risk group and exhibited an immunosuppressed state. Finally, hyaluronan-mediated motor receptor may play a role in inhibiting cancer, while fibroblast growth factor 2 and nuclear receptor 0B2 may play roles in promoting cancer. In this study, we established a new prognostic prediction model for LUAD patients that combines glycolysis ability and immune status.

A Risk-Assessing Signature Based on Hypoxia- and Immune-Related Genes for Prognosis of Lung Adenocarcinoma Patients.

Lung Adenocarcinoma (LUAD) drastically influences human health. Tumor hypoxia and immunity impact hugely on the immunotherapeutic effect of LUAD patients. This study is aimed at exploring the prognostic markers associated with hypoxia and immunity in LUAD patients and evaluates their reliability. The relationship between hypoxia and immune-related genes and prognoses of LUAD patients was investigated by the univariate regression analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) methods were used to reveal the enriched pathways and biological processes of prognosis-related genes. Univariate, LASSO, and multivariate Cox regression analyses were used to construct a prognostic signature and verify its independence. The reliability of the signature was evaluated by the Principal Component Analysis (PCA), the Kaplan-Meier (K-M) curve, and the receiver operating characteristic (ROC) curve. Gene set enrichment analysis (GSEA), tumor mutational burden (TMB), and single-sample GSEA (ssGSEA) further verified the performance of the signature. Finally, a prognostic signature for LUAD was constructed based on 7 hypoxia- and immune-related genes. According to riskScores acquired from the signature, the test set was divided into groups, where the prognosis of high-risk patients was poor. The feature genes had good reliability, and the riskScore could be used as an independent prognostic factor for LUAD patients. Meanwhile, high TMB scores and low immune scores were found in high-risk patients, and feature genes were enriched in signaling pathways such as cell cycle and p53 signaling pathway. In sum, a prognostic signature based on 7 hypoxia- and immune-related genes was constructed.

PLK1 Is a Potential Prognostic Factor Associated with the Tumor Microenvironment in Lung Adenocarcinoma.

More than 40% of lung cancers are lung adenocarcinoma (LUAD) worldwide. However, the prognosis of LUAD is poor for the lack of effective treatment methods. Our study identified PLK1 as a novel prognosis biomarker and treatment target for LUAD. Based on the Cancer Genome Atlas (TCGA) database, differentially expressed genes (DEGs) from 551 LUAD cases were analyzed for the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. To explore the biological pathways and the tumor-infiltrating immune cells (TICs) using gene set variation analysis (GSVA) and the CIBERSORT, as well as to analyze DEGs, a protein-protein interaction (PPI) network and Cox regression analysis were performed. Validation of DEGs was achieved through quantitative real-time PCR (qPCR) and immunoblotting. DEGs associated with the cell cycle were sorted out. Cell cycle scores were positively correlated with age, clinical stages, and metastasis and negatively correlated with overall survival of LUAD patients. PPI and Cox analyses showed that PLK1 could be a prognostic factor for LUAD patients. CIBERSORT analysis revealed a positive correlation between the transcription level of PLK1 and the function of CD8+ and activated memory CD4+ T cells, as well as a negative correlation with activated natural killer cells. Furthermore, PLK1 overexpression increased immune cytotoxicity, as measured by the cytolytic activity score, IFN- score, and IFN- level. There is a strong correlation between PLK1 and key features of TICs, indicating its potential as a promising prognostic biomarker for LUAD.

AMICA1 is a diagnostic and prognostic biomarker and induces immune cells infiltration by activating cGAS-STING signaling in lung adenocarcinoma

BackgroundAdhesion molecule interacting with CXADR antigen 1 (AMICA1), also known as Junction Adhesion Molecule Like (JAML), a recently identified member of the JAMs family, plays a critical role in mediating cancer development and immune cells transmigration. However, AMICA1 has never been reported to be related to the genesis, development and immunotherapy effect of lung adenocarcinoma (LUAD). In this research, we investigated the role of AMICA1 in LUAD through bioinformatic analysis and in vitro experiments.MethodsBioinformatic analysis from TCGA and GEO databases were used to investigate the expression level of AMICA1 and the correlation between AMICA1 and clinical parameters in LUAD patients. The LinkedOmics database was analyzed to investigate the co-expression network of AMICA1. TIMER and TISIDB databases were used to analyze the correlation between AMICA1 expression and immune infiltration level. Except for bioinformatic analysis, the AMICA1 mRNA (26 patients) and protein level (6 patients) were also detected by real-time PCR and western blot. The infiltration level of CD8+ T cells (15 patients) and PD1+ T cells (13 patients) were detected by immunohistochemistry. The diagnostic value of AMICA1 was revealed by receiver operating characteristic (ROC) curves. The Spearman correlation coefficient was used to analyze the correlation between AMICA1 expression and CD8+ T cells and PD1+ T cells infiltration level.ResultsBioinformatic data from public database and our data showed that AMICA1 was significantly downregulated in LUAD. Decreased AMICA1 expression in LUAD was associated with higher T stage, M stage and pathological stage. Kaplan–Meier survival analysis indicated that patients with low AMICA1 expression had a worse prognosis. ROC curves showed that AMICA1 had high diagnostic accuracy for LUAD patients. Multivariate Cox analysis further displayed that AMICA1 expression level was an independent prognostic factor for LUAD patients. Moreover, the expression of AMICA1 was significantly different in the immune cells subtype and was obviously linked to immune cells infiltration. In vitro experiments suggested that AMICA1 significantly suppressed the proliferation of LUAD cells and played an important role in activating cGAS-STING signaling.ConclusionsOur study suggested that AMICA1 might function as a diagnostic and prognostic biomarker and significantly suppressed the proliferation of LUAD cells. Besides, AMICA1 is positively correlated with immune cells infiltration in LUAD, and cGAS-STING signaling might play an important role in the process.

Identification of the Signature Genes and Network of Reactive Oxygen Species Related Genes and DNA Repair Genes in Lung Adenocarcinoma.

Reactive Oxygen Species (ROS) are present in excess amounts in patients with tumors, and these ROS can kill and destroy tumor cells. Therefore, tumor cells upregulate ROS-related genes to protect them and reduce their destructing effects. Cancer cells already damaged by ROS can be repaired by expressing DNA repair genes consequently promoting their proliferation. The present study aimed to identify the signature genes of and regulating network of ROS-related genes and DNA repair genes in lung adenocarcinoma (LUAD) using transcriptomic data of public databases. The LUAD transcriptome data in the TCGA database and gene expressions from Gene Expression Omnibus (GEO) were analyzed and samples were clustered into 5 ROS-related categories and 6 DNA repair categories. Survival analysis revealed a significant difference in patient survival between the two classification methods. In addition, the samples corresponding to the two categories overlap, thus, the gene expression profile of the same sample with different categories and survival prognosis was further explored, and the connection between ROS-related and DNA repair genes was investigated. The interactive sample recombination classification was used, revealing that the patient's prognosis was worse when the ROS-related and DNA repair genes were expressed at the same time. The further research on the potential regulatory network of the two categories of genes and the correlation analysis revealed that ROS-related genes and DNA repair genes have a mutual regulatory relationship. The ROS-related genes namely NQO1, TXNRD1, and PRDX4 could establish links with other DNA repair genes through the DNA repair gene NEIL3, thereby balancing the level of ROS. Therefore, targeting ROS-related genes and DNA repair genes might be a promising strategy in the treatment of LUAD. Finally, a survival prognostic model of ROS-related genes and DNA repair genes was established (TERT, PRKDC, PTTG1, SMUG1, TXNRD1, CAT, H2AFX, and PFKP). The risk score obtained from our survival prognostic model could be used as an independent prognostic factor in LUAD patients.

Identification of a Four-Gene Signature Associated with the Prognosis Prediction of Lung Adenocarcinoma Based on Integrated Bioinformatics Analysis.

Lung adenocarcinoma (LUAD) is often diagnosed at an advanced stage, so it is necessary to identify potential biomarkers for the early diagnosis and prognosis of LUAD. In our study, a gene co-expression network was constructed using weighted gene co-expression network analysis (WGCNA) in order to obtain the key modules and genes correlated with LUAD prognosis. Four hub genes (HLF, CHRDL1, SELENBP1, and TMEM163) were screened out using least absolute shrinkage and selection operator (LASSO)–Cox regression analysis; then, a prognostic model was established for predicting overall survival (OS) based on these four hub genes..Furthermore, the prognostic values of this four-gene signature were verified in four validation sets (GSE26939, GSE31210, GSE72094, and TCGA-LUAD) as well as in the GEPIA database. To assess the prognostic values of hub genes, receiver operating characteristic (ROC) curves were constructed and a nomogram was created. We found that a higher expression of four hub genes was associated with a lower risk of patient death. In a training set, it was demonstrated that this four-gene signature was a better prognostic factor than clinical factors such as age and stage of disease. Moreover, our results revealed that these four genes were suppressor factors of LUAD and that their high expression was associated with a lower risk of death. In summary, we demonstrated that this four-gene signature could be a potential prognostic factor for LUAD patients. These findings provide a theoretical basis for exploring potential biomarkers for LUAD prognosis prediction in the future.

Identification of seven-gene marker to predict the survival of patients with lung adenocarcinoma using integrated multi-omics data analysis.

BackgroundThe mechanism of cancer occurrence and development could be understood with multi‐omics data analysis. Discovering genetic markers is highly necessary for predicting clinical outcome of lung adenocarcinoma (LUAD).MethodsClinical follow‐up information, copy number variation (CNV) data, single nucleotide polymorphism (SNP), and RNA‐Seq were acquired from The Cancer Genome Atlas (TCGA). To obtain robust biomarkers, prognostic‐related genes, genes with SNP variation, and copy number differential genes in the training set were selected and further subjected to feature selection using random forests. Finally, a gene‐based prediction model for LUAD was validated in validation datasets.ResultsThe study filtered 2071 prognostic‐related genes and 230 genomic variants, 1878 copy deletions, and 438 significant mutations. 218 candidate genes were screened through integrating genomic variation genes and prognosis‐related genes. 7 characteristic genes (RHOV, CSMD3, FBN2, MAGEL2, SMIM4, BCKDHB, and GANC) were identified by random forest feature selection, and many genes were found to be tumor progression‐related. A 7‐gene signature constructed by Cox regression analysis was an independent prognostic factor for LUAD patients, and at the same time a risk factor in the test set, external validation set, and training set. Noticeably, the 5‐year AUC of survival in the validation set and training set was all ˃ 0.67. Similar results were obtained from multi‐omics validation datasets.ConclusionsThe study builds a novel 7‐gene signature as a prognostic marker for the survival prediction of patients with LUAD. The current findings provided a set of new prognostic and diagnostic biomarkers and therapeutic targets.

Identification of a Six-Gene SLC Family Signature With Prognostic Value in Patients With Lung Adenocarcinoma.

Given the importance of solute carrier (SLC) proteins in maintaining cellular metabolic homeostasis and that their dysregulation contributes to cancer progression, here we constructed a robust SLC family signature for lung adenocarcinoma (LUAD) patient stratification. Transcriptomic profiles and relevant clinical information of LUAD patients were downloaded from the TCGA and GEO databases. SLC family genes differentially expressed between LUAD tissues and adjacent normal tissues were identified using limma in R. Of these, prognosis-related SLC family genes were further screened out and used to construct a novel SLC family-based signature in the training cohort. The accuracy of the prognostic signature was assessed in the testing cohort, the entire cohort, and the external GSE72094 cohort. Correlations between the prognostic signature and the tumor immune microenvironment and immune cell infiltrates were further explored. We found that seventy percent of SLC family genes (279/397) were differentially expressed between LUAC tissues and adjacent normal. Twenty-six genes with p-values < 0.05 in univariate Cox regression analysis and Kaplan-Meier survival analysis were regarded as prognosis-related SLC family genes, six of which were used to construct a prognostic signature for patient classification into high- and low-risk groups. Kaplan-Meier survival analysis in all internal and external cohorts revealed a better overall survival for patients in the low-risk group than those in the high-risk group. Univariate and multivariate Cox regression analyses indicated that the derived risk score was an independent prognostic factor for LUAD patients. Moreover, a nomogram based on the six-gene signature and clinicopathological factors was developed for clinical application. High-risk patients had lower stromal, immune, and ESTIMATE scores and higher tumor purities than those in the low-risk group. The proportions of infiltrating naive CD4 T cells, activated memory CD4 T cells, M0 macrophages, resting dendritic cells, resting mast cells, activated mast cells, and eosinophils were significantly different between the high- and low-risk prognostic groups. In all, the six-gene SLC family signature is of satisfactory accuracy and generalizability for predicting overall survival in patients with LUAD. Furthermore, this prognostics signature is related to tumor immune status and distinct immune cell infiltrates in the tumor microenvironment.

Comprehensive Bioinformatics Analysis to Identify the Gene HMMR Associated With Lung Adenocarcinoma Prognosis and Its Mechanism of Action in Multiple Cancers.

BackgroundLung cancer is the third most frequently diagnosed cancer in the world, with lung adenocarcinoma (LUAD) as the most common pathological type. But studies on the predictive effect of a single gene on LUAD are limited. We aimed to discover new predictive markers for LUAD.MethodsDifferentially high-expressed genes at each stage were obtained from the TCGA and GTEx databases. The functions of these genes were investigated through GO enrichment and KEGG pathway analyses. Then, the key genes were selected by applying whole gene overall survival time. The expression of the key gene was studied in LUAD, and survival analysis was performed using Kaplan-Meier mapper, followed by univariate and multifactorial COX analysis. Finally, the gene expression and its prognostic significance in the pan-cancer were examined.ResultsA total of 10,106 DEGs were obtained from the two datasets. The top 266 differentially upregulated genes intersected with the top 1,497 overall survival-related genes, and 87 key genes were identified. High-expressed HMMR was associated with a poor prognosis of LUAD. Univariate and multifactorial Cox analysis showed that HMMR was an independent prognostic factor for LUAD patients. A high HMMR expression was strongly associated with the overall survival (OS) and disease-specific survival (DSS) in 11 cancer types and with poorer OS, DSS, and PFI in 10 cancer types.ConclusionHMMR may be an independent prognostic indicator and an important biomarker in diagnosing and predicting the survival of LUAD patients. Also, HMMR may be a key predictor of a variety of cancers.

Comprehensive Analysis of a Nine-Gene Signature Related to Tumor Microenvironment in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common malignancy, leading to more than 1 million related deaths each year. Due to low long-term survival rates, the exploration of molecular mechanisms underlying LUAD progression and novel prognostic predictors is urgently needed to improve LUAD treatment. In our study, cancer-specific differentially expressed genes (DEGs) were identified using the robust rank aggregation (RRA) method between tumor and normal tissues from six Gene Expression Omnibus databases (GSE43458, GSE62949, GSE68465, GSE115002, GSE116959, and GSE118370), followed by a selection of prognostic modules using weighted gene co-expression network analysis. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were applied to identify nine hub genes (CBFA2T3, CR2, SEL1L3, TM6SF1, TSPAN32, ITGA6, MAPK11, RASA3, and TLR6) that constructed a prognostic risk model. The RNA expressions of nine hub genes were validated in tumor and normal tissues by RNA-sequencing and single-cell RNA-sequencing, while immunohistochemistry staining from the Human Protein Atlas database showed consistent results in the protein levels. The risk model revealed that high-risk patients were associated with poor prognoses, including advanced stages and low survival rates. Furthermore, a multivariate Cox regression analysis suggested that the prognostic risk model could be an independent prognostic factor for LUAD patients. A nomogram that incorporated the signature and clinical features was additionally built for prognostic prediction. Moreover, the levels of hub genes were related to immune cell infiltration in LUAD microenvironments. A CMap analysis identified 13 small molecule drugs as potential agents based on the risk model for LUAD treatment. Thus, we identified a prognostic risk model including CBFA2T3, CR2, SEL1L3, TM6SF1, TSPAN32, ITGA6, MAPK11, RASA3, and TLR6 as novel biomarkers and validated their prognostic and predicted values for LUAD.

Integrated analysis of gene expression and DNA methylation datasets identified key genes and a 6-gene prognostic signature for primary lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the main subtype of non-small cell lung cancer with a poor survival prognosis. In our study, gene expression, DNA methylation, and clinicopathological data of primary LUAD were utilized to identify potential prognostic markers for LUAD, which were recruited from The Cancer Genome Atlas (TCGA) database. Univariate regression analysis showed that there were 21 methylation-associated DEGs related to overall survival (OS), including 9 down- and 12 up-regulated genes. The 12 up-regulated genes with hypomethylation may be risky genes, whereas the other 9 down-regulated genes with hypermethylation might be protective genes. By using the Step-wise multivariate Cox analysis, a methylation-associated 6-gene (consisting of CCL20, F2, GNPNAT1, NT5E, B3GALT2, and VSIG2) prognostic signature was constructed and the risk score based on this gene signature classified patients into high- or low-risk groups. Patients of the high-risk group had shorter OS than those of the low-risk group in both the training and validation cohort. Multivariate Cox analysis and the stratified analysis revealed that the risk score was an independent prognostic factor for LUAD patients. The methylation-associated gene signature may serve as a prognostic factor for LUAD patients and the represent hypermethylated or hypomethylated genes might be potential targets for LUAD therapy.

Systematic Analyses of a Chemokine Family-Based Risk Model Predicting Clinical Outcome and Immunotherapy Response in Lung Adenocarcinoma

Chemokines exhibited complicated functions in antitumor immunity, with their expression profile and clinical importance of lung adenocarcinoma (LUAD) patients remaining largely undetermined. This study aimed to explore the expression patterns of chemokine family in LUAD and construct a predictive chemokine family-based signature. A total of 497 samples were downloaded from the Cancer Genome Atlas (TCGA) data portal as the training set, and the combination of 4 representative Gene Expression Omnibus (GEO) datasets, including GSE30219, GSE50081, GSE37745, and GSE31210, were utilized as the validation set. A three gene-based signature was constructed using univariate and stepwise multivariate Cox regression analysis, classifying patients into high and low risk groups according to the overall survival. The independent GEO datasets were utilized to validate this signature. Another multivariate analysis revealed that this signature remained an independent prognostic factor in LUAD patients. Furthermore, patients in the low risk group featured immunoactive tumor microenvironment (TME), higher IPS scores and lower TIDE scores, and was regarded as the potential beneficiaries of immunotherapy. Finally, the role of risky CCL20 was validated by immunohistochemistry (IHC), and patients possessed higher CCL20 expression presented shorter overall survival (P = 0.011).

DNA Repair-Based Gene Expression Signature and Distinct Molecular Subtypes for Prediction of Clinical Outcomes in Lung Adenocarcinoma.

Objective: To conduct a robust prognostic gene expression signature and characterize molecular subtypes with distinct clinical characteristics for lung adenocarcinoma (LUAD).Methods: Based on DNA repair genes from the GSEA database, a prognostic signature was conducted in the TCGA-LUAD training set via univariate and multivariate cox regression analysis. Its prediction power was validated by overall survival analysis, relative operating characteristic (ROC) curves and stratification analysis in the GSE72094 verification set. Involved pathways in the high- and low-risk groups were analyzed by GSEA. A nomogram was built based on the signature and clinical features and its performance was assessed by calibration plots. LUAD samples were clustered via the ConsensusClusterPlus package. The differences in clinical outcomes, single nucleotide polymorphism (SNP) and sensitivity to chemotherapy drugs between molecular subtypes were analyzed.Results: A 13-DNA repair gene-signature was constructed for LUAD prognosis. Following validation, it can robustly and independently predict patients' clinical outcomes. The GSEA results exhibited the differences in pathways between high- and low- risk groups. A nomogram combining the signature and stage could accurately predict 1-, 3-, and 5-year survival probability. Two distinct molecular subtypes were characterized based on DNA repair genes. Patients in the Cluster 2 exhibited a worse prognosis and were more sensitive to common chemotherapy than those in the Cluster 1.Conclusion:This study proposed a 13-DNA repair gene-signature as a prognostic factor for LUAD patients, which can independently predict clinical outcomes by complement of the stage. Moreover, we characterized two LUAD subtypes with distinct clinical outcomes, somatic gene mutations, and drug sensitivity in cancer based on DNA repair genes.