Molecular subtypes based on CNVs related gene signatures identify candidate prognostic biomarkers in lung adenocarcinoma.
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ABSTRACT:
SUBMITTER: Li B
PROVIDER: S-EPMC8208901 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background: Lung adenocarcinoma (LUAD) is a life-threatening malignant tumor, contributing for the largest cancer burden worldwide. Tumor microenvironment (TME) is composed of various immune cells, stromal cells and tumor cells, which is highly associated with the cancer prognosis and the response to immunotherapy, in which macrophages in TME have been revealing a potential target for cancer treatment. In this study, we sought to further explore the role of macrophages in LUAD progression and establish a risk model related to macrophages for LUAD. Methods: We explored immune-related pathways that might be affected by counting positively associated genes in macrophages. Molecular typing was also constructed by mining macrophage-associated genes with prognostic value through COX regression and other analyses. RiskScore prognostic models were constructed using lasso regression and stepwise multifactorial regression analysis. The differences on clinical characteristics among three subtypes (C1, C2, and C3) and RiskScore subtypes were analyzed in TCGA dataset. Immunological algorithms such as TIMER, ssGSEA, MCP-Counter, ESTIMATE, and TIDE were used to calculate the level of difference in immune infiltration between the different subtypes. The TCGA mutation dataset processed by mutect2 was used to demonstrate the frequency of mutations between different molecular subtypes. Finally, nomograms, calibration curves, and decision curves were created to assess the predictive accuracy and reliability of the model. Results: The C1 subtype demonstrated the best prognostic outcome, accompanied by higher levels of immune infiltration and lower mutation frequency, while the majority of patients in the C1 subtype were women under 65 years of age. Myeloid-derived suppressor cell (MDSC) scores were higher in the C3 subtype, suggesting a more severe immune escape, which may have contributed to the tumor evading the immune system resulting in a poorer prognosis for patients. In addition, our RiskScore prognostic model had good predictive accuracy and reliability. Conclusion: This paper provides a study of macrophage-related pathways, immunosuppression, and their mechanisms of action in lung cancer, along with targets for future treatment to guide the optimal treatment of lung cancer.
| S-EPMC9574025 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is the major subtype of lung cancer and the most lethal malignant disease worldwide. However, the molecular mechanisms underlying LUAD are not fully understood.MethodsFour datasets (GSE118370, GSE85841, GSE43458 and GSE32863) were obtained from the gene expression omnibus (GEO). Identification of differentially expressed genes (DEGs) and functional enrichment analysis were performed using the limma and clusterProfiler packages, respectively. A protein-protein interaction (PPI) network was constructed via Search Tool for the Retrieval of Interacting Genes (STRING) database, and the module analysis was performed by Cytoscape. Then, overall survival analysis was performed using the Kaplan-Meier curve, and prognostic candidate biomarkers were further analyzed using the Oncomine database.ResultsTotally, 349 DEGs were identified, including 275 downregulated and 74 upregulated genes which were significantly enriched in the biological process of extracellular structure organization, leukocyte migration and response to peptide. The mainly enriched pathways were complement and coagulation cascades, malaria and prion diseases. By extracting key modules from the PPI network, 11 hub genes were screened out. Survival analysis showed that except VSIG4, other hub genes may be involved in the development of LUAD, in which MYH10, METTL7A, FCER1G and TMOD1 have not been reported previously to correlated with LUAD. Briefly, novel hub genes identified in this study will help to deepen our understanding of the molecular mechanisms of LUAD carcinogenesis and progression, and to discover candidate targets for early detection and treatment of LUAD.
| S-EPMC6626531 | biostudies-literature
Project description:BACKGROUND We aimed to screen and identify central genetic and molecular targets involved in advancement of lung adenocarcinoma (LUAD) and to perform an integrated analysis and clinical validation. MATERIAL AND METHODS The GEO2R technique was utilized to assess differentially expressed genes (DEGs) among the gene sets GSE75037, GSE85716, and GSE118370. Subsequently, gene Ontology (GO) analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) analytical methods were executed to determine related biofunctions and signaling pathways, which were annotated with tools from the Database for Annotation, Visualization and Integrated Discovery (DAVID) resource. Then, a protein-protein interaction (PPI) network complex consisting of all detected DEGs was built with the STRING web interface. Cytohubba and MCODE plug-ins for Cytoscape software and Gene Expression Profiling Interactive Analysis (GEPIA) were employed to identify the hub genes. Finally, the mRNA expression of the identified hub genes was quantitatively validated by The Cancer Genome Atlas (TCGA) database analysis and real-time quantitative polymerase chain reaction (RT-qPCR). RESULTS We screened 146 upregulated DEGs and 431 downregulated DEGs with the criteria of |logFC| >1 and P<0.05, and the GO analysis indicated that DEGs were implicated in mitotic nuclear division (biological process, BP), the nucleus (cellular component, CC), and protein binding (molecular function, MF) and were associated with multiple KEGG pathways, such as the p53 signaling pathway in cancer. Then, the top 8 genes that predicted significantly different outcomes in LUAD patients were filtered from the DEGs and selected as hub genes. The TCGA database analysis and RT-qPCR results demonstrated that these genes were differentially expressed with the same trends in LUAD tissues compared with normal tissues. CONCLUSIONS Overall, we propose that 8 genes (PECAM1, CDK1, MKI67, SPP1, TOP2A, CHEK1, CCNB1, and RRM2) might be novel hub genes strongly associated with the progression and prognosis of LUAD.
| S-EPMC7331474 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is the major non-small-cell lung cancer pathological subtype with poor prognosis worldwide. Herein, we aimed to build an energy metabolism-associated prognostic gene signature to predict patient survival.MethodsThe gene expression profiles of patients with LUAD were downloaded from the TCGA and GEO databases, and energy metabolism (EM)-related genes were downloaded from the GeneCards database. Univariate Cox and LASSO analyses were performed to identify the prognostic EM-associated gene signatures. Kaplan-Meier and receiver operating characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signatures. A CIBERSORT analysis was used to evaluate the correlation between the risk model and immune cells. A nomogram was used to predict the survival probability of LUAD based on a risk model.ResultsWe constructed a prognostic signature comprising 13 EM-related genes (AGER, AHSG, ALDH2, CIDEC, CYP17A1, FBP1, GNB3, GZMB, IGFBP1, SORD, SOX2, TRH and TYMS). The Kaplan-Meier curves validated the good predictive ability of the prognostic signature in TCGA AND two GEO datasets (p<0.0001, p=0.00021, and p=0.0034, respectively). The area under the curve (AUC) of the ROC curves also validated the predictive accuracy of the risk model. We built a nomogram to predict the survival probability of LUAD, and the calibration curves showed good predictive ability. Finally, a functional analysis also unveiled the different immune statuses between the two different risk groups.ConclusionOur study constructed and verified a novel EM-related prognostic gene signature that could improve the individualized prediction of survival probability in LUAD.
| S-EPMC9047773 | biostudies-literature
Project description:The extracellular matrix (ECM) is vital to normal cellular function and has emerged as a key factor in cancer initiation and metastasis. However, the prognostic and oncological values of ECM organization-related genes have not been comprehensively explored in lung adenocarcinoma (LUAD) patients. In this study, we included LUAD samples from The Cancer Genome Atlas (TCGA, training set) and other three validation sets (GSE87340, GSE140343 and GSE115002), then we constructed a three-gene prognostic signature based on ECM organization-related genes. The prognostic signature involving COL4A6, FGA and FSCN1 was powerful and robust in both the training and validation datasets. We further constructed a composite prognostic nomogram to facilitate clinical practice by integrating an ECM organization-related signature with clinical characteristics, including age and TNM stage. Patients with higher risk scores were characterized by proliferation, metastasis and immune hallmarks. It is worth noting that high-risk group showed higher fibroblast infiltration in tumor tissue. Accordingly, factors (IGFBP5, CLCF1 and IL6) reported to be secreted by cancer-associated fibroblasts (CAFs) showed higher expression level in the high-risk group. Our findings highlight the prognostic value of the ECM organization signature in LUAD and provide insights into the specific clinical and molecular features underlying the ECM organization-related signature, which may be important for patient treatment.
| S-EPMC9197387 | biostudies-literature
Project description:Prostate cancer is the most common form of non-skin cancer in American men. Although the 5-year survival rate is 100%, an important proportion of patients will recur after seven years following treatment. Despite attempts to delineate clinical and molecular prognostic signatures of recurrence, accurate prediction remains an elusive task. The purpose of this project was, therefore, to identify candidate genomic signatures of biochemical failure present at the moment of diagnosis. To do this, we carried out array comparative genomic hybridization on surgical specimens from a training cohort of patients with PRCA who were followed on average for ten years. Due to the unstable nature of malignant genomes, we selected non-random genomic aberrations through Significance Testing of Aberrant Copy Number (STAC), a statistical permutation algorithm. Further analysis of STAC regions between recurrent and non-recurrent groups showed that recurring cases had a greater proportion of gain in 4p14-12, 11p14.3-14.1 or 15q21.1-22.2 in comparison to non-recurrent cases. All three genomic signatures were independently, better predictors of recurrence than previously known clinical covariates. The importance of these regions in PRCA progression was further validated in publicly available expression data sets, where over-expression of ADAM10, BDNF and CYP19A1 (all coded in the aforementioned regions) was found to increase with progression of disease. We hypothesize that amplification of ADAM10, BDNF, and CYP19A1 may serve as candidate biomarkers of recurrence to be validated in larger datasets. Keywords: BAC Array CGH, Biomarker study
2007-08-01 | GSE7949 | GEO
Project description:Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy. Here we show that shotgun and targeted protein sequencing can be used to identify potential prognostic biomarkers in formalin-fixed paraffin-embedded specimens from 9 patients with PDAC with "short" survival (<12 months) and 10 patients with "long" survival (>45 months) undergoing surgical resection. A total of 24 and 147 proteins were significantly upregulated [fold change ?2 or ?0.5 and P<0.05; or different detection frequencies (?5 samples)] in patients with "short" survival (including GLUT1) and "long" survival (including C9orf64, FAM96A, CDH1 and CDH17), respectively. STRING analysis of these proteins indicated a tight protein-protein interaction network centered on TP53. Ingenuity pathway analysis linked proteins representing "activated stroma factors" and "basal tumor factors" to poor prognosis of PDAC. It also highlighted TCF1 and CTNNB1 as possible upstream regulators. Further parallel reaction monitoring verified that seven proteins were upregulated in patients with "short" survival (MMP9, CLIC3, MMP8, PRTN3, P4HA2, THBS1 and FN1), while 18 proteins were upregulated in patients with "long" survival, including EPCAM, LGALS4, VIL1, CLCA1 and TPPP3. Thus, we verified 25 protein biomarker candidates for PDAC prognosis at the tissue level. Furthermore, an activated stroma status and protein-protein interactions with TP53 might be linked to poor prognosis of PDAC.
| S-EPMC5839402 | biostudies-literature
Project description:BackgroundCancer stem cells (CSCs) refer to cells with self-renewal capability in tumors. CSCs play important roles in proliferation, metastasis, recurrence, and tumor heterogeneity. This study aimed to identify immune-related gene-prognostic models based on stemness index (mRNAsi) in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), respectively.MethodsX-tile software was used to determine the best cutoff value of survival data in LUAD and LUSC based on mRNAsi. Tumor purity and the scores of infiltrating stromal and immune cells in lung cancer tissues were predicted with ESTIMATE R package. Differentially expressed immune-related genes (DEIRGs) between higher- and lower-mRNAsi subtypes were used to construct prognostic models.ResultsmRNAsi was negatively associated with StromalScore, ImmuneScore, and ESTIMATEScore, and was positively associated with tumor purity. LUAD and LUSC samples were divided into higher- and lower-mRNAsi groups with X-title software. The distribution of immune cells was significantly different between higher- and lower-mRNAsi groups in LUAD and LUSC. DEIRGs between those two groups in LUAD and LUSC were enriched in multiple cancer- or immune-related pathways. The network between transcriptional factors (TFs) and DEIRGs revealed potential mechanisms of DEIRGs in LUAD and LUSC. The eight-gene-signature prognostic model (ANGPTL5, CD1B, CD1E, CNTFR, CTSG, EDN3, IL12B, and IL2)-based high- and low-risk groups were significantly related to overall survival (OS), tumor microenvironment (TME) immune cells, and clinical characteristics in LUAD. The five-gene-signature prognostic model (CCL1, KLRC3, KLRC4, CCL23, and KLRC1)-based high- and low-risk groups were significantly related to OS, TME immune cells, and clinical characteristics in LUSC. These two prognostic models were tested as good ones with principal components analysis (PCA) and univariate and multivariate analyses. Tumor T stage, pathological stage, or metastasis status were significantly correlated with DEIRGs contained in prognostic models of LUAD and LUSC.ConclusionCancer stemness was not only an important biological process in cancer progression but also might affect TME immune cell infiltration in LUAD and LUSC. The mRNAsi-related immune genes could be potential biomarkers of LUAD and LUSC. Evaluation of integrative characterization of multiple immune-related genes and pathways could help to understand the association between cancer stemness and tumor microenvironment in lung cancer.
| S-EPMC8567176 | biostudies-literature
Project description:Background: Increasing evidence supports that lactate plays an important role in tumor proliferation, invasion and within the tumor microenvironment (TME). This is particularly relevant in lung adenocarcinoma (LUAD). Therefore, there is a current need to investigate lactate metabolism in LUAD patients and how lactate metabolism is affected by different therapies. Methods: Data from LUAD patients were collected from The Cancer Genome Atlas (TCGA) and patients were divided into two subtypes according to 12 lactate metabolism-related genes to explore the effect of lactate metabolism in LUAD. We established a lactate-related prognostic indicator (LRPI) based on different gene expression profiles. Subsequently, we investigated associations between this LRPI and patient survival, molecular characteristics and response to therapy. Some analyses were conducted using the Genomics of Drug Sensitivity in Cancer (GDSC) database. Results: The two LUAD subtypes exhibited different levels of lactate metabolism, in which patients that displayed high lactate metabolism also had a worse prognosis and a poorer immune environment. Indeed, LRPI was shown to accurately predict the prognosis of LUAD patients. Patients with a high LRPI showed a poor prognosis coupled with high sensitivity to chemotherapy using GDSC data. Meanwhile, these patients exhibited a high responsiveness to immunotherapy in TMB (Tumor mutation burden) and TIDE (Tumor Immune Dysfunction and Exclusion) analyses. Conclusion: We validated the effect of lactate metabolism on the prognosis of LUAD patients and established a promising biomarker. LRPI can predict LUAD patient survival, molecular characteristics and response to therapy, which can aid the individualized treatment of LUAD patients.
| S-EPMC9449370 | biostudies-literature
Project description:Background and aim: Competing endogenous RNA (ceRNA) is believed to play vital roles in tumorigenesis. The goal of this study was to screen prognostic biomarkers in lung adenocarcinoma (LUAD). Methods: Common differentially expressed genes (DEGs) were collected from Gene Expression Omnibus (GEO) databases and The Cancer Genome Atlas databases (TCGA) using GEO2R and "limma" package in R, respectively. Overlapping DEGs were conducted using enrichment of functions and protein-protein interaction (PPI) network to discover significant candidate genes. By using a comprehensive analysis, we constructed an mRNA mediated ceRNA network. Survival rates were used Kaplan-Meier analysis. Statistical analysis was used to further identify the prognosis of studied genes. Results: Integrated analysis of GSE32863 and TCGA databases, a total of 886 overlapping DEGs, including 279 up-regulated and 607 down-regulated genes were identified. Considering the highest term of candidate genes in PPI, we identified TPX2, which was enriched in cell division signaling pathway. Besides, 35 differentially expressed miRNAs (DEmiRNAs) were predicted to target TPX2 and only 7 DEmiRNAs were identified to be prognostic biomarkers in LUAD. Then, 30 differentially expressed lncRNAs (DElncRNAs) were predicted to bind these 7 DEmiRNAs. Finally, we found that 7 DElncRNAs were correlated with the overall survival (all p <0.05). Furthermore, we identified elevated TPX2 was strongly correlated with the worse survival rate among 458 samples. Univariate and multivariate cox analysis showed TPX2 may act as an independent factor for prognosis in LUAD (p <0.05). Then pathway enrichment results suggested that TPX2 may facilitate tumorigenesis by participating in several cancer-related signaling pathways in LUAD, especially in Notch signal pathway. Conclusions: TPX2-related lncRNAs and miRNAs are related to the survival of LUAD. 7 lncRNAs, 7 miRNAs and TPX2 may serve as prognostic biomarkers in LUAD.
| S-EPMC7532481 | biostudies-literature