scholarly journals Tumor Purity Coexpressed Genes Related to Immune Microenvironment and Clinical Outcomes of Lung Adenocarcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ming Bai ◽  
Qi Pan ◽  
Chen Sun
Keyword(s):  
Lung Cancer ◽  
Single Cell ◽  
Clinical Outcomes ◽  
Lung Carcinoma ◽  
Functional Enrichment ◽  
Coexpression Network ◽  
Support Vector ◽  
Biological Processes ◽  
Tumor Purity ◽  
Coexpressed Genes

Purpose. Lung cancer tissue includes tumor tissue, stromal cells, immune cells, and epithelial cells. These nontumor cells dilute the tumor purity in lung cancer tissues. Tumor purity plays an essential role in the immune response to lung cancer. At present, the biological processes related to the purity of lung cancer tumors remains unclear. Methods. We measured tumor purity in 486 lung carcinoma tissues from TCGA-LUAD FPKM by using the “estimate” R package. Lung carcinoma tumor mutation burden was calculated by analyzing TCGA single nucleotide polymorphism data. The immune cell proportion was also evacuated via the CIBERSORT method. Lung carcinoma samples with P < 0.05 were considered significant. Based on the tumor purity and lung carcinoma gene matrix, we performed weighted gene coexpression network analysis (WGCNA), and the tumor purity-related module was identified. Then, we analyzed the functions of the factors involved in the module. We screened the coexpressed factors related to clinical outcome and immunophenotype. Finally, expression levels of these factors were measured at tissue and single-cell levels. Results. A lung cancer tumor purity correlated coexpression network was determined. Five coexpressed genes (CD4, CD53, EVI2B, PLEK, and SASH3) were identified as tumor purity coexpressed genes that negatively correlated with tumor purity. Because the factors in the coexpression network often participate in similar biological processes, we found that CD4, CD53, EVI2B, PLEK, and SASH3 were most related to positive regulation of cytokine production and interleukin−2 production through functional enrichment. In a clinical phenotype analysis, we found that these five factors can be used as independent prognostic risk factors. We found that these factors were significantly negatively correlated with tumor purity and positively correlated with the immune score in the immunophenotyping analysis. Using GSEA analysis, we found that the antigen processing and presentation pathway were related to the five tumor coexpressed genes mentioned above. SASH3 and CD53 were used to conduct a prognostic model based on the interaction analysis of the Support Vector Machine and the Least Absolute Shrinkage and Selection Operator. SASH3 was verified to be related to CD8A using a single-cell analysis. Conclusion. Tumor purity-related coexpression factors in the tumor microenvironment have essential clinical, genomic, and biological significance in lung cancer. These coexpression factors (SASH3 and CD53) can be used to classify tumor purity phenotypes and to predict clinical outcomes.

scholarly journals Identification of Gene Signatures and Expression Patterns During Epithelial-to-Mesenchymal Transition From Single-Cell Expression Atlas

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiangtian Yu ◽  
XiaoYong Pan ◽  
ShiQi Zhang ◽  
Yu-Hang Zhang ◽  
Lei Chen ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Single Cell ◽  
Epithelial To Mesenchymal Transition ◽  
Expression Patterns ◽  
Cancer Recurrence ◽  
Functional Enrichment ◽  
Support Vector ◽  
Pathogenic Potential ◽  
Mesenchymal Transition ◽  
Cell Expression

Cancer, which refers to abnormal cell proliferative diseases with systematic pathogenic potential, is one of the leading threats to human health. The final causes for patients’ deaths are usually cancer recurrence, metastasis, and drug resistance against continuing therapy. Epithelial-to-mesenchymal transition (EMT), which is the transformation of tumor cells (TCs), is a prerequisite for pathogenic cancer recurrence, metastasis, and drug resistance. Conventional biomarkers can only define and recognize large tissues with obvious EMT markers but cannot accurately monitor detailed EMT processes. In this study, a systematic workflow was established integrating effective feature selection, multiple machine learning models [Random forest (RF), Support vector machine (SVM)], rule learning, and functional enrichment analyses to find new biomarkers and their functional implications for distinguishing single-cell isolated TCs with unique epithelial or mesenchymal markers using public single-cell expression profiling. Our discovered signatures may provide an effective and precise transcriptomic reference to monitor EMT progression at the single-cell level and contribute to the exploration of detailed tumorigenesis mechanisms during EMT.

scholarly journals Potential lncRNA Biomarkers for HBV-Related Hepatocellular Carcinoma Diagnosis Revealed by Analysis on Coexpression Network

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Shunqiang Nong ◽  
Xiaohao Chen ◽  
Zechen Wang ◽  
Guidan Xu ◽  
Wujun Wei ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Functional Annotation ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Coexpression Network ◽  
Support Vector ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment

Background. Increasing evidence demonstrated that long noncoding RNA (lncRNA) could affect inflammatory tumor immune microenvironment by modulating gene expression and could be used as a biomarker for HBC-related hepatocellular carcinoma (HCC) but still needs further research. The aim of the present study was to determine an lncRNA signature for the diagnosis of HBV-related HCC. Methods. HBV-related HCC expression profiles (GSE55092, GSE19665, and GSE84402) were abstracted from the GEO (Gene Expression Omnibus) data resource, and R package limma and RobustRankAggreg were employed to identify common differentially expressed genes (DEGs). Using machine learning, optimal diagnostic lncRNA molecular markers for HBV-related HCC were identified. The expression of candidate lncRNAs was cross-validated in GSE121248, and an ROC (receiver operating characteristic) curve of lncRNA biomarkers was carried out. Additionally, a coexpression network and functional annotation was built, after which a PPI (protein-protein interaction) network along with module analysis were conducted with the Cytoscape open source software. Result. A total of 38 DElncRNAs and 543 DEmRNAs were identified with a fold change larger than 2.0 and a P value < 0.05. By machine learning, AL356056.2, AL445524.1, TRIM52-AS1, AC093642.1, EHMT2-AS1, AC003991.1, AC008040.1, LINC00844, and LINC01018 were screened out as optional diagnostic lncRNA biosignatures for HBV-related HCC. The AUC (areas under the curve) of the SVM (support vector machine) model and random forest model were 0.957 and 0.904, respectively, and the specificity and sensitivity were 95.7 and 100% and 94.3 and 86.5%, respectively. The results of functional enrichment analysis showed that the integrated coexpressed DEmRNAs shared common cascades in the p53 signaling pathway, retinol metabolism, PI3K-Akt signaling cascade, and chemical carcinogenesis. The integrated DEmRNA PPI network complex was found to be comprised of 87 nodes, and two vital modules with a high degree were selected with the MCODE app. Conclusion. The present study identified nine potential diagnostic biomarkers for HBV-related HCC, all of which could potentially modulated gene expression related to inflammatory conditions in the tumor immune microenvironment. The functional annotation of the target DEmRNAs yielded novel evidence in evaluating the precise functions of lncRNA in HBV-related HCC.

scholarly journals Lung Cancer Prediction using Data Mining Techniques

2019 ◽  
Vol 8 (4) ◽  
pp. 12301-12305
Keyword(s):  
Lung Cancer ◽  
Data Mining ◽  
Lung Carcinoma ◽  
Small Cell Lung Carcinoma ◽  
Small Cell ◽  
Support Vector ◽  
Human Beings ◽  
Small Cell Lung ◽  
Cell Lung Carcinoma ◽  
Data Mining Techniques

The major cause for death in human beings is because of cancer .Lung cancer is one of the most common and serious types of cancer that severely harms the human body. In order to cure the cancer early cancer detection is required. If lung cancer is diagnosed at early stages many lives will be saved. The other name for lung cancer is lung carcinoma, an uncontrolled malignant tumor distinguished by undisciplined cell growth in lung cells. There are many people suffering from this kind of cancer and confining to death. If this is left untreated, this may grow later than lung by metastasis into other parts of body. Many of the cancers starts from lungs, called as primary lung carcinoma. There are two types of small cell lung carcinoma (SCLC), non small cell lung carcinoma(NSCLC). The main reason for lung cancer is smoking of cigarette. There are many researches targeting on exact approaches for treating cancer. To predict the survival rate for NSCLC patients data mining techniques can be used with selection of algorithms. The algorithms used to detect the lung cancer are Support vector machine (SVM), Decision tree, k-Nearest neighbour, Random forest, Logistic regression. In this paper By implementing 2 different datasets and various packages and libraries in python, it is compared and on implementation found suitable algorithms have more accuracy on certain data sets for optimum prediction rate of lung cancer..

scholarly journals Identification of CD8+ T Cell-Related Genes: Correlations with Immune Phenotypes and Outcomes of Liver Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Qi Pan ◽  
Ying Cheng ◽  
Donghua Cheng
Keyword(s):  
T Lymphocytes ◽  
Liver Cancer ◽  
T Lymphocyte ◽  
Biological Processes ◽  
Single Cell Sequencing ◽  
Cd8 T Lymphocytes ◽  
Tumor Purity ◽  
Cd8 T Lymphocyte ◽  
Coexpression Module ◽  
Coexpressed Genes

Purpose. Treatment outcomes for advanced liver cancer are poor. Immunotherapy is a treatment strategy that has been widely used to treat other cancers. Studies have shown that CD8+ T lymphocytes are essential factors affecting the efficacy of immunotherapy. We used computational biology methods to determine the coexpressed gene network that promotes CD8+ T lymphocyte infiltration. Method. We obtained the liver cancer gene matrix and clinical follow-up information data from TCGA liver hepatocellular carcinoma FPKM. We obtained single nucleotide polymorphism (SNP) data to evaluate the tumor mutation burden. The “estimate” package and the CIBERSORT algorithm were used to evaluate tumor purity and the proportion of CD8+ T lymphocytes in the liver cancer cohort. We used the gene expression matrix of liver cancer and the relative proportion of CD8+ T lymphocytes as input files and performed WGCNA based on this analysis. The weighted coexpression network identified the most CD8+ T lymphocyte-related coexpression modules in liver cancer. Then, we analyzed the biological processes involved in the module. We determined the coexpression module with CD8+ T lymphocyte infiltration in terms of data and function. We then screened the factors in the coexpression module correlated with CD8+ T lymphocyte content greater than 0.4. Finally, the expression levels of these factors were verified at the protein level using immunohistochemistry and single-cell sequencing. Results. We determined the CD8+ T lymphocyte proportions that correlated with coexpression networks. Four coexpressed genes (C1QC, CD3D, GZMA, and PSMB9) were identified as CD8+ T cell coexpression genes that promoted infiltration of CD8+ T cells. Because the factors in the coexpression network often participate in similar biological processes, we found that these factors were most related to antigen processing and presentation of peptide antigen through functional enrichment. In the clinical phenotype analysis, we found that 18 factors can be used as independent prognostic protective factors. We found that these factors were significantly negatively correlated with tumor purity and negatively correlated with M2 macrophages in the immunophenotyping analysis. Using immunohistochemistry and single-cell sequencing analysis, we found that CD3D antibody staining was weaker in tumor tissues than normal tissues and was related to CD8+ T cells. Conclusion. These coexpressed genes were positively related to the high infiltration proportion of CD8+ T lymphocytes in an antigen presentation process. The biological process might provide new directions for patients who are insensitive to immune therapy.

scholarly journals A CT-Based Radiomics Approach to Predict Nivolumab Response in Advanced Non-Small-Cell Lung Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Chang Liu ◽  
Jing Gong ◽  
Hui Yu ◽  
Quan Liu ◽  
Shengping Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Clinical Outcomes ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Support Vector ◽  
Small Cell Lung ◽  
Primary Tumors ◽  
Significant Difference ◽  
Nsclc Patients

PurposeThis study aims to develop a CT-based radiomics model to predict clinical outcomes of advanced non-small-cell lung cancer (NSCLC) patients treated with nivolumab.MethodsForty-six stage IIIB/IV NSCLC patients without EGFR mutation or ALK rearrangement who received nivolumab were enrolled. After segmenting primary tumors depicting on the pre-anti-PD1 treatment CT images, 1,106 radiomics features were computed and extracted to decode the imaging phenotypes of these tumors. A L1-based feature selection method was applied to remove the redundant features and build an optimal feature pool. To predict the risk of progression-free survival (PFS) and overall survival (OS), the selected image features were used to train and test three machine-learning classifiers namely, support vector machine classifier, logistic regression classifier, and Gaussian Naïve Bayes classifier. Finally, the overall patients were stratified into high and low risk subgroups by using prediction scores obtained from three classifiers, and Kaplan–Meier survival analysis was conduct to evaluate the prognostic values of these patients.ResultsTo predict the risk of PFS and OS, the average area under a receiver operating characteristic curve (AUC) value of three classifiers were 0.73 ± 0.07 and 0.61 ± 0.08, respectively; the corresponding average Harrell’s concordance indexes for three classifiers were 0.92 and 0.79. The average hazard ratios (HR) of three models for predicting PFS and OS were 6.22 and 3.54, which suggested the significant difference of the two subgroup’s PFS and OS (p&lt;0.05).ConclusionThe pre-treatment CT-based radiomics model provided a promising way to predict clinical outcomes for advanced NSCLC patients treated with nivolumab.

scholarly journals Weighted Gene Coexpression Network Analysis Reveals Essential Genes and Pathways in Bipolar Disorder

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhen-Qing Zhang ◽  
Wei-Wei Wu ◽  
Jin-Dong Chen ◽  
Guang-Yin Zhang ◽  
Jing-Yu Lin ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Network Analysis ◽  
Actin Filament ◽  
Functional Enrichment ◽  
Coexpression Network ◽  
Biological Processes ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

Bipolar disorder (BD) is a major and highly heritable mental illness with severe psychosocial impairment, but its etiology and pathogenesis remains unclear. This study aimed to identify the essential pathways and genes involved in BD using weighted gene coexpression network analysis (WGCNA), a bioinformatic method studying the relationships between genes and phenotypes. Using two available BD gene expression datasets (GSE5388, GSE5389), we constructed a gene coexpression network and identified modules related to BD. The analyses of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways were performed to explore functional enrichment of the candidate modules. A protein-protein interaction (PPI) network was further constructed to identify the potential hub genes. Ten coexpression modules were identified from the top 5,000 genes in 77 samples and three modules were significantly associated with BD, which were involved in several biological processes (e.g., the actin filament-based process) and pathways (e.g., MAPK signaling). Four genes (NOTCH1, POMC, NGF, and DRD2) were identified as candidate hub genes by PPI analysis and CytoHubba. Finally, we carried out validation analyses in a separate dataset, GSE12649, and verified NOTCH1 as a hub gene and the involvement of several biological processes such as actin filament-based process and axon development. Taken together, our findings revealed several candidate pathways and genes (NOTCH1) in the pathogenesis of BD and call for further investigation for their potential research values in BD diagnosis and treatment.

scholarly journals Identification of prognosis-related genes in the tumor microenvironment of stomach adenocarcinoma by TCGA and GEO datasets

2020 ◽  
Vol 40 (10) ◽  
Author(s):  
Na Ren ◽  
Bin Liang ◽  
Yunhui Li
Keyword(s):  
Tumor Microenvironment ◽  
Clinical Outcomes ◽  
Computational Algorithm ◽  
Individual Therapy ◽  
Biological Processes ◽  
Expression Data ◽  
Tumor Purity ◽  
Stomach Adenocarcinoma ◽  
Geo Dataset ◽  
Tcga Dataset

Abstract Accumulating evidence has demonstrated that tumor microenvironment (TME) plays a crucial role in stomach adenocarcinoma (STAD) development, progression, prognosis and immunotherapeutic responses. How the genes in TME interact and behave is extremely crucial for tumor investigation. In the present study, we used gene expression data of STAD available from TCGA and GEO datasets to infer tumor purity using ESTIMATE algorithms, and predicted the associations between tumor purity and clinical features and clinical outcomes. Next, we calculated the differentially expressed genes (DEGs) from the comparisons of immune and stromal scores, and postulated key biological processes and pathways that the DEGs mainly involved in. Then, we analyzed the prognostic values of DEGs in TCGA dataset, and validated the results by GEO dataset. Finally, we used CIBERSORT computational algorithm to estimate the 22 tumor infiltrating immune cells (TIICs) subsets in STAD tissues. We found that stromal and immune scores were significantly correlated with STAD subtypes, clinical stages, Helicobacter polyri infection, and stromal scores could predict the clinical outcomes in STAD patients. Moreover, we screened 307 common DEGs in TCGA and GSE51105 datasets. In the prognosis analyses, we only found OGN, JAM2, RERG, OLFML2B, and ADAMTS1 genes were significantly associated with overall survival in TCGA and GSE84437 datasets, and these genes were correlated with the fractions of T cells, B cells, macrophages, monocytes, NK cells and DC cells, respectively. Our comprehensive analyses for transcriptional data not only improved the understanding of characteristics of TME, but also provided the targets for individual therapy in STAD.

scholarly journals Coexpressed Genes That Promote the Infiltration of M2 Macrophages in Melanoma Can Evaluate the Prognosis and Immunotherapy Outcome

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Kexin Yan ◽  
Yutao Wang ◽  
Yuxiu Lu ◽  
Zhangyong Yan
Keyword(s):  
Regression Analysis ◽  
Cox Regression ◽  
Essential Genes ◽  
Assessment Model ◽  
Functional Enrichment ◽  
Coexpression Network ◽  
M2 Macrophages ◽  
Cox Regression Analysis ◽  
Coexpression Module ◽  
Coexpressed Genes

Purpose. To improve immunotherapy efficacy for melanoma, a coexpression network and key genes of M2 macrophages in melanoma were explored. A prognostic risk assessment model was established for M2-related coexpressed genes, and the role of M2 macrophages in the immune microenvironment of melanoma was elucidated. Method. We obtained mRNA data from melanoma and peritumor tissue samples from The Cancer Genome Atlas-skin cutaneous melanoma (TCGA-SKCM). Then, we used CIBERSORT to calculate the proportion of M2 macrophage cells. A coexpression module most related to M2 macrophages in TCGA-SKCM was determined by analyzing the weighted gene coexpression network, and a coexpression network was established. After survival analysis, factors with significant results were incorporated into a Cox regression analysis to establish a model. The model’s essential genes were analyzed using functional enrichment, GSEA, and subgroup and total carcinoma. Finally, external datasets GSE65904 and GSE78220 were used to verify the prognostic risk model. Results. The yellow-green module was the coexpression module most related to M2 macrophages in TCGA-SKCM; NOTCH3, DBN1, KDELC2, and STAB1 were identified as the essential genes that promoted the infiltration of M2 macrophages in melanoma. These genes are concentrated in antigen treatment and presentation, chemokine, cytokine, the T cell receptor pathway, and the IFN-γ pathway. These factors were analyzed for survival, and factors with significant results were included in a Cox regression analysis. According to the methods, a model related to M2-TAM coexpressed gene was established, and the formula was risk   score = 0.25 ∗ NOTCH 3 + 0.008 ∗   DBN 1 − 0.031 ∗ KDELC 2 − 0.032 ∗ STAB 1 . The new model was used to perform subgroup evaluation and external queue validation. The results showed good prognostic ability. Conclusion. We proposed a Cox proportional hazards regression model associated with coexpression genes of melanoma M2 macrophages that may provide a measurement method for generating prognosis scores in patients with melanoma. Four genes coexpressed with M2 macrophages were associated with high levels of infiltration of M2 macrophages. Our findings may provide significant candidate biomarkers for the treatment and monitoring of melanoma.

scholarly journals Heterogeneity and targeted therapy-induced adaptations in lung cancer revealed by longitudinal single-cell RNA sequencing

2019 ◽  
Author(s):  
Ashley Maynard ◽  
Caroline E. McCoach ◽  
Julia K. Rotow ◽  
Lincoln Harris ◽  
Franziska Haderk ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Targeted Therapy ◽  
Single Cell ◽  
Cancer Cells ◽  
Rna Sequencing ◽  
Clinical Outcomes ◽  
Residual Disease ◽  
Metastatic Cancer ◽  
Immunosuppressive Cell ◽  
Single Cell Rna Sequencing

Lung cancer, the leading cause of cancer mortality, exhibits heterogeneity that enables adaptability, limits therapeutic success, and remains incompletely understood. Single-cell RNA sequencing (scRNAseq) of metastatic lung cancer was performed using 44 tumor biopsies obtained longitudinally from 27 patients before and during targeted therapy. Over 20,000 cancer and tumor microenvironment (TME) single-cell profiles exposed a rich and dynamic tumor ecosystem. scRNAseq of cancer cells illuminated targetable oncogenes beyond those detected clinically. Cancer cells surviving therapy as residual disease (RD) expressed an alveolar-regenerative cell signature suggesting a therapy-induced primitive cell state transition, whereas those present at on-therapy progressive disease (PD) upregulated kynurenine, plasminogen, and gap junction pathways. Active T-lymphocytes and decreased macrophages were present at RD and immunosuppressive cell states characterized PD. Biological features revealed by scRNAseq were biomarkers of clinical outcomes in independent cohorts. This study highlights how therapy-induced adaptation of the multi-cellular ecosystem of metastatic cancer shapes clinical outcomes.

Radiosurgery for patients with recurrent small cell lung carcinoma metastatic to the brain: outcomes and prognostic factors

2005 ◽  
Vol 102 (Special_Supplement) ◽  
pp. 247-254 ◽  
Author(s):  
Jason Sheehan ◽  
Douglas Kondziolka ◽  
John Flickinger ◽  
L. Dade Lunsford
Keyword(s):  
Lung Cancer ◽  
Brain Metastases ◽  
Lung Carcinoma ◽  
Small Cell Lung Carcinoma ◽  
Small Cell ◽  
Small Cell Lung ◽  
Cell Lung Carcinoma ◽  
Content Type ◽  
The Brain ◽  
Fine Print

Object. Lung carcinoma is the leading cause of death from cancer. More than 50% of those with small cell lung cancer develop a brain metastasis. Corticosteroid agents, radiotherapy, and resection have been the mainstays of treatment. Nonetheless, median survival for patients with small cell lung carcinoma metastasis is approximately 4 to 5 months after cranial irradiation. In this study the authors examine the efficacy of gamma knife surgery for treating recurrent small cell lung carcinoma metastases to the brain following tumor growth in patients who have previously undergone radiation therapy, and they evaluate factors affecting survival. Methods. A retrospective review of 27 patients (47 recurrent small cell lung cancer brain metastases) undergoing radiosurgery was performed. Clinical and radiographic data obtained during a 14-year treatment period were collected. Multivariate analysis was utilized to determine significant prognostic factors influencing survival. The overall median survival was 18 months after the diagnosis of brain metastases. In multivariate analysis, factors significantly affecting survival included: 1) tumor volume (p = 0.0042); 2) preoperative Karnofsky Performance Scale score (p = 0.0035); and 3) time between initial lung cancer diagnosis and development of brain metastasis (p = 0.0127). Postradiosurgical imaging of the brain metastases revealed that 62% decreased, 19% remained stable, and 19% eventually increased in size. One patient later underwent a craniotomy and tumor resection for a tumor refractory to radiosurgery and radiation therapy. In three patients new brain metastases were demonstrating on follow-up imaging. Conclusions. Stereotactic radiosurgery for recurrent small cell lung carcinoma metastases provided effective local tumor control in the majority of patients. Early detection of brain metastases, aggressive treatment of systemic disease, and a therapeutic strategy including radiosurgery can extend survival.

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