scholarly journals Identification of Key Pathways and Genes Related to Immunotherapy Resistance of LUAD Based on WGCNA Analysis

2022 ◽  
Vol 11 ◽  
Author(s):  
Weina Yu ◽  
Fengsen Liu ◽  
Qingyang Lei ◽  
Peng Wu ◽  
Li Yang ◽  
...  
Keyword(s):  
Checkpoint Inhibitors ◽  
The Cancer Genome Atlas ◽  
Normal Lung ◽  
Hub Genes ◽  
Major Barrier ◽  
Tumor Tissues ◽  
Cancer Genome Atlas ◽  
Potential Factors ◽  
Tcga Dataset ◽  
Immunosuppressive Cells

Immunotherapy resistance is a major barrier in the application of immune checkpoint inhibitors (ICI) in lung adenocarcinoma (LUAD) patients. Although recent studies have found several mechanisms and potential genes responsible for immunotherapy resistance, ways to solve this problem are still lacking. Tumor immune dysfunction and exclusion (TIDE) algorithm is a newly developed method to calculate potential regulators and indicators of ICI resistance. In this article, we combined TIDE and weighted gene co-expression network analysis (WGCNA) to screen potential modules and hub genes that are highly associated with immunotherapy resistance using the Cancer Genome Atlas (TCGA) dataset of LUAD patients. We identified 45 gene co-expression modules, and the pink module was most correlated with TIDE score and other immunosuppressive features. After considering the potential factors in immunotherapy resistance, we found that the pink module was also highly related to cancer stemness. Further analysis showed enriched immunosuppressive cells in the extracellular matrix (ECM), immunotherapy resistance indicators, and common cancer-related signaling pathways in the pink module. Seven hub genes in the pink module were shown to be significantly upregulated in tumor tissues compared with normal lung tissue, and were related to poor survival of LUAD patients. Among them, THY1 was the gene most associated with TIDE score, a gene highly related to suppressive immune states, and was shown to be strongly expressed in late-stage patients. Immunohistochemistry (IHC) results demonstrated that THY1 level was higher in the progressive disease (PD) group of LUAD patients receiving a PD-1 monoclonal antibody (mAb) and positively correlated with SOX9. Collectively, we identified that THY1 could be a critical biomarker in predicting ICI efficiency and a potential target for avoiding tumor immunotherapy resistance.

scholarly journals Screening of immunosuppressive cells from colorectal adenocarcinoma and identification of prognostic markers

2021 ◽  
Author(s):  
Fazhan Li ◽  
Jun Zhou ◽  
Li Zedong ◽  
Leiyi Zhang
Keyword(s):  
Th17 Cells ◽  
Colorectal Adenocarcinoma ◽  
Expression Profiles ◽  
Malignant Tumour ◽  
Common Type ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Hub Genes ◽  
Cancer Genome Atlas ◽  
Immunosuppressive Cells

Background: Colorectal cancer (CRC) is the most common type of gastrointestinal malignant tumour. Colorectal adenocarcinoma (COAD)—the most common type of CRC—is particularly dangerous. The role of the immune system in the development of tumour-associated inflammation and cancer has received increasing attention recently. Methods: In this study, we compiled the expression profiles of 262 patients with complete follow-up data from the Cancer Genome Atlas (TCGA) database as an experimental group and selected 65 samples from the GEO dataset (of which 46 samples with M0) as a verification group. First, we screened the immune Th17 cells related to the prognosis of COAD. Subsequently, we identified Th17-cells-related hub genes by utilizing co-expression network analysis (WGCNA) and LASSO regression analysis. Six genes associated with the prognosis in patients with COAD were identified ,including : KRT23, ULBP2, ASRGL1, SERPINA1, SCIN, and SLC28A2. We constructed a clinical prediction model and analysed its predictive power. Results: The identified hub genes are involved in developing many diseases and closely linked to digestive disorders. Our results suggested that the hub genes could influence the prognosis of COAD by regulating Th17 cells’ infiltration. Conclusions: These newly discovered hub genes contribute to clarifying the mechanisms of COAD development and metastasis. Given that they promote COAD development, they may become new therapeutic targets and biomarkers of COAD.

scholarly journals Deep Learning for Automatic Subclassification of Gastric Carcinoma Using Whole-Slide Histopathology Images

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3811
Author(s):  
Hyun-Jong Jang ◽  
In-Hye Song ◽  
Sung-Hak Lee
Keyword(s):  
Deep Learning ◽  
Relative Proportion ◽  
The Cancer Genome Atlas ◽  
Cancer Tissue ◽  
Mucinous Tumor ◽  
Receiver Operating Characteristic Curves ◽  
Tumor Tissues ◽  
Cancer Genome Atlas ◽  
Stomach Adenocarcinoma ◽  
Tumor Types

Histomorphologic types of gastric cancer (GC) have significant prognostic values that should be considered during treatment planning. Because the thorough quantitative review of a tissue slide is a laborious task for pathologists, deep learning (DL) can be a useful tool to support pathologic workflow. In the present study, a fully automated approach was applied to distinguish differentiated/undifferentiated and non-mucinous/mucinous tumor types in GC tissue whole-slide images from The Cancer Genome Atlas (TCGA) stomach adenocarcinoma dataset (TCGA-STAD). By classifying small patches of tissue images into differentiated/undifferentiated and non-mucinous/mucinous tumor tissues, the relative proportion of GC tissue subtypes can be easily quantified. Furthermore, the distribution of different tissue subtypes can be clearly visualized. The patch-level areas under the curves for the receiver operating characteristic curves for the differentiated/undifferentiated and non-mucinous/mucinous classifiers were 0.932 and 0.979, respectively. We also validated the classifiers on our own GC datasets and confirmed that the generalizability of the classifiers is excellent. The results indicate that the DL-based tissue classifier could be a useful tool for the quantitative analysis of cancer tissue slides. By combining DL-based classifiers for various molecular and morphologic variations in tissue slides, the heterogeneity of tumor tissues can be unveiled more efficiently.

Identification of novel mRNA-miRNA-lncRNA competing endogenous RNA network associated with prognosis of breast cancer

Epigenomics ◽  
2019 ◽  
Vol 11 (13) ◽  
pp. 1501-1518 ◽  
Author(s):  
Guansheng Zhong ◽  
Weiyang Lou ◽  
Minya Yao ◽  
Chengyong Du ◽  
Haiyan Wei ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Competing Endogenous Rna ◽  
Hub Genes ◽  
Cerna Network ◽  
Competing Endogenous Rna Network ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Aim: To identify novel competing endogenous RNA (ceRNA) network related to patients prognosis in breast cancer. Materials & methods: Dysregulated mRNA based on intersection of three Gene Expression Omnibus and The Cancer Genome Atlas datasets were analyzed by bioinformatics. Results: In total 72 upregulated and 208 downregulated genes were identified. Functional analysis showed that some pathways related to cancer were significantly enriched. By means of stepwise reverse prediction and validation from mRNA to lncRNA, 19 hub genes, nine key miRNA and four key lncRNAs were identified by expression and survival analysis. Ultimately, the coexpression analysis identified RRM2-let-7a-5p- SNHG16/ MAL2 as key ceRNA subnetwork associated with prognosis of breast cancer. Conclusion: We successfully constructed a novel ceRNA network, among which each component was significantly associated with breast cancer prognosis.

scholarly journals Integrated Dissection of lncRNA-Perturbated Triplets Reveals Novel Prognostic Signatures Across Cancer Types

2020 ◽  
Vol 21 (17) ◽  
pp. 6087
Author(s):  
Yunzhen Wei ◽  
Limeng Zhou ◽  
Yingzhang Huang ◽  
Dianjing Guo
Keyword(s):  
Cancer Biology ◽  
Noncoding Rna ◽  
The Cancer Genome Atlas ◽  
Dynamic Changes ◽  
Computational Framework ◽  
Potential Biomarker ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Tcga Dataset ◽  
Pan Cancer

Long noncoding RNA (lncRNA)/microRNA(miRNA)/mRNA triplets contribute to cancer biology. However, identifying significative triplets remains a major challenge for cancer research. The dynamic changes among factors of the triplets have been less understood. Here, by integrating target information and expression datasets, we proposed a novel computational framework to identify the triplets termed as “lncRNA-perturbated triplets”. We applied the framework to five cancer datasets in The Cancer Genome Atlas (TCGA) project and identified 109 triplets. We showed that the paired miRNAs and mRNAs were widely perturbated by lncRNAs in different cancer types. LncRNA perturbators and lncRNA-perturbated mRNAs showed significantly higher evolutionary conservation than other lncRNAs and mRNAs. Importantly, the lncRNA-perturbated triplets exhibited high cancer specificity. The pan-cancer perturbator OIP5-AS1 had higher expression level than that of the cancer-specific perturbators. These lncRNA perturbators were significantly enriched in known cancer-related pathways. Furthermore, among the 25 lncRNA in the 109 triplets, lncRNA SNHG7 was identified as a stable potential biomarker in lung adenocarcinoma (LUAD) by combining the TCGA dataset and two independent GEO datasets. Results from cell transfection also indicated that overexpression of lncRNA SNHG7 and TUG1 enhanced the expression of the corresponding mRNA PNMA2 and CDC7 in LUAD. Our study provides a systematic dissection of lncRNA-perturbated triplets and facilitates our understanding of the molecular roles of lncRNAs in cancers.

scholarly journals Deciphering N6-Methyladenosine-Related Genes Signature to Predict Survival in Lung Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jie Zhu ◽  
Min Wang ◽  
Daixing Hu
Keyword(s):  
Lung Adenocarcinoma ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Lasso Regression ◽  
Gene Expressions ◽  
Tumor Tissues ◽  
Cancer Genome Atlas ◽  
Selection Operator ◽  
Cox Analysis

Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer-related death. Among these, lung adenocarcinoma (LUAD) accounts for most cases. Due to the improvement of precision medicine based on molecular characterization, the treatment of LUAD underwent significant changes. With these changes, the prognosis of LUAD becomes diverse. N6-methyladenosine (m6A) is the most predominant modification in mRNAs, which has been a research hotspot in the field of oncology. Nevertheless, little has been studied to reveal the correlations between the m6A-related genes and prognosis in LUAD. Thus, we conducted a comprehensive analysis of m6A-related gene expressions in LUAD patients based on The Cancer Genome Atlas (TCGA) database by revealing their relationship with prognosis. Different expressions of the m6A-related genes in tumor tissues and non-tumor tissues were confirmed. Furthermore, their relationship with prognosis was studied via Consensus Clustering Analysis, Principal Components Analysis (PCA), and Least Absolute Shrinkage and Selection Operator (LASSO) Regression. Based on the above analyses, a m6A-based signature to predict the overall survival (OS) in LUAD was successfully established. Among the 479 cases, we found that most of the m6A-related genes were differentially expressed between tumor and non-tumor tissues. Six genes, HNRNPC, METTL3, YTHDC2, KIAA1429, ALKBH5, and YTHDF1 were screened to build a risk scoring signature, which is strongly related to the clinical features pathological stages (p<0.05), M stages (p<0.05), T stages (p < 0.05), gender (p=0.04), and survival outcome (p=0.02). Multivariate Cox analysis indicated that risk value could be used as an independent prognostic factor, revealing that the m6A-related genes signature has great predictive value. Its efficacy was also validated by data from the Gene Expression Omnibus (GEO) database.

scholarly journals Elevated ZNF703 Protein Expression Is an Independent Unfavorable Prognostic Factor for Survival of the Patients with Head and Neck Squamous Cell Carcinoma

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hang Yang ◽  
Wen-Qi Jiang ◽  
Ye Cao ◽  
Yong-An Sun ◽  
Jing Wei ◽  
...  
Keyword(s):  
Prognostic Factor ◽  
Head And Neck ◽  
Squamous Cell ◽  
The Cancer Genome Atlas ◽  
Cancer Center ◽  
Protein Overexpression ◽  
Prognostic Effect ◽  
Tumor Tissues ◽  
Cancer Genome Atlas

Aim. Data from The Cancer Genome Atlas (TCGA) show that the ZNF703 gene amplifies and overexpresses in head and neck squamous cell carcinomas (HNSCC). However, the clinical relevance of this observation in HNSCC is unclear. The purpose of this study was to clarify the expression of ZNF703 protein and its prognostic effect on HNSCC.Methods. Two hundred ten HNSCC patients from Sun Yat-Sen University Cancer Center with complete survival follow-up were included in this study. Tumor samples from primary sites were collected. The expression of the ZNF703 protein was tested by immunohistochemistry (IHC).Results. The high expression of ZNF703 in HNSCC tumor tissues was significantly higher than that of the matched noncancerous tissues (48.6% versus 11.6%,P<0.001). ZNF703 overexpression was correlated with tumor position (laryngeal carcinoma) and recurrence (allP<0.05). Multivariate analysis revealed that ZNF703 protein overexpression was an independent prognostic factor (P=0.022, hazard ratio = 1.635, 95% CI 1.073–2.493) in HNSCC patients.Conclusion. ZNF703 overexpression is associated with adverse prognosis in HNSCC, which might be a novel biomarker of HNSCC.

The impact of BMI on outcomes of patients with metastatic renal cell carcinoma treated with targeted therapy: An external validation data set and analysis of underlying biology from The Cancer Genome Atlas.

2015 ◽  
Vol 33 (7_suppl) ◽  
pp. 405-405 ◽  
Author(s):  
Laurence Albiges ◽  
A. Ari Hakimi ◽  
Xun Lin ◽  
Ronit Simantov ◽  
Emily C. Zabor ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Targeted Therapy ◽  
Prognostic Factor ◽  
Renal Cell ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Tcga Dataset ◽  
The Impact ◽  
Genome Atlas ◽  
High Bmi

405 Background: Obesity is a risk factor for renal cell carcinoma (RCC) and a poor prognostic factor across many tumor types. However, reports have suggested that RCC developing in an obesogenic environment may be more indolent. We recently reported on the favorable impact of body mass index (BMI) on survival in the International mRCC Database Consortium (IMDC). The current work aims to externally validate this finding and characterize the underlying biology. Methods: We conducted an analysis of 4,657 metastatic RCC (mRCC) patients (pts) treated on phase II-III clinical trials sponsored by Pfizer from 2003-2013. We assessed the impact of BMI on overall survival (OS), progression-free survival (PFS) and overall response rate (ORR). Additionally, we analysed metastatic pts from the clear cell RCC (ccRCC) cohort of TCGA dataset to correlate the expression of Fatty Acid Synthase (FASN) with BMI and OS. Results: At targeted therapy (TT) initiation, 1,829 (39%) pts were normal or underweight (BMI <25 kg/m2) and 2,828 (61%) were overweight or obese (BMI ≥25 kg/m2). Overall, the high BMI group had a longer median OS (23.4 months) than the low BMI group (14.5 months) (hazard ratio (HR) = 0.830, p= 0.0008, 95% CI 0.743-0.925) after adjusting for the IMDC prognostic risk group and other risks factors. In addition, pts with high BMI had improved PFS (HR=0.821, 95% CI 0.746-0.903, p<0.0001) and ORR (odds ratio =1.527, 95% CI 1.258-1.855, p<0.001). These results remain valid when stratified by line of therapy. When stratified by histological subtype, the favorable outcome associated with high BMI was only observed in ccRCC. Toxicity patterns did not differ between BMI groups. In the the Cancer Genome Atlas (TCGA) dataset (n=61), there was a trend towards improved OS in the high BMI group (p=0.07). FASN gene expression inversely correlated with both OS (p=0.002) and BMI (p=0.034). Conclusions: In an external cohort,we validate BMI as an independent prognostic factor for improved survival in mRCC. Given that this finding was observed in ccRCC only, we hypothesize that lipid metabolism may be modulated by the fat laden tumors cells. FASN staining in the IMDC cohort is ongoing to better investigate the obesity paradox in mRCC.

scholarly journals Development of an Aerobic Glycolysis Index for Predicting the Sorafenib Sensitivity and Prognosis of Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Yu Pan ◽  
Geng-yuan Hu ◽  
Shi Jiang ◽  
Shun-jie Xia ◽  
Hendi Maher ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Aerobic Glycolysis ◽  
Disease Free Survival ◽  
The Cancer Genome Atlas ◽  
Free Survival ◽  
Sorafenib Therapy ◽  
Tumor Tissues ◽  
Cancer Genome Atlas ◽  
Advanced Stages ◽  
Disease Free

Hepatocellular carcinoma (HCC) is a deadly tumor with high heterogeneity. Aerobic glycolysis is a common indicator of tumor growth and plays a key role in tumorigenesis. Heterogeneity in distinct metabolic pathways can be used to stratify HCC into clinically relevant subgroups, but these have not yet been well-established. In this study, we constructed a model called aerobic glycolysis index (AGI) as a marker of aerobic glycolysis using genomic data of hepatocellular carcinoma from The Cancer Genome Atlas (TCGA) project. Our results showed that this parameter inferred enhanced aerobic glycolysis activity in tumor tissues. Furthermore, high AGI is associated with poor tumor differentiation and advanced stages and could predict poor prognosis including reduced overall survival and disease-free survival. More importantly, the AGI could accurately predict tumor sensitivity to Sorafenib therapy. Therefore, the AGI may be a promising biomarker that can accurately stratify patients and improve their treatment efficacy.

scholarly journals ITGB1-DT/ARNTL2 axis may be a novel biomarker in lung adenocarcinoma: a bioinformatics analysis and experimental validation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bai-Quan Qiu ◽  
Xia-Hui Lin ◽  
Song-Qing Lai ◽  
Feng Lu ◽  
Kun Lin ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Prognostic Effect ◽  
Cancer Genome Atlas

Abstract Background Lung cancer is one of the most lethal malignant tumors that endangers human health. Lung adenocarcinoma (LUAD) has increased dramatically in recent decades, accounting for nearly 40% of all lung cancer cases. Increasing evidence points to the importance of the competitive endogenous RNA (ceRNA) intrinsic mechanism in various human cancers. However, behavioral characteristics of the ceRNA network in lung adenocarcinoma need further study. Methods Groups based on SLC2A1 expression were used in this study to identify associated ceRNA networks and potential prognostic markers in lung adenocarcinoma. The Cancer Genome Atlas (TCGA) database was used to obtain the patients' lncRNA, miRNA, and mRNA expression profiles, as well as clinical data. Informatics techniques were used to investigate the effect of hub genes on prognosis. The Cox regression analyses were performed to evaluate the prognostic effect of hub genes. The methylation, GSEA, and immune infiltration analyses were utilized to explore the potential mechanisms of the hub gene. The CCK-8, transwell, and colony formation assays were performed to detect the proliferation and invasion of lung cancer cells. Results We eventually identified the ITGB1-DT/ARNTL2 axis as an independent fact may promote lung adenocarcinoma progression. Furthermore, methylation analysis revealed that hypo-methylation may cause the dysregulated ITGB1-DT/ARNTL2 axis, and immune infiltration analysis revealed that the ITGB1-DT/ARNTL2 axis may affect the immune microenvironment and the progression of lung adenocarcinoma. The CCK-8, transwell, and colonu formation assays suggested that ITGB1-DT/ARNTL2 promotes the progression of lung adenocarcinoma. And hsa-miR-30b-3p reversed the ITGB1/ARNTL2-mediated oncogenic processes. Conclusion Our study identified the ITGB1-DT/ARNTL2 axis as a novel prognostic biomarker affects the prognosis of lung adenocarcinoma.

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