The correlation of WDR76 expression with survival outcomes and immune infiltrates in lung adenocarcinoma

Background WD repeat domain 76 (WDR76) is a predicted member of the WD40-repeat-containing domain superfamily and possibly involves in various biological processes, but its function in cancers is poorly characterized. This study aimed to evaluate the role of WDR76 in the prognosis and immune infiltrates of lung adenocarcinoma (LUAD). Methods WDR76 expressions in LUAD tissues and normal tissues were primarily compared by The Cancer Genome Atlas (TCGA) database, and were validated in cohorts from Gene Expression Omnibus (GEO) database. The associations between WDR76 expression and clinicopathologic characteristics were analyzed. Kaplan–Meier and Cox regression analyses were performed to determine the impact of WDR76 expression on survival outcomes. The protein interaction network of WDR76 was built using STRING website. TIMER and GEPIA databases were used to investigate the correlation between WDR76 expression and immune infiltrates. Results WDR76 expression was elevated in LUAD (P < 0.001) and high WDR76 expression was associated with advanced N stage, M stage and pathologic stage. Expectedly, high WDR76 expression significantly correlated with poor survival outcomes and was the independent risk factor for overall survival (OS) (HR 1.468, 95% CI [1.031–2.089], P = 0.033) and disease specific survival (DSS) (HR 1.764, 95% CI [1.095–2.842], P = 0.020). DDB1 and LSH were the important proteins interacting with WDR76. WDR76 expression correlated with CD8+ T cells presence and was also positively associated with levels of inhibitory receptors. Conclusion WDR76 expression was involved in the regulation of immune infiltrates and had predictive value for prognosis in LUAD.

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Identification and Validation of a Novel Immune-Related Four-lncRNA Signature for Lung Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.639254 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jixin Wang ◽

Xiangjun Yin ◽

Yin-Qiang Zhang ◽

Xuming Ji

Keyword(s):

Gene Expression ◽

Lung Adenocarcinoma ◽

Immune Cell ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Lasso Regression ◽

Kaplan Meier

Lung adenocarcinoma (LUAD) is a major subtype of lung cancer, the prognosis of patients with which is associated with both lncRNAs and cancer immunity. In this study, we collected gene expression data of 585 LUAD patients from The Cancer Genome Atlas (TCGA) database and 605 subjects from the Gene Expression Omnibus (GEO) database. LUAD patients were divided into high and low immune-cell-infiltrated groups according to the single sample gene set enrichment analysis (ssGSEA) algorithm to identify differentially expressed genes (DEGs). Based on the 49 immune-related DE lncRNAs, a four-lncRNA prognostic signature was constructed by applying least absolute shrinkage and selection operator (LASSO) regression, univariate Cox regression, and stepwise multivariate Cox regression in sequence. Kaplan–Meier curve, ROC analysis, and the testing GEO datasets verified the effectiveness of the signature in predicting overall survival (OS). Univariate Cox regression and multivariate Cox regression suggested that the signature was an independent prognostic factor. The correlation analysis revealed that the infiltration immune cell subtypes were related to these lncRNAs.

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

Frontiers in Genetics ◽

10.3389/fgene.2021.699242 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jianlin Chen ◽

Junping Ding ◽

Wenjie Huang ◽

Lin Sun ◽

Jinping Chen ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Prognostic Biomarker ◽

Function Analysis ◽

Mrna Level ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Immune Checkpoints ◽

Diagnostic Efficiency ◽

Protein Levels ◽

Normal Tissues

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

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Identification and validation of a hub gene prognostic index for hepatocellular carcinoma

Future Oncology ◽

10.2217/fon-2020-1112 ◽

2021 ◽

Author(s):

Q Shi ◽

Z Meng ◽

XX Tian ◽

YF Wang ◽

WH Wang

Keyword(s):

Hepatocellular Carcinoma ◽

Cox Regression ◽

Prognostic Index ◽

Interaction Network ◽

Gene Signature ◽

Gene Expression Omnibus ◽

Cox Regression Analysis ◽

Protein Protein Interaction ◽

Normal Tissues ◽

Significant Independent Prognostic Factor

Aims: We aim to provide new insights into the mechanisms of hepatocellular carcinoma (HCC) and identify key genes as biomarkers for the prognosis of HCC. Materials & methods: Differentially expressed genes between HCC tissues and normal tissues were identified via the Gene Expression Omnibus tool. The top ten hub genes screened by the degree of the protein nodes in the protein–protein interaction network also showed significant associations with overall survival in HCC patients. Results: A prognostic model containing a five-gene signature was constructed to predict the prognosis of HCC via multivariate Cox regression analysis. Conclusion: This study identified a novel five-gene signature ( CDK1, CCNB1, CCNB2, BUB1 and KIF11) as a significant independent prognostic factor.

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Co-Expression of Coxsackievirus/Adenovirus Receptors and Desmoglein 2 in Lung Adenocarcinoma: A Comprehensive Analysis of Bioinformatics and Tissue Microarrays

Journal of Clinical Medicine ◽

10.3390/jcm9113693 ◽

2020 ◽

Vol 9 (11) ◽

pp. 3693

Author(s):

Ching-Fu Weng ◽

Chi-Jung Huang ◽

Mei-Hsuan Wu ◽

Henry Hsin-Chung Lee ◽

Thai-Yen Ling

Keyword(s):

Overall Survival ◽

Survival Analysis ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Early Stage ◽

The Cancer Genome Atlas ◽

Cancer Tissue ◽

Mesenchymal Transition ◽

Kaplan Meier

Introduction: Coxsackievirus/adenovirus receptors (CARs) and desmoglein-2 (DSG2) are similar molecules to adenovirus-based vectors in the cell membrane. They have been found to be associated with lung epithelial cell tumorigenesis and can be useful markers in predicting survival outcome in lung adenocarcinoma (LUAD). Methods: A gene ontology enrichment analysis disclosed that DSG2 was highly correlated with CAR. Survival analysis was then performed on 262 samples from the Cancer Genome Atlas, forming “Stage 1A” or “Stage 1B”. We therefore analyzed a tissue microarray (TMA) comprised of 108 lung samples and an immunohistochemical assay. Computer counting software was used to calculate the H-score of the immune intensity. Cox regression and Kaplan–Meier analyses were used to determine the prognostic value. Results: CAR and DSG2 genes are highly co-expressed in early stage LUAD and associated with significantly poorer survival (p = 0.0046). TMA also showed that CAR/DSG2 expressions were altered in lung cancer tissue. CAR in the TMA was correlated with proliferation, apoptosis, and epithelial–mesenchymal transition (EMT), while DSG2 was associated with proliferation only. The Kaplan–Meier survival analysis revealed that CAR, DSG2, or a co-expression of CAR/DSG2 was associated with poorer overall survival. Conclusions: The co-expression of CAR/DSG2 predicted a worse overall survival in LUAD. CAR combined with DSG2 expression can predict prognosis.

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Upregulation of GNPNAT1 Predicts Poor Prognosis and Correlates With Immune Infiltration in Lung Adenocarcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.605754 ◽

2021 ◽

Vol 8 ◽

Author(s):

Wenting Liu ◽

Kaiting Jiang ◽

Jingya Wang ◽

Ting Mei ◽

Min Zhao ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Signaling Pathways ◽

Poor Prognosis ◽

Cox Regression ◽

Clinical Stage ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Clinical Value ◽

Cox Regression Analysis ◽

Normal Tissues

BackgroundGlucosamine 6-phosphate N-acetyltransferase (GNPNAT1) is a key enzyme in the hexosamine biosynthetic pathway (HBP), which functions as promoting proliferation in some tumors, yet its potential biological function and mechanism in lung adenocarcinoma (LUAD) have not been explored.MethodsThe mRNA differential expression of GNPNAT1 in LUAD and normal tissues was analyzed using the Cancer Genome Atlas (TCGA) database and validated by real-time PCR. The clinical value of GNPNAT1 in LUAD was investigated based on the data from the TCGA database. Then, immunohistochemistry (IHC) of GNPNAT1 was applied to verify the expression and clinical significance in LUAD from the protein level. The relationship between GNPNAT1 and epigenetics was explored using the cBioPortal database, and the miRNAs regulating GNPNAT1 were found using the miRNA database. The association between GNPNAT1 expression and tumor-infiltrating immune cells in LUAD was observed through the Tumor IMmune Estimation Resource (TIMER). Finally, Gene set enrichment analysis (GSEA) was used to explore the biological signaling pathways involved in GNPNAT1 in LUAD.ResultsGNPNAT1 was upregulated in LUAD compared with normal tissues, which was verified through qRT-PCR in different cell lines (P < 0.05), and associated with patients’ clinical stage, tumor size, and lymphatic metastasis status (all P < 0.01). Kaplan–Meier (KM) analysis suggested that patients with upregulated GNPNAT1 had a relatively poor prognosis (P < 0.0001). Furthermore, multivariate Cox regression analysis indicated that GNPNAT1 was an independent prognostic factor for LUAD (OS, TCGA dataset: HR = 1.028, 95% CI: 1.013–1.044, P < 0.001; OS, validation set: HR = 1.313, 95% CI: 1.130–1.526, P < 0.001). GNPNAT1 overexpression was correlated with DNA copy amplification (P < 0.0001), low DNA methylation (R = −0.52, P < 0.0001), and downregulation of hsa-miR-30d-3p (R = −0.17, P < 0.001). GNPNAT1 expression was linked to B cells (R = −0.304, P < 0.0001), CD4+T cells (R = −0.218, P < 0.0001), and dendritic cells (R = −0.137, P = 0.002). Eventually, GSEA showed that the signaling pathways of the cell cycle, ubiquitin-mediated proteolysis, mismatch repair and p53 were enriched in the GNPNAT1 overexpression group.ConclusionGNPNAT1 may be a potential prognostic biomarker and novel target for intervention in LUAD.

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An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Adenocarcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.685379 ◽

2021 ◽

Vol 9 ◽

Author(s):

Qian Xu ◽

Yurong Chen

Keyword(s):

Lung Adenocarcinoma ◽

Cox Regression ◽

Characteristic Curve ◽

Staging System ◽

Risk Groups ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Related Gene ◽

Net Benefit ◽

Cox Regression Analysis

Aging is an inevitable time-dependent process associated with a gradual decline in many physiological functions. Importantly, some studies have supported that aging may be involved in the development of lung adenocarcinoma (LUAD). However, no studies have described an aging-related gene (ARG)-based prognosis signature for LUAD. Accordingly, in this study, we analyzed ARG expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). After LASSO and Cox regression analyses, a six ARG-based signature (APOC3, EPOR, H2AFX, MXD1, PLCG2, and YWHAZ) was constructed using TCGA dataset that significantly stratified cases into high- and low-risk groups in terms of overall survival (OS). Cox regression analysis indicated that the ARG signature was an independent prognostic factor in LUAD. A nomogram based on the ARG signature and clinicopathological factors was developed in TCGA cohort and validated in the GEO dataset. Moreover, to visualize the prediction results, we established a web-based calculator yurong.shinyapps.io/ARGs_LUAD/. Calibration plots showed good consistency between the prediction of the nomogram and actual observations. Receiver operating characteristic curve and decision curve analyses indicated that the ARG nomogram had better OS prediction and clinical net benefit than the staging system. Taken together, these results established a genetic signature for LUAD based on ARGs, which may promote individualized treatment and provide promising novel molecular markers for immunotherapy.

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N6-Methyladenosine-Regulated mRNAs: Potential Prognostic Biomarkers for Patients With Lung Adenocarcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.705962 ◽

2021 ◽

Vol 9 ◽

Author(s):

Junjun Sun ◽

Yili Ping ◽

Jingjuan Huang ◽

Bingjie Zeng ◽

Ping Ji ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Mrna Expression ◽

Cox Regression ◽

Interaction Network ◽

Prognostic Biomarkers ◽

Cox Regression Analysis ◽

Receiver Operating Characteristic Curves ◽

Network Analyses ◽

Kaplan Meier ◽

Selection Operator

Aberrant regulation of m6A mRNA modification can lead to changes in gene expression, thus contributing to tumorigenesis in several types of solid tumors. In this study, by integrating analyses of m6A methylation and mRNA expression, we identified 84 m6A-regulated mRNAs in lung adenocarcinoma (LUAD). Although the m6A methylation levels of total RNA in LUAD patient tumor tissue were reduced, the majority (75.2%) of m6A-regulated mRNAs were hypermethylated. The m6A-hypermethylated mRNAs were mainly enriched in terms related to transcription factor activity. We established a 10-m6A-regulated-mRNA signature score system through least absolute shrinkage and selection operator Cox regression analysis, with its predictive value validated by Kaplan–Meier curve and time-dependent receiver operating characteristic curves. RFXAP and KHDRBS2 from the signature also exhibited an independent prognostic value. The co-expression and interaction network analyses demonstrated the strong correlation between m6A regulators and the genes in the signature, further supporting the results of the m6A methylation modification patterns. These findings highlight the potential utility of integrating multi-omics data (m6A methylation level and mRNA expression) to accurately obtain potential prognostic biomarkers, which may provide important insights into developing novel and effective therapies for LUAD.

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Outcomes of Patients With Advanced Gastrointestinal Cancer in Relationship to Opioid Use: Findings From Eight Clinical Trials

Journal of the National Comprehensive Cancer Network ◽

10.6004/jnccn.2019.7382 ◽

2020 ◽

Vol 18 (5) ◽

pp. 575-581

Author(s):

Omar Abdel-Rahman ◽

Hatim Karachiwala ◽

Jacob C. Easaw

Keyword(s):

Clinical Trials ◽

Regression Analysis ◽

Cox Regression ◽

Survival Outcomes ◽

Disease Entity ◽

Gastrointestinal Cancers ◽

Opioid Use ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

The Impact

Background: This study assessed the patterns of opioid use among patients with advanced gastrointestinal cancers who were included in 8 clinical trials and evaluated the impact of opioid use on survival outcomes of included patients. Methods: Deidentified datasets from 8 clinical trials evaluating first-line systemic treatment of advanced gastrointestinal cancers were accessed from the Project Data Sphere platform (ClinicalTrial.gov identifiers: NCT01124786, NCT00844649, NCT00290966, NCT00678535, NCT00699374, NCT00272051, NCT00305188, and NCT00384176). These trials evaluated patients with pancreatic carcinoma, gastric carcinoma, hepatocellular carcinoma (HCC), and colorectal carcinoma. Multivariable logistic regression analysis was used to evaluate factors predicting the use of opioids. Kaplan-Meier survival estimates were used to compare survival outcomes in each disease entity among patients who did or did not receive opioid treatment. Multivariable Cox regression analysis was then used to further assess the impact of opioid use on survival outcomes in each disease entity. Results: A total of 3,441 participants were included in the current analysis. The following factors predicted a higher probability of opioid use within logistic regression analysis: younger age at diagnosis (odds ratio [OR], 0.990; 95% CI, 0.984–0.997; P=.004), nonwhite race (OR for white vs nonwhite, 0.749; 95% CI, 0.600–0.933; P=.010), higher ECOG score (OR for 1 vs 0, 1.751; 95% CI, 1.490–2.058; P<.001), and pancreatic primary site (OR for colorectal vs pancreatic, 0.241; 95% CI, 0.198–0.295; P<.001). Use of opioids was consistently associated with worse overall survival (OS) in Kaplan-Meier survival estimates of each disease entity (P=.008 for pancreatic cancer; P<.001 for gastric cancer, HCC, and colorectal cancer). In multivariable Cox regression analysis, opioid use was associated with worse OS among patients with pancreatic cancer (hazard ratio [HR], 1.245; 95% CI, 1.063–1.459; P=.007), gastric cancer (HR, 1.725; 95% CI, 1.403–2.122; P<.001), HCC (HR, 1.841; 95% CI, 1.480–2.290; P<.001), and colorectal cancer (HR, 1.651; 95% CI, 1.380–1.975; P<.001). Conclusions: Study findings suggest that opioid use is consistently associated with worse OS among patients with different gastrointestinal cancers. Further studies are needed to understand the underlying mechanisms of this observation and its potential implications.

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Comprehensive Analysis of the Immune and Prognostic Implication of TRIM8 in Breast Cancer

10.21203/rs.3.rs-1152767/v1 ◽

2021 ◽

Author(s):

Cheng Yan ◽

Qingling Liu ◽

Mingkun Nie ◽

Wei Hu ◽

Ruoling Jia

Keyword(s):

Breast Cancer ◽

Cox Regression ◽

Molecular Taxonomy ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Survival Prediction ◽

Mrna Levels ◽

Breast Cancer Patients ◽

Prognostic Signature ◽

Normal Tissues

Abstract Background: Breast cancer remains one of most lethal illnesses for female and the most common malignancies among women, making it important to discover novel biomarkers and therapeutic targets for breast cancer. Immunotherapy has become a promising therapeutic tool for breast cancer. The role of TRIM8 in breast cancer has rarely been reported. Method: Here we identified TRIM8 expression and its potential functions on survival in patients with breast cancer using TCGA (The cancer genome atlas), GEO (Gene expression omnibus) database and METABRIC (Molecular Taxonomy of Breast Cancer International Consortium). Then, TIMER and TISIDB databases were used to investigate the correlations between TRIM8 mRNA levels and immune characteristics. Using stepwise cox regression, we established an immune prognostic signature based on five differentially expression immune-related genes (DE-IRGs). Finally, a nomogram, accompanied by a calibration curve was proposed to predict 1-, 3-, and 5-year survival for breast cancer patients. Results: We found that TRIM8 expression was dramatically lower in breast cancer tissues in comparison with normal tissues. Lower TRIM8 expression was related with worse prognosis in breast cancer. TIMER and TISIDB analysis showed that there were strong correlations between TRIM8 expression and immune characteristics. The receiver operating characteristic (ROC) curve confirmed the good performance in survival prediction, showing good accuracy of the immune prognostic signature. We demonstrated the model usefulness of predictions by nomogram and calibration curves. Our findings indicated that TRIM8 might be a potential link between progression and prognosis survival of breast cancer.Conclusion: This is a comprehensive study to reveal that TRIM8 may serve as a potential prognostic biomarker associating with immune characteristics and provide a novel therapeutic target for the treatment of breast cancer.

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Lipid metabolic gene-wide profile and survival signature of lung adenocarcinoma

10.21203/rs.3.rs-31951/v3 ◽

2020 ◽

Author(s):

Jinyou Li ◽

Qiang Li ◽

Zhenyu Su ◽

Qi Sun ◽

Yong Zhao ◽

...

Keyword(s):

Overall Survival ◽

Lipid Metabolism ◽

Lung Adenocarcinoma ◽

Interaction Network ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

High Morbidity ◽

Hub Genes ◽

Histologic Subtype ◽

Normal Tissues

Abstract Background: Lung cancer is the cancer with high morbidity and mortality across the globe, and lung adenocarcinoma (LUAD) is the most common histologic subtype. The disorder of lipid metabolism is related to the development of cancer. Analysis of lipid-related transcriptome helps shed light on diagnosis and prognostic biomarkers of LUAD. Methods: In this study, we performed an expression analysis of 1045 lipid metabolism-related genes between LUAD tumors and normal tissues from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) cohort. The interaction network of differential expression genes (DEGs) was constructed to identify. The association between hub genes and overall survival was evaluated and formed a model to predict the prognosis of LUAD using a nomogram, and the model was validated by another cohort (GSE13213). Results: Finally, a total of 217 lipid metabolism-related DEGs were detected in LUAD. They were significantly enriched in Glycerophospholipid metabolism, fatty acid metabolic process, and Eicosanoid Signaling. Then we identified 6 hub genes through network and cytoHubba, including INS, LPL, HPGDS, DGAT1, UGT1A6, and CYP2C9. The high expression of CYP2C9, UGT1A6, and INS, whereas low expressions of DGAT1, HPGDS, and LPL, were associated with worse overall survival (OS) for 1925 LUAD patients. Our model found that the high-risk score group had a worse OS, and the validated cohort had the same result.Conclusion: This study constructed a signature of six lipid metabolic genes, which was significantly associated with the diagnosis and prognosis of LUAD patients. The gene signature can be used as a biomarker for LUAD in the term of lipid metabolic.

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