scholarly journals Development and Validation of a Prognostic Signature Based on Immune Genes in Cervical Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Yu Chen ◽  
Hao Lin ◽  
Ya-Nan Pi ◽  
Xi-Xi Chen ◽  
Hu Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Prognostic Score ◽  
Progression Free Survival ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Validation Dataset ◽  
Prognostic Signature ◽  
Immune Genes ◽  
Immune Signature

BackgroundCervical cancer is one of the most common types of gynecological malignancies worldwide. This study aims to develop an immune signature to predict survival in cervical cancer.MethodThe gene expression data of 296 patients with cervical cancer from The Cancer Genome Atlas database (TCGA) and immune-related genes from the Immunology Database and Analysis Portal (ImmPort) database were included in this study. The immune signature was developed based on prognostic genes. The validation dataset was downloaded from the Gene Expression Omnibus (GEO) database.ResultThe immune signature namely immune-based prognostic score (IPRS) was developed with 229 genes. Multivariate analysis revealed that the IPRS was an independent prognostic factor for overall survival (OS) and progression-free survival (PFS) in patients with cervical cancer. Patients were stratified into high IPRS and low IPRS groups, and those in the high IPRS group were associated with better survival, which was validated in the validation set. A nomogram with IPRS and stage was constructed to predict mortality in cervical cancer.ConclusionsWe developed a robust prognostic signature IPRS that could be used to predict patients’ survival outcome.

scholarly journals Development and validation of a 13-m6a related lncRNA prognostic signature for lung adenocarcinoma

2021 ◽  
Author(s):  
Pingfan Wu ◽  
Xiaowen Zhao ◽  
Ling Xue ◽  
Xiaojing Yang ◽  
Yuxiang Shi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Treatment Strategies ◽  
Risk Groups ◽  
Gene Expression Omnibus ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
P53 Signaling ◽  
Selection Operator

Abstract Considerable evidence suggests that N6-methyladenosine (m6A) is involved in the regulation of long non-coding RNA (lncRNA), whichparticipates in the occurrence, development and prognosis of tumorscancerBut the relationship between m6A regulators-related lncRNA (mRlncRNA) and lung adenocarcinoma (LUAD) remains unclear. This study aims to determine a feature based on mRlncRNA for prognostic evaluation of LUAD patients. By integrating the gene expression data of LUAD and normal samples from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, the m6A gene and mRlncRNA with imbalanced expression were screened out. Then we used the least absolute shrinkage and selection operator (LASSO) to obtain the 13-lncRNA prognostic signature in the TCGA training cohort. Patients were divided into two risk groups based on the risk score of lncRNAs characteristics, and their overall survival (OS) was significantly different. The predictive power of this signature was verified in TCGA testing cohort and entire TCGA cohort. These landmark lncRNAs were involved in several biologiocal processes and pathways related to cell cycle, DNA replication, P53 signaling pathway and mismatch repair. Besides, the high-risk group was low-response to cisplatin, while high-response to mitomycin, docetaxel and immunotherapy. In conclusion, we identified a 13-mRlncRNA model associated with prognosis and treatment sensitivity in LUAD, which may provide clues about the influence of m6A on lncRNA in LUAD and promote the further improvement of LUAD individualized treatment strategies.

scholarly journals Identification of ARHGEF38, NETO2, GOLM1, and SAPCD2 Associated With Prostate Cancer Progression by Bioinformatic Analysis and Experimental Validation

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhuolun Sun ◽  
Yunhua Mao ◽  
Xu Zhang ◽  
Shuo Lu ◽  
Hua Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cancer Progression ◽  
Cox Regression ◽  
Prognostic Indicator ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Tcga Dataset

Prostate cancer (PCa) represents one of the most prevalent types of cancers and is a large health burden for men. The pathogenic mechanisms of PCa still need further investigation. The aim of this study was to construct an effective signature to predict the prognosis of PCa patients and identify the biofunctions of signature-related genes. First, we screened differentially expressed genes (DEGs) between PCa and normal control tissues in The Cancer Genome Atlas (TCGA) and GSE46602 datasets, and we performed weighted gene co-expression network analysis (WGCNA) to determine gene modules correlated with tumors. In total, 124 differentially co-expressed genes were retained. Additionally, five genes (ARHGEF38, NETO2, PRSS21, GOLM1, and SAPCD2) were identified to develop the prognostic signature based on TCGA dataset. The five-gene risk score was verified as an independent prognostic indicator through multivariate Cox regression analyses. The expression of the five genes involved in the signature was detected in the Gene Expression Omnibus (GEO), Gene Expression Profiling Interactive Analysis (GEPIA), and Oncomine databases. In addition, we utilized DiseaseMeth 2.0 and MEXPRESS for further analysis and found that abnormal methylation patterns may be a potential mechanism for these five DEGs in PCa. Finally, we observed that these genes, except PRSS21, were highly expressed in tumor samples and PCa cells. Functional experiments revealed that silencing ARHGEF38, NETO2, GOLM1, and SAPCD2 suppressed the proliferation, migration, and invasiveness of PCa cells. In summary, this prognostic signature had significant clinical significance for treatment planning and prognostic evaluation of patients with PCa. Thus, ARHGEF38, NETO2, GOLM1, and SAPCD2 may serve as oncogenes in PCa.

scholarly journals Methylation-Based Signatures for Gastroesophageal Tumor Classification

Cancers ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1208 ◽  
Author(s):  
Nikolay Alabi ◽  
Dropen Sheka ◽  
Ashar Siddiqui ◽  
Edwin Wang
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Esophageal Cancer ◽  
Treatment Options ◽  
Gastroesophageal Junction ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Validation Dataset ◽  
Transcription Start Sites ◽  
Misclassification Rates

Contention exists within the field of oncology with regards to gastroesophageal junction (GEJ) tumors, as in the past, they have been classified as gastric cancer, esophageal cancer, or a combination of both. Misclassifications of GEJ tumors ultimately influence treatment options, which may be rendered ineffective if treating for the wrong cancer attributes. It has been suggested that misclassification rates were as high as 45%, which is greater than reported for junctional cancer occurrences. Here, we aimed to use the methylation profiles of GEJ tumors to improve classifications of GEJ tumors. Four cohorts of DNA methylation profiles, containing ~27,000 (27k) methylation sites per sample, were collected from the Gene Expression Omnibus and The Cancer Genome Atlas. Tumor samples were assigned into discovery (nEC = 185, nGC = 395; EC, esophageal cancer; GC gastric cancer) and validation (nEC = 179, nGC = 369) sets. The optimized Multi-Survival Screening (MSS) algorithm was used to identify methylation biomarkers capable of distinguishing GEJ tumors. Three methylation signatures were identified: They were associated with protein binding, gene expression, and cellular component organization cellular processes, and achieved precision and recall rates of 94.7% and 99.2%, 97.6% and 96.8%, and 96.8% and 97.6%, respectively, in the validation dataset. Interestingly, the methylation sites of the signatures were very close (i.e., 170–270 base pairs) to their downstream transcription start sites (TSSs), suggesting that the methylations near TSSs play much more important roles in tumorigenesis. Here we presented the first set of methylation signatures with a higher predictive power for characterizing gastroesophageal tumors. Thus, they could improve the diagnosis and treatment of gastroesophageal tumors.

scholarly journals Identification of Immune Infiltration-related Gene Signature Associated with Prognosis and Immune Features in Stomach Adenocarcinoma

2021 ◽  
Author(s):  
Ya Yang ◽  
Xintan Zhang ◽  
Tingxuan Li ◽  
Yue Zhang ◽  
Xiaoxiao Zuo
Keyword(s):  
Gene Expression ◽  
Clinical Data ◽  
Expression Profiles ◽  
Prognostic Score ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Validation Dataset ◽  
Prognostic Signature ◽  
Survival Prognosis ◽  
Stomach Adenocarcinoma

Abstract Background: Immune infiltrated genes (IIGs) have been identified to associated with the prognosis of various cancers, but their expression and prognostic significance remain largely unclear in stomach adenocarcinoma (STAD).Methods: Gene expression profiles and clinical data of STAD patients were downloaded from The Cancer Genome Atlas (TCGA) as a training dataset (n = 375) and Gene Expression Omnibus (GEO) databases as a validation dataset (n = 300). Construction of high and low immune cell infiltration groups was performed by single sample gene set enrichment analysis (ssGSEA) and evaluated by ESTIMATE algorithm-derived immune scores. The overlapping differentially expressed genes (DEGs) in tumor vs. normal and Immunity-H vs. Immunity-L were selected as differentially expressed immune infiltrated genes (DEIIGs), which were used to construct DEIIG prognostic signature and its performance was validated using validation dataset. Moreover, the association between clinical data and immune features were explored. Furthermore, ADH4 and ANGPT2 were selected for analyzing their expression and prognostic values in STAD patients.Results: A total of 191 overlapping DEGs, including 6 lnRNAs and 185 mRNA were identified. Consecutively, 9 DEIIG prognostic signature (LINC00843, ADH4, ANGPT2, APOA1, ASLC2, GFRA1, KIAA1549L, MTTP and PROC) were identified as risk signature and Kaplan-Meier curve and ROC curve verified its performance in TCGA and GEO datasets. Total five clinical outcomes (age, pathologic T, radiotherapy, tumor recurrence and prognostic score model status) were identified to be associated with the survival prognosis of STAD patients. The TIMER algorithm revealed that B cell, T cell CD4+, neutrophil, macrophage and myeloid dendritic cell were positively correlated with STAD prognosis, while CD8+ was negatively correlated with STAD prognosis. Additionally, we validated that higher ADH4 and lower ANGPT2 predicted better survival prognosis in STAD patients.Conclusion: We constructed and verified a robust signature of nine DEIIG prognostic signature for the prediction of STAD patient survival.

scholarly journals A novel four-gene prognostic signature as a risk biomarker in cervical cancer

2020 ◽  
Author(s):  
Jun Wang ◽  
Hua Zheng ◽  
Yatian Han ◽  
Geng Wang ◽  
Yanbin Li
Keyword(s):  
Cervical Cancer ◽  
Cox Regression ◽  
Treatment Options ◽  
Treatment Strategies ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Tissue Samples ◽  
Cox Analysis

Abstract Background: Cervical cancer (CC) is a major malignancy affecting women worldwide, with limited treatment options for patients with advanced disease. The aim of this study was to identify novel prognostic biomarkers for CC by a bioinformatics-based analysis using the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA)-CC cohort. Methods: RNA-Seq data from four GEO datasets (GSE5787, GSE6791, GSE26511, and GSE63514) were used to identify differentially expressed genes (DEGs) between CC and normal cervical tissues. Functional and enrichment analyses of the DEGs were performed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and the Database for Annotation, Visualization and Integrated Discovery (DAVID). The Oncomine database, Cytoscape software, and Kaplan–Meier survival analysis were used for in-depth screening for hub DEGs. Cox regression was then used to develop prognostic signature, which was in turn used to create a nomogram. Results: A total of 207 DEGs were identified in the tissue samples, eight of which were prognostically significant in terms of overall survival (OS). Thereafter, a novel four-gene signature consisting of DSG2, MMP1, SPP1, and MCM2 was developed and validated using stepwise Cox analysis. The area under the receiver operating characteristic (ROC) curve (AUC) values of 0.785, 0.609, and 0.686 in the training, verification, and combination groups, respectively. Moreover, the nomogram analysis showed that a combination of this four-gene signature plus lymph node metastasis (LNM) status effectively predicted the 1- and 3-year OS probabilities of CC patients with accuracies of 69.01% and 83.93%, respectively. Conclusions: We developed a four-gene signature that can accurately predict the prognosis, in terms of OS, of CC patients, and could be a valuable tool for designing treatment strategies.

scholarly journals Novel Epigenetic Eight-Gene Signature Predictive of Poor Prognosis and MSI-Like Phenotype in Human Metastatic Colorectal Carcinomas

Cancers ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 158
Author(s):  
Valentina Condelli ◽  
Giovanni Calice ◽  
Alessandra Cassano ◽  
Michele Basso ◽  
Maria Grazia Rodriquenz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Poor Prognosis ◽  
Cpg Island ◽  
Colon Adenocarcinoma ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Cpg Island Methylator Phenotype ◽  
Prognostic Information ◽  
Global Methylation

Epigenetics is involved in tumor progression and drug resistance in human colorectal carcinoma (CRC). This study addressed the hypothesis that the DNA methylation profiling may predict the clinical behavior of metastatic CRCs (mCRCs). The global methylation profile of two human mCRC subgroups with significantly different outcome was analyzed and compared with gene expression and methylation data from The Cancer Genome Atlas COlon ADenocarcinoma (TCGA COAD) and the NCBI GENE expression Omnibus repository (GEO) GSE48684 mCRCs datasets to identify a prognostic signature of functionally methylated genes. A novel epigenetic signature of eight hypermethylated genes was characterized that was able to identify mCRCs with poor prognosis, which had a CpG-island methylator phenotype (CIMP)-high and microsatellite instability (MSI)-like phenotype. Interestingly, methylation events were enriched in genes located on the q-arm of chromosomes 13 and 20, two chromosomal regions with gain/loss alterations associated with adenoma-to-carcinoma progression. Finally, the expression of the eight-genes signature and MSI-enriching genes was confirmed in oxaliplatin- and irinotecan-resistant CRC cell lines. These data reveal that the hypermethylation of specific genes may provide prognostic information that is able to identify a subgroup of mCRCs with poor prognosis.

scholarly journals Screening of gene markers related to the prognosis of metastatic skin cutaneous melanoma based on Logit regression and survival analysis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Guoliang Jia ◽  
Zheyu Song ◽  
Zhonghang Xu ◽  
Youmao Tao ◽  
Yuanyu Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cutaneous Melanoma ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Validation Dataset ◽  
Survival Prognosis ◽  
Gene Markers ◽  
Clinical Prognosis ◽  
Logit Regression

Abstract Background Bioinformatics was used to analyze the skin cutaneous melanoma (SKCM) gene expression profile to provide a theoretical basis for further studying the mechanism underlying metastatic SKCM and the clinical prognosis. Methods We downloaded the gene expression profiles of 358 metastatic and 102 primary (nonmetastatic) CM samples from The Cancer Genome Atlas (TCGA) database as a training dataset and the GSE65904 dataset from the National Center for Biotechnology Information database as a validation dataset. Differentially expressed genes (DEGs) were screened using the limma package of R3.4.1, and prognosis-related feature DEGs were screened using Logit regression (LR) and survival analyses. We also used the STRING online database, Cytoscape software, and Database for Annotation, Visualization and Integrated Discovery software for protein–protein interaction network, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses based on the screened DEGs. Results Of the 876 DEGs selected, 11 (ZNF750, NLRP6, TGM3, KRTDAP, CAMSAP3, KRT6C, CALML5, SPRR2E, CD3G, RTP5, and FAM83C) were screened using LR analysis. The survival prognosis of nonmetastatic group was better compared to the metastatic group between the TCGA training and validation datasets. The 11 DEGs were involved in 9 KEGG signaling pathways, and of these 11 DEGs, CALML5 was a feature DEG involved in the melanogenesis pathway, 12 targets of which were collected. Conclusion The feature DEGs screened, such as CALML5, are related to the prognosis of metastatic CM according to LR. Our results provide new ideas for exploring the molecular mechanism underlying CM metastasis and finding new diagnostic prognostic markers.

scholarly journals Functions and clinical significance of KLRG1 in the development of lung adenocarcinoma and immunotherapy

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaodong Yang ◽  
Yuexin Zheng ◽  
Zhihai Han ◽  
Xiliang Zhang
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Killer Cell ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Normal Lung ◽  
Using Data ◽  
Low Expression

Abstract Background As a marker of differentiation, Killer cell lectin like receptor G1 (KLRG1) plays an inhibitory role in human NK cells and T cells. However, its clinical role remains inexplicit. This work intended to investigate the predictive ability of KLRG1 on the efficacy of immune-checkpoint inhibitor in the treatment of lung adenocarcinoma (LUAD), as well as contribute to the possible molecular mechanisms of KLRG1 on LUAD development. Methods Using data from the Gene Expression Omnibus, the Cancer Genome Atlas and the Genotype-Tissue Expression, we compared the expression of KLRG1 and its related genes Bruton tyrosine kinase (BTK), C-C motif chemokine receptor 2 (CCR2), Scm polycomb group protein like 4 (SCML4) in LUAD and normal lung tissues. We also established stable LUAD cell lines with KLRG1 gene knockdown and investigated the effect of KLRG1 knockdown on tumor cell proliferation. We further studied the prognostic value of the four factors in terms of overall survival (OS) in LUAD. Using data from the Gene Expression Omnibus, we further investigated the expression of KLRG1 in the patients with different responses after immunotherapy. Results The expression of KLRG1, BTK, CCR2 and SCML4 was significantly downregulated in LUAD tissues compared to normal controls. Knockdown of KLRG1 promoted the proliferation of A549 and H1299 tumor cells. And low expression of these four factors was associated with unfavorable overall survival in patients with LUAD. Furthermore, low expression of KLRG1 also correlated with poor responses to immunotherapy in LUAD patients. Conclusion Based on these findings, we inferred that KLRG1 had significant correlation with immunotherapy response. Meanwhile, KLRG1, BTK, CCR2 and SCML4 might serve as valuable prognostic biomarkers in LUAD.

scholarly journals BART Cancer: a web resource for transcriptional regulators in cancer genomes

NAR Cancer ◽  
2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Zachary V Thomas ◽  
Zhenjia Wang ◽  
Chongzhi Zang
Keyword(s):  
Gene Expression ◽  
Cancer Research ◽  
Computational Prediction ◽  
Transcriptional Regulators ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Regulation Of Transcription ◽  
Data Resource ◽  
Web Resource ◽  
Cancer Types

Abstract Dysregulation of gene expression plays an important role in cancer development. Identifying transcriptional regulators, including transcription factors and chromatin regulators, that drive the oncogenic gene expression program is a critical task in cancer research. Genomic profiles of active transcriptional regulators from primary cancer samples are limited in the public domain. Here we present BART Cancer (bartcancer.org), an interactive web resource database to display the putative transcriptional regulators that are responsible for differentially regulated genes in 15 different cancer types in The Cancer Genome Atlas (TCGA). BART Cancer integrates over 10000 gene expression profiling RNA-seq datasets from TCGA with over 7000 ChIP-seq datasets from the Cistrome Data Browser database and the Gene Expression Omnibus (GEO). BART Cancer uses Binding Analysis for Regulation of Transcription (BART) for predicting the transcriptional regulators from the differentially expressed genes in cancer samples compared to normal samples. BART Cancer also displays the activities of over 900 transcriptional regulators across cancer types, by integrating computational prediction results from BART and the Cistrome Cancer database. Focusing on transcriptional regulator activities in human cancers, BART Cancer can provide unique insights into epigenetics and transcriptional regulation in cancer, and is a useful data resource for genomics and cancer research communities.

Screening of cervical cancer-related hub genes based on comprehensive bioinformatics analysis

2021 ◽  
pp. 1-13
Author(s):  
Simei Tu ◽  
Hao Zhang ◽  
Xiaocheng Yang ◽  
Wen Wen ◽  
Kangjing Song ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Molecular Mechanisms ◽  
Disease Free Survival ◽  
The Cancer Genome Atlas ◽  
Mrna Levels ◽  
Hub Genes ◽  
Normal Tissues ◽  
Significant Difference ◽  
Core Genes

BACKGROUND: Since the molecular mechanisms of cervical cancer (CC) have not been completely discovered, it is of great significance to identify the hub genes and pathways of this disease to reveal the molecular mechanisms of cervical cancer. OBJECTIVE: The study aimed to identify the biological functions and prognostic value of hub genes in cervical cancer. METHODS: The gene expression data of CC patients were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. The core genes were screened out by differential gene expression analysis and weighted gene co-expression network analysis (WGCNA). R software, the STRING online tool and Cytoscape software were used to screen out the hub genes. The GEPIA public database was used to further verify the expression levels of the hub genes in normal tissues and tumour tissues and determine the disease-free survival (DFS) rates of the hub genes. The protein expression of the survival-related hub genes was identified with the Human Protein Atlas (HPA) database. RESULTS: A total of 64 core genes were screened, and 10 genes, including RFC5, POLE3, RAD51, RMI1, PALB2, HDAC1, MCM4, ESR1, FOS and E2F1, were identified as hub genes. Compared with that in normal tissues, RFC5, POLE3, RAD51,RMI1, PALB2, MCM4 and E2F1 were all significantly upregulated in cervical cancer, ESR1 was significantly downregulated in cervical cancer, and high RFC5 expression in CC patients was significantly related to OS. In the DFS analysis, no significant difference was observed in the expression level of RFC5 in cervical cancer patients. Finally, RFC5 protein levels verified by the HPA database were consistently upregulated with mRNA levels in CC samples. CONCLUSIONS: RFC5 may play important roles in the occurrence and prognosis of CC. It could be further explored and validated as a potential predictor and therapeutic target for CC.

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