Implications of prognosis-associated genes in pancreatic tumor metastasis: lessons from global studies in bioinformatics

Kisling, Sophia G.; Gopalakrishnan, Natarajan; Pothuraju, Ramesh; Shah, Ashu; Batra, Surinder K.; Kaur, Sukhwinder

doi:10.1007/s10555-021-09991-1

Cited by 12 publications

(12 citation statements)

References 137 publications

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“…SERPINB5, a member of the serpin superfamily, is a serine protease inhibitor that suppresses tumor progression and metastasis (33)(34)(35). Higher SERPINB5 expressions have been reported to be associated with better prognosis in non-small cell lung cancer, esophageal squamous cell carcinoma, ovarian cancer, and bladder cancer (25)(26)(27)36).…”

Section: Discussionmentioning

confidence: 99%

Identification and in vitro validation of diagnostic and prognostic biomarkers for lung squamous cell carcinoma

Zhao¹,

Yuan²,

He³

et al. 2022

J Thorac Dis

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Background:The aim of the present study was to find diagnostic and prognostic biomarkers for lung squamous cell carcinoma (LUSC) and to validate key biomarkers in vitro.Methods: RNA sequencing was used to identify differentially expressed mRNAs (DEmRNAs) and differentially expressed long non-coding RNAs (DElncRNAs) in LUSC tissues. RNA sequencing results were validated using a published dataset. Diagnostic and prognostic values of candidate genes were evaluated by receiver-operating characteristic (ROC) curve analysis and survival analysis, respectively. To determine the effect of MIR205HG in LUSC, MIR205HG expression was knocked down in NCI-H520 cells. 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and Transwell assay were used to respectively detect the effect of MIR205HG on cell proliferation and migration. Results: In total, 1,946 DEmRNAs and 428 DElncRNAs were identified in LUSC compared with normal tissues. A total of 851 DElncRNA-DEmRNA co-expression pairs were obtained. With the exception of NEAT1, MCM2, SERPINB5, ITGB8, CASC19, and MIR205HG were upregulated in LUSC. ROC curve analysis indicated that MCM2, SERPINB5, ITGB8, CASC19, and MIR205HG could predict LUSC. Survival analysis suggested that SERPINB5, NEAT1, and MIR205HG had potential prognostic value for LUSC.MIR205HG knockdown inhibited cell proliferation and migration, and significantly reduced the expression of ITGB8. Conclusions:The findings of the present study could help determine the pathogenesis of LUSC and provide new and accurate therapeutic targets for its treatment.

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Section: Discussionmentioning

confidence: 99%

Identification and in vitro validation of diagnostic and prognostic biomarkers for lung squamous cell carcinoma

Zhao¹,

Yuan²,

He³

et al. 2022

J Thorac Dis

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“…TOP2A is another cell cycle-related gene that encodes an enzyme called DNA topoisomerase 2-alpha to alter the DNA strand topology states in the replication process. TOP2A is overexpressed in many types of cancers ( 27 ). TOP2A overexpression in HCC was reported to correlate with onset at an early age, shorter OS, and resistance to chemotherapy ( 28 ).…”

Section: Discussionmentioning

confidence: 99%

Development and validation of an eight-gene signature based predictive model to evaluate the prognosis of hepatocellular carcinoma patients: a bioinformatic study

Zhang¹,

Fu²,

Zhang³

et al. 2022

Ann Transl Med

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Background Hepatocellular carcinoma (HCC) is a malignant tumor with a poor prognosis, however, biomarkers for the prognostic assessment of HCC remain suboptimal. Consequently, we aimed to develop a reliable tool for prognostic estimation of HCC. Methods Differentially expressed genes (DEGs) between HCC and adjacent normal tissues in 3 Gene Expression Omnibus (GEO) datasets were identified, followed by hub gene selection and least absolute shrinkage and selection operator (LASSO) Cox regression to develop a prognostic gene signature. Kaplan-Meier survival analysis, univariate and multivariate Cox regression, time-dependent area under the curve (AUC), and integrated value of time-dependent AUC (iAUC) were used to assess the relationship between predictors and clinical outcomes in the training and validation datasets. Then we built nomograms including gene signature and clinicopathological factors to forecast the probability of death. Moreover, we performed quantitative real-time PCR (qPCR) to compare the expression of prognostic genes between HCC and adjacent normal tissues. Finally, the relationship between prognostic genes and tumor microenvironment (TME) was investigated using immune cell infiltration algorithms and single cell transcriptomic database. Results Eight prognostic genes ( CDC20 , PTTG1 , TOP2A , CXCL2 , CXCL14 , CYP2C9 , MT1F , and GHR ) were finally identified to construct the gene signature. Each patient’s risk score was calculated according to the gene signature. Patients with high-risk scores showed worse outcomes in the training set [hazard ratio (HR) =3.404, P<0.001]. Risk score, age, body mass index (BMI), and TNM stage were identified as independent prognostic factors for overall survival (OS) in the training set. The nomogram including risk score and other independent prognostic factors showed better performance as opposed to the clinicopathological model. In the validation dataset, we obtained the similar results as well. Moreover, we found a close relationship between risk score and immune cell infiltration. Patients with high-risk scores had elevated expression of immune checkpoint genes, indicating that these patients may be more suitable for immunotherapy. Conclusions We have established and validated an eight-gene based prognostic model, which could be an effective tool for the prognostic evaluation of HCC patients.

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“…ANLN is considered to be related to the incidence of CRC, pancreatic cancer 32 , breast cancer 33 and lung adenocarcinoma 34 . Up-regulation of ANLN will affect the invasiveness and size 35 of CRC, and it is related to the prognosis of patients.…”

Section: Discussionmentioning

confidence: 99%

ANLN, COL8A1, MMP3, MMP14 and WNT5A, as potential diagnostic and therapeutic targets for early-stage colorectal cancer: evidence from integrated bioinformatics analysis

Zhang

Xue

2022

Preprint

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Colorectal cancer (CRC) is one of the most common cancers worldwide. Intense efforts have been made to elucidate the pathogeny, but the molecular mechanisms of early-stage CRC are still not well understood. This study aims to identify the candidate genes in the carcinogenesis of early-stage CRC Microarray datasets GSE44076, GSE41328 and GSE9348 were obtained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and functional enrichment analyses were performed. The protein-protein interaction network (PPI) was constructed and the module analysis was performed by using STRING and Cytoscape. A total of 363 DEGs were identified, consisting of 48 downregulated genes and 315 upregulated genes. The enriched GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways of the DEGs mainly include collage catabolic process, extracellular matrix organization, skeleton system development and collagen fibril organization. 28 hub genes were identified, and biological process analysis revealed that these genes were mainly enriched in cell division, cell cycle, and nuclear division. Survival analysis showed that ANLN, COL8A1, MMP3, MMP14 and WNT5A may be related to the poor overall survival rate of patients. DEGs and hub genes identified in this study contribute to our understanding of differential genes and biological processes in the development and progression of early-stage CRC, providing possible targets for the early diagnosis and treatment of CRC.

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Implications of prognosis-associated genes in pancreatic tumor metastasis: lessons from global studies in bioinformatics

Cited by 12 publications

References 137 publications

Identification and in vitro validation of diagnostic and prognostic biomarkers for lung squamous cell carcinoma

Identification and in vitro validation of diagnostic and prognostic biomarkers for lung squamous cell carcinoma

Development and validation of an eight-gene signature based predictive model to evaluate the prognosis of hepatocellular carcinoma patients: a bioinformatic study

ANLN, COL8A1, MMP3, MMP14 and WNT5A, as potential diagnostic and therapeutic targets for early-stage colorectal cancer: evidence from integrated bioinformatics analysis

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