Dataset Information

Integrated Profiles Analysis Identified a Coding-Non-Coding Signature for Predicting Lymph Node Metastasis and Prognosis in Cervical Cancer.

ABSTRACT: Accumulating evidence has shown that lymph node metastasis (LNM) is not only an important prognostic factor but also an indicator of the need for postoperative chemoradiotherapy. Therefore, identifying risk factors or molecular markers related to LNM is critical for predicting the prognosis and guiding individualized treatment of patients with cervical cancer. In this study, we used the machine learning-based feature selection approach to identify eight optimal biomarkers from the list of 250 differentially expressed protein-coding genes and long non-coding RNAs (lncRNAs) in the TCGA cohort. Then a coding-non-coding signature (named CNC8SIG) was developed using the elastic-net logistic regression approach based on the expression levels of eight optimal biomarkers, which is useful in discriminating patients with LNM from those without LNM in the discovery cohort. The predictive performance of the CNC8SIG was further validated in two independent patient cohorts. Moreover, the CNC8SIG was significantly associated with patient's survival in different patient cohorts. In silico functional analysis suggested that the CNC8SIG-associated mRNAs are enriched in known cancer-related biological pathways such as the Wnt signaling pathway, the Ras signaling pathway, Rap1 signaling pathway, and PI3K-Akt signaling pathway.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC7859285 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Integrated Profiles Analysis Identified a Coding-Non-Coding Signature for Predicting Lymph Node Metastasis and Prognosis in Cervical Cancer.

Zhang Yu Y Sun Di D Song Jiayu J Yang Nan N Zhang Yunyan Y

Frontiers in cell and developmental biology 20210121

Accumulating evidence has shown that lymph node metastasis (LNM) is not only an important prognostic factor but also an indicator of the need for postoperative chemoradiotherapy. Therefore, identifying risk factors or molecular markers related to LNM is critical for predicting the prognosis and guiding individualized treatment of patients with cervical cancer. In this study, we used the machine learning-based feature selection approach to identify eight optimal biomarkers from the list of 250 di ...[more]

PMID: 33553172

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Project description:BackgroundmiRNAs and genes can serve as biomarkers for the prognosis and therapy of cervical tumors whose metastasis into lymph nodes is closely associated with disease progression and poor prognosis.MethodsR software and Bioconductor packages were employed to identify differentially expressed miRNAs (DEMs) from The Cancer Genome Atlas (TCGA) database. GEO2R detected differentially expressed genes (DEGs) in the GSE7410 dataset originating from the Gene Expression Omnibus (GEO). A Cox proportional hazard regression model was established to select prognostic miRNA biomarkers. Online tools such as TargetScan and miRDB predicted target genes, and overlapping DEGs and target genes were defined as consensus genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology (GO) function annotations were performed to discern the potential functions of consensus genes. STRING and Cytoscape screened key genes and constructed a regulatory network.ResultsA combination of four miRNAs (down-regulated miR-502 and miR-145, up-regulated miR-142 and miR-33b) was identified as an independent prognostic signature of cervical cancer. A total of 94 consensus genes were significantly enriched in 7 KEGG pathways and 19 GO function annotations including the cAMP signaling pathway, the plasma membrane, integral components of the plasma membrane, cell adhesion, etc. The module analysis suggested that CXCL12, IGF1, PTPRC CDH5, RAD51B, REV3L, and WDHD1 are key genes that significantly correlate with cervical cancer lymph node metastasis.ConclusionsThis study demonstrates that a four-miRNA signature can be a prognostic biomarker, and seven key genes are significantly associated with lymph node metastasis in cervical cancer patients. These miRNAs and key genes have the potential to be therapeutic targets for cervical cancer. Among them, two miRNAs (miR-502 and miR-33b) and two key genes (PTPRC and CDH5) were first reported to be potential novel biomarkers for cervical cancer. The current study further characterizes the progression of lymph node metastasis and mechanism of cervical tumors; therefore, it provides a novel diagnostic indicator and therapeutic targets for future clinical treatments.

Project description:Background. Papillary thyroid carcinoma (PTC) is the commonest thyroid malignancy originating from the follicle cells in the thyroid. Despite a good overall prognosis, certain high-risk cases as in those with lymph node metastasis (LNM) have progressive disease and poorer prognosis. MicroRNAs are a class of non-protein-coding, 19-24 nucleotides single-stranded RNAs which regulate gene expression and these molecules have been shown to play a role in LNM. The integrated analysis of miRNAs and gene expression profiles together with transcription factors (TFs) has been shown to improve the identification of functional miRNA-target gene-TF relationships, providing a more complete view of molecular events underlying metastasis process. Objectives. We reanalyzed The Cancer Genome Atlas (TCGA) datasets on PTC to identify differentially expressed miRNAs/genes in PTC patients with LNM-positive (LNM-P) versus lymph node negative (LNN) PTC patients and to investigate the miRNA-gene-TF regulatory circuit that regulate LNM in PTC. Results. PTC patients with LNM (PTC LNM-P) have a significantly shorter disease-free survival rate compared to PTC patients without LNM (PTC LNN) (Log-rank Mantel Cox test, p = 0.0049). We identified 181 significantly differentially expressed miRNAs in PTC LNM-P versus PTC LNN; 110 were upregulated and 71 were downregulated. The five topmost deregulated miRNAs were hsa-miR-146b, hsa-miR-375, hsa-miR-31, hsa-miR-7-2 and hsa-miR-204. In addition, 395 miRNAs were differentially expressed between PTC LNM-P and normal thyroid while 400 miRNAs were differentially expressed between PTC LNN and normal thyroid. We found four significant enrichment pathways potentially involved in metastasis to the lymph nodes, namely oxidative phosphorylation (OxPhos), cell adhesion molecules (CAMs), leukocyte transendothelial migration and cytokine-cytokine receptor interaction. OxPhos was the most significantly perturbed pathway (p = 4.70E-06) involving downregulation of 90 OxPhos-related genes. Significant interaction of hsa-miR-301b with HLF, HIF and REL/NFkB transcription factors were identified exclusively in PTC LNM-P versus PTC LNN. Conclusion. We found evidence of five miRNAs differentially expressed in PTC LNM-P. Alteration in OxPhos pathway could be the central event in metastasis to the lymph node in PTC. We postulate that hsa-miR-301b might be involved in regulating LNM in PTC via interactions with HLF, HIF and REL/NFkB. To the best of our knowledge, the roles of these TFs have been studied in PTC but the precise role of this miRNA with these TFs in LNM in PTC has not been investigated.

Project description:Background Pelvic lymph node metastasis (LNM) is a crucial independent prognostic factor in cervical cancer (CCa) and serves as an indicator for radiation therapy as the primary or an adjuvant treatment option. However, preoperative diagnosis of LNM remains challenging. Thus, we aimed to identify biomarkers of LNM in patients with presumed early-stage CCa. Methods The differentially expressed genes (DEGs) between tumours with different lymph node statuses were identified by using The Cancer Genome Atlas database. Then, univariate Cox regression analysis and Kaplan–Meier analyses were utilized to screen overall survival (OS)-associated genes. Multivariate Cox analysis and logistical analysis were utilized to evaluate independent risk factors for OS and LNM, respectively. Subsequently, the protein level of fatty acid binding protein 4 (FABP4) was detected in normal cervical and CCa tissues by immunohistochemistry assays. EdU assays were performed to determine whether FABP4 altered the proliferation of cervical cancer cells. Wound healing and Transwell assays were conducted to explore the effects of FABP4 depletion on migratory and invasive abilities of cervical cancer cells. F-actin fluorescence staining were performed to investigate morphological change and Western blotting analyses were performed to determine epithelial mesenchymal transition-related marker expression and downstream signalling pathways. Results A total of 243 DEGs, including 55 upregulated and 188 downregulated DEGs, were found in CCa patients with LNM versus those without LNM. Among these, FABP4 was found to be closely associated with poor OS. Multivariate analysis uncovered that FABP4 was an independent risk factor for OS and LNM in patients with CCa. The immunohistochemical results verified dramatically increased FABP4 expression in CCa tissues compared to normal cervical epithelia and its association with poor OS and LNM. In vitro, The proliferation, migration and invasion of cervical cancer cells were significantly inhibited after knocking down of FABP4, which was accompanied by elevated expression of E-cadherin and downregulated expression of N-cadherin, Vimentin and p-AKT. Conclusions FABP4 might be a promising biomarker of LNM and survival in patients with early-stage CCa and therefore could significantly contribute to the development of personalized prognosis prediction and therapy optimization. Supplementary Information The online version contains supplementary material available at 10.1186/s12935-021-02273-4.

Dataset Information

Integrated Profiles Analysis Identified a Coding-Non-Coding Signature for Predicting Lymph Node Metastasis and Prognosis in Cervical Cancer.

Publications

Integrated Profiles Analysis Identified a Coding-Non-Coding Signature for Predicting Lymph Node Metastasis and Prognosis in Cervical Cancer.

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