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Prognostic gene expression signature revealed the involvement of mutational pathways in cancer genome.

ABSTRACT: Background: Over the years, many efforts have been made to use the gene expression profiles of cancer types/subtypes to identify the prognostic genes with their potential clinical applications. However, one major challenge remains is to predict the common prognostic genes using simultaneously the dataset of multiple cancers, especially by considering the differences in survival, expression and the associated mutated pathways. Methods: Herein, we carried out a comprehensive examination for the prognostic genes and linked them to the mutational status of 29 cancers, so as to find independent prognostic genes and mechanisms. Additionally, their diagnostic value of them was also assessed. Results: our extensive analysis revealed: 1) the number of prognostic and diagnostic genes differs greatly across the cancers, 2) the potentially implicated 22 genes harbor the diagnostic as well as prognostic capacity, 3) the universal prognostic genes (CDC20, CDCA8, ASPM, ERCC6L, and GTSE1) were found to be involved in the spindle assembly checkpoint, 4) the prognostic genes were found to be statistically linked to the frequently mutated TP53-, MAPK-, PI3K- and AKT- related pathways. We also manually mined possible biological mechanisms for some of the statistical links in literatures. Conclusions: Taken together, we identified the prognostic genes and in addition we assessed their diagnostic capacity. Our analysis provides an important insight about the considerable overlapping between gene expression variation and the further associated altered mutational pathways across the cancer genome. We thus hypothesized that cancer related (mutated) genes are tightly connected and are capable to reshape the genome in multiple cancer types.

SUBMITTER: Liu H

PROVIDER: S-EPMC7255374 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Prognostic gene expression signature revealed the involvement of mutational pathways in cancer genome.

Liu Hongde H Li Huamei H Luo Kun K Sharma Amit A Sun Xiao X

Journal of Cancer 20200518 15

<b>Background</b>: Over the years, many efforts have been made to use the gene expression profiles of cancer types/subtypes to identify the prognostic genes with their potential clinical applications. However, one major challenge remains is to predict the common prognostic genes using simultaneously the dataset of multiple cancers, especially by considering the differences in survival, expression and the associated mutated pathways. <b>Methods</b>: Herein, we carried out a comprehensive examinat ...[more]

PMID: 32489468

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Project description:Cervical cancer (CC) remains high morbidity and mortality. We aimed to identify critical pathways underlying cervical carcinogenesis and establish a prognostic signature. Six datasets from the gene expression omnibus (GEO) database were used to screen the differentially expressed genes (DEGs) between CC and normal tissues. We used the unions of the DEGs to perform functional analysis. The 108 overlapped DEGs were analyzed to determine a prognostic signature by Cox regression and Lasso analysis based on The Cancer Genome Atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) and Immune Cell Abundance Identifier (ImmuCellAI) were used to determine the relationships between the signature and biological functions. The PI3K-Akt signaling pathway, the Ras signaling pathway, and the viral carcinogenesis pathway may be critical for CC development. We identified seven genes (PLOD2, DSG2, SPP1, CXCL8, MCM5, HLTF, and KLF4) to construct a risk score formula. Survival analysis showed that the high-risk group indicated a worse prognosis than the low-risk group (p < 0.0001). The AUC of the prognostic signature was 0.7449, 0.7641, and 0.8146 at 1, 3, and 5 years. We also identified that the signature is an independent prognostic factor. GSEA showed five pathways were relevant to the signature, such as the adherens junction pathway. The signature also affected the abundances of various types of immune cells, such as B cell, CD4+ T cell, and CD8+ T cell. Further, we found that SPP1 was co-expressed with HK3, CD163, CCL3, CLEC5A, MMP8, TREM1, OLR1, and TREM2. The results of Gene Ontology analysis showed that SPP1 and its co-expressed related proteins mainly affected metabolic process, multicellular organismal process, cell communication, cell proliferation, protein binding, and transporter activity. In conclusion, the present study explored the key pathways for CC development and the seven-gene signature can effectively make the prognosis evaluation of CC patients.

Project description:Background Currently, no gene-expression signature (GES) established from node-positive cohorts, able to predict breast cancer evolution after systemic adjuvant chemotherapy, exists. Methods Gene-expression profiles of 252 node-positive patients (median follow-up: 7.7 years), mostly included in a randomized clinical trial (PACS01), receiving systemic adjuvant regimen, were determined by means of cDNA custom array representing 5,776 distinct genes. Findings In the training cohort, we established a 38-GES for the purpose of predicting time to distant metastasis. The 38-GES yielded unadjusted hazard ratio of 4.86 (95% CI=2.76-8.56). Even when adjusted with the two best clinicopathological prognostic scoring: NPI and Adjuvant!, 38-GES HR were 3.30 (1.81-5.99) and 3.40 (1.85-6.24), respectively. Furthermore, 38-GES improved mostly NPI and Adjuvant! intermediate-risk classified patients. NPI intermediate-risk patients (7-year MFS=87.2%) were divided into 2/3 (7-year MFS=95.5%) close to NPI low-risk group and 1/3 (7-year MFS=69.3%) close to NPI high-risk group (HR=6.97 [2.51-19.36]). Adjuvant! intermediate-risk patients (7-year MFS=88.0%) were divided into 2/3 (7-year MFS=94.8%) close to Adjuvant! low-risk group and 1/3 (7-year MFS=71.7%) close to Adjuvant! high-risk group (HR=5.31 [5.38-11.87]). The 38-GES was validated on gene-expression datasets from three external node-positive breast cancer subcohorts (n=224) generated from different microarray platforms. The 38-GES yielded unadjusted HR=2.95 (1.74-5.01). Furthermore, 38-GES showed performance in supplementary cohorts with different lymph-node status and endpoint (1,031 new patients). Interpretation The 38-GES represents a robust tool able to type systemic adjuvant treated node-positive patients at high risk of metastatic relapse, and especially powerful to separate NPI or Adjuvant! intermediate-risk node-positive patients. Keywords: disease-state analysis 252 breast cancer patients at diagnosis examined with spotted cDNA nylon membrane. Patient details: PACS01x are 2 parts of a clinical trial : PACS01A : patients had received 6 cycles of FEC100 PACS01B : patients had received 3 cycles of FEC100 then 3 cycles of Docetaxel CCRG are patients from our local cancer center (Cancer Center René Gauducheau) included with the same criteria as PACS01A (6 cycles of FEC100).

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Prognostic gene expression signature revealed the involvement of mutational pathways in cancer genome.

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Prognostic gene expression signature revealed the involvement of mutational pathways in cancer genome.

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