&lt;p&gt;Integrated Analysis To Identify Molecular Biomarkers Of High-Grade Serous Ovarian Cancer&lt;/p&gt;

Si, Manfei; Zhang, Junji; Cao, Jian; Xie, Zhibo; Shu, Shan; Zhu, Yapei; Lang, Jinghe

doi:10.2147/ott.s228678

Cited by 6 publications

(6 citation statements)

References 120 publications

(105 reference statements)

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“…Serous ovarian cancer is a common malignant tumor in women, with a high incidence of gynecological malignancies and a low incidence of 5 percent of all malignant tumors [21]. Early detection and new treatment methods are important factors to reduce the occurrence and development of ovarian cancer, and the focus of clinical research [22]. At present, clinical treatment of serous ovarian cancer is mainly composed of surgical therapy and chemical therapy [23].…”

Section: Discussionmentioning

confidence: 99%

Long non-coding RNA HAL suppresses the migration and invasion of serous ovarian cancer by inhibiting EMT signaling pathway

et al. 2020

Bioscience Reports

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Objective: To investigate the specific function of long non-coding RNA HAL in serous ovarian cancer (SOC) and to further clarify the regulation of HAL on EMT pathway. Materials and methods: The expression of HAL and TWIST1 was detected by qRT-PCR. CCK8 assay, wound healing assay, transwell assay and flow cytometry were used to detect the HAL function on proliferation, migration, invasion and apoptosis in SOC cells. Western blot was used to calculate protein level of Vimentin, N-cadherin and E-cadherin. The effect of HAL on tumorigenesis of SOC was confirmed by xenograft nude mice model. Results: HAL was significantly decreased in SOC tissues and cells. Overexpression of HAL inhibited the proliferation, migration and invasion of SKOV3 cells, but promoted apoptosis. Furthermore, overexpression of HAL decreased the mRNA and protein levels of TWIST1 via a binding between HAL and TWIST1. Forced expression of TWIST1 reversed the inhibitory role of HAL on SOC cells’ migration and invasion. The in vivo tumor growth assay showed that HAL suppressed SOC tumorigenesis with inhibiting EMT pathway. Conclusions: Our research emphasized HAL acting as a tumor-inhibiting gene by regulating EMT signaling pathway, thus providing some novel experimental basis for clinical treatment of SOC.

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Section: Discussionmentioning

confidence: 99%

Long non-coding RNA HAL suppresses the migration and invasion of serous ovarian cancer by inhibiting EMT signaling pathway

et al. 2020

Bioscience Reports

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“…For instance, HELLS is involved in chromatin remodeling and has been shown to promote the progression of various cancers (Hou et al, 2021). ZWINT is involved in the spindle checkpoint during cell division and is overexpressed in various cancers (Si et al, 2019). ABCC5 is a member of the ATP-binding cassette transporter family, and its overexpression has been linked to multidrug resistance in cancer (Li et al, 2018a;Zhang et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of a novel anoikis-related prognostic model for prostate cancer

Zhang

Luan

et al. 2023

Preprint

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In this study, we collected gene expression profile, single nucleotide polymorphism mutation data, and copy number variation (CNV) info of 495 prostate cancer (PCa) patients from the TCGA database and downloaded 140 PCa samples from the MSKCC dataset as an external cohort. We extracted 434 anoikis-related genes from GeneCards and previous publications. We used unsupervised consensus cluster analysis to identify two molecular subtypes (C1 and C2). C1 exhibited a higher level of CNV amplification, immune score, stromal score, aneuploidy score, homologous recombination deficiency, intratumor heterogeneity, single-nucleotide variant neoantigens, and tumor mutational burden compared to C2. C2 showed a better survival outcome and was found to have a high level of gamma delta T cell and activated B cell infiltration. We then constructed a novel risk signature consisting of four genes (HELLS, ZWINT, ABCC5, and TPSB2) for predicting the overall survival of PCa patients using the univariate Cox regression analysis and the least absolute shrinkage and selection operator algorithm, which we validated using the MSKCC dataset. The receiver operating characteristic curve of the signature indicated that the area under the curve was 0.780, suggesting good predictive accuracy. We found that the risk signature was an independent prognostic factor for overall survival in PCa patients. Additionally, we identify four CTRP-derived compounds (cucurbitacin I, SB−743921, paclitaxel, and GSK461364) and four PRISM-derived compounds (volasertib, LY2606368, mitoxantrone, and dolastatin−10) for the treatment of high-risk group patients. Our findings may provide a new perspective for the treatment of anoikis-related PCa.

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“…The predictive value of multi‐gene signatures has been highlighted in many other cancer types 36‐39 . Nevertheless, recent researches on prognosis prediction are limited to the most common types of OV, namely high‐grade serous OV 17 . Therefore, there is an urgent need for a more widely used model.…”

Section: Discussionmentioning

confidence: 99%

“…Prognostic predictors need to be further developed, especially for patients with these poor outcome indicators. Previous studies have identified several potential genes for predicting the prognosis of OV but their comprehensiveness and clinical application remain limited 17‐19 . In this study, we discovered that differential expression genes (DEGs) between tumour and normal tissues were most significantly enriched in G2/M checkpoint signalling pathway based on the several data sets in the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data sets.…”

Section: Introductionmentioning

confidence: 97%

Novel gene signatures for prognosis prediction in ovarian cancer

Bao

Zhang

2020

J Cellular Molecular Medi

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Ovarian cancer (OV) is one of the leading causes of cancer deaths in women worldwide. Late diagnosis and heterogeneous treatment result to poor survival outcomes for patients with OV. Therefore, we aimed to develop novel biomarkers for prognosis prediction from the potential molecular mechanism of tumorigenesis. Eight eligible data sets related to OV in GEO database were integrated to identify differential expression genes (DEGs) between tumour tissues and normal. Enrichment analyses discovered DEGs were most significantly enriched in G2/M checkpoint signalling pathway. Subsequently, we constructed a multi‐gene signature based on the LASSO Cox regression model in the TCGA database and time‐dependent ROC curves showed good predictive accuracy for 1‐, 3‐ and 5‐year overall survival. Utility in various types of OV was validated through subgroup survival analysis. Risk scores formulated by the multi‐gene signature stratified patients into high‐risk and low‐risk, and the former inclined worse overall survival than the latter. By incorporating this signature with age and pathological tumour stage, a visual predictive nomogram was established, which was useful for clinicians to predict survival outcome of patients. Furthermore, SNRPD1 and EFNA5 were selected from the multi‐gene signature as simplified prognostic indicators. Higher EFNA5 expression or lower SNRPD1 indicated poorer outcome. The correlation between signature gene expression and clinical characteristics was observed through WGCNA. Drug‐gene interaction was used to identify 16 potentially targeted drugs for OV treatment. In conclusion, we established novel gene signatures as independent prognostic factors to stratify the risk of OV patients and facilitate the implementation of personalized therapies.

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<p>Integrated Analysis To Identify Molecular Biomarkers Of High-Grade Serous Ovarian Cancer</p>

Cited by 6 publications

References 120 publications

Long non-coding RNA HAL suppresses the migration and invasion of serous ovarian cancer by inhibiting EMT signaling pathway

Long non-coding RNA HAL suppresses the migration and invasion of serous ovarian cancer by inhibiting EMT signaling pathway

Identification and validation of a novel anoikis-related prognostic model for prostate cancer

Novel gene signatures for prognosis prediction in ovarian cancer

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