Identification of Novel lncRNAs in Ovarian Cancer and Their Impact on Overall Survival

Cardillo, Nicholas; Russo, D.; Newtson, A.M.; Pineda, Henry Reyes; Lyons, Yasmin; Devor, Eric J.; Bender, David; Goodheart, Michael J.; Bosquet, Jesús González

doi:10.3390/ijms22031079

Cited by 9 publications

(9 citation statements)

References 29 publications

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“…Gene CNV was estimated using SAMtools and superFreq [ 34 ]. BAM files were then used to identify lncRNA, as described previously [ 35 , 36 ]. Lastly, fusion transcripts were determined using the STAR-Fusion suite from fastq files [ 37 ].…”

Section: Methodsmentioning

confidence: 99%

Integrated Clinical and Genomic Models to Predict Optimal Cytoreduction in High-Grade Serous Ovarian Cancer

Cardillo

Devor

Nobre

et al. 2022

Cancers

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Advanced high-grade serous (HGSC) ovarian cancer is treated with either primary surgery followed by chemotherapy or neoadjuvant chemotherapy followed by interval surgery. The decision to proceed with surgery primarily or after chemotherapy is based on a surgeon’s clinical assessment and prediction of an optimal outcome. Optimal and complete cytoreductive surgery are correlated with improved overall survival. This clinical assessment results in an optimal surgery approximately 70% of the time. We hypothesize that this prediction can be improved by using biological tumor data to predict optimal cytoreduction. With access to a large biobank of ovarian cancer tumors, we obtained genomic data on 83 patients encompassing gene expression, exon expression, long non-coding RNA, micro RNA, single nucleotide variants, copy number variation, DNA methylation, and fusion transcripts. We then used statistical learning methods (lasso regression) to integrate these data with pre-operative clinical information to create predictive models to discriminate which patient would have an optimal or complete cytoreductive outcome. These models were then validated within The Cancer Genome Atlas (TCGA) HGSC database and using machine learning methods (TensorFlow). Of the 124 models created and validated for optimal cytoreduction, 21 performed at least equal to, if not better than, our historical clinical rate of optimal debulking in advanced-stage HGSC as a control. Of the 89 models created to predict complete cytoreduction, 37 have the potential to outperform clinical decision-making. Prospective validation of these models could result in improving our ability to objectively predict which patients will undergo optimal cytoreduction and, therefore, improve our ovarian cancer outcomes.

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Section: Methodsmentioning

confidence: 99%

Integrated Clinical and Genomic Models to Predict Optimal Cytoreduction in High-Grade Serous Ovarian Cancer

Cardillo

Devor

Nobre

et al. 2022

Cancers

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“…What is most noteworthy is that many studies have shown that there is a close relationship between the differential expression of miRNA and tumor, which provides a new idea for us to treat tumor. lncRNA is a non-coding RNA with a length of more than 200 nucleotides 16 . Previous studies have shown that it is involved in many important regulatory processes, such as X chromosome silencing, genomic imprinting, chromatin modi cation, transcriptional activation, transcriptional interference, and intranuclear transport 17 .…”

Section: Discussionmentioning

confidence: 99%

A mRNA-miRNA-lncRNA regulatory network related to potential pathogenesis and prognostic markers of Non- small cell lung cancer

Wang

Yuan

Yang

et al. 2021

Preprint

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Non-small cell lung cancer (NSCLC) is one of the most malignant tumors with the fastest increasing incidence and mortality rate, but the etiology of NSCLC is still not clear. Most of lncRNAs have some structural similarities with mRNAs, suggesting that miRNAs negatively regulate the expression of lncRNAs to affect the occurrence and development of tumor. Therefore, system bioinformatics was used to explore the potential biomarkers and possible pathogenesis of NSCLC in this study. Firstly, all the clinical information and transcriptome data were downloaded from GEO and TCGA databases. R language was used to analyze the differentially expressed genes (DEGs) in NSCLC and normal lung tissues. Then, 50 overlapped DEGs were obtained via Venn database, including 10 down-regulated mRNAs and 40 down-regulated mRNAs. Secondly, the top 20 DEGs were selected for KEGG pathway and GO enrichment analysis. After screening 4 HUB genes related to the survival and prognosis of NSCLC patients, their prognosis models were established. Meanwhile, HUB genes related miRNAs and lncRNAs were screened. Finally, a mRNA-miRNA-lncRNA network related to the survival and prognosis of NSCLC patients was established, including 4 up-regulated mRNAs, 3 up-regulated miRNAs, 10 down-regulated miRNAs, 6 up-regulated lncRNAs and 19 down-regulated lncRNAs. Subject terms: Non-small cell lung cancer, mRNA-miRNA-lncRNA, pathogenesis, prognostic biomarkers.

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“…TCGA-OV cohort composed of 373 samples with expression profiles was used to identify prognostic DEGs. In addition, a cisplatin response model was developed based on TCGA-OV dataset that included 209 complete response and 27 progressed disease samples after chemotherapy, which was validated in the GSE156699 dataset consisting of 38 cisplatin nonresponse patients and 50 response patients [ 29 ]. Possible associations of the model with other cisplatin resistance pathways as well as immune checkpoint genes were also investigated in TCGA-OV dataset.…”

Section: Methodsmentioning

confidence: 99%

Identification of an Immune Gene-Based Cisplatin Response Model and CD27 as a Therapeutic Target against Cisplatin Resistance for Ovarian Cancer

Guo¹,

Yang²,

Li³

et al. 2022

Journal of Immunology Research

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Objective. Evidence demonstrates that the immune microenvironment is extensively associated with chemotherapy response of ovarian cancer (OV). Herein, this study is aimed at establishing a cisplatin response prediction model for OV on the basis of immune genes. Methods. The expression profiles of cisplatin-sensitive and cisplatin-resistant OV specimens were integrated from multiple public datasets. The abundance scores of 22 immune cells were estimated with CIBERSORT algorithm. Differentially expressed immune genes (DEGs) were determined between cisplatin-sensitive and cisplatin-resistant groups. Thereafter, a cisplatin response model was constructed based on prognostic DEGs with logistic regression analysis. The prediction performance was validated in independent cohorts. The possible relationships between the model and immunotherapy were then assessed. Results. Treg scores were significantly decreased in cisplatin-resistant than cisplatin-sensitive OV specimens, with the opposite results for naïve B cells and activated dendritic cells. Fourteen prognostic DEGs were identified and used to develop a cisplatin-response model. The response scores, estimated by the model, showed favorable performance in discriminating cisplatin-response and nonresponse samples. The response scores also presented significantly negative correlations with three well-known cisplatin-resistant pathways and a positive correlation with the expression of CD274 (PD-L1). Moreover, the decreased CD27 expression was observed in cisplatin-resistant groups, and OV specimens with higher CD27 expressions were more sensitive to cisplatin treatment. Conclusion. Altogether, our findings proposed a cisplatin response prediction model and identified CD27 that might be involved in cisplatin resistance. Further investigations suggested that CD27 could be a promising immunotherapeutic target for cisplatin-resistant subset of OV.

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Identification of Novel lncRNAs in Ovarian Cancer and Their Impact on Overall Survival

Cited by 9 publications

References 29 publications

Integrated Clinical and Genomic Models to Predict Optimal Cytoreduction in High-Grade Serous Ovarian Cancer

Integrated Clinical and Genomic Models to Predict Optimal Cytoreduction in High-Grade Serous Ovarian Cancer

A mRNA-miRNA-lncRNA regulatory network related to potential pathogenesis and prognostic markers of Non- small cell lung cancer

Identification of an Immune Gene-Based Cisplatin Response Model and CD27 as a Therapeutic Target against Cisplatin Resistance for Ovarian Cancer

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