Integrated biological networks associated with platinum-based chemotherapy response in ovarian cancer
Topouza, D. G.; Choi, J.; Nesdoly, S.; Tarnouskaya, A.; Nicol, C. J. B.; Duan, Q. L.
Show abstract
BackgroundHigh-grade serous ovarian cancer (HGSOC) is a highly lethal gynecologic cancer, in part due to resistance to platinum-based chemotherapy reported among 20% of patients. This study aims to elucidate the biological mechanisms underlying chemotherapy resistance, which remain poorly understood. MethodsSequencing data (mRNA and microRNA) from HGSOC patients were analyzed to identify differentially expressed genes and co-expressed transcript networks associated with chemotherapy response. Initial analyses used datasets from The Cancer Genome Atlas and then replicated in two independent cancer cohorts. Moreover, transcript expression datasets and genomics data (i.e. single nucleotide polymorphisms) were integrated to determine potential regulation of the associated mRNA networks by microRNAs and expression quantitative trait loci (eQTLs). ResultsIn total, 196 differentially expressed mRNAs were enriched for adaptive immunity and translation, and 21 differentially expressed microRNAs were associated with angiogenesis. Moreover, co-expression network analysis identified two mRNA networks associated with chemotherapy response, which were enriched for ubiquitination and lipid metabolism, as well as three associated microRNA networks enriched for lipoprotein transport and oncogenic pathways. In addition, integrative analyses revealed potential regulation of the mRNA networks by the associated microRNAs and eQTLs. ConclusionWe report novel transcriptional networks and pathways associated with resistance to platinum-based chemotherapy among HGSOC patients. These results aid our understanding of the effector networks and regulators of chemotherapy response, which will improve drug efficacy and identify novel therapeutic targets for ovarian cancer.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A novel role for the tumor suppressor gene ITF2 in lung tumorigenesis and chemotherapy response 95%
- The MEK1/2 pathway as a therapeutic target in high-grade serous ovarian carcinoma 95%
- Genomic profile in TGCT Mexican patients reveals a potential biomarker of sensitivity to platinum-based therapy 95%
Similar papers in this journal
- Gene networks and expression quantitative trait loci associated with platinum-based chemotherapy response in high-grade serous ovarian cancer 96%
- Identification of novel exosomal miRNAs and their role in diagnosis and prognosis of Triple Negative Breast Cancer 94%
- NSMCE2, a Novel Super-Enhancer Regulated Gene, is Linked to Poor Prognosis and Therapy Resistance in Breast Cancer 94%
Similar papers in this journal
- Circulating serum miRNAs predict response to platinum chemotherapy in high-grade serous ovarian cancer 95%
- Added-value of whole exome and RNA Sequencing in advanced and refractory cancer patients with no molecular-based treatment recommendation based on a 90-gene panel 92%
- Macrophage Infiltration and ITGB2 Expression in ESCC: A Novel Correlation 92%
Similar papers in this journal
- SPARC in cancer-associated fibroblasts is an independent poor prognostic factor in non-metastatic triple-negative breast cancer and exhibits pro-tumor activity 93%
- Prelude to Malignancy: A Gene Expression Signature in Normal Mammary Gland from Breast Cancer Patients Suggests Pre-tumorous Alterations and Is Associated with Adverse Outcomes 92%
- Reprogramming SREBP1-dependent lipogenesis and inflammation in high-risk breast with licochalcone A: a novel path to cancer prevention 92%
Similar papers in this journal
- Loss of Y in regulatory T lymphocytes in the tumor micro-environment of primary colorectal cancers and liver metastases 94%
- Large-scale pan-cancer analysis reveals broad prognostic association between TGF-β ligands, not Hedgehog, and GLI1/2 expression in tumors 94%
- Periostin facilitates ovarian cancer recurrence by enhancing cancer stemness 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.