Detection and analysis of stable and flexible genes towards a genome signature framework in cancer

Šehović, Emir; Hadrovic, Adem; Dogan, Senol

doi:10.6026/97320630015772

Cited by 2 publications

(2 citation statements)

References 26 publications

(21 reference statements)

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“…SLC34A2 overexpression was reported related to development and progression of OC, brain cancer, and pancreatic cancer [35]. TRAFD1 suppression was observed in ovarian, colon, brain, and renal cancers [36]. CHD1L overexpression was reported to augment ovarian carcinoma metastasis [15].…”

Section: Resultsmentioning

confidence: 99%

Transcriptomic Gene Network Profiling and Weak Signal Detection for Prediction of Ovarian Cancer Occurrence, Survival, and Severity by Integrating Bulk and Single-cell RNAseq Data

Li,

Sardiu,

Koestler

et al. 2023

Preprint

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BackgroundOvarian cancer (OC) is a significant gynecological malignancy characterized by its high mortality rate, poor long-term survival rate, and late-stage diagnosis. OC is the 5th leading cause of cancer death among woman and counts 2.1% of all cancer death. OC survival rates are much lower than other cancers that affect woman. Its 5-year survival rate is less than 50%. Only ∼17% of OC patients are diagnosed within the early stage. The majority are diagnosed at an advanced stage, making early detection and effective treatment critical challenges. Currently, the identified OC predictive genes are still very sparse, resulting in pool prognostic performance. There exists unmet needs to identify novel prognostic gene biomarkers for OC occurrence, survival, and clinical stages to promote the likelihood of survival and to perform optimal treatments or therapeutic strategies at the earliest stage possible.MethodsPrevious RNAseq analysis on OC focused on detecting differentially expressed (DE) genes only. Many genes, although having weak marginal differential effects, may still exude strong predictive effects on disease outcomes though regulating other DE genes. In this work, we employed a new machine learning method, netLDA, to detect such predictive coregulating genes with weak marginal DE effects for predicting OC occurrence, 5-year survival, and clinical stage. The netLDA detects predictive gene networks (PGN) containing strong DE genes as hub genes and detects coregulating weak genes within the PGNs. The network structures of the detected PGNs along with the strong and weak genes therein are then used in outcome prediction on test datasets.ResultsWe identified different sets of signature genes for OC occurrence, survival, and clinical stage. Previously identified prognostic genes, such asEPCAM, UBE2C, CHD1L, TP53,CD24,WFDC2, andFANCI,were confirmed. We also identified novel predictive coregulating weak genes includingGIGYF2, GNPAT, RAD54L, andELL.Many of the detected predictive gene networks and coregulating weak genes therein overlapped with OC-related biological pathways such as KEGGtight junction, ribosome, andcell cyclepathways. The detection and incorporation of the gene networks and weak genes significantly improved the prediction performance. Cellular mapping of selected feature genes using single-cell RNAseq data further revealed the heterogeneous expression distributions of the signature genes on different cell types.ConclusionsWe established a transcriptomic gene network profile for OC prediction. The novel genes detected provide new targets for early diagnostics and new drug development for OC.

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Section: Resultsmentioning

confidence: 99%

Transcriptomic Gene Network Profiling and Weak Signal Detection for Prediction of Ovarian Cancer Occurrence, Survival, and Severity by Integrating Bulk and Single-cell RNAseq Data

Li,

Sardiu,

Koestler

et al. 2023

Preprint

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“…KCNT1 is a protein coding gene involved in the intracellular potassium activated channel activity. It has, to our knowledge never been associated to lung cancer risk, although recent work on gene expression identified low expression of KCNT1 consistently in four cancers, including lung [ 31 ]. Additional smoking-unrelated CpG sites selected in the CSI-adjusted model included PRDM16-cg00106196, CBX4-cg00192636, SSTR5-cg00610310, ALOX12-AS1-cg20216752, NTPCR-cg00069771, MAP3K9-cg01022840, TTYH3-cg00145404, GPR61-cg00521434, and MIR1251-cg03415518, which were all hypomethylated (β < -0.5), whereas ZNF664-FAM101A-cg0020446, RTKN-cg00610692, NFATC1-cg00348031, and RORA-cg02433545 were hypermethylated (β > 0.5).…”

Section: Discussionmentioning

confidence: 99%

Epigenetic mechanisms of lung carcinogenesis involve differentially methylated CpG sites beyond those associated with smoking

et al. 2022

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Smoking-related epigenetic changes have been linked to lung cancer, but the contribution of epigenetic alterations unrelated to smoking remains unclear. We sought for a sparse set of CpG sites predicting lung cancer and explored the role of smoking in these associations. We analysed CpGs in relation to lung cancer in participants from two nested case–control studies, using (LASSO)-penalised regression. We accounted for the effects of smoking using known smoking-related CpGs, and through conditional-independence network. We identified 29 CpGs (8 smoking-related, 21 smoking-unrelated) associated with lung cancer. Models additionally adjusted for Comprehensive Smoking Index-(CSI) selected 1 smoking-related and 49 smoking-unrelated CpGs. Selected CpGs yielded excellent discriminatory performances, outperforming information provided by CSI only. Of the 8 selected smoking-related CpGs, two captured lung cancer-relevant effects of smoking that were missed by CSI. Further, the 50 CpGs identified in the CSI-adjusted model complementarily explained lung cancer risk. These markers may provide further insight into lung cancer carcinogenesis and help improving early identification of high-risk patients.

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Detection and analysis of stable and flexible genes towards a genome signature framework in cancer

Cited by 2 publications

References 26 publications

Transcriptomic Gene Network Profiling and Weak Signal Detection for Prediction of Ovarian Cancer Occurrence, Survival, and Severity by Integrating Bulk and Single-cell RNAseq Data

Transcriptomic Gene Network Profiling and Weak Signal Detection for Prediction of Ovarian Cancer Occurrence, Survival, and Severity by Integrating Bulk and Single-cell RNAseq Data

Epigenetic mechanisms of lung carcinogenesis involve differentially methylated CpG sites beyond those associated with smoking

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