Improving gene function predictions using independent transcriptional components

Urzúa-Traslaviña, Carlos G.; Leeuwenburgh, Vincent Christiaan; Bhattacharya, Arkajyoti; Loipfinger, Stefan; Vugt, Marcel A.T.M. van; Vries, Elisabeth G.E. de; Fehrmann, Rudolf S.N.

doi:10.1038/s41467-021-21671-w

Cited by 26 publications

(23 citation statements)

References 35 publications

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“…4 ). Co-functionality analysis of commonly upregulated genes using the GenetICA algorithm 30 (see Supplementary Methods for details) pointed at roles for these genes in ncRNA processing, DNA repair, and ribosome biogenesis ( Fig. 3D ).…”

Section: Resultsmentioning

confidence: 99%

An mRNA expression-based signature for oncogene-induced replication-stress

et al. 2022

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Oncogene-induced replication stress characterizes many aggressive cancers. Several treatments are being developed that target replication stress, however, identification of tumors with high levels of replication stress remains challenging. We describe a gene expression signature of oncogene-induced replication stress. A panel of triple-negative breast cancer (TNBC) and non-transformed cell lines were engineered to overexpress CDC25A , CCNE1 or MYC, which resulted in slower replication kinetics. RNA sequencing analysis revealed a set of 52 commonly upregulated genes. In parallel, mRNA expression analysis of patient-derived tumor samples (TCGA, n=10,592) also revealed differential gene expression in tumors with amplification of oncogenes that trigger replication stress ( CDC25A , CCNE1 , MYC , CCND1 , MYB , MOS , KRAS , ERBB2 , and E2F1 ). Upon integration, we identified a six-gene signature of oncogene-induced replication stress ( NAT10 , DDX27 , ZNF48 , C8ORF33 , MOCS3, and MPP6) . Immunohistochemical analysis of NAT10 in breast cancer samples (n=330) showed strong correlation with expression of phospho-RPA (R=0.451, p=1.82x10 -20 ) and γH2AX (R=0.304, p=2.95x10 -9 ). Finally, we applied our oncogene-induced replication stress signature to patient samples from TCGA (n=8,862) and GEO (n=13,912) to define the levels of replication stress across 27 tumor subtypes, identifying diffuse large B cell lymphoma, ovarian cancer, TNBC and colorectal carcinoma as cancer subtypes with high levels of oncogene-induced replication stress.

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Section: Resultsmentioning

confidence: 99%

An mRNA expression-based signature for oncogene-induced replication-stress

et al. 2022

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“…To investigate LPAR6 effects on the inhibition of breast cancer progression, we investigated its significantly correlated or co-expressed genes via in silico analysis. An alternative approach is the concept of “guilt-by-association” (GBA) which assumes that if two proteins interact or share expression patterns, their functions are more likely to be related [ 36 , 37 ]. To reduce the influence of confounding factors, we sorted samples according to the expression level of LPAR6 and selected the first 200 and the last 200 samples to constitute two groups: “high”-level group and “low”-level group both in TCGA and METABRIC datasets, respectively.…”

Section: Resultsmentioning

confidence: 99%

Lysophosphatidic acid receptor 6 regulated by miR-27a-3p attenuates tumor proliferation in breast cancer

Lei

Guo

et al. 2021

Clin Transl Oncol

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Purpose Lysophosphatidic acid (LPA) is a bioactive molecule which participates in many physical and pathological processes. Although LPA receptor 6 (LPAR6), the last identified LPA receptor, has been reported to have diverse effects in multiple cancers, including breast cancer, its effects and functioning mechanisms are not fully known. Methods Multiple public databases were used to investigate the mRNA expression of LPAR6, its prognostic value, and potential mechanisms in breast cancer. Western blotting was performed to validate the differential expression of LPAR6 in breast cancer tissues and their adjacent tissues. Furthermore, in vitro experiments were used to explore the effects of LPAR6 on breast cancer. Additionally, TargetScan and miRWalk were used to identify potential upstream regulating miRNAs and validated the relationship between miR-27a-3p and LPAR6 via real-time polymerase chain reaction and an in vitro rescue assay. Results LPAR6 was significantly downregulated in breast cancer at transcriptional and translational levels. Decreased LPAR6 expression in breast cancer is significantly correlated with poor overall survival, disease-free survival, and distal metastasis-free survival, particularly for hormone receptor-positive patients, regardless of lymph node metastatic status. In vitro gain and loss-of-function assays indicated that LPAR6 attenuated breast cancer cell proliferation. The analyses of TCGA and METABRIC datasets revealed that LPAR6 may regulate the cell cycle signal pathway. Furthermore, the expression of LPAR6 could be positively regulated by miR-27a-3p. The knockdown of miR-27a-3p increased cell proliferation, and ectopic expression of LPAR6 could partly rescue this phenotype. Conclusion LPAR6 acts as a tumor suppressor in breast cancer and is positively regulated by miR-27a-3p.

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“…We compare TripletGO with two most recently developed gene function prediction approaches, i.e., GENETICA [7] and GeneNetwork [34], which are both based on expression profiles. Different from our work, these two approaches are designed at the term-centric level.…”

Section: Resultsmentioning

confidence: 99%

“…For example, according to official statistics in the neXtProt platform [5], nearly 2,000 protein-coding human genes have yet no known function; for many other organisms of biomedical or industrial importance, annotation rates are substantially lower. To fill the gap between sequence and function, it is urgent to develop efficient computational algorithms for function prediction [6, 7].…”

Section: Introductionmentioning

confidence: 99%

TripletGO: Integrating Transcript Expression Profiles with Protein Homology Inferences for High-Accuracy Gene Function Annotations

Zhu

Zhang

Liu

et al. 2021

Preprint

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Gene Ontology (GO) has been widely used to annotate functions of genes and gene products. We proposed a new method (TripletGO) to deduce GO terms of protein-coding and non-coding genes, through the integration of four complementary pipelines built on transcript expression profiling, genetic sequence alignment, protein sequence alignment and naive probability, respectively. TripletGO was tested on a large set of 5,754 genes from 8 species (human, mouse, arabidopsis, rat, fly, budding yeast, fission yeast, and nematoda) and 2,433 proteins with available expression data from the CAFA3 experiment and achieved function annotation accuracy significantly beyond the current state-of-the-art approaches. Detailed analyses show that the major advantage of TripletGO lies in the coupling of a new triplet-network based profiling method with the feature space mapping technique which can accurately recognize function patterns from transcript expressions. Meanwhile, the combination of multiple complementary models, especially those from transcript expression and protein-level alignments, improves the coverage and accuracy of the final GO annotation results. The standalone package and an online server of TripletGO are freely available at https://zhanglab.ccmb.med.umich.edu/TripletGO/.

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Improving gene function predictions using independent transcriptional components

Cited by 26 publications

References 35 publications

An mRNA expression-based signature for oncogene-induced replication-stress

An mRNA expression-based signature for oncogene-induced replication-stress

Lysophosphatidic acid receptor 6 regulated by miR-27a-3p attenuates tumor proliferation in breast cancer

TripletGO: Integrating Transcript Expression Profiles with Protein Homology Inferences for High-Accuracy Gene Function Annotations

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