Identifying Cancer Patient Subgroups by Finding Co-Modules From the Driver Mutation Profiles and Downstream Gene Expression Profiles

Song, Junrong; Peng, Wei; Wang, Feng

doi:10.1109/tcbb.2021.3106344

Cited by 9 publications

(3 citation statements)

References 64 publications

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“…Most somatic mutations of the EGFR gene associated with lung adenocarcinoma cause abnormal expression of the KRAS gene, which can continuously activate the EGFR signaling pathway and eventually enhance cell proliferation, leading to the generation of cancer. The ATM gene provides instructions for Entropy 2023, 25, 841 10 of 16 making proteins, mainly in the nucleus, where it helps control the rate at which cells grow and divide [37][38][39]. This protein also plays an important role in the normal development and activities of several body systems, including the nervous system and the immune system.…”

Section: K (Number Of Genes In Pathway)mentioning

confidence: 99%

Identifying Cancer Driver Pathways Based on the Mouth Brooding Fish Algorithm

Zhang,

Xiang,

Zhao

et al. 2023

Entropy

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Identifying the driver genes of cancer progression is of great significance in improving our understanding of the causes of cancer and promoting the development of personalized treatment. In this paper, we identify the driver genes at the pathway level via an existing intelligent optimization algorithm, named the Mouth Brooding Fish (MBF) algorithm. Many methods based on the maximum weight submatrix model to identify driver pathways attach equal importance to coverage and exclusivity and assign them equal weight, but those methods ignore the impact of mutational heterogeneity. Here, we use principal component analysis (PCA) to incorporate covariate data to reduce the complexity of the algorithm and construct a maximum weight submatrix model considering different weights of coverage and exclusivity. Using this strategy, the unfavorable effect of mutational heterogeneity is overcome to some extent. Data involving lung adenocarcinoma and glioblastoma multiforme were tested with this method and the results compared with the MDPFinder, Dendrix, and Mutex methods. When the driver pathway size was 10, the recognition accuracy of the MBF method reached 80% in both datasets, and the weight values of the submatrix were 1.7 and 1.89, respectively, which are better than those of the compared methods. At the same time, in the signal pathway enrichment analysis, the important role of the driver genes identified by our MBF method in the cancer signaling pathway is revealed, and the validity of these driver genes is demonstrated from the perspective of their biological effects.

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Section: K (Number Of Genes In Pathway)mentioning

confidence: 99%

Identifying Cancer Driver Pathways Based on the Mouth Brooding Fish Algorithm

Zhang,

Xiang,

Zhao

et al. 2023

Entropy

View full text Add to dashboard Cite

show abstract

“…These techniques identify SMG with genetic variation that are considerably greater than that of the background mutation rate and classify those as driving genes. The majority of cancer driver gene research has been on identifying single somatic point mutations [12]. Somatic mutations, on the other hand, are typically very diverse within cancer genomes, within the same kind of cancer, and account for only a tiny part of genomic changes [3] [19] [20] [21].…”

Section: Introductionmentioning

confidence: 99%

“…Other genomic elements, including as nonsense mutations, penetrations, and omissions, as well as germ line variations, have been previously overlooked but now have recently been discovered to play an important effect on cancer formation [9] [21] [22]. When genomic components, such as somatic mutations, germ line variations, insertions and deletions, are investigated jointly, particularly inside an interaction term, modeling intricacy as well as investigation strength are obstacles [12]. To evaluate the influence of genetic mutations on modifications in gene expression patterns, Tang et al [10] used a semi-local centrality measure and a gene mutation effect function in 2021.…”

Section: Introductionmentioning

confidence: 99%