SFMBT1 facilitates colon cancer cell metastasis and drug resistance combined with HMG20A

Pan, Ruijun; Yu, Du; Hu, Jiajia; Yang, Xia; Wang, Chenxing; Xue, Peng; Sun, Jing Ping; Zhang, Xiaoping; Cai, Wei

doi:10.1038/s41420-022-01057-7

Cited by 4 publications

(5 citation statements)

References 39 publications

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“…While ACTB and SFMBT1 are included in this gene interaction network, it is noteworthy that neither node exhibits a robust relationship with the rest of the nodes, particularly SFMBT1, which has not been reported in relation to lung cancer ( Pan et al, 2022 ). Although ACTB has been previously implicated in tumor development in lung cancer, its connection with other genes in this context requires further examination and understanding.…”

Section: Resultsmentioning

confidence: 94%

“…Moreover, within the NSCLC category, three primary histopathological subtypes are recognized. These subtypes include adenocarcinoma (LUAD), comprising 45%-50% of NSCLC cases, squamous cell carcinoma (LUSC), with a prevalence of 30%-35%, and large cell (undifferentiated) carcinoma, accounting for 5%-10% of cases (Wang et al, 2022). LC often manifests with subtle early symptoms, and it is frequently diagnosed at an advanced stage, rendering treatment more complex.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, as sequencing technologies have advanced, liquid biopsy has emerged as a non-invasive and efficient means of early cancer detection and targeted therapy (Crowley et al, 2013;Howlader et al, 2020). Furthermore, diverse techniques operating at various biological levels have been used, such as the assessment of single nucleotide variations, DNA methylation, and quantifying miRNA expression (Hao et al, 2017;Ahmed et al, 2022;Wang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…In the field of bioinformatics research, numerous multiomics methods have been introduced, to name a few, J. Wang et al (Wang et al, 2022) developed a precise multiomics risk model for predicting Tumor Mutational Burden (TMB) in patients with LUAD. This model integrated gene/miRNA expression and DNA methylation data sourced from The Cancer Genome Atlas (TCGA).…”

Section: Introductionmentioning

confidence: 99%

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Functional impact of multi-omic interactions in lung cancer

Díaz-Campos,

Vasquez-Arriaga,

Ochoa

et al. 2024

Front. Genet.

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Lung tumors are a leading cause of cancer-related death worldwide. Lung cancers are highly heterogeneous on their phenotypes, both at the cellular and molecular levels. Efforts to better understand the biological origins and outcomes of lung cancer in terms of this enormous variability often require of high-throughput experimental techniques paired with advanced data analytics. Anticipated advancements in multi-omic methodologies hold potential to reveal a broader molecular perspective of these tumors. This study introduces a theoretical and computational framework for generating network models depicting regulatory constraints on biological functions in a semi-automated way. The approach successfully identifies enriched functions in analyzed omics data, focusing on Adenocarcinoma (LUAD) and Squamous cell carcinoma (LUSC, a type of NSCLC) in the lung. Valuable information about novel regulatory characteristics, supported by robust biological reasoning, is illustrated, for instance by considering the role of genes, miRNAs and CpG sites associated with NSCLC, both novel and previously reported. Utilizing multi-omic regulatory networks, we constructed robust models elucidating omics data interconnectedness, enabling systematic generation of mechanistic hypotheses. These findings offer insights into complex regulatory mechanisms underlying these cancer types, paving the way for further exploring their molecular complexity.

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Section: Resultsmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Functional impact of multi-omic interactions in lung cancer

Díaz-Campos,

Vasquez-Arriaga,

Ochoa

et al. 2024

Front. Genet.

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“…HCT116 (2 × 10 3 cells/well) and SW620 (5 × 10 3 cells/well) cells were seeded in 96-well microplates and incubated for 24 h [ 20 ]. And then cells were treated with various concentrations of metformin (1, 5, 10, 20, and 25 mM) for 48 h. Cell viability was measured using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide (MTT) assay.…”

Section: Methodsmentioning

confidence: 99%

Synergistic apoptosis by combination of metformin and an O-GlcNAcylation inhibitor in colon cancer cells

Lee

et al. 2023

Cancer Cell Int

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Background Although autophagy is an important mediator of metformin antitumor activity, the role of metformin in the crosstalk between autophagy and apoptosis remains unclear. The aim was to confirm the anticancer effect by inducing apoptosis by co-treatment with metformin and OSMI-1, an inhibitor of O-GlcNAcylation, in colon cancer cells. Methods Cell viability was measured by MTT in colon cancer cell lines HCT116 and SW620 cells. Co-treatment with metformin and OSMI-1 induced autophagy and apoptosis, which was analyzed using western blot, reverse transcription-polymerase chain reaction (RT-PCR) analysis, and fluorescence-activated cell sorting (FACS). Combined treatment with metformin and OSMI-1 synergistically inhibit the growth of HCT116 was confirmed by xenograft tumors. Results We showed that metformin inhibited mammalian target of rapamycin (mTOR) activity by inducing high levels of C/EBP homologous protein (CHOP) expression through endoplasmic reticulum (ER) stress and activating adenosine monophosphate-activated protein kinase (AMPK) to induce autophagy in HCT116 cells. Interestingly, metformin increased O-GlcNAcylation and glutamine:fructose-6-phosphate amidotransferase (GFAT) levels in HCT116 cells. Thus, metformin also blocks autophagy by enhancing O-GlcNAcylation, whereas OSMI-1 increases autophagy via ER stress. In contrast, combined metformin and OSMI-1 treatment resulted in continuous induction of autophagy and disruption of O-GlcNAcylation homeostasis, resulting in excessive autophagic flux, which synergistically induced apoptosis. Downregulation of Bcl2 promoted apoptosis via the activation of c-Jun N-terminal kinase (JNK) and CHOP overexpression, synergistically inducing apoptosis. The activation of IRE1α/JNK signaling by OSMI-1 and PERK/CHOP signaling by metformin combined to inhibit Bcl2 activity, ultimately leading to the upregulation of cytochrome c release and activation of caspase-3. Conclusions In conclusion, combinatorial treatment of HCT116 cells with metformin and OSMI-1 resulted in more synergistic apoptosis being induced by enhancement of signal activation through ER stress-induced signaling rather than the cell protective autophagy function. These results in HCT116 cells were also confirmed in xenograft models, suggesting that this combination strategy could be utilized for colon cancer treatment.

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