MET as resistance factor for afatinib therapy and motility driver in gastric cancer cells

Ebert, Karolin; Mattes, Julian; Kunzke, Thomas; Zwingenberger, Gwen; Luber, Birgit

doi:10.1371/journal.pone.0223225

Cited by 13 publications

(9 citation statements)

References 43 publications

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“…The biological roles of CXCL12 also were founded in THCA, in which CXCR4/CXCR7/CXCL12 axis offer new valuable insight into the oncogenesis of metastatic follicular thyroid carcinoma ( Werner et al, 2018 ). The amplification of MET proto-oncogene, receptor tyrosine kinase (MET) is confirmed as a resistance factor in gastric cancer cells ( Ebert et al, 2019 ). And, MET may serve a role in regulating PD-L1 expression in hepatocellular carcinoma ( Chun & Hong, 2019 ) Meanwhile, MET is also involved in high-risk metastasis progression in thyroid cancer ( Garcia et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Identifying hub genes of papillary thyroid carcinoma in the TCGA and GEO database using bioinformatics analysis

Wang

Zhang²,

Leng³

et al. 2020

PeerJ

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Background Thyroid carcinoma (THCA) is a common endocrine malignant tumor. Papillary carcinoma with low degree of malignancy and good prognosis is the most common. It can occur at any age, but it is more common in young adults. Although the mortality rate is decreased due to early diagnosis, the survival rate varies depending on the type of tumor. Therefore, the purpose of this study is to identify hub biomarkers and novel therapeutic targets for THCA. Methods The GSE3467, GSE3678, GSE33630 and GSE53157 were obtained from the GEO database, including 100 thyroid tumors and 64 normal tissues to obtain the intersection of differentially expressed genes, and a protein-protein interaction network was constructed to obtain the HUB gene. The corresponding overall survival information from The Cancer Genome Atlas Project-THCA was then included in this research. The signature mechanism was studied by analyzing the gene ontology and the Kyoto Encyclopedia of Genes and Genome database. Results In this research, we identified eight candidate genes (FN1, CCND1, CDH2, CXCL12, MET, IRS1, DCN and FMOD) from the network. Also, expression verification and survival analysis of these candidate genes based on the TCGA database indicate the robustness of the above results. Finally, our hospital samples validated the expression levels of these genes. Conclusion The research identified eight mRNA (four up–regulated and four down–regulated) which serve as signatures and could be a potential prognostic marker of THCA.

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Section: Discussionmentioning

confidence: 99%

Identifying hub genes of papillary thyroid carcinoma in the TCGA and GEO database using bioinformatics analysis

Wang

Zhang²,

Leng³

et al. 2020

PeerJ

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“…Only cells not undergoing cell division are considered. For a trajectory (x, y, t), 1 ≤ t ≤ N, its approximate average speed is calculated as described earlier [ 17 ]: the approximate average speed is defined as the approximate trajectory length divided by the total elapsed time of the video sequence.…”

Section: Methodsmentioning

confidence: 99%

“…Several mutations in HER2 were reported, which might be associated with trastuzumab resistance [ 8 – 10 ]. Various RTKs, such as AXL [ 11 ], EGFR [ 12 ], HER3 [ 13 , 14 ] and MET [ 15 – 17 ] can compensate the inhibition of signaling pathways by anti-HER therapies. Moreover, the downstream signaling pathway can be maintained by genetic alterations, such as mutations of the PI3K catalytic subunit (PIK3CA) or low PTEN expression [ 18 – 20 ].…”

Section: Introductionmentioning

confidence: 99%

Determining the effects of trastuzumab, cetuximab and afatinib by phosphoprotein, gene expression and phenotypic analysis in gastric cancer cell lines

et al. 2020

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Background Gastric cancer is the fifth most frequently diagnosed cancer and the third leading cause of cancer death worldwide. The molecular mechanisms of action for anti-HER-family drugs in gastric cancer cells are incompletely understood. We compared the molecular effects of trastuzumab and the other HER-family targeting drugs cetuximab and afatinib on phosphoprotein and gene expression level to gain insights into the regulated pathways. Moreover, we intended to identify genes involved in phenotypic effects of anti-HER therapies. Methods A time-resolved analysis of downstream intracellular kinases following EGF, cetuximab, trastuzumab and afatinib treatment was performed by Luminex analysis in the gastric cancer cell lines Hs746T, MKN1, MKN7 and NCI-N87. The changes in gene expression after treatment of the gastric cancer cell lines with EGF, cetuximab, trastuzumab or afatinib for 4 or 24 h were analyzed by RNA sequencing. Significantly enriched pathways and gene ontology terms were identified by functional enrichment analysis. Furthermore, effects of trastuzumab and afatinib on cell motility and apoptosis were analyzed by time-lapse microscopy and western blot for cleaved caspase 3. Results The Luminex analysis of kinase activity revealed no effects of trastuzumab, while alterations of AKT1, MAPK3, MEK1 and p70S6K1 activations were observed under cetuximab and afatinib treatment. On gene expression level, cetuximab mainly affected the signaling pathways, whereas afatinib had an effect on both signaling and cell cycle pathways. In contrast, trastuzumab had little effects on gene expression. Afatinib reduced average speed in MKN1 and MKN7 cells and induced apoptosis in NCI-N87 cells. Following treatment with afatinib, a list of 14 genes that might be involved in the decrease of cell motility and a list of 44 genes that might have a potential role in induction of apoptosis was suggested. The importance of one of these genes (HBEGF) as regulator of motility was confirmed by knockdown experiments. Conclusions Taken together, we described the different molecular effects of trastuzumab, cetuximab and afatinib on kinase activity and gene expression. The phenotypic changes following afatinib treatment were reflected by altered biological functions indicated by overrepresentation of gene ontology terms. The importance of identified genes for cell motility was validated in case of HBEGF.

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“…Thirteen of 55 PmBs (Table 2) were confirmed by the data of the literature to play certain roles in occurrence and development of GA and were biomarkers or potential therapeutic targets of GA. For example, GPRC5A and SOX9 have been shown to be related to occurrence and development of GA (Liu et al, 2016;, and their expression levels changed more than fourfold in the GA vs. adjacent control samples according to the result of DE RNA analysis (log 2 FC > 2). FCMET has been confirmed as a resistance factor in GA (Ebert et al, 2019), and Wang R. G. et al (2020) demonstrated that FKBP10 may be a crucial player mediating cell proliferation, invasion, and migration by regulating the PI3K signaling pathway in GA. Twenty-seven of 55 PmBs (Table 2) were shown to be associated with other cancers according to the data of the literature. Thus, mRBioM identified some new GA-related mRNAs.…”

Section: Discussionmentioning

confidence: 98%

mRBioM: An Algorithm for the Identification of Potential mRNA Biomarkers From Complete Transcriptomic Profiles of Gastric Adenocarcinoma

Dong

Rao

et al. 2021

Front. Genet.

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PurposeIn this work, an algorithm named mRBioM was developed for the identification of potential mRNA biomarkers (PmBs) from complete transcriptomic RNA profiles of gastric adenocarcinoma (GA).MethodsmRBioM initially extracts differentially expressed (DE) RNAs (mRNAs, miRNAs, and lncRNAs). Next, mRBioM calculates the total information amount of each DE mRNA based on the coexpression network, including three types of RNAs and the protein-protein interaction network encoded by DE mRNAs. Finally, PmBs were identified according to the variation trend of total information amount of all DE mRNAs. Four PmB-based classifiers without learning and with learning were designed to discriminate the sample types to confirm the reliability of PmBs identified by mRBioM. PmB-based survival analysis was performed. Finally, three other cancer datasets were used to confirm the generalization ability of mRBioM.ResultsmRBioM identified 55 PmBs (41 upregulated and 14 downregulated) related to GA. The list included thirteen PmBs that have been verified as biomarkers or potential therapeutic targets of gastric cancer, and some PmBs were newly identified. Most PmBs were primarily enriched in the pathways closely related to the occurrence and development of gastric cancer. Cancer-related factors without learning achieved sensitivity, specificity, and accuracy of 0.90, 1, and 0.90, respectively, in the classification of the GA and control samples. Average accuracy, sensitivity, and specificity of the three classifiers with machine learning ranged within 0.94–0.98, 0.94–0.97, and 0.97–1, respectively. The prognostic risk score model constructed by 4 PmBs was able to correctly and significantly (∗∗∗p < 0.001) classify 269 GA patients into the high-risk (n = 134) and low-risk (n = 135) groups. GA equivalent classification performance was achieved using the complete transcriptomic RNA profiles of colon adenocarcinoma, lung adenocarcinoma, and hepatocellular carcinoma using PmBs identified by mRBioM.ConclusionsGA-related PmBs have high specificity and sensitivity and strong prognostic risk prediction. MRBioM has also good generalization. These PmBs may have good application prospects for early diagnosis of GA and may help to elucidate the mechanism governing the occurrence and development of GA. Additionally, mRBioM is expected to be applied for the identification of other cancer-related biomarkers.

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MET as resistance factor for afatinib therapy and motility driver in gastric cancer cells

Cited by 13 publications

References 43 publications

Identifying hub genes of papillary thyroid carcinoma in the TCGA and GEO database using bioinformatics analysis

Identifying hub genes of papillary thyroid carcinoma in the TCGA and GEO database using bioinformatics analysis

Determining the effects of trastuzumab, cetuximab and afatinib by phosphoprotein, gene expression and phenotypic analysis in gastric cancer cell lines

mRBioM: An Algorithm for the Identification of Potential mRNA Biomarkers From Complete Transcriptomic Profiles of Gastric Adenocarcinoma

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