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1.
Sci Rep ; 14(1): 15173, 2024 07 02.
Artigo em Inglês | MEDLINE | ID: mdl-38956143

RESUMO

Metastatic gastric cancer (GC) presents significant clinical challenges due to its poor prognosis and limited treatment options. To address this, we conducted a targeted protein biomarker discovery study to identify markers predictive of metastasis in advanced GC (AGC). Serum samples from 176 AGC patients (T stage 3 or higher) were analyzed using the Olink Proteomics Target panels. Patients were retrospectively categorized into nonmetastatic, metastatic, and recurrence groups, and differential protein expression was assessed. Machine learning and gene set enrichment analysis (GSEA) methods were applied to discover biomarkers and predict prognosis. Four proteins (MUC16, CAIX, 5'-NT, and CD8A) were significantly elevated in metastatic GC patients compared to the control group. Additionally, GSEA indicated that the response to interleukin-4 and hypoxia-related pathways were enriched in metastatic patients. Random forest classification and decision-tree modeling showed that MUC16 could be a predictive marker for metastasis in GC patients. Additionally, ELISA validation confirmed elevated MUC16 levels in metastatic patients. Notably, high MUC16 levels were independently associated with metastatic progression in T3 or higher GC. These findings suggest the potential of MUC16 as a clinically relevant biomarker for identifying GC patients at high risk of metastasis.


Assuntos
Biomarcadores Tumorais , Antígeno Ca-125 , Neoplasias Gástricas , Humanos , Neoplasias Gástricas/patologia , Neoplasias Gástricas/sangue , Masculino , Feminino , Biomarcadores Tumorais/sangue , Pessoa de Meia-Idade , Antígeno Ca-125/sangue , Prognóstico , Idoso , Proteínas de Membrana/sangue , Proteínas de Membrana/genética , Proteínas de Membrana/metabolismo , Metástase Neoplásica , Estudos Retrospectivos , Adulto
2.
Genomics Inform ; 22(1): 6, 2024 Jun 17.
Artigo em Inglês | MEDLINE | ID: mdl-38907287

RESUMO

Here, we investigated that the heat shock protein 47 (HSP47) plays a crucial role in the progression of gastric cancer (GC). We analyzed HSP47 gene expression in GC cell lines and patient tissues. The HSP47 mRNA and protein expression levels were significantly higher in GC cell lines and tumor tissues compared to normal gastric mucosa. Using siRNA to silence the expression of HSP47 in GC cells resulted in a significant reduction in their proliferation, wound healing, migration, and invasion capacities. Additionally, we also showed that the mRNA expression of matrix metallopeptidase-7 (MMP-7), a metastasis-promoting gene, was significantly reduced in HSP47 siRNA-transfected GC cells. We confirmed that the HSP47 promoter region was methylated in the SNU-216 GC cell line expressing low levels of HSP47 and in most non-cancerous gastric tissues. It means that the expression of HSP47 is regulated by epigenetic regulatory mechanisms. These findings suggest that targeting HSP47, potentially through its promoter methylation, could be a useful new therapeutic strategy for treating GC.

3.
Br J Cancer ; 130(9): 1571-1584, 2024 May.
Artigo em Inglês | MEDLINE | ID: mdl-38467827

RESUMO

BACKGROUND: Molecular analysis of advanced tumors can increase tumor heterogeneity and selection bias. We developed a robust prognostic signature for gastric cancer by comparing RNA expression between very rare early gastric cancers invading only mucosal layer (mEGCs) with lymph node metastasis (Npos) and those without metastasis (Nneg). METHODS: Out of 1003 mEGCs, all Npos were matched to Nneg using propensity scores. Machine learning approach comparing Npos and Nneg was used to develop prognostic signature. The function and robustness of prognostic signature was validated using cell lines and external datasets. RESULTS: Extensive machine learning with cross-validation identified the prognostic classifier consisting of four overexpressed genes (HDAC5, NPM1, DTX3, and PPP3R1) and two downregulated genes (MED12 and TP53), and enabled us to develop the risk score predicting poor prognosis. Cell lines engineered to high-risk score showed increased invasion, migration, and resistance to 5-FU and Oxaliplatin but maintained sensitivity to an HDAC inhibitor. Mouse models after tail vein injection of cell lines with high-risk score revealed increased metastasis. In three external cohorts, our risk score was identified as the independent prognostic factor for overall and recurrence-free survival. CONCLUSION: The risk score from the 6-gene classifier can successfully predict the prognosis of gastric cancer.


Assuntos
Biomarcadores Tumorais , Mucosa Gástrica , Neoplasias Gástricas , Neoplasias Gástricas/genética , Neoplasias Gástricas/patologia , Neoplasias Gástricas/mortalidade , Humanos , Prognóstico , Animais , Camundongos , Biomarcadores Tumorais/genética , Biomarcadores Tumorais/metabolismo , Mucosa Gástrica/patologia , Mucosa Gástrica/metabolismo , Metástase Linfática/genética , Feminino , Masculino , Linhagem Celular Tumoral , Regulação Neoplásica da Expressão Gênica , Aprendizado de Máquina , Pessoa de Meia-Idade
4.
Curr Microbiol ; 80(2): 83, 2023 Jan 21.
Artigo em Inglês | MEDLINE | ID: mdl-36680647

RESUMO

The wetland is an important ecosystem for purifying pollutants and circulating nutrients. Numerous microorganisms contribute to maintaining this function. We obtained Flavobacterium enshiense R6S-5-6 which was isolated from Ungok (Ramsar) Wetland and conducted whole-genome sequencing to investigate what contribution R6S-5-6 could make to the wetland community. The complete genome sequence of R6S-5-6 has a size of 3,251,289 bp with 37.68% of GC content. Gene annotation revealed that R6S-5-6 has several pathways to break down pollutants, including denitrification, assimilatory sulfate reduction (ASR), and polyphosphate-accumulating process. Furthermore, R6S-5-6 has genes that can have a positive effect on plants living in wetlands, such as storing essential nutrients, promoting plant growth, and protecting plants against pathogens.


Assuntos
Ecossistema , Poluentes Ambientais , Áreas Alagadas , Desenvolvimento Vegetal
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