Peripheral myelin protein 2 is underexpressed in early-onset colorectal cancer and inhibits metastasis
BackgroundThe occurrence and fatality rates of early-onset colorectal cancer (EOCRC) are increasing, with metastasis being one of the primary causes of the high mortality rate in EOCRC patients. Currently, there is a shortage of biomarkers for diagnosis and targets for treatment with optimal efficac...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Molecular Biosciences |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2025.1610003/full |
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| Summary: | BackgroundThe occurrence and fatality rates of early-onset colorectal cancer (EOCRC) are increasing, with metastasis being one of the primary causes of the high mortality rate in EOCRC patients. Currently, there is a shortage of biomarkers for diagnosis and targets for treatment with optimal efficacy and reliability. This study aims to identify biomarkers associated with metastasis to provide effective diagnostic strategies for EOCRC patients.MethodsExpression datasets were retrieved from The Cancer Genome Atlas (TCGA) database and analyzed for differentially expressed genes (DEGs). Subsequently, weighted gene co-expression network analysis (WGCNA) was performed to identify gene modules associated with EOCRC metastasis. Feature genes were selected using machine learning and tdiagnostic performance of these genes was assessed with receiver operating characteristic (ROC) curves. We validated peripheral myelin protein 2 (PMP2) expression levels in EOCRC tissues at the molecular and protein levels using quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC). Finally, the invasive and migratory capabilities of PMP2 were assessed in this study using Transwell assays.ResultsThe analysis identified 1,411 DEGs in EOCRC and 3,434 DEGs in late-onset colorectal cancer (LOCRC). Subsequently, WGCNA filtered out module genes specifically associated with metastasis in EOCRC, leading to 44 genes. We identified four feature genes by analyzing these genes using machine learning algorithms and taking the intersection: LINC02268, AC092652.1, GRIK1, and PMP2. The ROC curve indicated that these four genes are vitally involved in EOCRC. Further, qRT-PCR and IHC highlighted that PMP2 was downregulated in EOCRC tumor tissues compared to normal tissues. Moreover, Transwell assays revealed that PMP2 inhibited the invasion and migration of EOCRC.ConclusionPMP2 has low expression in EOCRC tissues and inhibits EOCRC metastasis, serving as a potential biomarker and therapeutic target for EOCRC treatment. |
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| ISSN: | 2296-889X |