Identification of MYC and STAT3 for early diagnosis based on the long noncoding RNA-mRNA network and bioinformatics in colorectal cancer
BackgroundColorectal cancer (CRC) ranks among the top three cancers globally in both incidence and mortality, posing a significant public health challenge. Most CRC cases are diagnosed at intermediate to advanced stages, and reliable biomarkers for early detection are lacking. Long non-coding RNAs (...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1497919/full |
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| Summary: | BackgroundColorectal cancer (CRC) ranks among the top three cancers globally in both incidence and mortality, posing a significant public health challenge. Most CRC cases are diagnosed at intermediate to advanced stages, and reliable biomarkers for early detection are lacking. Long non-coding RNAs (lncRNAs) have been implicated in various cancers, including CRC, playing key roles in tumor development, progression, and prognosis.MethodsA comprehensive search of the PubMed database was conducted to identify relevant studies on the early diagnosis of CRC. Bioinformatics analysis was performed to explore lncRNA-mRNA networks, leading to the identification of five potential blood biomarkers. Expression analysis was carried out using the GEPIA and GEO online databases, focusing on MYC and STAT3. Differential expression between normal and CRC tissues was assessed, followed by Receiver Operating Characteristic (ROC) analysis to evaluate the diagnostic potential of these markers. Quantitative Real-Time PCR (qRT-PCR) was performed to validate MYC and STAT3 expression levels, and findings were further confirmed using the Human Protein Atlas (HPA) database.ResultsDatabase analysis revealed significant differential expression of MYC and STAT3 between normal and CRC tissues. ROC analysis demonstrated the diagnostic potential of these markers. qRT-PCR validation confirmed the differential expression patterns observed in the databases. Validation through the HPA database further supported these findings, confirming the potential of MYC and STAT3 as diagnostic biomarkers for CRC.ConclusionOur results suggest that MYC and STAT3 are promising diagnostic biomarkers for CRC, offering new insights into its pathophysiology and potential for targeted therapies. |
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| ISSN: | 1664-3224 |