ArticleFrontiers in immunology2024
Identification of MYC and STAT3 for early diagnosis based on the long noncoding RNA-mRNA network and bioinformatics in colorectal cancer.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Colorectal 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. Methods: A 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. Results: Database 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. Conclusion: Our results suggest that MYC and STAT3 are promising diagnostic biomarkers for CRC, offering new insights into its pathophysiology and potential for targeted therapies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.