ArticleDiscover oncology2025
Screening and validation of long non-coding RNAs associated with colorectal cancer based on random forest and LASSO regression algorithm.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
objectiveColorectal cancer (CRC) ranks as the third most prevalent contributor to global disease burden and represents the second highest mortality rate among all malignancies worldwide. Long non-coding RNAs (lncRNAs) are a new class of regulatory RNAs, which play a crucial role in the occurrence and development of colorectal cancer. Therefore, it is potentially important to use bioinformatics and machine learning methods to study novel biomarkers for CRC.
methodsThe RNA-seq data of colorectal cancer and normal colorectal tissue were downloaded from the GEO database. Random forest (RF) and LASSO (Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithms were constructed to screen lncRNAs closely related to CRC, and their screening efficiency was verified. Predict the regulatory genes of lncRNA and construct the ceRNA regulatory network of lncRNA-miRNA-mRNA. Quantitative real-time PCR (qRT-PCR) was used to verify its expression in colorectal cancer tissues and adjacent tissues, as well as its relationship with clinical features of CRC patients.
resultA total of 3028 CRC-related lncRNAs were initially screened from the GEO database, and 55 differentially expressed lncRNAs (DE lncRNAs) were finally selected through difference analysis. The key lncRNAs were further screened using RF and LASSO. The same gene in the screening results of the above two methods was selected as the key lncRNA of CRC. Finally, five key lncRNAs (NCAL1, CRNDE, HMGA1P4, EPIST and MT1JP) were selected, among them, the expressions of NCAL1, CRNDE and HMGA1P4 were upregulated compared with normal CRC tissues, while the expressions of EPIST and MT1JP were downregulated compared with normal colorectal tissues. The expression of 5 key CRC lncRNAs was verified, and each AUC is greater than 0.7, indicating a good screening effect. Since CRNDE has been studied by members of this research group before, it will not be further studied. It was predicted that 4 lncRNAs would interact with 16 miRNAs and 57 mRNAs. Four key lncRNAs, namely NCAL1, HMGA1P4, EPIST and MT1JP, were experimentally verified. qRT-PCR results showed that the expression of four key lncRNAs in CRC tissues and adjacent tissues had statistical significance (p < 0.001).
conclusionIn summary, we obtained 5 lncRNAs that may be closely related to colorectal cancer, including NCAL1, CRNDE, HMGA1P4, EPIST and MT1JP. This study found that NCAL1, HMGA1P4, EPIST and MT1JP may be candidate biomarkers for colorectal cancer.
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.