ArticleCancer informatics2026
iCRCexp: An Integrative Database for Colorectal Cancer-Associated Gene Expression Profiles.
Article in Cancer informatics, 2026. 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Colorectal cancer (CRC) is a leading cause of tumor-related mortality. Recent studies have shown that the transcriptome plays an important role in the development and occurrence of CRC. However, a comprehensive repository of CRC transcriptome sequencing data is unavailable. In the present study, we constructed a colorectal database (iCRCexp; http://icrcexp.omicsbio.info/). Method: We collected CRC-related transcriptome datasets from The Cancer Genome Atlas (TCGA) and National Center for Biotechnology Information (NCBI) Gene Ontology Omnibus (GEO) databases up to 2022. The sequencing data were preprocessed through a unified pipeline and subsequently analyzed. CRC-related genes and drugs were identified via text mining of the PubMed abstracts. Results: A total of 18 466 tissue samples from 231 studies, 2429 CRC-related genes, and 1852 CRC-related drugs were collected and integrated into iCRCexp. Among these studies, 251 CRC-related datasets were identified with abundant characteristic information, including tissue source, baseline characteristics, therapeutic responses, recurrence and metastasis, and survival. We conducted differential correlation and survival analyses. We predicted potential target drugs for CRC-related genes by calculating connectivity scores. Consequently, we integrated these analysis results through network construction and presented them in a CRC database. Conclusion: A comprehensive resource, including CRC-related gene and medication information and an expression analysis platform, was constructed for the CRC community.
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.