Evidence map›Paper›PMID 40538516›Full record

ReviewWorld journal of gastroenterology2025

Recent advances and challenges in colorectal cancer: From molecular research to treatment.

Gao-Xiu Qi, Rui-Xia Zhao, Chen Gao, Zeng-Yan Ma, Shang Wang, Jing Xu

Abstract readReview
In one paragraph

Review in World journal of gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

  1. Review
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  5. Role of CaCancer medicine · 2026
    Article
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  9. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Gao-Xiu QiDepartment of Pathology, Qingdao Central Hospital, University of Health and Rehabilitation Sciences (Qingdao Central Medical Group), Qingdao 266042, Shandong Province, China.
Rui-Xia ZhaoDepartment of Pathology, Qingdao Central Hospital, University of Health and Rehabilitation Sciences (Qingdao Central Medical Group), Qingdao 266042, Shandong Province, China.
Chen GaoDepartment of Pathology, Qingdao Central Hospital, University of Health and Rehabilitation Sciences (Qingdao Central Medical Group), Qingdao 266042, Shandong Province, China.
Zeng-Yan MaDepartment of Pathology, Qingdao Central Hospital, University of Health and Rehabilitation Sciences (Qingdao Central Medical Group), Qingdao 266042, Shandong Province, China.
Shang WangDepartment of Pathology, School of Basic Medicine, Qingdao University, Qingdao 266071, Shandong Province, China.
Jing XuDepartment of Pathology, Qingdao Central Hospital, University of Health and Rehabilitation Sciences (Qingdao Central Medical Group), Qingdao 266042, Shandong Province, China. katherine_xu@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Colorectal cancer (CRC) ranks among the top causes of cancer-related fatalities globally. Recent progress in genomics, proteomics, and bioinformatics has greatly improved our comprehension of the molecular underpinnings of CRC, paving the way for targeted therapies and immunotherapies. Nonetheless, obstacles such as tumor heterogeneity and drug resistance persist, hindering advancements in treatment efficacy. In this context, the integration of artificial intelligence (AI) and organoid technology presents promising new avenues. AI can analyze genetic and clinical data to forecast disease risk, prognosis, and treatment responses, thereby expediting drug development and tailoring treatment plans. Organoids replicate the genetic traits and biological behaviors of tumors, acting as platforms for drug testing and the formulation of personalized treatment approaches. Despite notable strides in CRC research and treatment - from genetic insights to therapeutic innovations - numerous challenges endure, including the intricate tumor microenvironment, tumor heterogeneity, adverse effects of immunotherapies, issues related to AI data quality and privacy, and the need for standardization in organoid culture. Future initiatives should concentrate on clarifying the pathogenesis of CRC, refining AI algorithms and organoid models, and creating more effective therapeutic strategies to alleviate the global impact of CRC.

Indexed as

Colorectal NeoplasmsArtificial IntelligenceBiomarkers, TumorGenomicsHumansImmunotherapyMolecular Targeted TherapyOrganoidsPrecision MedicinePrognosisTumor MicroenvironmentBiomarkers, TumorArtificial intelligenceColorectal cancerMolecularOrganoidTreatment

Identifiers

PMID40538516
PMCPMC12175868

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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 Socratic graph.