Evidence map›Paper›PMID 42169872›Full record

ArticleJournal of gastrointestinal oncology2026

Identification of an autophagy-related prognostic signature and validation of WDR45 in colorectal cancer.

Cuiying Qin, Youwu He, Wenling Wu, Binliang Gan, Xinning Luo, Xianjie Feng, Ganlu Deng

Abstract read
In one paragraph

Article in Journal of gastrointestinal oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Cuiying Qin *Department of Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Youwu He *Department of Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Wenling WuDepartment of Radiation Oncology, The Second Affiliated Hospital of Hainan Medical College, Haikou, China.
Binliang GanDepartment of Oncology, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China.
Xinning LuoDepartment of Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Xianjie FengThe First Clinical College of Guangxi Medical University, Nanning, China.
Ganlu DengDepartment of Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.ORCID https://orcid.org/0009-0008-4467-461X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Colorectal cancer (CRC) is a common cancer and leading cause of cancer-related death with an insidious onset and a high recurrence rate after surgery. Autophagy has been shown to be closely related to the progression of CRC. This study aimed to identify novel biomarkers based on autophagy-related genes (ATGs). Methods: RNA-sequencing and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database, and combined with ATG data to identify differentially expressed autophagy-related genes (DEATGs). A prognostic risk model of the DEATGs was constructed using multivariable Cox regression. Genomic alteration, DNA methylation, and the tumor mutational burden (TMB) of the hub ATGs were analyzed, along with pan-cancer expression, interaction proteins, prognostic values, pharmaceutical sensitivity, and immune infiltration. The expression of the hub ATGs was verified in local CRC samples and cell lines. Functional assays were performed. The expression of autophagy-related proteins was detected. Results: A total of 320 DEATGs were identified, and a two-gene prognostic signature (comprising Conclusions: This study constructed an ATG-based signature that accurately predicted the prognosis of CRC patients. It also identified

Indexed as

Autophagybiomarkercolorectal cancer (CRC)immune infiltrationprognosis

Identifiers

PMID42169872
PMCPMC13188033

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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.