Evidence map›Paper›PMID 40622593›Full record

ArticleNaunyn-Schmiedeberg's archives of pharmacology2026

AQP9 and IFITM1 as drivers of immune infiltration and tumor progression in IBD-associated colorectal cancer: from computational insights to experimental validation.

Rohan Gupta, Ayesha Siddiqua, M Yasmin Begum, Siva Parsad Panda, Mosleh Mohammad Abomughaid, Kunal Thakur, Sorabh Lakhanpal

Abstract read
PubMed Publisher
In one paragraph

Article in Naunyn-Schmiedeberg's archives of pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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.

Rohan GuptaSchool of Basic and Applied Sciences, Department of Biotechnology and Bioengineering, Galgotias University, Greater Noida, Uttar Pradesh, 203201, India. rohan.gupta@galgotiasuniversity.edu.in.ORCID 0000-0002-6635-6558
Ayesha SiddiquaDepartment of Clinical Pharmacy, College of Pharmacy, King Khalid University, Abha, Saudi Arabia.
M Yasmin BegumDepartment of Pharmaceutics, College of Pharmacy, King Khalid University, Abha, Saudi Arabia.
Siva Parsad PandaInstitute of Pharmaceutical Research, GLA University, Mathura, Uttar Pradesh, 281406, India.
Mosleh Mohammad AbomughaidDepartment of Medical Laboratory Sciences, College of Applied Medical Sciences, University of Bisha, 61922, Bisha, Saudi Arabia.
Kunal ThakurCentre for Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India.
Sorabh LakhanpalSchool of Pharmaceutical Sciences, Lovely Professional University, Phagwara, Punjab, 144411, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract associated with an increased risk of colorectal cancer. Despite extensive research, high treatment failure rates have intensified the search for new therapeutic and diagnostic biomarkers. This study used an integrative approach, combining gene expression analysis with machine learning, to identify novel gene candidates for IBD-related colorectal cancer. RNA-sequencing data (GSE165512) from 170 samples-84 Crohn's disease (CD), 40 ulcerative colitis (UC), and 46 controls-were normalized using R. Analysis revealed 1361 differentially expressed mRNAs in CD and 4916 in UC. Venn diagram analysis identified 428 commonly upregulated and 562 downregulated genes. Cross-referencing with MSigDB inflammatory signatures highlighted 31 candidate genes. XGBoost classification achieved an overall accuracy of 88% (IBD: 71%, controls: 93%), with SHAP analysis pinpointing nine key genes, including AQP9, CXCL8, IFITM1, and ITGA5. Validation with blood RNA-seq data (GSE169568, n = 205) showed ITGA5 (AUC: 0.856) and CXCL8 (AUC: 0.786) had the highest diagnostic performance. Further, RT-PCR in DSS- and Anti-CD40-induced mouse colitis models confirmed significant overexpression of AQP9, IFITM1, and ITGA5 (P < 0.0001). Additionally, oncogenomic analysis via cBioPortal revealed mutation rates of 2.3% (AQP9), 1% (IFITM1), and 3% (ITGA5) in colon adenocarcinoma. Gene set variation analysis and TIMER 2.0 further implicated AQP9 and IFITM1 in EMT, PI3K/Akt signaling, and immune infiltration pathways, highlighting their potential as diagnostic and therapeutic targets.

Indexed as

Antigens, DifferentiationColitis, UlcerativeColorectal NeoplasmsCrohn DiseaseAnimalsDisease ProgressionFemaleGene Expression Regulation, NeoplasticHumansMaleMiceMice, Inbred C57BLAntigens, Differentiationleu-13 antigenColorectal cancerFeature extractionInflammatory bowel diseaseMachine learningTherapeutic genesTumor infiltration

Identifiers

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.