Evidence map›Paper›PMID 40302018›Full record

ArticleACS nano2025

Artificial Intelligence-Guided Cancer Engineering for Tumor Normalization Executed by Tumor Lysosomal-Triggered Supramolecular Chiral Peptide.

Jingmei Wang, Weiming You, Wangxiao He, Jin Yan, Yanmin Zhang

Abstract read
In one paragraph

Article in ACS nano, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

5 authors.

Jingmei WangDepartment of Infectious Diseases and Hepatology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, P. R. China.
Weiming YouDepartment of Infectious Diseases and Hepatology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, P. R. China.
Wangxiao HeDepartment of Medical Oncology and Department of Talent Highland, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, P. R. China.ORCID 0000-0002-2054-6022
Jin YanDepartment of Infectious Diseases and Hepatology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, P. R. China.ORCID 0000-0001-6468-6003
Yanmin ZhangDepartment of Infectious Diseases and Hepatology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, P. R. China.ORCID 0000-0001-7307-9408

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer engineering for tumor normalization offers a promising therapeutic strategy to reverse malignant cells and their supportive tumor microenvironment into a more benign state. Herein, an artificial intelligence (AI) approach was developed using mRNA data from patients with lung adenocarcinoma to facilitate the identification of aberrant signaling pathways, specifically focusing on PD-L1, Wnt, and macropinocytosis. Targeting these characteristics, we have developed a supramolecular construct called cancer corrector (CCtor) with the aim of harnessing the enhanced macropinocytosis observed in cancer cells. Undergoing cleavage and subsequent drug release triggered by the lysosomal protease in cancer cells, CCtor rectifies the aberrant hyperactivity of both Wnt and PD-L1 signaling pathways. This dual-action therapeutic strategy not only restores normalcy to cancer cells but also exerts an exceptionally robust therapeutic effect. This work exemplifies a future direction for cancer therapies by combining AI with molecular engineering to significantly improve patient outcomes through tumor behavior normalization.

Indexed as

Adenocarcinoma of LungAntineoplastic AgentsArtificial IntelligenceLung NeoplasmsLysosomesPeptidesCell Line, TumorHumansTumor MicroenvironmentAntineoplastic AgentsPeptidesantitumor immunitycancer engineeringcancer therapyLUADtumor normalization

Identifiers

PMID40302018
PMCPMC12080376

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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.