Evidence map›Paper›PMID 39443755›Full record

ArticleCancer reports (Hoboken, N.J.)2024

A Four-Gene Autophagy-Related Prognostic Model Signature and Its Association With Immune Phenotype in Lung Squamous Cell Carcinoma.

Lumeng Luo, Jiaying Deng, Qiu Tang

Abstract read
In one paragraph

Article in Cancer reports (Hoboken, N.J.), 2024. 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

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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

3 citing papers in PubMed.

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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

3 authors.

Lumeng LuoDepartment of Radiation Oncology, Women's Hospital, School of Medicine, Zhejiang University, Zhejiang, China.
Jiaying DengDepartment of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
Qiu TangDepartment of Radiation Oncology, Women's Hospital, School of Medicine, Zhejiang University, Zhejiang, China.ORCID 0009-0007-0264-1089

Funding

National Natural Science Foundation of China Research, China 81903127Shanghai Sailing Program, China 19YF1409100
6 · The paper itself

Abstract

backgroundIn the era of immunotherapy, there is a critical need for effective biomarkers to improve outcome prediction and guide treatment decisions for patients with lung squamous cell carcinoma (LUSC). We hypothesized that the immune contexture of LUSC may be influenced by tumor intrinsic events, such as autophagy.

aimsWe aimed to develop an autophagy-related risk signature and assess its predictive value for immune phenotype. METHODS AND

resultsExpression profiles of autophagy-related genes (ARGs) in LUSC samples were obtained from the TCGA and GEO databases. Survival analyses were conducted to identify survival-related ARGs and construct a risk signature using the Random Forest algorithm. Four ARGs (CFLAR, RGS19, PINK1, and CTSD) with the most significant prognostic value were selected to construct the risk signature. Patients in the high-risk group exhibited worse prognosis than those in the low-risk group (p < 0.0001 in TCGA; p < 0.01 in GEO) and the risk score was identified as an independent prognostic factor. We observed that the high-risk group displayed an immune-suppressive status and showed higher levels of infiltrating regulatory T cells and macrophages, which are associated with poorer outcomes. Additionally, the risk score exhibited a significantly positive correlation with the expression of PD-1 and CTLA4, as well as the estimate score and immune score.

conclusionThis study provided an effective autophagy-related prognostic signature, which could also predict the immune phenotype.

Indexed as

AutophagyBiomarkers, TumorCarcinoma, Squamous CellLung NeoplasmsAgedFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMiddle AgedPhenotypePrognosisTumor MicroenvironmentBiomarkers, Tumorautophagybioinformatic analysisimmune infiltrationimmune landscapelung squamous cell carcinomaprognostic signature

Identifiers

PMID39443755
PMCPMC11499073

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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.