Evidence map›Paper›PMID 37814076›Full record

ArticleCellular oncology (Dordrecht, Netherlands)2024

Plasma cell signatures predict prognosis and treatment efficacy for lung adenocarcinoma.

Long Shu, Jun Tang, Shuang Liu, Yongguang Tao

Abstract read
In one paragraph

Article in Cellular oncology (Dordrecht, Netherlands), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
5.2field-weighted citation impact, top 4% of its field
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

18 citing papers in PubMed, 20 citations in OpenAlex.

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  13. A Glycolysis and gluconeogenesis-related model for breast cancer prognosis.Cancer biomarkers : section A of Disease markers · 2024
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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

4 authors at 3 institutions in 2 countries.

Long ShuHunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China.
Jun TangNHC Key Laboratory of Carcinogenesis (Central South University), Cancer Research Institute, School of Basic Medicine, Central South University, Changsha, 410078, Hunan, China.
Shuang LiuDepartment of Oncology, Institute of Medical Sciences, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China. shuangliu2016@csu.edu.cn.
Yongguang TaoHunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China. taoyong@csu.edu.cn.
Hunan Cancer Hospital · CNCentral South University · CNXiangya Hospital Central South University · CN

Funding

National Natural Science Foundation of China 82072594National Natural Science Foundation of China 82073097Natural Science Foundation of Hunan Province, and Hunan Provincial Key Area R&D Programs 2021SK2013The Science and Technology Innovation Program of Hunan Province 2022RC3072
6 · The paper itself

Abstract

purposeThis study aims to identify key genes regulating tumor infiltrating plasma cells (PC) and provide new insights for innovative immunotherapy.

methodsKey genes related to PC were identified using machine learning in lung adenocarcinoma (LUAD) patients. A prognostic model called PC scores was developed using TCGA data and validated with GEO cohorts. We assessed the molecular background, immune features, and drug sensitivity of the high PC scores group. Real-time PCR was utilized to assess the expression of hub genes in both localized LUAD patients and LUAD cell lines.

resultsWe constructed PC scores based on seventeen PC-related hub genes (ELOVL6, MFI2, FURIN, DOK1, ERO1LB, CLEC7A, ZNF431, KIAA1324, NUCB2, TXNDC11, ICAM3, CR2, CLIC6, CARNS1, P2RY13, KLF15, and SLC24A4). Higher age, TNM stage, and PC scores independently predicted shorter overall survival. The AUC value of PC scores for one year, three years, and five years of overall survival were 0.713, 0.716, and 0.690, separately. The nomogram model that integrated age, stage, and PC scores showed significantly higher predictive value than stage alone (P < 0.01). High PC scores group exhibited an immune suppressing microenvironment with lower B, CD8 + T, CD4 + T, and dendritic cell infiltration. Docetaxel, gefitinib, and erlotinib had lower IC50 in high PC groups (P < 0.001). After validation through the local cohort and in vitro experiments, we ultimately confirmed three key potential targets: MFI2, KLF15, and CLEC7A.

conclusionWe proposed a prediction mode which can effectively identify high-risk LUAD patients and found three novel genes closely correlated with PC tumor infiltration.

Indexed as

Adenocarcinoma of LungLung NeoplasmsPlasma CellsAgedBiomarkers, TumorCell Line, TumorFemaleGene Expression Regulation, NeoplasticHumansMaleMiddle AgedNomogramsPrognosisTreatment OutcomeBiomarkers, TumorLung adenocarcinomaMachine learningOverall survivalPlasma cells

Identifiers

PMID37814076
PMCPMC12973951
OpenAlexW4387450869

What Socratic 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 Socratic graph.