Evidence map›Paper›PMID 39415861›Full record

ArticleIranian journal of public health2024

Effect on Non-Small Cell Lung Cancer after Combination of Driver Gene Mutations and Anti-PD-1/PD-L1 Immunotherapy as Well as Chemotherapy.

Zhengming Huang, Long Yu, Weisong Chen, Dan Zhu, Hui Chen

Abstract read
In one paragraph

Article in Iranian journal of public health, 2024. 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

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

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

5 authors.

Zhengming HuangPulmonary and Critical Care Medicine, Jinhua Municipal Central Hospital, Zhejiang University, Zhejiang, China.
Long YuPulmonary and Critical Care Medicine, Jinhua Municipal Central Hospital, Zhejiang University, Zhejiang, China.
Weisong ChenPulmonary and Critical Care Medicine, Jinhua Municipal Central Hospital, Zhejiang University, Zhejiang, China.
Dan ZhuPulmonary and Critical Care Medicine, Jinhua Municipal Central Hospital, Zhejiang University, Zhejiang, China.
Hui ChenPulmonary and Critical Care Medicine, Jinhua Municipal Central Hospital, Zhejiang University, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: We aimed to reveal the correlation between pathological indicators and PD-L1, between gene mutation status in lung cancer through clinico-pathological data and lung cancer-related gene mutation and PD-L1 expression analysis. Methods: The study was conducted in Jinhua Municipal Central Hospital, Zhejiang, China from 2017 to 2022. PD-L1 testing and targeted gene mutations detection were evaluated. The clinical characteristics of these non-small cell lung cancer (NSCLC) samples have been obtained. The groups (LUAD, n=142; LUSC, n=143) were grouped according to clinico-pathological features and PD-L1 expression (Yes/No or High/Low), and the clinico-pathological and genetic and molecular features and correlation with PD-L1 expression were compared across the above groups. Comparisons and analyses were made between different treatment schemes. Results: Lung adenocarcinoma (LUAD, n=142) and lung squamous carcinoma (LUSC, n=143) samples were enrolled (median age: 64 years old). Pleural invasion and M staging were significantly different from PD-L1 alterations ( Conclusion: There are individual differences in PD-L1 expression, which can also vary depending on the different clinical features. Specific molecular features correlate with differential PD-L1 expression and may influence the response to therapy.

Indexed as

Gene mutationsImmunotherapyNon-small cell lung cancer (NSCLC)Prognostic analysis

Identifiers

PMID39415861
PMCPMC11475187

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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.