Evidence map›Paper›PMID 41262872›Full record

ReviewFrontiers in cellular and infection microbiology2025

Revisiting lung cancer immunotherapy in the era of long COVID: mechanistic insights and therapeutic implications.

Xingyao Lv, Lin Ji, Weijia Cao, Yanyan Xue, Hua Dai, Shengzhe Zhang

Abstract readReview
In one paragraph

Review in Frontiers in cellular and infection microbiology, 2025. 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

6 authors.

Xingyao Lv *Institute of Translational Medicine, School of Medicine, Yangzhou University, Yangzhou, Jiangsu, China.
Lin Ji *Institute of Translational Medicine, School of Medicine, Yangzhou University, Yangzhou, Jiangsu, China.
Weijia CaoInstitute of Translational Medicine, School of Medicine, Yangzhou University, Yangzhou, Jiangsu, China.
Yanyan XueInstitute of Translational Medicine, School of Medicine, Yangzhou University, Yangzhou, Jiangsu, China.
Hua DaiInstitute of Translational Medicine, School of Medicine, Yangzhou University, Yangzhou, Jiangsu, China.
Shengzhe ZhangInstitute of Translational Medicine, School of Medicine, Yangzhou University, Yangzhou, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the post-COVID-19 era, understanding the long-term impact of Long COVID on the immune system is essential for deciphering its influence on lung cancer pathogenesis and immunotherapeutic efficacy. This review comprehensively examines how persistent COVID-19 sequelae-manifested as chronic inflammation, pulmonary fibrosis, cytokine dysregulation, and T-cell exhaustion can reshape the lung cancer microenvironment. In addition, the emerging roles of memory B cells and altered neutrophil function in promoting tumorigenesis are discussed. Importantly, we analyze recent clinical evidence suggesting that COVID-19 vaccination may enhance the efficacy of immune checkpoint inhibitors, potentially by modulating host immunity. By integrating mechanistic insights with clinical observations, this review aims to illuminate the challenges and opportunities at the intersection of Long COVID and lung cancer treatment, thereby fostering the development of personalized therapeutic strategies in the post-pandemic era.

Indexed as

COVID-19ImmunotherapyLung NeoplasmsHumansImmune Checkpoint InhibitorsSARS-CoV-2Tumor MicroenvironmentImmune Checkpoint InhibitorsCOVID-19 vaccinationimmune checkpoint inhibitorslong covidlung cancerpulmonary fibrosist-cell exhaustion

Identifiers

PMID41262872
PMCPMC12623366

What OpenQuestion holds

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LicenceCC BY
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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.