Evidence map›Paper›PMID 34268412›Full record

ArticleAnnals of translational medicine2021

The prognostic landscape of tumor-infiltrating immune cells in lung squamous cell carcinoma.

Liang Guo, Jiaoyu Yi, Ming Liu, Juanjuan Li

Open access · diamondAbstract read
In one paragraph

Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 42% 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

4 citing papers in PubMed, 4 citations in OpenAlex.

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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 1 country.

Liang GuoDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Jiaoyu YiDepartment of Thyroid and Neck Tumor, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Ming LiuDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Juanjuan LiDepartment of Medical Oncology, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai, China.
Tongji University · CNShanghai Pulmonary Hospital · CNTianjin Medical University Cancer Institute and Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundExploring novel biomarkers and developing effective therapeutic strategies can improve the prognosis of lung squamous cell carcinoma (LUSC) in the future. The prognostic value of tumor-infiltrating immune cells (TICs) in solid tumors has been extensively studied. However, the landscape of TICs involved in the prognosis of non-small cell lung cancer (NSCLC), especially in LUSC, remains unclear and should be systematically investigated.

methodsThis retrospective study analyzed the immune-related transcriptional profiles of 490 LUSC patients from The Cancer Genome Atlas (TCGA) cohort. Using the CIBERSORT method, TICs were evaluated and examined for their association with overall survival (OS) in LUSC.

resultsOut of the 27 TICs, 14 were correlated with prognosis in LUSC. A novel prognostic model characterized by fewer memory B cells and more central memory CD8 T cells, regulatory T cells (Tregs), and plasmacytoid dendritic cell (pDC) infiltration predicted poor OS in LUSC with high accuracy. The 1-, 3-, and 5-year areas under the curve (AUC) were 0.95, 0.98, and 0.96, respectively, in the training cohort. This finding was further validated in the validation cohort, where the 1-, 3-, and 5-year AUCs were 0.95, 0.98, and 0.95, respectively.

conclusionsThese findings may provide more effective prognostic biomarkers and potential therapeutic targets for the immunotherapy of LUSC.

Indexed as

biomarkerimmune checkpointlung squamous cell carcinoma (LUSC)prognosisTumor-infiltrating immune cells (TICs)

Identifiers

PMID34268412
PMCPMC8246229
OpenAlexW3163467991

What OpenQuestion holds

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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.