Evidence map›Paper›PMID 40035834›Full record

ArticleCancer immunology, immunotherapy : CII2025

Exploring the role of TIGIT in patients with Small Cell Lung Cancer as a novel predictor of prognosis and immunotherapy response.

Li Liu, Peng Wu, Bingzhi Wang, Jiyan Dong, Chaoqi Zhang, Wenchao Liu, Jianming Ying

Abstract read
In one paragraph

Article in Cancer immunology, immunotherapy : CII, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

7 authors.

Li LiuDepartment of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
Peng WuDepartment of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
Bingzhi WangDepartment of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
Jiyan DongDepartment of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
Chaoqi ZhangDepartment of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
Wenchao LiuDepartment of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
Jianming YingDepartment of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China. jmying@cicams.ac.cn.ORCID http://orcid.org/0000-0002-7301-4118

Funding

Beijing Municipal Health Commission 2022-2-4027Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences 2021-I2M-1-012National Natural Science Foundation of China 82172876
6 · The paper itself

Abstract

backgroundT-cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domains (TIGIT) is a novel immune checkpoint playing a crucial role in immunosuppression and immune evasion. This study aims to elucidate the expression patterns, characteristics, and possible mechanisms of TIGIT in small cell lung cancer (SCLC).

methodsTIGIT expression was analyzed across various cancers and normal tissues using The Cancer Genome Atlas (TCGA). Transcriptomic data from SCLC patients, sourced from the Gene Expression Omnibus (GEO) and literature, were analyzed to assess TIGIT-related characteristics. Immunohistochemistry (IHC) was used to verify TIGIT expression in post-surgical and advanced SCLC samples, focusing on expression characteristics, prognostic value, and treatment response.

resultsTIGIT was significantly overexpressed in various tumors, including SCLC (p < 0.05). Higher expression was associated with better overall survival (OS) (p < 0.05). Notably, a significant positive correlation was observed between TIGIT expression and immune-related metagenes, such as HCK, interferon, and LCK (p < 0.05). Immune infiltration analysis revealed a strong positive correlation between TIGIT expression and immune score in multiple cohorts. Additionally, TIGIT expression correlated positively with immune cells, including CD8 T cells, cytotoxic lymphocytes, and B cells (p < 0.05), and multiple immune checkpoints like BTLA, ICOS, and LAG3 (p < 0.05), while it had a significant negative correlation with the TIDE score (p < 0.05). In the validation section, patients with high TIGIT expression showed significantly prolonged disease-free survival (DFS) and OS (p < 0.05), and demonstrated a better response to adjuvant chemotherapy (ACT) and immunotherapy.

conclusionTIGIT serves as a biomarker in SCLC, with its high expression indicating favorable prognosis and treatment response. These effects may be due to TIGIT's unique immune landscape and its association with other immune checkpoints.

Indexed as

Biomarkers, TumorImmunotherapyLung NeoplasmsReceptors, ImmunologicSmall Cell Lung CarcinomaAgedFemaleHumansMaleMiddle AgedPrognosisBiomarkers, TumorReceptors, ImmunologicTIGIT protein, humanCheckpointImmunotherapyPD-L1/PD-1PrognosisSmall cell lung cancerTIGIT

Identifiers

PMID40035834
PMCPMC11880484

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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.