Prognostic Role of TIGIT Expression in Patients with Solid Tumors: A Meta-Analysis
Bibliographic record
Abstract
Background. T cell immunoglobulin and ITIM domain (TIGIT) is a recently identified immunosuppressive receptor. The expression levels of TIGIT affect the prognosis of patients with solid tumors. To fully comprehend the role of TIGIT on the prognosis of patients with solid tumors, we conducted a meta-analysis. Methods. We performed an online search of PubMed, Embase, Web of Science (WOS), and MEDLINE databases for literature published till March 31, 2021. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the literature, and Stata 16.0 and Engauge Digitizer 4.1 software were used for data analysis. Results. Our literature search identified eight papers comprising 1426 patients with solid tumors. Increased expression of TIGIT was associated with poor prognosis. High expression of TIGIT was a risk factor for overall survival (OS) { hazard ratio HR = 1.66 , 95% confidence interval (CI) [1.26, 2.20], P < 0.001 } and progression-free survival (PFS) ( HR = 1.44 , 95% CI [1.15, 1.81], P = 0.01 ). We performed subgroup analysis to explore the source of heterogeneity, colorectal cancer ( HR = 2.07 , 95% CI [0.23, 18.82], P = 0.518 ), lung cancer ( HR = 1.29 , 95% CI [0.96, 1.72], P = 0.094 ), esophageal cancer ( HR = 1.70 , 95% CI [1.20, 2.40], P = 0.003 ), and other cancers ( HR = 1.83 , 95% CI [1.25, 2.68], P = 0.002 ). In addition to cancer type, expression location, sample size, and different statistical analysis methods are also considered the possible causes of heterogeneity between studies. Funnel plots suggested no publication bias for OS ( P = 0.902 ), and Egger’s test supported this conclusion ( P = 0.537 ). Conclusion. TIGIT expression was associated with OS and PFS in patients with solid tumors. Patients with elevated TIGIT expression have a shorter OS and PFS, and TIGIT expression could be a novel biomarker for prognosis prediction and a valuable therapeutic target for solid tumors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.031 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".