Cancer-associated fibroblasts, clinicopathological characteristics and prognosis of liver cancer: A systematic review and meta-analysis based on real-world research
Bibliographic record
Abstract
BACKGROUND Cancer-associated fibroblasts (CAFs), crucial components of the tumor microenvironment in primary and metastatic tumors, can impact the activity of cancer cells and contribute to their progression. Given their extensive interactions with cancer cells and other stromal cells, we aimed to evaluate the prognostic value of CAFs in patients with liver cancer (LC). AIM To investigate the association between CAF expression and clinicopathological characteristics as well as overall survival (OS) in patients with LC, including hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA). METHODS We performed a meta-analysis of cohort studies with available data on the effects of CAF expression on both clinicopathological characteristics and OS via hazard ratios (HRs) and risk ratios with 95% confidence intervals (CIs). Studies were subgrouped on the basis of CAF markers and cancer type, and the subgroup effects of CAF expression on both HCC and iCCA were analyzed through meta-regression. The Newcastle-Ottawa Scale was used to evaluate the included studies to guarantee their quality and minimize the possibility of bias. RESULTS Nine trials were selected and included a total of 1518 patients. According to our primary meta-analysis, the expression of CAFs in LC patients was significantly associated with a decrease in OS (LC: HR: 1.62; 95%CI: 1.34-1.97; P < 0.001; HCC: HR: 1.67; 95%CI: 1.34-2.07; P < 0.001; iCCA: HR: 1.47; 95%CI: 0.97-2.23; P = 0.07); nevertheless, it was not significantly associated with almost all clinicopathologic characteristics, including tumor size, venous infiltration, alpha-fetoprotein level, and differentiation grade. According to the subgroup analysis of smooth muscle actin (SMA) markers in both HCC patients and iCCA patients, high CAF expression in HCC (HR: 2.29; 95%CI: 1.01-5.22; P = 0.048) and iCCA (HR: 2.04; 95%CI: 1.09-3.81; P = 0.025) patients was a significant indicator of poor OS. Moreover, the clinicopathological characteristics were also verified by the SMA marker, which had a nearly significant effect on the venous infiltration of iCCA (risk ratio: 2.70; 95%CI: 0.97-7.49; P = 0.057). CONCLUSION High CAF expression, evaluated by both mixed markers and SMAs, is significantly associated with poor OS in patients with LC, including both HCC patients and iCCA patients. However, further research is necessary since how CAF expression and clinicopathologic features are related is yet unknown.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".