Bidirectional Mendelian Randomization identifies plasma proteins associated with Cervical Cancer risk
Bibliographic record
Abstract
BACKGROUND: Cervical cancer continues to pose a considerable challenge to global health, necessitating innovative approaches for improved diagnostics and personalized treatment strategies. Prior investigations have suggested that plasma proteins may play a role in the pathogenesis of cervical cancer; however, these studies do not confirm a causal relationship. To address this gap, conducted a large-scale Mendelian randomization (MR) study of the plasma proteome. METHODS: We performed a two-sample bidirectional Mendelian randomization analysis involving 4,907 plasma proteins, utilizing publicly accessible genome-wide association study (GWAS) summary statistics, to examine the causal association between the plasma proteome and the risk of cervical cancer. Analytical methods included inverse variance weighting (IVW), weighted median, MR-Egger regression, and simple and weighted models. Additionally, we performed sensitivity analyses to evaluate heterogeneity and horizontal pleiotropy through Cochran's Q test, MR-Egger intercept, MR-PRESSO test, and leave-one-out analysis. We also applied false discovery rate (FDR) correction to the results of all IVW methods to identify the plasma proteins most strongly associated with cervical cancer. Finally, we enriched the most relevant plasma protein genes using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses and GeneMANIA to identify disease-related pathways. RESULTS: According to the IVW method, seven plasma proteins are significantly associated with cervical cancer risk (P < 0.05). Specifically, six proteins demonstrated protective factors: DEFB135 (OR = 0.201, 95% CI = 0.082-0.492, P < 0.001), FGL2 (OR = 0.104, 95% CI = 0.032-0.338, P < 0.001), FTMT (OR = 0.612, 95% CI = 0.465-0.804, P < 0.001), PDIA4 (OR = 0.088, 95% CI = 0.026-0.295, P < 0.001), SPHK2 (OR = 0.102, 95% CI = 0.030-0.350, P < 0.001), and TMED2 (OR = 0.045, 95% CI = 0.008-0.246, P < 0.001). In contrast, RACGAP1 (OR = 1.755, 95% CI = 1.286-2.395, P < 0.001) was identified as a risk factor. Reverse MR analysis revealed no significant evidence of reverse causation (P > 0.05) between cervical cancer and these plasma proteins. Functional enrichment analysis identified several biologically relevant pathways potentially involved in cervical cancer pathogenesis, including the establishment of organelle localization, regulation of oxidoreductase activity, Ferroptosis, and Porphyrin metabolism. CONCLUSION: These findings suggest that DEFB135, FGL2, FTMT, PDIA4, SPHK2, and TMED2 may protect against cervical cancer, while RACGAP1 may represent a potential risk factor. The identified tumor markers provide mechanistic insights into the molecular basis of cervical cancer and warrant further investigation in functional studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".