Epidemiology, molecular typing, microbiome-immune interactions and treatment strategies of endometrial cancer: a review
Bibliographic record
Abstract
This review focuses on the field of endometrial cancer. Since 2020, there have been 417,367 new cases of endometrial cancer diagnosed globally and 97,370 deaths reported. Endometrial cancer ranks second in terms of incidence among female genital malignancies and third in terms of mortality among gynecological cancers. The stage, grade, and histological subtype of endometrial cancer were closely correlated with the risk of recurrence and prognosis for survival. Meanwhile, endometrial cancer exhibits significant biological heterogeneity. The complex interactions among the reproductive tract, host cells, and the microbial environment may harbor novel disease mechanisms. In this review, we provide an overview of the epidemiological characteristics, major risk factors, histological and molecular subtypes of endometrial cancer, as well as explore the associations between the female reproductive tract microbiome, immunity, and cancer progression. We also identify the specific roles of different cytokines in the pathophysiology of endometrial cancer. By integrating findings from diverse research fields, this comprehensive review offers an in-depth understanding of the multidimensional nature of endometrial cancer and highlights the significant potential and promising avenues that microbiological factors present for advancing future cancer research and guiding the development of innovative therapeutic strategies.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".