SPARC is a Novel Prognostic Biomarker for Ovarian Cancer and Associated with Immune Signatures and Drug Response
Bibliographic record
Abstract
Background: The calcium-binding matricellular glycoprotein (SPARC, secreted protein, acidic and rich in cysteine) belongs to the extracellular-matrix-protein family, and its functions mainly focus on tissue injury, remodeling, and tumorigenesis. The role of SPARC in ovarian cancer remains controversial at present. Methods: We searched SPARC using The Cancer Genome Atlas/Genotype-Tissue Expression (TCGA/GTEx) and other databases to analyze the relationship between its expression level and survival, immunity signatures, and chemical drug response, in ovarian cancer. Additionally, we overexpressed SPARC with plasmids in ovarian cancer SKOV3 and ID8 cell lines, then measured the effects of SPARC on the proliferation, migration, invasiveness, clonality, and stemness of ovarian cancer cells by Cell Counting Kit-8 (CCK8), Transwell, wound healing assay, adhesion assay, plate cloning assay, and soft agar spheroid formation in vitro. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed the potential signaling pathway for SPARC. Results: The higher expression of SPARC in ovarian cancer is related to more advanced tumor stage, poorer clinical survival, and worse chemical drug response, whereas it is positively correlated with immune signatures. For ovarian cancer phenotypes, higher SPARC expression level promotes cell proliferation, migration, colony formation, and spheroid formation. The GO and KEGG enrichment highlighted the potential molecular mechanisms for SPARC with PI3K-AKT and MAPK signaling regulation. Conclusions: SPARC promotes ovarian cancer progression through proliferation, migration, invasiveness, clonality, and stemness. A high level of expression of SPARC in ovarian cancer patients can be used as a marker of poor prognosis and poor drug response.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".