Abstract A033: Secretogranin V as a Potential Biomarker for Esophageal Squamous Cell Carcinoma
Bibliographic record
Abstract
Abstract Background: Oesophageal squamous cell carcinoma (ESCC) remains one of the most poorly diagnosed and deadly cancers worldwide. Identification of biomarkers to accurately predict the risk of recurrence and survival after surgery is therefore crucial to improving patient outcomes. This study examined the expression of Secretogranin V (SCG5) and its correlation with the prognosis of patients with ESCC. Methods: Transcription levels of SCG5 were evaluated in 22 cell lines of ESCC. The biological roles of SCG5 in cell invasion, proliferation, and migration were verified by small interfering RNA- mediated knockdown experiments. The expression of SCG5 was measured in 165 ESCC tissues by using quantitative reverse-transcription (qRT)-PCR, and its association with clinicopathological parameters was analyzed. Results: SCG5 mRNA expression levels varied widely in ESCC cell lines. Knockdown of SCG5 expression significantly suppressed cell invasion, proliferation, and migration of ESCC cells in vitro. Analysis of clinical specimens revealed that the expression of SCG5 mRNA was overexpressed in the ESCC compared to the adjacent normal oesophageal tissues. The high SCG5 expression group had significantly shorter overall and disease-free survival times. In the multivariable analysis, the high expression of SCG5 was determined to be an independent poor prognostic factor. Conclusion: SCG5 may have a significant role as a diagnostic and prognostic biomarker for ESCC. Citation Format: Mohammad Hussain Hamrah. Secretogranin V as a Potential Biomarker for Esophageal Squamous Cell Carcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr A033.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".