VSTM2A Overexpression Is a Sensitive and Specific Biomarker for Mucinous Tubular and Spindle Cell Carcinoma (MTSCC) of the Kidney
Bibliographic record
Abstract
Our recent study revealed recurrent chromosomal losses and somatic mutations of genes in the Hippo pathway in mucinous tubular and spindle cell carcinoma (MTSCC). Here, we performed an integrative analysis of 907 renal cell carcinoma (RCC) samples (combined from The Cancer Genome Atlas and in-house studies) and the Knepper data set of microdissected rat nephrons. We identified VSTM2A and IRX5 as novel cancer-specific and lineage-specific biomarkers in MTSCC. We then assessed their expression by RNA in situ hybridization (ISH) in 113 tumors, including 33 MTSCC, 40 type 1 papillary RCC, 8 type 2 papillary RCC, 2 unclassified RCC, 15 clear cell RCC, and 15 chromophobe RCC. Sensitivity and specificity were calculated as the area under the receiver operating characteristics curve (AUC). All MTSCC tumors demonstrated moderate to high expression of VSTM2A (mean ISH score=255). VSTM2A gene expression assessed by RNA sequencing strongly correlated with VSTM2A ISH score (r(2)=0.81, P=0.00016). The majority of non-MTSCC tumors demonstrated negative or low expression of VSTM2A. IRX5, nominated as a lineage-specific biomarker, showed moderate to high expression in MTSCC tumors (mean ISH score=140). IRX5 gene expression assessed by RNA sequencing strongly correlated with IRX5 ISH score (r(2)=0.69, P=0.00291). VSTM2A (AUC: 99.2%) demonstrated better diagnostic efficacy than IRX5 (AUC: 87.5%), and may thus serve as a potential diagnostic marker to distinguish tumors with overlapping histology. Furthermore, our results suggest MTSCC displays an overlapping phenotypic expression pattern with the loop of Henle region of normal nephrons.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".