MRNAs marker and kit for early diagnosis of pulmonary signet-ring cell carcinoma

12 mRNA markers were discovered through RNA sequencing and machine learning methods, and the ultimate random tree risk score model was constructed, which solved the problem of insufficient sensitivity and specificity of early diagnosis of lung mark ring cell carcinoma in the prior art, and achieved high sensitivity and specificity early diagnosis.

CN120330331AInactive Publication Date: 2025-07-18HANGZHOU FIRST PEOPLES HOSPITAL +1
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Patent Information

Application Number
CN202510463064.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-14
Publication Date
2025-07-18
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The prior art is difficult to provide high sensitivity and specific biomarkers for the early diagnosis of lung marking ring cell carcinoma, there are problems with high false positives in imaging and pathological examinations, and insufficient sensitivity and specificity of immunohistochemistry examinations, which cannot meet the needs of early diagnosis.

Method used

The mRNA expression profiles of cancer tissues and control tissues of lung marker cell carcinoma patients were detected by RNA sequencing technology. Combined with statistical and machine learning methods, 12 differentially expressed mRNA markers FASN, RGPD2, ACSL1, ABCA3, MT2A, ITPKC, FKBP5, CEACAM6, HP, CAPN8, FCGBP and MUC16 were found, and the ultimate random tree risk score model was constructed for diagnosis.

Benefits of technology

The constructed risk scoring model is sensitive to early lung mark ring cell carcinoma diagnosis >80% and specificity >60%, improving the accuracy of early diagnosis.

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Abstract

The invention discloses an mRNAs marker and a kit for early diagnosis of pulmonary signet-ring cell carcinoma. The marker is a combination of FASN, RGPD2, ACSL1, ABCA3, MT2A, ITPKC, FKBP5, CEACAM6, HP, CAPN8, FCGBP and MUC16, and is characterized in that the marker is a combination of FASN, RGPD2, ACSL1, ABCA3, MT2A, ITPKC, FKBP5, CEACAM6, HP, CAPN8 and MUC16. According to the invention, FFPE cancer tissue of a patient with primary pulmonary signet-ring cell carcinoma diagnosed by an existing detection means in clinic is used as a sample, para-carcinoma tissue is used as a control sample, mRNA expression profiles of the cancer tissue and the control tissue are detected by adopting an RNA sealing technology, and 12 differentially expressed mRNA markers are found by applying statistics and machine learning methods. Based on the found 12 markers, a risk scoring model with high sensitivity and specificity is constructed, and the sensitivity gt of the model to early-stage lung signet-ring cell carcinoma diagnosis; the specificity is gt; 60%.
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