Biomarker related to oral squamous carcinoma
A biomarker, oral squamous cell carcinoma technology, applied in biochemical equipment and methods, microbial determination/examination, drug combination, etc., can solve the problem of low overall survival rate of OSCC patients
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Embodiment 1
[0051] Example 1 QPCR detection of expression levels of biomarkers
[0052] 1. Sample collection
[0053] Fifteen oral squamous cell carcinoma tissues and 15 corresponding paracancerous tissues were collected. The patients did not receive any treatment before operation. The cancer tissues were used as the experimental group, and the paracancerous tissues were used as the normal control group.
[0054] 2. RNA extraction
[0055] The RNA in the sample was extracted using QIAGEN tissue RNA extraction kit, and the detailed operation was carried out according to the instructions.
[0056] 3. QPCR detection
[0057] 1) Use the FastQμant cDNA First Strand Synthesis Kit (Product No.: KR106) for mRNA reverse transcription;
[0058] 2) Design QPCR amplification primers based on the sequences of RP13-297E16.4 and C20orf197, synthesized by Biomed Biotech, using GAPDH as an internal reference gene.
[0059] Use SuperReal PreMix Plus (SYBR Green) (product number: FP205) to amplify, and ...
Embodiment 2
[0062] Example 2 Diagnostic efficacy verification of biomarkers
[0063] The preprocessed oral squamous cell carcinoma sequencing data and clinical information were downloaded from the TCGA database, and the sample size was paracancerous:carcinoma=44:331.
[0064] Using R software DESeq2 to analyze differential lncRNA expression, the results showed that RP13-297E16.4 and C20orf197 were significantly upregulated in OSCC tissues.
[0065] The R package "pROC" was used to draw the receiver operating curve (ROC), analyze the AUC value, sensitivity and specificity, and judge the diagnostic efficacy of indicators alone or in combination. When judging the diagnostic efficacy of the combination of indicators, perform logistic regression on the expression level of each gene, calculate the probability of each individual suffering from cancer or not through the fitted regression curve, determine different probability division thresholds, and divide according to the determined probability...
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