Cen Yixuan, Tu Mengyan, Zhang Yanan, Ren Yan, Xu Junfen
Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Department of Gynecologic Oncology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
J Gynecol Oncol. 2025 Sep;36(5):e81. doi: 10.3802/jgo.2025.36.e81. Epub 2025 Mar 4.
Squamous cell carcinoma (SCC) and adenocarcinoma (ADC) represent predominant histological subtypes of cervical cancer. To improve screening efficacy, we leveraged RNA sequencing data from 4 cervical SCC samples, 4 cervical ADC samples, and 8 normal cervix samples and conducted a comprehensive mRNA and long noncoding RNA (lncRNA) profiling analysis followed with a multi-phase study comprising 556 samples. Validating the RNA sequencing data in a clinical sample set comprising 45 normal cervix tissues, 45 SCC tissues, and 45 ADC tissues, we identified 9 mRNAs (SMC1B, OTX1, GRP, CELSR3, HOXC6, ITGB6, WDR62, SEPT3, and KLHL34) and 4 lncRNAs (FEZF1-AS1, LINC01305, LINC00857, and LINC00673) differentially expressed in both SCC and ADC samples. Utilizing quantitative reverse transcription polymerase chain reaction analysis and receiver operating characteristic (ROC) curve analysis in a training set (45 normal, 126 SCC, and 82 ADC tissues), we refined a novel mRNA-lncRNA-based panel (SMC1B/CELSR3/FEZF1-AS1/LINC01305). Employing logistic regression model and ROC analysis, this panel exhibited significant distinctions and promising area under the curve (AUC) values in both SCC (AUC=0.9520, p<0.0001) and ADC (AUC=0.9748, p<0.0001) tissues. Subsequent validation in an independent set (11 normal, 32 SCC, and 20 ADC tissues) demonstrated its diagnostic accuracy in both SCC (AUC=0.9659, p<0.0001) and ADC (AUC=0.9636, p<0.0001) patients. Notably, this tissue-based biomarker panel robustly discriminated precancerous lesion and cervical cancer patients from non-disease controls in a blood-based validation set (30 normal, 25 HSIL and 50 cervical cancer) with an AUC value of 0.9320. This study presents a non-invasive, efficient diagnostic panel for cervical cancer screening.
鳞状细胞癌(SCC)和腺癌(ADC)是宫颈癌的主要组织学亚型。为提高筛查效率,我们利用了来自4例宫颈SCC样本、4例宫颈ADC样本和8例正常宫颈样本的RNA测序数据,进行了全面的mRNA和长链非编码RNA(lncRNA)谱分析,随后开展了一项包含556个样本的多阶段研究。在一个由45例正常宫颈组织、45例SCC组织和45例ADC组织组成的临床样本集中验证RNA测序数据后,我们鉴定出9种在SCC和ADC样本中均差异表达的mRNA(SMC1B、OTX1、GRP、CELSR3、HOXC6、ITGB6、WDR62、SEPT3和KLHL34)以及4种lncRNA(FEZF1-AS1、LINC01305、LINC00857和LINC00673)。在一个训练集(45例正常、126例SCC和82例ADC组织)中利用定量逆转录聚合酶链反应分析和受试者工作特征(ROC)曲线分析,我们优化出了一个基于mRNA-lncRNA的新型检测组合(SMC1B/CELSR3/FEZF1-AS1/LINC01305)。采用逻辑回归模型和ROC分析,该检测组合在SCC(AUC=0.9520,p<0.0001)和ADC(AUC=0.9748,p<0.0001)组织中均表现出显著差异和良好的曲线下面积(AUC)值。随后在一个独立数据集(11例正常、32例SCC和20例ADC组织)中进行验证,结果表明其在SCC(AUC=0.9659,p<0.0001)和ADC(AUC=0.9636,p<0.0001)患者中均具有诊断准确性。值得注意的是,在一个基于血液的验证集(30例正常、25例高级别鳞状上皮内病变和50例宫颈癌患者)中,这个基于组织的生物标志物检测组合能够有力地区分癌前病变和宫颈癌患者与非疾病对照,AUC值为0.9320。本研究提出了一种用于宫颈癌筛查的非侵入性、高效的诊断检测组合。