Furthermore, abnormal cells were found in 76% sputum by detecting combined HYAL2 and FHIT deletions whereas in 47% sputum by cytology, of the cancer cases, implying that detecting the combination of HYAL2 and FHIT deletions had higher sensitivity than that of sputum cytology for lung cancer diagnosis.
Detecting FHIT deletions for lung cancer diagnosis produced 58% sensitivity in the enriched sputum, whereas there was 42% sensitivity in the unenriched samples (P = .02).
The aim of this study was to identify FHIT gene alterations in bronchoscopy specimens of patients with suspected lung cancer and to determine the molecular relevance, if any, of FHIT alterations in the development of cancer.
These findings suggest FHIT methylation is associated with a higher susceptibility and has a prognostic significance in early stage lung cancer in the Han population of southern-central China and may represent a marker for progressive disease.
Expression of these genes was evaluated by real-time polymerase chain reaction in 55 primary lung cancer samples characterized for FHIT and p53 expression by immunehistochemistry.
These results suggest that the high methylation statuses of p16, RASSF1A, or FHIT genes were associated with a significantly increased risk of lung cancer; the risk of lung cancer increased as the methylation status increased.
The SVM and DT models for diagnosing lung cancer were successfully developed through the combined detection of p16, RASSF1A and FHIT promoter methylation and RTL, which provided useful tools for screening lung cancer.
The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length.
Using a two-stage study design including discovery and replication studies, and stringent Bonferroni correction for multiple statistical analysis, we identified significant genetic interactions between SNPs in <i>RGL1:RAD51B</i> (OR=0.44, <i>p</i> value=3.27x10<sup>-11</sup> in overall lung cancer and OR=0.41, <i>p</i> value=9.71x10<sup>-11</sup> in non-small cell lung cancer), <i>SYNE1:RNF43</i> (OR=0.73, <i>p</i> value=1.01x10<sup>-12</sup> in adenocarcinoma) and <i>FHIT:TSPAN8</i> (OR=1.82, <i>p</i> value=7.62x10<sup>-11</sup> in squamous cell carcinoma) in our analysis.