Genomic analyses based on pulmonary adenocarcinoma in situ reveal early lung cancer signature

Abstract

BackgroundNon-small cell lung cancer (NSCLC) represents more than about 80% of the lung cancer. The early stages of NSCLC can be treated with complete resection with a good prognosis. However, most cases are detected at late stage of the disease. The average survival rate of the patients with invasive lung cancer is only about 4%. Adenocarcinoma in situ (AIS) is an intermediate subtype of lung adenocarcinoma that exhibits early stage growth patterns but can develop into invasion.MethodsIn this study, we used RNA-seq data from normal, AIS, and invasive lung cancer tissues to identify a gene module that represents the distinguishing characteristics of AIS as AIS-specific genes. Two differential expression analysis algorithms were employed to identify the AIS-specific genes. Then, the subset of the best performed AIS-specific genes for the early lung cancer prediction were selected by random forest. Finally, the performances of the early lung cancer prediction were assessed using random forest, support vector machine (SVM) and artificial neural networks (ANNs) on four independent early lung cancer datasets including one tumor-educated blood platelets (TEPs) dataset.ResultsBased on the differential expression analysis, 107 AIS-specific genes that consisted of 93 protein-coding genes and 14 long non-coding RNAs (lncRNAs) were identified. The significant functions associated with these genes include angiogenesis and ECM-receptor interaction, which are highly related to cancer development and contribute to the smoking-free lung cancers. Moreover, 12 of the AIS-specific lncRNAs are involved in lung cancer progression by potentially regulating the ECM-receptor interaction pathway. The feature selection by random forest identified 20 of the AIS-specific genes as early stage lung cancer signatures using the dataset obtained from The Cancer Genome Atlas (TCGA) lung adenocarcinoma samples. Of the 20 signatures, two were lncRNAs, BLACAT1 and CTD-2527I21.15 which have been reported to be associated with bladder cancer, colorectal cancer and breast cancer. In blind classification for three independent tissue sample datasets, these signature genes consistently yielded about 98% accuracy for distinguishing early stage lung cancer from normal cases. However, the prediction accuracy for the blood platelets samples was only 64.35% (sensitivity 78.1%, specificity 50.59%, and AUROC 0.747).ConclusionsThe comparison of AIS with normal and invasive tumor revealed diseases-specific genes and offered new insights into the mechanism underlying AIS progression into an invasive tumor. These genes can also serve as the signatures for early diagnosis of lung cancer with high accuracy. The expression profile of gene signatures identified from tissue cancer samples yielded remarkable early cancer prediction for tissues samples, however, relatively lower accuracy for boold platelets samples.Electronic supplementary materialThe online version of this article (10.1186/s12920-018-0413-3) contains supplementary material, which is available to authorize...

A seven‑miRNA expression‑based prognostic signature and its corresponding potential competing endogenous RNA network in early pancreatic cancer

“…HOTAIR has been reported to be a negative prognostic factor with an oncogenic role in PC (42), and the present study indicated that it may promote the development of PC by upregulating TGFBR2 and ACVR1 via adsorbing miR-454-3p. The ECM receptor interaction pathway has been reported to be involved in the development of various cancer types, including lung cancer (43), bladder cancer (44) and breast cancer (45). The present study suggests that miR-424-5p is downregulated by adsorption of PVTI and LINC00339, leading to upregulation of ITGA2 and LAMC1 involved in the ECM receptor interaction pathway to promote PC.…”

Identification and Integrated Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer

“…DEGs functional enrichment analysis provides major signaling pathways in the occurrence and development of NSCLC. Although it is unclear which is the pivotal culprit in the course of aggravation of the disease, these signaling pathways are closely associated with NSCLC [15–18]. After the PPI network was constructed, a critical network module was identified.…”

Tumor-educated platelets

“…Detecting cancer using spliced TEP profiles was confirmed in a large follow-up study on platelets of NSCLC patients, 12 and confirmed by others. [90][91][92] Various individual spliced transcripts were detected in a validation study of NSCLC patients; the most significantly increased transcripts were CFL1, ACOT7, and ARPC1B, whereas DDX5, RPS5, and EEF1B2 were decreased, independent of age, smoking status, and blood storage, as well as various inflammatory conditions. 12 Although no direct experiments were performed, it is likely that the spliced RNA detected in platelets from NSCLC patients is derived mostly from megakaryocytes and spliced in response to tumor-associated signals rather than tumor derived and ingested by the platelets.…”