Cross-disease analysis identified novel common genes for both lung adenocarcinoma and lung squamous cell carcinoma

Five genes influenced by obesity may contribute to the development of thyroid cancer through the regulation of insulin levels

Previous studies indicate that obesity is an important contributor to the proceeding of thyroid cancer (TC) with limited knowledge of the underlying mechanism. Here, we hypothesize that molecules affected by obesity may play roles in the development of TC. To test the hypothesis above, we first conducted a large-scale literature-based data mining to identify genes influenced by obesity and genes related to TC. Then, a mega-analysis was conducted to study the expression changes of the obesity-specific genes in the case of TC, using 16 independent TC array-expression datasets (783 TC cases and 439 healthy controls). After that, pathway analysis was performed to explore the functional profile of the selected target genes and their potential connections with TC. We identified 1,036 genes associated with TC and 534 regulated by obesity, demonstrating a significant overlap (N = 176, p-value = 4.07e−112). Five out of the 358 obesity-specific genes, FABP4, CFD, GHR, TNFRSF11B, and LTF, presented significantly decreased expression in TC patients (LFC<−1.44; and p-value < 1e⁻⁷). Multiple literature-based pathways were identified where obesity could promote the pathologic development of TC through the regulation of these five genes and INS levels. The five obesity genes uncovered could be novel genes that play roles in the etiology of TC through the modulation of INS levels.

Detection and Independent Validation of Model-Based Quantitative Transcriptional Regulation Relationships Altered in Lung Cancers

Differential expressions of genes are widely evaluated for the diagnosis and prognosis correlations with diseases. But limited studies investigate how transcriptional regulations are quantitatively altered in diseases. This study proposes a novel model-based quantitative measurement of transcriptional regulatory relationships between mRNA genes and Transcription Factor (TF) genes (mqTrans features). This study didn't consider the regulatory relationships between TF genes, so the mRNA genes were the protein-coding genes excluding the TF genes. The models are trained in the control samples in a lung cancer dataset and evaluated in two independent datasets and the hold-out testing samples from the third dataset. Twenty-nine mRNA genes are detected with transcriptional regulations quantitatively altered in lung cancers. The transcriptional modification technologies like RNA interference (RNAi) may be utilized to restore the altered transcriptional regulations in lung cancers.