Integrative analyses identified ion channel genes GJB2 and SCNN1B as prognostic biomarkers and therapeutic targets for lung adenocarcinoma

The gut microbial dysbiosis is a risk of colorectal cancer (CRC) and some bacteria have been reported as potential markers for CRC diagnosis. However, heterogeneity among studies with different populations and technologies lead to inconsistent results. Here, we investigated six metagenomic profiles of stool samples from healthy controls (HC), colorectal adenoma (CA) and CRC, and six and four genera were consistently altered between CRC and HC or CA across populations, respectively. In FengQ cohort, which composed with 61 HC, 47 CA, and 46 CRC samples, a random forest (RF) model composed of the six genera, denoted as signature-HC, distinguished CRC from HC with an area under the curve (AUC) of 0.84. Similarly, another RF model composed of the four universal genera, denoted as signature-CA, discriminated CRC from CA with an AUC of 0.73. These signatures were further validated in five metagenomic sequencing cohorts and six independent 16S rRNA gene sequencing cohorts. Interestingly, three genera overlapped in the two models (Porphyromonas, Parvimonas and Peptostreptococcus) were with very low abundance in HC and CA, but sharply increased in CRC. A concise RF model on the three genera distinguished CRC from HC or CA with AUC of 0.87 and 0.67, respectively. Functional gene family analysis revealed that Kyoto Encyclopedia of Genes and Genomes Orthogroups categories which were significantly correlated with markers in signature-HC and signature-CA were mapped into pathways related to lipopolysaccharide and sulfur metabolism, which might be vital risk factors of CRC development. Conclusively, our study identified universal bacterial markers across populations and technologies as potential aids in non-invasive diagnosis of CRC.

Recurrence is the main factor affecting the prognosis of early hepatocellular carcinoma (HCC), which is not accurately evaluated by clinical indicators. The metabolic heterogeneity of HCC hints at the possibility of constructing a stratification model to predict the clinical outcome. On the basis of the relative expression orderings of 2939 metabolism-related genes, an individualized signature with 10 metabolism-related gene pairs (10-GPS) was developed from 250 early HCC samples in the discovery datasets, which stratified HCC patients into the high- and low-risk subgroups with significantly different survival rates. The 10-GPS was validated in 311 public transcriptomic samples from two independent validation datasets. A nomogram that included the 10-GPS, age, gender, and stage was constructed for eventual clinical evaluation. The low-risk group was characterized by lower proliferation, higher metabolism, increased activated immune microenvironment, and lower TIDE scores, suggesting a better response to immunotherapy. The high-risk group displayed hypomethylation, higher copy number alterations, mutations, and more overexpression of immune-checkpoint genes, which might jointly lead to poor outcomes. The prognostic accuracy of the 10-GPS was further validated in 47 institutional transcriptomic samples and 101 public proteomic samples. In conclusion, the 10-GPS is a robust predictor of the clinical outcome for early HCC patients and could help evaluate prognosis and characterize molecular heterogeneity.

BackgroundMedullary thyroid carcinoma (MTC), a thyroid C cell-derived malignancy, is poorly differentiated and more aggressive than papillary, follicular and oncocytic types of thyroid cancer. The current therapeutic options are limited, with a third of population suffering resistance. The differential gene expression pattern among thyroid cancer subtypes remains unclear. This study intended to explore the exclusive gene profile of MTC and construct a comprehensive regulatory network via integrated analysis, to uncover the potential key biomarkers.MethodsMultiple datasets of thyroid and other neuroendocrine tumors were obtained from GEO and TCGA databases. Differentially expressed genes (DEGs) specific in MTC were identified to construct a transcription factor (TF)-mRNA-miRNA network. The impact of the TF-mRNA-miRNA network on tumor immune characteristics and patient survival was further explored by single-sample GSEA (ssGSEA) and ESTIMATE algorithms, as well as univariate combined with multivariate analyses. RT-qPCR, cell viability and apoptosis assays were performed for in vitro validation.ResultsWe identified 81 genes upregulated and 22 downregulated in MTC but not in other types of thyroid tumor compared to the normal thyroid tissue. According to the L1000CDS2 database, potential targeting drugs were found to reverse the expressions of DEGs, with panobinostat (S1030) validated effective for tumor repression in MTC by in vitro experiments. The 103 DEGs exclusively seen in MTC were involved in signal release, muscle contraction, pathways of neurodegeneration diseases, neurotransmitter activity and related amino acid metabolism, and cAMP pathway. Based on the identified 15 hub genes, a TF-mRNA-miRNA linear network, as well as REST-cored coherent feed-forward loop networks, namely REST-KIF5C-miR-223 and REST-CDK5R2-miR-130a were constructed via online prediction and validation by public datasets and our cohort. Hub-gene, TF and miRNA scores in the TF-mRNA-miRNA network were related to immune score, immune cell infiltration and immunotherapeutic molecules in MTC as well as in neuroendocrine tumor of lung and neuroblastoma. Additionally, a high hub-gene score or a low miRNA score indicated good prognoses of neuroendocrine tumors.ConclusionThe present study uncovers underlying molecular mechanisms and potential immunotherapy-related targets for the pathogenesis and drug discovery of MTC.