Low Expression of ADCY4 Predicts Worse Survival of Lung Squamous Cell Carcinoma Based on Integrated Analysis and Immunohistochemical Verification

Pterostilbene exerts anti-lung squamous cell carcinoma function by suppressing the level of KANK3

Early detection of lung squamous cell carcinoma (LUSC) has a significant impact on clinical outcomes, and pterostilbene (PT) is a natural compound with promising anti-oncogenic activities. This study aimed to identify potential LUSC biomarkers through a series of bioinformatic analyses and clinical verification and explored the interaction between PT and selected biomarkers during the treatment of LUSC. The analysis of the expression profile of the clinical samples of LUSC was performed to identify dysexpressed genes (DEGs) and validated by IHC. The role of KANK3 in the anti-LUSC effects of PT was assessed with a series of in vitro and in vivo assays. 4335 DEGs were identified, including 1851 upregulated genes and 2484 downregulated genes. Survival analysis showed that KANK3 was significantly higher in patients with LUSC with an advanced tumor stage. In in vitro assays, PT suppressed cell viability, induced apoptosis, and inhibited migration and invasion in LUSC cell lines, which was associated with downregulation of KANK3. After the reinduction of the KANK3 level in LUSC cells, the anti-LUSC function of PT was impaired. In mice model, reinduction of KANK3 increased tumor growth and metastasis even under the treatment of PT. The findings outlined in the current study indicated that PT exerted anti-LUSC function in a KANK3 inhibition-dependent manner.

Lung proteomics combined with metabolomics reveals molecular characteristics of inflammation-related lung tumorigenesis induced by B(a)P and LPS

Inflammatory microenvironment may take a promoting role in lung tumorigenesis. However, the molecular characteristics underlying inflammation-related lung cancer remains unknown. In this work, the inflammation-related lung tumorigenesis mouse model was established by treated with B(a)P (1 mg/mouse, once a week for 4 weeks), followed by LPS (2.5 μg/mouse, once every 3 weeks for five times), the mice were sacrificed 30 weeks after exposure. TMT-labeled quantitative proteomics and untargeted metabolomics were used to interrogate differentially expressed proteins and metabolites in different mouse cancer tissues, followed by integrated crosstalk between proteomics and metabolomics through Spearman's correlation analysis. The result showed that compared with the control group, 103 proteins and 37 metabolites in B(a)P/LPS group were identified as significantly altered. By searching KEGG pathway database, proteomics pathways such as Leishmaniasis, Asthma and Intestinal immune network for IgA production, metabolomics pathways such as Vascular smooth muscle contraction, Linoleic acid metabolism and cGMP-PKG signaling pathway were enriched. A total of 22 pathways were enriched after conjoint analysis of the proteomic and metabolomics, and purine metabolism pathway, the unique metabolism-related pathway, which included significantly altered protein (adenylate cyclase 4, ADCY4) and metabolites (L-Glutamine, guanosine monophosphate (GMP), adenosine and guanosine) was found. Results suggested purine metabolism may contribute to the inflammation-related lung tumorigenesis, which may provide novel clues for the therapeutic strategies of inflammation-related lung cancer.

Identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysis

Introduction

Cocaine is a highly addictive drug that is abused due to its excitatory effect on the central nervous system. It is critical to reveal the mechanisms of cocaine addiction and identify key genes that play an important role in addiction.

Methods

In this study, we proposed a centrality algorithm integration strategy to identify key genes in a protein-protein interaction (PPI) network constructed by deferential genes from cocaine addiction-related datasets. In order to investigate potential therapeutic drugs for cocaine addiction, a network of targeted relationships between nervous system drugs and key genes was established.

Results

Four key genes (JUN, FOS, EGR1, and IL6) were identified and well validated using CTD database correlation analysis, text mining, independent dataset analysis, and enrichment analysis methods, and they might serve as biomarkers of cocaine addiction. A total of seventeen drugs have been identified from the network of targeted relationships between nervous system drugs and key genes, of which five (disulfiram, cannabidiol, dextroamphetamine, diazepam, and melatonin) have been shown in the literature to play a role in the treatment of cocaine addiction.

Discussion

This study identified key genes and potential therapeutic drugs for cocaine addiction, which provided new ideas for the research of the mechanism of cocaine addiction.

A Deep Learning-Based Framework for Supporting Clinical Diagnosis of Glioblastoma Subtypes

Understanding molecular features that facilitate aggressive phenotypes in glioblastoma multiforme (GBM) remains a major clinical challenge. Accurate diagnosis of GBM subtypes, namely classical, proneural, and mesenchymal, and identification of specific molecular features are crucial for clinicians for systematic treatment. We develop a biologically interpretable and highly efficient deep learning framework based on a convolutional neural network for subtype identification. The classifiers were generated from high-throughput data of different molecular levels, i.e., transcriptome and methylome. Furthermore, an integrated subsystem of transcriptome and methylome data was also used to build the biologically relevant model. Our results show that deep learning model outperforms the traditional machine learning algorithms. Furthermore, to evaluate the biological and clinical applicability of the classification, we performed weighted gene correlation network analysis, gene set enrichment, and survival analysis of the feature genes. We identified the genotype-phenotype relationship of GBM subtypes and the subtype-specific predictive biomarkers for potential diagnosis and treatment.