Integrating genomics and proteomics-oriented biomarkers to comprehend lung cancer

Unveiling the potential of proteomic and genetic signatures for precision therapeutics in lung cancer management

Lung cancer's enduring global significance necessitates ongoing advancements in diagnostics and therapeutics. Recent spotlight on proteomic and genetic biomarker research offers a promising avenue for understanding lung cancer biology and guiding treatments. This review elucidates genetic and proteomic lung cancer biomarker progress and their treatment implications. Technological strides in mass spectrometry-based proteomics and next-generation sequencing enable pinpointing of genetic abnormalities and abnormal protein expressions, furnishing vital data for precise diagnosis, patient classification, and customized treatments. Biomarker-driven personalized medicine yields substantial treatment improvements, elevating survival rates and minimizing adverse effects. Integrating omics data (genomics, proteomics, etc.) enhances understanding of lung cancer's intricate biological milieu, identifying novel treatment targets and biomarkers, fostering precision medicine. Liquid biopsies, non-invasive tools for real-time treatment monitoring and early resistance detection, gain popularity, promising enhanced management and personalized therapy. Despite advancements, biomarker repeatability and validation challenges persist, necessitating interdisciplinary efforts and large-scale clinical trials. Integrating artificial intelligence and machine learning aids analyzing vast omics datasets and predicting treatment responses. Single-cell omics reveal cellular connections and intratumoral heterogeneity, valuable for combination treatments. Biomarkers enable accurate diagnosis, tailored medicines, and treatment response tracking, significantly impacting personalized lung cancer care. This approach spurs patient-centered trials, empowering active patient engagement. Lung cancer proteomic and genetic biomarkers illuminate disease biology and treatment prospects. Progressing towards individualized efficient therapies is imminent, alleviating lung cancer's burden through ongoing research, omics integration, and technological strides.

Comprehensive analysis of competing endogenous RNA networks associated with cholangiocarcinoma

Cholangiocarcinoma (CCA) is the second most common type of primary malignancy of the liver. Certain long non-coding RNAs (lncRNAs) have been demonstrated to have key roles in tumor pathogenesis by binding to microRNAs (miRNAs). However, the competing endogenous RNA (ceRNA) network of CCA remains to be fully determined. In the present study, the RNA expression profiles for CCA were downloaded from The Cancer Genome Atlas and further analyzed. A total of 318 differentially expressed (DE) lncRNAs, 87 DE miRNAs and 3,851 DE mRNAs were identified from 36 CCA samples and 9 adjacent non-tumor samples (for lncRNAs and miRNAs, fold change ≥2.5 and P<0.01; for mRNAs, fold change ≥2 and P<0.01). Further bioinformatics analyses were performed and the ceRNA network for CCA was constructed, which included 16 lncRNAs, 55 miRNAs and 373 mRNAs. Survival analysis of all genes in the network revealed that high expression of the mRNAs fucosyltransferase 4 (P<0.005) and huntingtin-interacting protein 1 related (P<0.001) has a positive impact on the overall survival of patients with CAA. Furthermore, the lncRNAs H19 and PVT1, and the miRNAs Homo sapiens (hsa)-miR-16-5p and hsa-miR-424-5p, together with peroxisome proliferator-activated receptors, may also have important roles in the pathogenesis of CCA. The present study provided data to further the understanding of and research into the molecular mechanisms implicated in CCA.

Using genomics to develop novel antibacterial therapeutics

As the genomics era matures, the availability of complete microbial genome sequences is facilitating computational approaches to understand bacterial genomes and DNA structure/function relationships. From the genome of pathogens, we can derive invaluable information on potential targets for new antimicrobial agents. Advancements in high-throughput 'omics' technologies and the availability of multiple isolates of the same species have significantly changed the time frame and scope for identifying novel therapeutic targets. This article aims to discuss selected aspects of the bacterial genome, and advocates 'omics'-based techniques to advance the discovery of new therapeutic targets against extracellular bacterial pathogens.

Proteomics: a strategy to understand the novel targets in protein misfolding and cancer therapy

Proteins carry out important functions as they fold themselves. Protein misfolding occurs during different biochemical processes and may lead to the development of diseases such as cancer, which is characterized by genetic instability. The cancer microenvironment exposes malignant cells to a variety of stressful conditions that may further promote protein misfolding. Tumor development and progression often arises from mutations that interfere with the appropriate function of tumor-suppressor proteins and oncogenes. These may be due to alteration of catalytic activity of the protein, loss of binding sites for effector proteins or alterations of the native folded protein conformation. Src family kinases, p53, mTOR and C-terminus of HSC70 interacting protein (CHIPs) are some examples associated with protein misfolding and tumorigenesis. Molecular chaperones, such as heat-shock protein (HSP)70 and HSP90, assist protein folding and recognize target misfolded proteins for degradation. It is likely that this misfolding in cancer is linked by common principles, and may, therefore, present an exciting possibility to identify common targets for therapeutic intervention. Here we aim to review a number of examples that show how alterations in the folding of tumor-suppressor proteins or oncogenes lead to tumorigenesis. The possibility of targeting the targets to repair or degrade protein misfolding in cancer therapy is discussed.