Parallel-reaction monitoring revealed altered expression of a number of epitranscriptomic reader, writer, and eraser proteins accompanied with colorectal cancer metastasis

High-throughput intact Glycopeptide quantification strategy with targeted-MS (HTiGQs-target) reveals site-specific IgG N-glycopeptides as biomarkers for hepatic disorder diagnosis and staging

Liver disease is one of the leading causes of global mortality, and identifying biomarkers for diagnosing the progression of liver diseases is crucial for improving its outcomes. Targeted mass spectrometry technology is a powerful tool with unique advantages for verifying biomarker candidates and clinical applications. It is particularly useful in validating protein biomarkers with post-translational modifications, eliminating the need for site-specific antibodies. Especially, targeted mass spectrometry technique is particularly critical for translation of glycoproteins into clinical applications as there are no site-specific antibodies for N-glycosylation. Nevertheless, its limitation in analyzing only one sample per run has become apparent when dealing with a large number of clinical samples. Herein, we developed a high-throughput intact N-glycopeptides quantification strategy with targeted-MS (HTiGQs-Target), which allows the validation of 20 samples per run with an average analysis time of only 3 min per sample. We applied HTiGQs-Target in a cohort of 461 serum samples (including 120 healthy controls (HC), 127 chronic hepatitis B (CHB) cases, 106 liver cirrhosis (LC) cases, and 108 hepatocellular carcinomas (HCC) cases) and found that a panel of 10 IgG N-glycopeptides have strong clinical utility in evaluating the severity of the liver disease.

AI-powered therapeutic target discovery

Disease modeling and target identification are the most crucial initial steps in drug discovery, and influence the probability of success at every step of drug development. Traditional target identification is a time-consuming process that takes years to decades and usually starts in an academic setting. Given its advantages of analyzing large datasets and intricate biological networks, artificial intelligence (AI) is playing a growing role in modern drug target identification. We review recent advances in target discovery, focusing on breakthroughs in AI-driven therapeutic target exploration. We also discuss the importance of striking a balance between novelty and confidence in target selection. An increasing number of AI-identified targets are being validated through experiments and several AI-derived drugs are entering clinical trials; we highlight current limitations and potential pathways for moving forward.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

LncRNA BBOX1-AS1 Contributes to the Progression of Esophageal Carcinoma by Targeting the miR-361-3p/COL5A1 Axis

Long noncoding RNAs (lncRNAs) are known to participate in the progression of several cancers, including esophageal carcinoma (EC), a common malignancy of the digestive system. Although the role of the lncRNA-miRNA-mRNA regulatory network is crucial for the growth and progression of EC, the regulation of lncRNA BBOX1-AS1 (BBOX1 antisense RNA1) remains unclear. We performed reverse transcription-quantitative PCR (RT-qPCR) and western blotting to evaluate miR-361-3p, collagen type V alpha 1 chain (COL5A1), and BBOX1-AS1 expression levels in EC cells and tissues. The colony formation assay (CFA) and Cell Counting Kit-8 (CCK-8) were employed to identify EC cell proliferation, while western blotting was used to examine EC cell apoptosis and Bax and Bcl-2 expression levels. The effect of BBOX1-AS1 on EC proliferation was determined using an in vivo carcinogenesis assay. Correlation between COL5A1, BBOX1-AS1, and miR-361-3p was examined using the luciferase reporter system and RNA immunoprecipitation assay (RIP). Herein, we observed that BBOX1-AS1 expression levels were upregulated in EC cells and tissues. BBOX1-AS1 knockdown inhibited EC cell proliferation and conferred a pro-apoptotic effect. These results indicated a positive interaction between BBOX1-AS1 and miR-361-3p in EC and a negative association with miR-361-3p. COL5A1 was recognized as a downstream miR-361-3p target and was inversely related to miR-361-3p in EC. Therefore, BBOX1-AS1 expression suppressed cell apoptosis and promoted cell proliferation via the downregulation of miR-361-3p and upregulation of COL5A1 expression. Overall, BBOX1-AS1 facilitates EC progression via the miR-361-3p or COL5A1 axis, indicating that BBOX1-AS1 might be a potential therapeutic target for EC therapy.

The functions of N6-methyladenosine (m6A) RNA modifications in colorectal cancer

Colorectal cancers (CRC), which includes colon cancer (CC) and rectal cancer (RC), are some of the most common malignant tumors that are prone to distant metastasis. Its high incidence rate and high mortality rate have attracted much attention. In recent years, epigenetics has attracted increasing attention and has been the focus of many research studies. N6-methyladenosine(m6A) RNA modifications can modify eukaryotic mRNA to impact metabolism. The changes in the m6A regulatory genes are related to the occurrence and development of CRC and play an important role in the pathogenesis of CRC. The effect of m6A RNA modification is regulated by its related regulatory factors ("writer", "eraser", "reader"). In this review, we comprehensively analyzed the effect of m6A methylation on CRC and the relationship between the expression of related regulatory factors and the development and occurrence of CRC. Then, we summarized the roles of m6A and its regulatory factors in CRC and its potential clinical value, which provides a basis for further research on the mechanism of m6A methylation in CRC.