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The jumonji domain-containing protein 6 (JMJD6) gene catalyzes the arginine demethylation and lysine hydroxylation of histone and a growing list of its known substrate molecules, including p53 and U2AF65, suggesting a possible role in mRNA splicing and transcription in cancer progression. Mass spectrometry-based technology offers the opportunity to detect SNP variants accurately and effectively. In our study, we conducted a combined computational and filtration workflow to predict the nonsynonymous single nucleotide polymorphisms (nsSNPs) present in JMJD6, followed by a liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis and validation. The computational approaches SIFT, PolyPhen-2, SNAP, I-Mutant 2.0, PhD-SNP, PANTHER, and SNPS&GO were integrated to screen out the predicted damaging/deleterious nsSNPs. Through the three-dimensional structure of JMJD6, H187R (rs1159480887) was selected as a candidate for validation. The validation experiments showed that the mutation of this nsSNP in JMJD6 obviously affected mRNA splicing or the transcription of downstream genes through the reduced lysyl-hydroxylase activity of its substrates, U2AF65 and p53, further indicating the accuracy of this prediction method. This research provides an effective computational workflow for researchers with an opportunity to select prominent deleterious nsSNPs and, thus, remains promising for examining the dysfunction of proteins.

Colorectal cancer (CRC) is a prevalent malignant tumor worldwide, with a high mortality rate due to its complex etiology and limited early screening techniques. This study aimed to identify potential biomarkers for early detection of CRC utilizing targeted metabolite profiling of platelet‐rich plasma (PRP). Based on multiple reaction monitoring (MRM) mode, liquid chromatography tandem mass spectrometry (LC–MS/MS) analysis identified metabolites in PRP collected from patients with CRC (n = 70) and healthy controls (n = 30). A total of 302 metabolites were identified and quantified in this study, including various categories such as lipids, lipid mediators, amino acids, and derivatives, organic acids and derivatives, nucleotides and derivatives, alkaloids, carbohydrates, vitamins and derivatives, and others. The differential analysis revealed that five carbohydrates and organic acids (lactose, glycerol‐3‐phosphate, 2−hydroxyglutaric acid, isocitric acid, and citric acid) involved in the carbohydrate metabolism pathway displayed consistent upregulation within PRP derived from patients with CRC. To further validate the abundance of differential metabolites, 10 pairs of CRC tissues, adjacent tissues, and matched PRP were collected. Ultimately, five carbohydrate metabolites were validated in PRP, and compared with carcinoembryonic antigen (CEA) and cancer antigen 19‐9 (CA199), the five carbohydrate metabolites significantly improved the specificity of differentiating patients with CRC from healthy controls. Furthermore, the diagnostic efficacy of the combined five‐carbohydrate metabolite panel was superior to that of individual metabolites, CEA and CA199. The sensitivity, specificity, and AUC of the metabolite panel in distinguishing patients with CRC from healthy controls were 90.00%, 96.67%, and 0.961 (95% CI 0.922–0.998), respectively. Collectively, metabolomics was used to identify and validate differential metabolites in the PRP of CRC, which may serve as potential early screening markers for patients with CRC. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is a deadly cancer that emerges from a continuous progression of liver cells from normal to abnormal, often following infections by hepatitis B/C viruses (HBV/HCV), liver fibrosis, and liver cirrhosis (LC), ultimately culminating in cancer. However, there is currently limited systematic molecular analysis of biomarkers at different stages of HCC progression using multi‐omics approaches. We carried out an innovative pipeline by utilizing targeted proteomics and metabolomics to identify potential biomarkers for early detection of HCC in 316 participants, including healthy adults and patients diagnosed with HBV, HCV, LC, and HCC from three independent cohorts. We first established a detailed database of candidate biomarkers for HCC containing 3059 proteins and 103 metabolites, and identified pivotal candidates implicated in the progressive trajectory of liver cancers. Through our developed DeepPRM, scheduled multiple reaction monitoring (MRM)‐targeted approach, and machine learning‐based computational pipeline, we identified an eight‐biomolecular‐based combination with an accuracy rate of 91.43% for early diagnosis of HCC, and a 12‐biomolecular‐based combination with an accuracy rate of 80.00% for detecting changes in HBV–LC progression. These two biomarker combinations significantly improved accuracy compared to traditional tumor biomarkers. Our extensive analysis provides valuable proteomic and metabolomic data resources that will contribute to a deeper understanding of liver disease progression and enhance the identification of potential therapeutic targets. [ABSTRACT FROM AUTHOR]

In article number 20240031, the authors have showed us the work identified protein‐metabolite biomarkers in serum for hepatitis B liver cancer progression..By Jin Xiao; Hang Liu; Jun Yao; Shuang Yang; Fenglin Shen; KunPeng Bu; Zhenxin Wang; Fan Liu; Ningshao Xia; Quan Yuan; Hong Shu; Yueting Xiong and Xiaohui LiuReported by Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author [Extracted from the article]

Biomarker discovery and application are paramount for the early diagnosis, treatment, and prognosis assessment of diseases. Novel proteomic strategies have been developed for high-efficiency biomarker screening. However, evaluating various strategies and applying them for the in-depth mining of biomarkers from blood need to be elucidated. Herein, we systematically evaluated the technical characteristics of three representative biomarker discovery strategies, including the most popular DIA proteomics, and two promising strategies targeting the cancer-secreted proteome or extracellular vesicle proteome, and integrated them into one multidimensional serum proteomic strategy. The results showed that the three strategies each have unique characteristics in terms of sensitivity, reproducibility, and protein coverage and are highly complementary in biomarker discovery. The integrated multidimensional serum proteomic strategy achieves deep and comprehensive coverage of the serum proteome, discovers more cancer markers, and helps achieve a more accurate multicancer (breast, lung, stomach, liver, and colorectum) diagnosis with 87.5% localization accuracy.

Background/aim: Pancreatic cancer (PC) is currently the fourth leading cause of cancer-related mortality worldwide. Peripheral blood mononuclear cells (PBMCs) is a subpopulation of accessible and functional immune cells. Comparative analysis of the proteome of PBMCs can help us elucidate the mechanism of disease and find potential biomarkers for diagnosis., Materials and Methods: PBMCs were collected from healthy individuals, patients with benign diseases, and pancreatic cancer. iTRAQ-2DLC-MS/MS and SWATH methodologies were applied to make a comparative proteomics analysis of PBMCs., Results: A total of 3,357 proteins with a false discovery rate (FDR) <1% were identified, of which 114 proteins were found dysregulated in the PC group. An extensive SWATH library was constructed which showed a potential application for large scale clinical sample analysis., Conclusion: A PBMCs proteome with extensive protein representation was achieved, which will potentially allow the identification of novel biomarkers for PC., (Copyright© 2019, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)