Label-free quantitative proteomics reveals aberrant expression levels of LRG, C9, FN, A1AT and AGP1 in the plasma of patients with colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is one of the major causes of cancer-related death worldwide. Although commercial biomarkers of CRC are currently available, they are still lacking in terms of sensitivity and specificity; thus, searching for reliable blood-based biomarkers are important for the primary screening of CRC. METHODS: Plasma samples of patients with non-metastatic (NM) and metastatic (M) CRC and healthy controls were fractionated using MARS-14 immunoaffinity chromatography. The flow-through and elute fractions representing low- and high-abundant proteins, respectively, were analyzed by label-free quantitative proteomics mass spectrometry. The functional analysis of the proteins with greater than 1.5-fold differential expression level between the CRC and the healthy control groups were analyzed for their biological processes and molecular functions. In addition, the levels of plasma proteins showing large alterations in CRC patients were confirmed by immunoblotting using two independent cohorts. Moreover, receiver operating characteristic (ROC) curve analysis was performed for individual and combinations of biomarker candidates so as to evaluate the diagnostic performance of biomarker candidates. RESULTS: From 163 refined identifications, five proteins were up-regulated and two proteins were down-regulated in NM-CRC while eight proteins were up-regulated and three proteins were down-regulated in M-CRC, respectively. Altered plasma proteins in NM-CRC were mainly involved in complement activation, while those in M-CRC were clustered in acute-phase response, complement activation, and inflammatory response. Results from the study- and validation-cohorts indicate that the levels of leucine-rich alpha-2-glycoprotein-1(LRG), complement component C9 (C9), alpha-1-acid glycoprotein 1 (AGP1), and alpha-1-antitrypsin (A1AT) were statistically increased, while fibronectin (FN) level was statistically decreased in CRC patients compared to healthy controls, with most alterations found in a metastatic stage-dependent manner. ROC analysis revealed that FN exhibited the best diagnostic performance to discriminate CRC patients and healthy controls while AGP1 showed the best discrimination between the disease stages in both cohorts. The combined biomarker candidates, FN + A1AT + AGP1, exhibited perfect discriminatory power to discriminate between the CRC population and healthy controls whereas LRG + A1AT + AGP1 was likely to be the best panel to discriminate the metastatic stages in both cohorts. CONCLUSIONS: This study identified and quantified distinct plasma proteome profiles of CRC patients. Selected CRC biomarker candidates including FN, LRG, C9, A1AT, and AGP1 may be further applied for screening larger cohorts including disease groups from other types of cancer or other diseases.

Machine learning applications for transcription level and phenotype predictions.

Predicting phenotypes and complex traits from genomic variations has always been a big challenge in molecular biology, at least in part because the task is often complicated by the influences of external stimuli and the environment on regulation of gene expression. With today's abundance of omic data and advances in high-throughput computing and machine learning (ML), we now have an unprecedented opportunity to uncover the missing links and molecular mechanisms that control gene expression and phenotypes. To empower molecular biologists and researchers in related fields to start using ML for in-depth analyses of their large-scale data, here we provide a summary of fundamental concepts of machine learning, and describe a wide range of research questions and scenarios in molecular biology where ML has been implemented. Due to the abundance of data, reproducibility, and genome-wide coverage, we focus on transcriptomics, and two ML tasks involving it: (a) predicting of transcriptomic profiles or transcription levels from genomic variations in DNA, and (b) predicting phenotypes of interest from transcriptomic profiles or transcription levels. Similar approaches can also be applied to more complex data such as those in multi-omic studies. We envisage that the concepts and examples described here will raise awareness and promote the application of ML among molecular biologists, and eventually help improve a framework for systematic design and predictions of gene expression and phenotypes for synthetic biology applications.

Three-Decade Successive Establishment of Care for Women/Girls from Families with Haemophilia.

Objective: The study aimed to report a 3-decade successive establishment of care for women/girls from families with haemophilia. Methods: A retrospective analysis was conducted on 462 women/girls from 243 families from 1987 to 2021. Results: Combining phenotypic analysis of coagulation factor and genotypic analysis of either linkage analysis or mutation detection confirmed the status of all obligate haemophilia carriers (A118, B19). For potential carrier, 159 proven carriers (A130, B29) and 146 noncarrier status (A126, B20) were diagnosed except 20 potential carriers (A16, B4). Only 54 prenatal diagnoses were requested resulting in normal males (n = 21), males with haemophilia A (n = 12) and females with either normal or carrier status (n = 21). Additionally, 40 women/girls with haemophilia carrier received a diagnosis of severe haemophilia A with Turner's syndrome (n = 2) and mild haemophilia (A31, B7). The skewed X-chromosome inactivation of the nonmutant factor VIII/IX carrying X-chromosome of 8% (2/25) was found in mild haemophilia. Factor concentrate and desmopressin are prescribed for these affected women/girls. The response of women/girls with either haemophilia carrier or haemophilia was amazement with their religious beliefs and cultural acceptance. Conclusion: Appropriate care for women/girls from families with haemophilia concerning diagnosis and management of haemophilia and carrier has been successively established.

A Simple and Applicable Method for Human Platelet Lysate Preparation Using Citrate Blood.

OBJECTIVES: To determine and compare the platelet growth factors in human platelet lysate (HPL) prepared from citrated whole blood, with final centrifugations at 4oC and 25oC. METHODS: We collected specimens of citrated whole blood from 27 healthy volunteers. The platelet-rich plasma (PRP) was separated to prepare the HPL, which was further divided into 2 portions for the final centrifugation, at 4oC and 25oC, respectively. Platelet growth factors were measured and compared between the 2 groups. RESULTS: All platelet growth factors were higher than those in PRP prepared from citrated whole blood. Moreover, the final centrifugation at 25oC resulted in noninferiority of platelet-growth-factor level. CONCLUSION: This study provided a simple method for small-volume of HPL preparation using only 10-15 mL of citrated whole blood. Further, the entire process of centrifugation can be performed at room temperature of 25oC, which is more applicable than lower temperatures for other laboratories.

Glycoproteomic Analysis Reveals Aberrant Expression of Complement C9 and Fibronectin in the Plasma of Patients with Colorectal Cancer.

Colorectal cancer (CRC) is a major cause of cancer mortality. Currently used CRC biomarkers provide insufficient sensitivity and specificity; therefore, novel biomarkers are needed to improve the CRC detection. Label-free quantitative proteomics were used to identify and compare glycoproteins, enriched by wheat germ agglutinin, from plasma of CRC patients and age-matched healthy controls. Among 189 identified glycoproteins, the levels of 7 and 15 glycoproteins were significantly altered in the non-metastatic and metastatic CRC groups, respectively. Protein-protein interaction analysis revealed that they were predominantly involved in immune responses, complement pathways, wound healing and coagulation. Of these, the levels of complement C9 (C9) was increased and fibronectin (FN1) was decreased in both CRC states in comparison to those of the healthy controls. Moreover, their levels detected by immunoblotting were validated in another independent cohort and the results were consistent with in the study cohort. Combination of CEA, a commercial CRC biomarker, with C9 and FN1 showed better diagnostic performance. Interestingly, predominant glycoforms associated with acetylneuraminic acid were obviously detected in alpha-2 macroglobulin, haptoglobin, alpha-1-acid glycoprotein 1, and complement C4-A of CRC patient groups. This glycoproteomic approach provides invaluable information of plasma proteome profiles of CRC patients and identification of CRC biomarker candidates.

Urinary biomarkers for the diagnosis of cervical cancer by quantitative label-free mass spectrometry analysis.

Due to the invasive procedure associated with Pap smears for diagnosing cervical cancer and the conservative culture of developing countries, identifying less invasive biomarkers is of great interest. Quantitative label-free mass spectrometry was performed to identify potential biomarkers in the urine samples of patients with cervical cancer. This technique was used to study the differential expression of urinary proteomes between normal individuals and cancer patients. The alterations in the levels of urinary proteomes in normal and cancer patients were analyzed by Progenesis label-free software and the results revealed that 60 proteins were upregulated while 73 proteins were downregulated in patients with cervical cancer. This method could enrich high molecular weight proteins from 100 kDa. The protein-protein interactions were obtained by Search Tool for the Retrieval of Interacting Genes/Proteins analysis and predicted the biological pathways involving various functions including cell-cell adhesion, blood coagulation, metabolic processes, stress response and the regulation of morphogenesis. Two notable upregulated urinary proteins were leucine-rich α-2-glycoprotein (LRG1) and isoform-1 of multimerin-1 (MMRN1), while the 3 notable downregulated proteins were S100 calcium-binding protein A8 (S100A8), serpin B3 (SERPINB3) and cluster of differentiation-44 antigen (CD44). The validation of these 5 proteins was performed by western blot analysis and the biomarker sensitivity of these proteins was analyzed individually and in combination with receiver operator characteristic curve (ROC) analysis. Quantitative mass spectrometry analysis may allow for the identification of urinary proteins of high molecular weight. The proteins MMRN1 and LRG1 were presented, for the first time, to be highly expressed urinary proteins in cervical cancer. ROC analysis revealed that LRG1 and SERPINB3 could be individually used, and these 5 proteins could also be combined, to detect the occurrence of cervical cancer.