Data_Sheet_1_Nutritional Challenges and Dietary Practices of Ethnic Minority (Indigenous) Groups in China: A Critical Appraisal.xlsx

Indigenous food systems can affect multiple aspects of Indigenous people's health. In China, the government declared that there are no Indigenous people in China and used the term “ethnic minority groups” instead. However, to date, no attempt has been made to investigate the nutrition status and dietary practices of all 55 ethnic minority groups. To understand this pertinent issue, a systematic review is required. The main selection criteria were publications should be about nutrition status or dietary practices among ethnic minority groups in China, specify the name of the ethnic minority group, and be published within the past 10 years. For this literature review, 111 publications were selected through Wanfang Med Online for Chinese publications and Google Scholar for English publications. Linear regressions were applied to explore what factors can affect the total number of publications for an ethnic minority group. The main findings include that only 15 ethnic minority groups have dietary intake data representing the general people of the ethnic group; only seven ethnic minority groups have data for both nutrition status (anthropometric and nutrients intake/deficiency) and dietary practices (dietary intake and dietary habits); there are still 10 ethnic minority groups with a total number of population 845,420 that lack studies on both nutrition status and dietary practices; ethnic minority groups are suffering from double-burden malnutrition and consuming unbalanced diets; primary and middle school students are the most prevalent study population than any other age group due to easy access; and an ethnic minority group is likely to have more publications about nutrition status and dietary practices if they have a larger population or are unique to a region. The results indicate that more national-level programs and timely nutrition and dietary reports should be implemented to address double-burden malnutrition and unbalanced diets among ethnic minority groups in China. More studies involving maternal nutrition, targeting underrepresented ethnic minority groups and age groups, and exploring traditional food systems in China are also essential to better understand and address this issue.</p

DataSheet2_Gamma distribution based predicting model for breast cancer drug response based on multi-layer feature selection.ZIP

In the pursuit of precision medicine for cancer, a promising step is to predict drug response based on data mining, which can provide clinical decision support for cancer patients. Although some machine learning methods for predicting drug response from genomic data already exist, most of them focus on point prediction, which cannot reveal the distribution of predicted results. In this paper, we propose a three-layer feature selection combined with a gamma distribution based GLM and a two-layer feature selection combined with an ANN. The two regression methods are applied to the Encyclopedia of Cancer Cell Lines (CCLE) and the Cancer Drug Sensitivity Genomics (GDSC) datasets. Using ten-fold cross-validation, our methods achieve higher accuracy on anticancer drug response prediction compared to existing methods, with an R2 and RMSE of 0.87 and 0.53, respectively. Through data validation, the significance of assessing the reliability of predictions by predicting confidence intervals and its role in personalized medicine are illustrated. The correlation analysis of the genes selected from the three layers of features also shows the effectiveness of our proposed methods.</p

DataSheet1_Gamma distribution based predicting model for breast cancer drug response based on multi-layer feature selection.PDF

In the pursuit of precision medicine for cancer, a promising step is to predict drug response based on data mining, which can provide clinical decision support for cancer patients. Although some machine learning methods for predicting drug response from genomic data already exist, most of them focus on point prediction, which cannot reveal the distribution of predicted results. In this paper, we propose a three-layer feature selection combined with a gamma distribution based GLM and a two-layer feature selection combined with an ANN. The two regression methods are applied to the Encyclopedia of Cancer Cell Lines (CCLE) and the Cancer Drug Sensitivity Genomics (GDSC) datasets. Using ten-fold cross-validation, our methods achieve higher accuracy on anticancer drug response prediction compared to existing methods, with an R2 and RMSE of 0.87 and 0.53, respectively. Through data validation, the significance of assessing the reliability of predictions by predicting confidence intervals and its role in personalized medicine are illustrated. The correlation analysis of the genes selected from the three layers of features also shows the effectiveness of our proposed methods.</p

Immobilized Titanium (IV) Ion Affinity Chromatography Contributes to Efficient Proteomics Analysis of Cellular Nucleic Acid-Binding Proteins

Cellular nucleic acid-binding proteins (NABPs), namely, DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs), play important roles in many biological processes. However, extracting NABPs with high efficiency in living cells is challenging, which greatly limited their proteomics analysis and comprehensive characterization. Here, we discovered that titanium (IV) ion-immobilized metal affinity chromatography (Ti4+-IMAC) material could enrich DNA and RNA with high efficiency (96.82 ± 2.67 and 85.75 ± 2.99%, respectively). We therefore developed a Ti4+-IMAC method for the joint extraction of DBPs and RBPs. Through utilizing formaldehyde (FA) cross-linking, DBPs and RBPs were covalently linked to nucleic acids (NAs) and further denatured by organic solvents. After Ti4+-IMAC capture, 2000 proteins were identified in 293T cells, among which 417 DBPs and 999 RBPs were revealed, showing promising selectivity for NABPs. We further applied the Ti4+-IMAC capture method to lung cancer cell lines 95C and 95D, which have different tumor progression abilities. The DNA- and RNA-binding capabilities of many proteins have been dysregulated in 95D. Under our conditions, Ti4+-IMAC can be used as a selective and powerful tool for the comprehensive characterization of both DBPs and RBPs, which might be utilized to study their dynamic interactions with nucleic acids

Integrated <i>N</i>‑glycoproteomics Analysis of Human Saliva for Lung Cancer

Aberrant protein N-glycosylation is a cancer hallmark, which has great potential for cancer detection. However, large-scale and in-depth analysis of N-glycosylation remains challenging because of its high heterogeneity, complexity, and low abundance. Human saliva is an attractive diagnostic body fluid, while few efforts explored its N-glycoproteome for lung cancer. Here, we utilized a zwitterionic–hydrophilic interaction chromatography-based strategy to specifically enrich salivary glycopeptides. Through quantitative proteomics analysis, 1492 and 1234 intact N-glycopeptides were confidently identified from pooled saliva samples of 10 subjects in the nonsmall-cell lung cancer group and 10 subjects in the normal control group. Accordingly, 575 and 404 N-glycosites were revealed for the lung cancer group and normal control group. In particular, 154 N-glycosites and 259 site-specific glycoforms were significantly dysregulated in the lung cancer group. Several N-glycosites located at the same glycoprotein and glycans attached to the same N-glycosites were observed with differential expressions, including haptoglobin, Mucin-5B, lactotransferrin, and α-1-acid glycoprotein 1. These N-glycoproteins were mainly related to inflammatory responses, infectious diseases, and cancers. Our study achieved comprehensive characterization of salivary N-glycoproteome, and dysregulated site-specific glycoforms hold promise for noninvasive detection of lung cancer

Integrated <i>N</i>‑glycoproteomics Analysis of Human Saliva for Lung Cancer

Aberrant protein N-glycosylation is a cancer hallmark, which has great potential for cancer detection. However, large-scale and in-depth analysis of N-glycosylation remains challenging because of its high heterogeneity, complexity, and low abundance. Human saliva is an attractive diagnostic body fluid, while few efforts explored its N-glycoproteome for lung cancer. Here, we utilized a zwitterionic–hydrophilic interaction chromatography-based strategy to specifically enrich salivary glycopeptides. Through quantitative proteomics analysis, 1492 and 1234 intact N-glycopeptides were confidently identified from pooled saliva samples of 10 subjects in the nonsmall-cell lung cancer group and 10 subjects in the normal control group. Accordingly, 575 and 404 N-glycosites were revealed for the lung cancer group and normal control group. In particular, 154 N-glycosites and 259 site-specific glycoforms were significantly dysregulated in the lung cancer group. Several N-glycosites located at the same glycoprotein and glycans attached to the same N-glycosites were observed with differential expressions, including haptoglobin, Mucin-5B, lactotransferrin, and α-1-acid glycoprotein 1. These N-glycoproteins were mainly related to inflammatory responses, infectious diseases, and cancers. Our study achieved comprehensive characterization of salivary N-glycoproteome, and dysregulated site-specific glycoforms hold promise for noninvasive detection of lung cancer

Immobilized Titanium (IV) Ion Affinity Chromatography Contributes to Efficient Proteomics Analysis of Cellular Nucleic Acid-Binding Proteins

Cellular nucleic acid-binding proteins (NABPs), namely, DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs), play important roles in many biological processes. However, extracting NABPs with high efficiency in living cells is challenging, which greatly limited their proteomics analysis and comprehensive characterization. Here, we discovered that titanium (IV) ion-immobilized metal affinity chromatography (Ti4+-IMAC) material could enrich DNA and RNA with high efficiency (96.82 ± 2.67 and 85.75 ± 2.99%, respectively). We therefore developed a Ti4+-IMAC method for the joint extraction of DBPs and RBPs. Through utilizing formaldehyde (FA) cross-linking, DBPs and RBPs were covalently linked to nucleic acids (NAs) and further denatured by organic solvents. After Ti4+-IMAC capture, 2000 proteins were identified in 293T cells, among which 417 DBPs and 999 RBPs were revealed, showing promising selectivity for NABPs. We further applied the Ti4+-IMAC capture method to lung cancer cell lines 95C and 95D, which have different tumor progression abilities. The DNA- and RNA-binding capabilities of many proteins have been dysregulated in 95D. Under our conditions, Ti4+-IMAC can be used as a selective and powerful tool for the comprehensive characterization of both DBPs and RBPs, which might be utilized to study their dynamic interactions with nucleic acids

Integrated <i>N</i>‑glycoproteomics Analysis of Human Saliva for Lung Cancer

Aberrant protein N-glycosylation is a cancer hallmark, which has great potential for cancer detection. However, large-scale and in-depth analysis of N-glycosylation remains challenging because of its high heterogeneity, complexity, and low abundance. Human saliva is an attractive diagnostic body fluid, while few efforts explored its N-glycoproteome for lung cancer. Here, we utilized a zwitterionic–hydrophilic interaction chromatography-based strategy to specifically enrich salivary glycopeptides. Through quantitative proteomics analysis, 1492 and 1234 intact N-glycopeptides were confidently identified from pooled saliva samples of 10 subjects in the nonsmall-cell lung cancer group and 10 subjects in the normal control group. Accordingly, 575 and 404 N-glycosites were revealed for the lung cancer group and normal control group. In particular, 154 N-glycosites and 259 site-specific glycoforms were significantly dysregulated in the lung cancer group. Several N-glycosites located at the same glycoprotein and glycans attached to the same N-glycosites were observed with differential expressions, including haptoglobin, Mucin-5B, lactotransferrin, and α-1-acid glycoprotein 1. These N-glycoproteins were mainly related to inflammatory responses, infectious diseases, and cancers. Our study achieved comprehensive characterization of salivary N-glycoproteome, and dysregulated site-specific glycoforms hold promise for noninvasive detection of lung cancer

Integrated <i>N</i>‑glycoproteomics Analysis of Human Saliva for Lung Cancer

Aberrant protein N-glycosylation is a cancer hallmark, which has great potential for cancer detection. However, large-scale and in-depth analysis of N-glycosylation remains challenging because of its high heterogeneity, complexity, and low abundance. Human saliva is an attractive diagnostic body fluid, while few efforts explored its N-glycoproteome for lung cancer. Here, we utilized a zwitterionic–hydrophilic interaction chromatography-based strategy to specifically enrich salivary glycopeptides. Through quantitative proteomics analysis, 1492 and 1234 intact N-glycopeptides were confidently identified from pooled saliva samples of 10 subjects in the nonsmall-cell lung cancer group and 10 subjects in the normal control group. Accordingly, 575 and 404 N-glycosites were revealed for the lung cancer group and normal control group. In particular, 154 N-glycosites and 259 site-specific glycoforms were significantly dysregulated in the lung cancer group. Several N-glycosites located at the same glycoprotein and glycans attached to the same N-glycosites were observed with differential expressions, including haptoglobin, Mucin-5B, lactotransferrin, and α-1-acid glycoprotein 1. These N-glycoproteins were mainly related to inflammatory responses, infectious diseases, and cancers. Our study achieved comprehensive characterization of salivary N-glycoproteome, and dysregulated site-specific glycoforms hold promise for noninvasive detection of lung cancer

Integrated <i>N</i>‑glycoproteomics Analysis of Human Saliva for Lung Cancer

Aberrant protein N-glycosylation is a cancer hallmark, which has great potential for cancer detection. However, large-scale and in-depth analysis of N-glycosylation remains challenging because of its high heterogeneity, complexity, and low abundance. Human saliva is an attractive diagnostic body fluid, while few efforts explored its N-glycoproteome for lung cancer. Here, we utilized a zwitterionic–hydrophilic interaction chromatography-based strategy to specifically enrich salivary glycopeptides. Through quantitative proteomics analysis, 1492 and 1234 intact N-glycopeptides were confidently identified from pooled saliva samples of 10 subjects in the nonsmall-cell lung cancer group and 10 subjects in the normal control group. Accordingly, 575 and 404 N-glycosites were revealed for the lung cancer group and normal control group. In particular, 154 N-glycosites and 259 site-specific glycoforms were significantly dysregulated in the lung cancer group. Several N-glycosites located at the same glycoprotein and glycans attached to the same N-glycosites were observed with differential expressions, including haptoglobin, Mucin-5B, lactotransferrin, and α-1-acid glycoprotein 1. These N-glycoproteins were mainly related to inflammatory responses, infectious diseases, and cancers. Our study achieved comprehensive characterization of salivary N-glycoproteome, and dysregulated site-specific glycoforms hold promise for noninvasive detection of lung cancer