hccTAAb Atlas: An Integrated Knowledge Database for Tumor-Associated Autoantibodies in Hepatocellular Carcinoma.

Tumor-associated autoantibodies (TAAbs) have demonstrated potential as biomarkers for cancer detection. However, the understanding of their role in hepatocellular carcinoma (HCC) remains limited. In this study, we aimed to systematically collect and standardize information about these TAAbs and establish a comprehensive database as a platform for in-depth research. A total of 170 TAAbs were identified from published papers retrieved from PubMed, Web of Science, and Embase. Following normative reannotation, these TAAbs were referred to as 162 official symbols. The hccTAAb (tumor-associated autoantibodies in hepatocellular carcinoma) atlas was developed using the R Shiny framework and incorporating literature-based and multiomics data sets. This comprehensive online resource provides key information such as sensitivity, specificity, and additional details such as official symbols, official full names, UniProt, NCBI, HPA, neXtProt, and aliases through hyperlinks. Additionally, hccTAAb offers six analytical modules for visualizing expression profiles, survival analysis, immune infiltration, similarity analysis, DNA methylation, and DNA mutation analysis. Overall, the hccTAAb Atlas provides valuable insights into the mechanisms underlying TAAb and has the potential to enhance the diagnosis and treatment of HCC using autoantibodies. The hccTAAb Atlas is freely accessible at https://nscc.v.zzu.edu.cn/hccTAAb/.

ZPR1 is an immunodiagnostic biomarker and promotes tumor progression in esophageal squamous cell carcinoma.

To evaluate the potential of zinc finger protein 1 (ZPR1) as a diagnostic biomarker and explore the underlying role for esophageal squamous cell carcinoma (ESCC). A human proteome microarray was customized to identify anti-ZPR1 autoantibody, and enzyme-linked immunosorbent assay (ELISA) was adopted to assess the diagnostic performance of anti-ZPR1 autoantibody in 294 patients with ESCC and 294 normal controls. The expression of ZPR1 protein was measured by immunohistochemistry. The effect of ZPR1 on the proliferation, migration, and invasion of ESCC cells was investigated through CCK-8, wound healing, and Transwell assays. The expression level of anti-ZPR1 autoantibody (fold change = 2.77) in ESCC patients was higher than that in normal controls. The receiver operating characteristic (ROC) analysis manifested anti-ZPR1 autoantibody achieved area under the ROC curve (AUC) of 0.726 and 0.734 to distinguish ESCC from normal controls with sensitivity of 50.0% and 42.3%, and specificity of 91.0% and 92.0% in the test group and validation group, respectively. The positive rate of ZPR1 protein was significantly higher in ESCC tissues (75.5%, 80/106) than paracancerous tissues (9.4%, 5/53). Compared with the human normal esophageal cell line, the expression level of ZPR1 mRNA and protein in ESCC lines (KYSE150, Eca109, and TE1) had an increased trend. The knockdown or overexpression of ZPR1 reduced and enhanced the proliferation, migration, and invasion of ESCC cell, respectively. ZPR1 was a potential immunodiagnostic biomarker for noninvasive detection and could be a promotional factor in tumor progression of ESCC.

ESCCPred: a machine learning model for diagnostic prediction of early esophageal squamous cell carcinoma using autoantibody profiles.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a deadly cancer with no clinically ideal biomarkers for early diagnosis. The objective of this study was to develop and validate a user-friendly diagnostic tool for early ESCC detection. METHODS: The study encompassed three phases: discovery, verification, and validation, comprising a total of 1309 individuals. Serum autoantibodies were profiled using the HuProtTM human proteome microarray, and autoantibody levels were measured using the enzyme-linked immunosorbent assay (ELISA). Twelve machine learning algorithms were employed to construct diagnostic models, and evaluated using the area under the receiver operating characteristic curve (AUC). The model application was facilitated through R Shiny, providing a graphical interface. RESULTS: Thirteen autoantibodies targeting TAAs (CAST, FAM131A, GABPA, HDAC1, HDGFL1, HSF1, ISM2, PTMS, RNF219, SMARCE1, SNAP25, SRPK2, and ZPR1) were identified in the discovery phase. Subsequent verification and validation phases identified five TAAbs (anti-CAST, anti-HDAC1, anti-HSF1, anti-PTMS, and anti-ZPR1) that exhibited significant differences between ESCC and control subjects (P < 0.05). The support vector machine (SVM) model demonstrated robust performance, with AUCs of 0.86 (95% CI: 0.82-0.89) in the training set and 0.83 (95% CI: 0.78-0.88) in the test set. For early-stage ESCC, the SVM model achieved AUCs of 0.83 (95% CI: 0.79-0.88) in the training set and 0.83 (95% CI: 0.77-0.90) in the test set. Notably, promising results were observed for high-grade intraepithelial neoplasia, with an AUC of 0.87 (95% CI: 0.77-0.98). The web-based implementation of the early ESCC diagnostic tool is publicly accessible at https://litdong.shinyapps.io/ESCCPred/ . CONCLUSION: This study provides a promising and easy-to-use diagnostic prediction model for early ESCC detection. It holds promise for improving early detection strategies and has potential implications for public health.

A novel autoantibody signatures for enhanced clinical diagnosis of pancreatic ductal adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease that requires precise diagnosis for effective treatment. However, the diagnostic value of carbohydrate antigen 19 - 9 (CA19-9) is limited. Therefore, this study aims to identify novel tumor-associated autoantibodies (TAAbs) for PDAC diagnosis. METHODS: A three-phase strategy comprising discovery, test, and validation was implemented. HuProt™ Human Proteome Microarray v3.1 was used to screen potential TAAbs in 49 samples. Subsequently, the levels of potential TAAbs were evaluated in 477 samples via enzyme-linked immunosorbent assay (ELISA) in PDAC, benign pancreatic diseases (BPD), and normal control (NC), followed by the construction of a diagnostic model. RESULTS: In the discovery phase, protein microarrays identified 167 candidate TAAbs. Based on bioinformatics analysis, fifteen tumor-associated antigens (TAAs) were selected for further validation using ELISA. Ten TAAbs exhibited differentially expressed in PDAC patients in the test phase (P < 0.05), with an area under the curve (AUC) ranging from 0.61 to 0.76. An immunodiagnostic model including three TAAbs (anti-HEXB, anti-TXLNA, anti-SLAMF6) was then developed, demonstrating AUCs of 0.81 (58.0% sensitivity, 86.0% specificity) and 0.78 (55.71% sensitivity, 87.14% specificity) for distinguishing PDAC from NC. Additionally, the model yielded AUCs of 0.80 (58.0% sensitivity, 86.25% specificity) and 0.83 (55.71% sensitivity, 100% specificity) for distinguishing PDAC from BPD in the test and validation phases, respectively. Notably, the combination of the immunodiagnostic model with CA19-9 resulted in an increased positive rate of PDAC to 92.91%. CONCLUSION: The immunodiagnostic model may offer a novel serological detection method for PDAC diagnosis, providing valuable insights into the development of effective diagnostic biomarkers.

Identification of novel tumor-associated antigens and evaluation of a panel of autoantibodies in detecting oral cancer.

BACKGROUND: We aimed to identify tumor-associated antigen (TAA) biomarkers through bioinformatic analysis and experimental verification, and to evaluate a panel of autoantibodies against tumor-associated antigens (TAAbs) for the detection of oral cancer (OC). METHODS: GEO and TCGA databases were used to screen significantly up-regulated genes related to OC, and protein-protein interaction (PPI) analysis and Cystoscope software were used to identify key genes. Enzyme-linked immunosorbent assay (ELISA) was used to detect the expression levels of autoantibodies in 173 OC patients and 173 normal controls, and binary logistic regression analysis was used to build a diagnostic model. RESULTS: Using bioinformatics, we identified 10 key genes (AURKA, AURKB, CXCL8, CXCL10, COL1A1, FN1, FOXM1, MMP9, SPP1 and UBE2C) that were highly expressed in OC. Three autoantibodies (anti-AURKA, anti-CXCL10, anti-FOXM1) were proven to have diagnostic value for OC in the verification set and the validation set. The combined assessment of these three autoantibodies improved the diagnostic value for OC, with an area under the curve (AUC), sensitivity and specificity of 0.741(95%CI:0.690-0.793),58.4% and 80.4%, respectively. In addition, the combination of these three autoantibodies also had high diagnostic value for oral squamous cell carcinoma (OSCC), with an AUC, sensitivity and specificity of 0.731(95%CI:0.674,0.786), 53.8% and 82.1%, respectively. CONCLUSIONS: Our study revealed that AURKA, CXCL10 and FOXM1 may be potential biomarkers and the panel of three autoantibodies (anti-AURKA, anti-CXCL10 and anti-FOXM1) had good diagnostic value for OC.

A novel immunodiagnosis panel for hepatocellular carcinoma based on bioinformatics and the autoantibody-antigen system.

Hepatocellular carcinoma (HCC) is a malignancy with a dismal survival rate. The novel autoantibodies panel may provide new insights for the diagnosis of HCC. Biomarkers screened by two methods (bioinformatics and the antigen-antibody system) were taken as candidate tumor-associated antigens (TAAs). Enzyme-linked immunosorbent assay was used to detect the corresponding autoantibodies in 888 samples of verification and validation cohorts. The verification cohort was used to verify the autoantibodies. Samples in the validation cohort were randomly divided into a train set and a test set with the ratio of 6:4. A diagnostic model was established by support vector machines within the train set. The test set further verified the model. Eleven TAAs were selected (AAGAB, C17orf75, CDC37L1, DUSP6, EID3, PDIA2, RGS20, PCNA, TAF7L, TBC1D13, and ZIC2). The titer of six autoantibodies (PCNA, AAGAB, CDC37L1, TAF7L, DUSP6, and ZIC2) had a significant difference in any of the pairwise comparisons among the HCC, liver cirrhosis, and normal control groups. The titer of these autoantibodies had an increasing tendency. Finally, an optimum diagnostic model was constructed with the six autoantibodies. The AUCs were 0.826 in the train set and 0.773 in the test set. The area under the curve (AUC) of this panel for diagnosing early HCC was 0.889. The diagnostic ability of the panel reduced with the progress of HCC. The positive rate of the panel in diagnosing alpha-fetoprotein (AFP)-negative patients was 75.6%. For early HCC, the sensitivity of the combination of AFP with the panel was 90.9% and superior to 53.2% of AFP alone. The novel immunodiagnosis panel combining AFP may be a new approach for the diagnosis of HCC, especially for early-HCC cases.

Alterations in the gut microbiome and metabolome profiles of septic rats treated with aminophylline.

The treatment of sepsis remains a major challenge worldwide. Aminophylline has been shown to have anti-inflammatory effects; however, the role of aminophylline in sepsis, a disease characterized by immune dysregulation, is unknown. In this study, we combined microbiome sequencing and metabolomic assays to investigate the effect of aminophylline administration on the intestinal flora and metabolites in septic rats. Sixty SD rats were randomly divided into three groups: a sham-operated (SC) group, a sepsis model (CLP) group and a CLP + aminophylline treatment (Amino) group. The intestinal flora and metabolic profile of rats in the CLP group were significantly different than those of the SC group, while aminophylline administration resulted in a return to a state similar to healthy rats. Differential abundance analysis showed that aminophylline significantly back-regulated the abundance of Firmicutes, unidentified_Bacteria, Proteobacteria, Lactobacillus, Escherichia-Shigella and other dominant bacteria (P < 0.05) and altered chenodeoxycholic acid, isolithocholic acid and a total of 26 metabolites (variable importance in the projection (VIP) > 1, P < 0.05). In addition, we found that there were significant correlations between differential metabolites and bacterial genera of the Amino and CLP groups. For example, Escherichia-Shigella was associated with 12 metabolites, and Lactobacillus was associated with two metabolites (P < 0.05), suggesting that differences in the metabolic profiles caused by aminophylline were partly dependent on its influence on the gutmicrobiome. In conclusion, this study identified a novel protective mechanism whereby aminophylline could regulate disordered intestinal flora and metabolites in septic rats.

Serum anti-SPP1 autoantibody as a potential novel biomarker in detection of esophageal squamous cell carcinoma.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) has poor prognosis mainly due to lacking of effective diagnostic biomarkers. Aberrant expression of secreted phosphoprotein 1 (SPP1) protein has been observed in several cancers. The purpose of this study is to assess the feasibility of serum autoantibody to SPP1 in detection of ESCC. METHODS: The SPP1 protein levels in 108 ESCC tissues and 72 adjacent normal tissues were analyzed by immunohistochemistry. Discovery group containing 62 serum samples from ESCC patients and 62 serum samples from normal controls (NC) were used to detect the levels of anti-SPP1 autoantibody by enzyme-linked immunosorbent assay (ELISA). Validation group containing another 100 ESCC and 100 NC serum samples were tested to confirm the levels of autoantibody to SPP1. Western blotting was performed to further confirm the results of ELISA. RESULTS: SPP1 protein was significantly overexpressed in ESCC tissues compared to adjacent normal tissues. ELISA results showed that serum autoantibody to SPP1 was significantly increased in ESCC compared to NC in both discovery and validation groups. Autoantibody to SPP1 could discriminate patients with ESCC from NC with the area under curve (AUC) values of 0.653 and 0.739 in discovery and validation group, respectively. The results of ELISA and the occurrence of immunoreactivity to SPP1 in ESCC sera were confirmed by western blotting. CONCLUSION: Our study indicated the potential significance of anti-SPP1 autoantibody as a novel biomarker for detection of ESCC.

Trend of the mortality of major liver diseases and its impact on life expectancy in China from 2006 to 2017.

BACKGROUND: Liver diseases are the serious cause of death in China. We aim to describe the trends and disparities of major liver disease mortality rates and the loss of life expectancy (LLE) in China. METHODS: Annual percentage change (APC) and average APC (AAPC) were calculated using the Joinpoint regression model. LLE was calculated using cause eliminated life table. RESULTS: From 2006 to 2017, the overall age-standardized mortality rate (ASMR) of liver cirrhosis lightly declined (AAPC: -2.97%), whereas the ASMR of viral hepatitis and liver cancer remained stable. Viral hepatitis (AAPC: -4.36%) and liver cirrhosis (AAPC: -4.35%) ASMRs both declined for females. The highest ASMRs of viral hepatitis and liver cirrhosis were in the west region, while that of liver cancer was in the middle region. The ASMRs of liver cirrhosis in the middle region and liver cancer in the east region significantly decreased. The means of LLE on viral hepatitis, liver cirrhosis and liver cancer were 0.05, 0.1 and 0.46 years, respectively. CONCLUSIONS: The burden of liver diseases is still severe and there are disparities between genders and different regions in China. Accurate early diagnostic approaches for high-risk populations should be established to eliminate the burden of liver diseases.

The Relationship between MALAT1 Polymorphism rs3200401 C > T and the Risk of Overall Cancer: A Meta-Analysis.

Background and Objectives: At present, the association between the long non-coding RNA (lncRNA) metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) polymorphism rs3200401 C > T and cancer risk remain controversial. The aim of this meta-analysis was to assess the association between rs3200401 C > T and cancer susceptibility. Materials and Methods: The databases of PubMed, EMBASE and Web of Science were searched for literature published in English until 1 September 2021. The odd ratios (ORs) and 95% confidence intervals (CIs) were applied to evaluate the strength of association in five genetic models. Heterogeneity was assessed using the Q-test and I2 test. Begg's funnel plot and Egger's linear regression test were conducted to assess publication bias. Meta-regression analysis was used to explore potential sources of heterogeneity. Trial sequential analysis (TSA) was performed to validate the reliability of the results. Results: A total of 10 case-control studies involving 6630 cases and 7457 controls were included in this study. The pooled ORs showed no significant association between MALAT1 rs3200401 C > T and cancer risk in five genetic models. Similarly, the association was not found in the subgroups of control source, ethnicity and study quality. In the cancer type subgroup, the results demonstrated that the T allele increased the risk of colorectal cancer (CRC) compared with the C allele. (C vs. T: OR, 1.16; 95% CI, 1.01-1.33). Conclusion: In the current meta-analysis, we found no significant association between MALAT1 polymorphism rs3200401 C > T and overall cancer risk. However, the rs3200401 C > T may be linked to a higher risk of CRC, which needs more studies to be further confirmed.

Anti-CXCL8 Autoantibody: A Potential Diagnostic Biomarker for Esophageal Squamous Cell Carcinoma.

Background and Objectives: Esophageal squamous cell carcinoma (ESCC) is one of the most common malignancies. Anti-tumor associated antigen autoantibodies (TAAbs) can be used as biomarkers for tumor detection. The aim of this study was to identify a reliable TAAb as the diagnostic marker for ESCC. Materials and Methods: The Cancer Genome Atlas (TCGA) database was used to screen candidate genes. The mRNA expression of the key gene was then verified by micro array dataset GSE44021 from the Gene Expression Omnibus (GEO) database and the diag nostic value of the corresponding autoantibody to the key gene in ESCC was detected by enzyme-linked im muno sorbent assay (ELISA). Results: CXCL8 was identified as the key gene. The dataset GSE44021 showed that CXCL8 mRNA expression was prominently over-expressed in ESCC tissues compared with normal tissues. ELISA results showed that the level of anti-CXCL8 autoantibody in ESCC patients was significantly higher than in normal controls and the receiver operating char ac teristic (ROC) curve indicated that anti-CXCL8 autoantibody could discriminate ESCC patients from normal controls, with the area under the ROC curve (AUC) for the verification cohort, and the validation cohort were 0.713 and 0.751, respectively. Conclusions: Our study illustrated that anti-CXCL8 autoantibody had good diagnostic value, and may become a candidate biomarker for ESCC.

Identification of novel autoantibody signatures and evaluation of a panel of autoantibodies in breast cancer.

Tumor-associated autoantibodies (TAAb) could be serological tumor markers. This study aims to discover novel TAAb signatures for breast cancer (BC) detection. The protein microarray was used to identify candidate TAAb, which were further validated in 1197 sera from BC, benign breast diseases (BD), and healthy controls (HC) by enzyme-linked immunosorbent assay. In addition, 319 preoperative and postoperative sera were evaluated. A panel was determined using four different classifiers. Twelve TAAb were identified with frequencies of 15.8%-59.2%; their levels were significantly decreased in postoperative sera compared to those in preoperative sera (P < .05). A panel with six TAAb was developed and evaluated. The area under the curve (AUC) was 0.879 (74.3% sensitivity, 91.9% specificity) and 0.865 (69.7% sensitivity, 91.7% specificity) for distinguishing BC from HC in the training set and test set, respectively. The panel had an AUC of .884 (71.2% sensitivity, 90.5% specificity) for discriminating BC from BD. For identifying BC from all controls (HC+BD), the AUC was .916 (78.9% sensitivity, 90.2% specificity). The AUC of the panel was .920 and .934 for distinguishing stage I-II and age < 50 BC from HC, respectively. These identified TAAb have the potential to provide a non-invasive approach to detect BC.

Using protein microarray to identify and evaluate autoantibodies to tumor-associated antigens in ovarian cancer.

The aim of this study was to develop a noninvasive serological diagnostic approach in identifying and evaluating a panel of candidate autoantibodies to tumor-associated antigens (TAAs) based on protein microarray technology for early detection of ovarian cancer (OC). Protein microarray based on 154 proteins encoded by 138 cancer driver genes was used to screen candidate anti-TAA autoantibodies in a discovery cohort containing 17 OC and 27 normal controls (NC). Indirect enzyme-linked immunosorbent assay (ELISA) was used to detect the content of candidate anti-TAA autoantibodies in sera from 140 subjects in the training cohort. Differential anti-TAA autoantibodies were further validated in the validation cohort with 328 subjects. Subsequently, 112 sera from the patients with ovarian benign diseases with 104 OC sera and 104 NC sera together were recruited to identify the specificity of representative autoantibodies to OC among ovarian diseases. Five TAAs (GNAS, NPM1, FUBP1, p53, and KRAS) were screened out in the discovery phase, in which four of them presented higher levels in OC than controls (P < .05) in the training cohort, which was consistent with the result in the subsequent validation cohort. An optimized panel of three anti-TAA (GNAS, p53, and NPM1) autoantibodies was identified to have relatively high sensitivity (51.2%), specificity (86.0%), and accuracy (68.6%), respectively. This panel can identify 51% of OC patients with CA125 negative. This study supports our assumption that anti-TAA autoantibodies can be considered as potential diagnostic biomarkers for detection of OC; especially a panel of three anti-TAA autoantibodies could be a good tool in immunodiagnosis of OC.

Pinitol suppresses TNF-α-induced chondrocyte senescence.

Osteoarthritis (OA) is a highly prevalent joint disorder that is tightly correlated with age. As the body ages, cell replication and function decline until homeostasis can no longer be maintained. This process involves cellular senescence as well as replicative senescence. Telomere length, cell cycle arrest, expression of p16 and p53, and the release of senescence-associated ß-galactosidase (SA-ß-Gal) are all markers of cell senescence. In OA joints, chondrocytes undergo cellular senescence prematurely, thereby ceasing to synthesize and maintain cartilage tissue. Upregulation of proinflammatory cytokines, such as tumor necrosis factor-α (TNF-α), and oxidative stress induced by overproduction of reactive oxygen species (ROS) are key events in the pathogenesis of OA. In the present study, we investigated the effects of pinitol, a naturally occurring compound, on the effects of TNF-α on chondrocyte senescence and cell cycle arrest. We found that pinitol has a favorable safety profile in terms of cell viability. Pinitol significantly inhibited cellular senescence and cell cycle arrest in the G0/G1 phase induced by TNF-α. We also found that pinitol could inhibit TNF-α-induced increased telomerase activity and expression of p16 and p53. Importantly, we found that the effects of pinitol may be mediated through rescue of Nrf2 signaling, which is recognized as a key protective factor in OA. This finding was verified through a Nrf2 silencing experiment using Nrf2 siRNA. Together, our findings reveal the potential of pinitol as a safe therapeutic option for the prevention of OA-associated chondrocyte senescence and oxidative stress.

MiR-194-5p inhibits cell migration and invasion in bladder cancer by targeting E2F3.

PURPOSE: MicroRNA (miR)-194-5p is downregulated in bladder cancer (BC), but its role in BC has not been determined mechanistically. METHODS: The expression levels of miR-194-5p and E2F transcription factor 3 (E2F3) were determined by means of quantitative reverse transcription and polymerase chain reaction in BC specimens. In addition, T24 BC cells were transfected with a miR-194-5p mimic, a miR-194-5p inhibitor, or E2F3 small interfering (si)RNA, and the level of E2F3 protein expressed by these cells was assessed by western blotting. A dual luciferase reporter assay was applied to verify the binding site between miR-194-5p and the 3' untranslated region of E2F3. Transwell assays were performed to examine cell migration and invasion. RESULTS: Dysregulation of miR-194-5p in BC was closely associated with node metastasis and differentiation. In BC specimens and cell lines, miR-194-5p mRNA was downregulated, while E2F3 mRNA was upregulated. Overexpression of miR-194-5p suppressed the expression of E2F3 mRNA and protein. By regulating E2F3, miR-194-5p inhibited cell migration and invasion in BC. Treatment of BC cells with E2F3 siRNA had the same effect as did overexpression of miR-194-5p. CONCLUSIONS: MiR-194-5p directly targets E2F3 and inhibits cell migration and invasion in BC.

Serum anti-CFL1, anti-EZR, and anti-CYPA autoantibody as diagnostic markers in ovarian cancer.

The purpose of this study was to identify novel autoantibodies against tumor-associated antigens (TAAs) and explore a diagnostic panel for Ovarian cancer (OC). Enzyme-linked immunosorbent assay was used to detect the expression of five anti-TAA autoantibodies in the discovery (70 OC and 70 normal controls) and validation cohorts (128 OC and 128 normal controls). Machine learning methods were used to construct a diagnostic panel. Serum samples from 81 patients with benign ovarian disease were used to identify the specificity of anti-TAA autoantibodies for OC. In both the discovery and validation cohorts, the expression of anti-CFL1, anti-EZR, anti-CYPA, and anti-PFN1 was higher in patients with OC than that in normal controls. The area under the receiver operating characteristic curve, sensitivity, and specificity of the panel containing anti-CFL1, anti-EZR, and anti-CYPA were 0.762, 55.56%, and 81.31%. The panel identified 53.06%, 53.33%, and 51.11% of CA125 negative, HE4 negative and the Risk of Ovarian Malignancy Algorithm negative OC patients, respectively. The combination of the three anti-TAA autoantibodies can serve as a favorable diagnostic tool for OC and has the potential to be a complementary biomarker for CA125 and HE4 in the diagnosis of ovarian cancer.

Role of miR-21 in the diagnosis of colorectal cancer: Meta-analysis and bioinformatics.

Advanced colorectal cancer (CRC) has a bad prognosis and is challenging to cure. Therefore, there is an urgent need for an effective early diagnosis marker. MicroRNA-21 (miR-21) regulates the expression of multiple cancer target genes. The objective of this study was to assess the diagnostic role of miR-21 in CRC.A meta-analysis of PubMed, Cochrane Library, EMBASE, and Web of Science databases was performed with a carefully designed search strategy to identify records related to the diagnostic role of miR-21 in CRC. TCGA data was used to search for different microRNAs in colorectal cancer samples and surrounding tissues. In addition, potential target genes for miR-21 were predicted and evaluated by functional analysis. We conducted a meta-analysis for 10 studies, including 728 blood samples of patients with CRC and 472 healthy controls. The combined sensitivity and specificity of miR-21 to diagnose colorectal cancer were 0.79 (95% CI: 0.67-0.87) and 0.92 (95% CI: 0.85-0.96), respectively. The combined positive likelihood ratio (PLR) was 10.20 (95% CI: 4.8-21.5), the combined negative likelihood ratio (NLR) was 0.23 (95% CI: 0.14-0.37), the diagnostic odds ratio (DOR) was 45.00 (95% CI:15-132), the area under the summary receiver operating characteristic curve (SROC) for the included studies was 0.93(95%CI: 0.91-0.95). Simultaneously, TCGA data showed that miR-21 was a differential microRNA in colorectal cancer tissues and adjacent tissues, and it was an up-regulated gene. After verification by three databases, 48 target genes of miR-21 were obtained. Through GO enrichment analysis, it was found that the target genes were mainly distributed in the fiber center, the molecular function was mainly focused on cytokine receptor binding, and the biological process was mainly focused on ubiquitin-dependent protein catabolism mediated by the proteasome. KEGG pathway analysis showed that the target genes were mainly distributed in tumor pathways.

Spatial and temporal effect and driving factors of ecosystem service trade-off in the Qingjiang River Basin, China.

Identifying the spatiotemporal differentiation characteristics of trade-offs/synergies relationships of ecosystem service in watersheds and their influencing factors is essential for ecosystem management and regulation. It is of great significance for the efficient allocation of environmental resources and the rational formulation of ecological and environmental policies. We used correlation analysis and root mean square deviation to analyze the trade-offs/synergies relationships among grain provision, net primary productivity (NPP), soil conservation, and water yield service in the Qingjiang River Basin from 2000 to 2020. Then, we analyzed the critical factors affecting the trade-offs of ecosystem services by using the geographical detector. The results showed that grain provision service in the Qingjiang River Basin presented a decreasing trend from 2000 to 2020, and that NPP, soil conservation, as well as water yield service showed an increasing trend. There was a decreasing trend in the degree of trade-offs between grain provision and soil conservation services, NPP and water yield service, and an increasing trend in the intensity of trade-offs between other services. Grain provision and NPP, soil conservation and water yield showed trade-off in the northeast and synergy in the southwest. There was a synergistic relationship between NPP with soil conservation and water yield in the central part and a trade-off relationship in the surrounding area. Soil conservation and water yield showed a high degree of synergy. Land use and normalized difference of vegetation index were the dominant factors in the intensity of trade-offs between grain provision and other ecosystem services. Precipitation, temperature, and elevation were the dominant factors in the intensity of trade-offs between water yield service and other ecosystem services. The intensity of ecosystem service trade-offs was not only affected by a single factor. In contrast, the interaction between the two services or the common factors behind the two services was the determining factor. Our results could provide a reference for developing ecological restoration planning strategies in the national land space.

Single-cell transcriptional gene signature analysis identifies IL-17 signaling pathway as the key pathway in sepsis.

Sepsis is a multiple dysregulated systemic inflammatory response with high mortality and leads to public concern. This study was designed to identify possible critical pathways associated with sepsis clinical severity and outcome, which offer potential biomarkers and therapeutic targets for sepsis diagnosis and treatment. Single-cell transcriptome profiles of human peripheral blood mononuclear (PBMC) in the healthy control population and sepsis patients were downloaded from the sepsis database GSE167363 and performed quality control before subsequent analysis. The bulk-RNA sequencing of blood samples in the sepsis-associated databases GSE100159 and GSE133822 was also used to confirm the association between critical pathways and sepsis pathology after processing raw data. We found there was a total of 18 distinct clusters in PBMC of sepsis, which was identified by the t-SNE and UMAP dimension reduction analysis. Meanwhile, the main cell types including B, NK, T, and monocyte cells were identified via the cell maker website and the "Single R" package cell-type annotation analysis. Subsequently, GO and KEGG enrichment analysis of differential expression genes in each cluster found that DEGs between healthy control and sepsis patients were significantly enriched in the IL-17 signaling pathway in monocyte, NK, and T cells. Finally, GSE100159 and GSE133822 confirmed IL-17 signaling pathway-associated genes including IL-17R, TRAF6, RELB, TRAF5, CEBPB, JUNB, CXCL1, CXCL3, CXCL8, CXCR1, and CXCR2 were significantly up-regulated in sepsis blood samples compared with the age-matched healthy control population. Taken together, we concluded that the IL-17 signaling pathway serves as a significant potential mechanism of sepsis and provides a promising therapeutic target for sepsis treatment. This research will further deepen our understanding of sepsis development.

Human Proteome Microarray identifies autoantibodies to tumor-associated antigens as serological biomarkers for the diagnosis of hepatocellular carcinoma.

The identification of the high-efficiency and non-invasive biomarkers for hepatocellular carcinoma (HCC) detection is urgently needed. This study aims to screen out potential autoantibodies to tumor-associated antigens (TAAbs) and to assess their diagnostic value for HCC. Fifteen potential TAAbs were screened out from the Human Proteome Microarray by 30 HCC sera and 22 normal control sera, of which eight passed multiple-stage validations by ELISA with a total of 1625 human serum samples from normal controls (NCs) and patients with HCC, liver cirrhosis, chronic hepatitis B, gastric cancer, esophageal cancer, and colorectal cancer. Finally, an immunodiagnostic model including six TAAbs (RAD23A, CAST, RUNX1T1, PAIP1, SARS, PRKCZ) was constructed by logistic regression, and yielded the area under curve (AUC) of 0.835 and 0.788 in training and validation sets, respectively. The serial serum samples from HCC model mice were tested to explore the change in TAAbs during HCC formation, and an increasing level of autoantibodies was observed. In conclusion, the panel of six TAAbs can provide potential value for HCC detection, and the strategy to identify novel serological biomarkers can also provide new clues in understanding immunodiagnostic biomarkers.