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.

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.

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/.

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.

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.

Diagnostic performance of anti-MAGEA family protein autoantibodies in esophageal squamous cell carcinoma.

MAGEA family proteins are immunogenic and can produce corresponding autoantibodies, and we aim to evaluate the diagnostic value of anti-MAGEA family protein autoantibodies in esophageal squamous cell carcinoma (ESCC). Protein chip was used to detect the expression level of anti-MAGEA autoantibodies (IgG and IgM) in 20 mixed serum samples. Enzyme linked immunosorbent assay was adopted to determine the expression level of autoantibodies in 1019 serum samples (423 ESCC, 423 healthy control (HC), 173 benign esophageal disease (BED)), and stepwise logistic regression analysis was used for developing a diagnostic model. Eight anti-MAGEA autoantibodies were screened out based on the protein chip. The levels of 7 autoantibodies (MAGEA1-IgG, MAGEA3-IgG, MAGEA3-IgM, MAGEA4-IgG, MAGEA6-IgG, MAGEA10-IgG, MAGEA12-IgG) in ESCC were significantly higher than that in HC, and the levels of anti-MAGEA1 IgG, anti-MAGEA3-IgG, anti-MAGEA4-IgG, anti-MAGEA10-IgG and anti-MAGEA12-IgG autoantibodies in ESCC group were significantly higher than those in BED group. The area under curve (AUC), sensitivity and specificity of the logistic regression model (MAGEA1-IgG, MAGEA4-IgG, MAGEA6-IgG, MAGEA12-IgG) in the training set and the validation set were 0.725 and 0.698, 55.2% and 51.8%, 80.4% and 84.5%, respectively, in distinguishing ESCC and HC. The model also could distinguish between ESCC and BED, with the AUC of 0.743, sensitivity of 55.4% and specificity of 89.0%. The positive rate of the model combined with cytokeratin 19 fragment to diagnose ESCC reached 78.0%. The study identified anti-MAGEA autoantibodies with potential diagnostic value for ESCC, which may provide new promising for the detection of the disease.

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.

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.

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.

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.

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.

Metformin Mitigates Sepsis-Related Neuroinflammation via Modulating Gut Microbiota and Metabolites.

Gut microbiota affects the functions of brains. However, its mechanism in sepsis remains unclear. This study evaluated the effect of metformin on ameliorating sepsis-related neurodamage by regulating gut microbiota and metabolites in septic rats. Cecal ligation and puncture (CLP) was used to establish the sepsis-related neurodamage animal models. Metformin therapy by gavage at 1 h after CLP administration was followed by fecal microbiota transplantation (FMT) to ensure the efficacy and safety of metformin on the sepsis-related neurodamage by regulating gut microbiota. The gut microbiota and metabolites were conducted by 16S rRNA sequencing and liquid chromatography-tandem mass spectrometry metabolomic analysis. The brain tissue inflammation response was analyzed by histopathology and reverse transcription-polymerase chain reaction (RT-PCR). This study reported brain inflammatory response, hemorrhage in sepsis-related neurodamage rats compared with the control group (C group). Surprisingly, the abundance of gut microbiota slightly increased in sepsis-related neurodamage rats than C group. The ratio of Firmicutes/Bacteroidetes was significantly increased in the CLP group than the C group. However, no difference was observed between the CLP and the metformin-treated rats (MET group). Interestingly, the abundance of Escherichia_Shigella increased in the MET group than the C and CLP groups, while Lactobacillaceae abundance decreased. Furthermore, Prevotella_9, Muribaculaceae, and Alloprevotella related to short-chain fatty acids production increased in the sepsis-related neurodamage of metformin-treated rats. Additionally, Prevotella_9 and Muribaculaceae correlated positively to 29 metabolites that might affect the inflammatory factors in the brain. The FMT assay showed that metformin improved sepsis-related neurodamage by regulating the gut microbiota and metabolites in septic rats. The findings suggest that metformin improves the sepsis-related neurodamage through modulating the gut microbiota and metabolites in septic rats, which may be an effective therapy for patients with sepsis-related neurodamage.

Identification of metabolomics-based prognostic prediction models for ICU septic patients.

Sepsis-related global mortality remains unacceptably high in intensive care units. Identifying the various molecular processes between survival and death in septic patients may assist in better treatment. Accurate prognostic evaluation of sepsis is an essentially unmet need. This study analyzed the metabolite changes in plasma between healthy controls and septic patients, as well as between survival and dead septic patients using liquid chromatography/mass spectrometry. Univariate and multivariate analyses were applied to identify differential metabolites. The differential metabolites and clinical indicators within 24 h after sepsis diagnosis were run through multivariate logistic regression models to determine the 28-day, hospital, and 90-day septic mortality prediction models. The results suggested markedly changed amino acids metabolism in septic patients compared to healthy controls; 10, 4, and 22 primary differential metabolites related to amino acid and fatty acid metabolisms were identified in the survival and death groups at 28-day, hospital, and 90-day, respectively. Further, we found that model 1 (indoleacetic acid, 3-methylene-indolenine, heart rate, respiratory support, and application of pressure drugs), model 2 (lymphocyte count, alkaline phosphatase, SOFA, and L-alpha-amino-1H-pyrrole-1-hexanoic acid), and model 3 (dopamine, delta-12-prostaglandin J2, heart rate, respiratory support, and application of pressure drugs) could predict 28-day, hospital, and 90-day mortality of sepsis with a sensitivity of 75.51%, 73.58%, and 83.33%, specificity of 78.72%, 72.09%, and 78.57%, and the area under the receiver operating characteristic curve of 0.881, 0.830, 0.886, respectively. Thus, this research presents three multiple-biomarker-based prognostic models for 28-day, hospital, and 90-day mortality septic patients and could be used to guide sepsis treatment.

Anti-POSTN and Anti-TIMP1 Autoantibodies as Diagnostic Markers in Esophageal Squamous Cell Carcinoma.

Esophageal cancer is one of the most commonly diagnosed malignant gastrointestinal tumors. The aim of the study was to explore the diagnostic values of anti-POSTN and anti-TIMP1 autoantibodies in esophageal squamous cell carcinoma (ESCC). Differentially expressed genes (DEGs) associated with esophageal cancer were screened out by the LIMMA method in the Gene Expression Profiling Interactive Analysis (GEPIA) platform. Search Tool for the Retrieval of Interacting Genes (STRING) was used to construct the protein-protein interaction (PPI) based on highly DEGs. The candidate hub genes were the intersection genes calculated based on degree and Maximal Clique Centrality (MCC) algorithms via Cytoscape. A total of 370 participants including 185 ESCC patients and 185 matched normal controls were enrolled in enzyme-linked immunosorbent assay (ELISA) to detect the expression levels of autoantibodies corresponding to POSTN and TIMP1 proteins. A total of 375 DEGs with high expression were obtained in esophageal cancer. A total of 20 hub genes were acquired using the cytoHubba plugin by degree and MCC algorithms. The expression levels of anti-POSTN and anti-TIMP1 autoantibodies were higher in the sera of ESCC patients (p < 0.05). Anti-POSTN autoantibody can diagnose ESCC patients with an AUC of 0.638 at the specificity of 90.27% and sensitivity of 27.57%, and anti-TIMP1 autoantibody can diagnose ESCC patients with an AUC of 0.585 at the specificity of 90.27% and sensitivity of 20.54% (p < 0.05). In addition, anti-POSTN and anti-TIMP1 autoantibodies can distinguish ESCC patients from normal controls in most clinical subgroups (p < 0.05). In conclusion, anti-POSTN and anti-TIMP1 autoantibodies may be considered the potential biomarkers in the clinical diagnosis of ESCC.

Serum Autoantibodies against LRDD, STC1, and FOXA1 as Biomarkers in the Detection of Ovarian Cancer.

Purpose: This study is aimed at evaluating serum autoantibodies against four tumor-associated antigens, including LRDD, STC1, FOXA1, and EDNRB, as biomarkers in the immunodiagnosis of ovarian cancer (OC). Methods: The autoantibodies against LRDD, STC1, FOXA1, and EDNRB were measured using an enzyme-linked immunosorbent assay (ELISA) in 94 OC patients and 94 normal healthy controls (NHC) in the research group. In addition, the diagnostic values of different autoantibodies were validated in another independent validation group, which comprised 136 OC patients, 136 NHC, and 181 patients with benign ovarian diseases (BOD). Results: In the research group, autoantibodies against LRDD, STC1, and FOXA1 had higher serum titer in OC patients than NHC (P < 0.001). The area under receiver operating characteristic curves (AUCs) of these three autoantibodies were 0.910, 0.879, and 0.817, respectively. In the validation group, they showed AUCs of 0.759, 0.762, and 0.817 and sensitivities of 49.3%, 42.7%, and 48.5%, respectively, at specificity over 90% for discriminating OC patients from NHC. For discriminating OC patients from BOD, they showed AUCs of 0.718, 0.729, and 0.814 and sensitivities of 47.1%, 39.0%, and 51.5%, respectively, at specificity over 90%. The parallel analyses demonstrated that the combination of anti-LRDD and anti-FOXA1 autoantibodies achieved the optimal diagnostic performance with the sensitivity of 58.1% at 87.5% specificity and accuracy of 72.8%. The positive rate of the optimal autoantibody panel improved from 62.4% to 87.1% when combined with CA125 in detecting OC patients. Conclusion: Serum autoantibodies against LRDD, STC1, and FOXA1 have potential diagnostic values in detecting OC.

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.

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.

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.