A polygenetic risk score combined with environmental factors better predict susceptibility to hepatocellular carcinoma in Chinese population.

AIMS: This study aimed to investigate environmental factors and genetic variant loci associated with hepatocellular carcinoma (HCC) in Chinese population and construct a weighted genetic risk score (wGRS) and polygenic risk score (PRS). METHODS: A case-control study was applied to confirm the single nucleotide polymorphisms (SNPs) and environmental variables linked to HCC in the Chinese population, which had been screened by meta-analyses. wGRS and PRS were built in training sets and validation sets. Area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were applied to evaluate the performance of the models. RESULTS: A total of 13 SNPs were included in both risk prediction models. Compared with wGRS, PRS had better accuracy and discrimination ability in predicting HCC risk. The AUC for PRS in combination with drinking history, cirrhosis, HBV infection, and family history of HCC in training sets and validation sets (AUC: 0.86, 95% CI: 0.84-0.89; AUC: 0.85, 95% CI: 0.81-0.89) increased at least 20% than the AUC for PRS alone (AUC: 0.63, 95% CI: 0.60-0.67; AUC: 0.65, 95% CI: 0.60-0.71). CONCLUSIONS: A novel model combining PRS with alcohol history, HBV infection, cirrhosis, and family history of HCC could be applied as an effective tool for risk prediction of HCC, which could discriminate at-risk individuals for precise prevention.

SPP1 represents a therapeutic target that promotes the progression of oesophageal squamous cell carcinoma by driving M2 macrophage infiltration.

BACKGROUND: Tumour-associated macrophages (TAMs) are an important component of the tumour microenvironment (TME). However, the crosstalk between oesophageal squamous cell carcinoma (ESCC) cells and TAMs remains largely unexplored. METHODS: Clinical samples and the TCGA database were used to evaluate the relevance of SPP1 and TAM infiltration in ESCC. Mouse models were constructed to investigate the roles of macrophages educated by SPP1 in ESCC. Macrophage phenotypes were determined using qRT‒PCR and immunohistochemical staining. RNA sequencing was performed to elucidate the mechanism. RESULTS: Increasing expression of SPP1 correlated with M2-like TAM accumulation in ESCC, and they both predicted poor prognosis in the ESCC cohort. Knockdown of SPP1 significantly inhibited the infiltration of M2 TAMs in xenograft tumours. In vivo mouse model experiments showed that SPP1-mediated education of macrophages plays an essential role in the progression of ESCC. Mechanistically, SPP1 recruited macrophages and promoted M2 polarisation via CD44/PI3K/AKT signalling activation and then induced VEGFA and IL6 secretion to sustain ESCC progression. Finally, blockade of SPP1 with RNA aptamer significantly inhibited tumour growth and M2 TAM infiltration in xenograft mouse models. CONCLUSIONS: This study highlights SPP1-mediated crosstalk between ESCC cells and TAMs in ESCC. SPP1 could serve as a potential target in ESCC therapy.

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.

Combination of an Autoantibody Panel and Alpha-Fetoprotein for Early Detection of Hepatitis B Virus-Associated Hepatocellular Carcinoma.

The purpose of this study was to identify biomarkers associated with hepatitis B virus-associated hepatocellular carcinoma (HBV-HCC) and to develop a new combination with good diagnostic performance. This study was divided into four phases: discovery, verification, validation, and modeling. A total of four candidate tumor-associated autoantibodies (TAAb; anti-ZIC2, anti-PCNA, anti-CDC37L1, and anti-DUSP6) were identified by human proteome microarray (52 samples) and bioinformatics analysis. Subsequently, these candidate TAAbs were further confirmed by indirect ELISA with two testing cohorts (120 samples for verification and 663 samples for validation). The AUC for these four TAAbs to identify patients with HBV-HCC from chronic hepatitis B (CHB) patients ranged from 0.693 to 0.739. Finally, a diagnostic panel with three TAAbs (anti-ZIC2, anti-CDC37L1, and anti-DUSP6) was developed. This panel showed superior diagnostic efficiency in identifying early HBV-HCC compared with alpha-fetoprotein (AFP), with an AUC of 0.834 [95% confidence interval (CI), 0.772-0.897] for this panel and 0.727 (95% CI, 0.642-0.812) for AFP (P = 0.0359). In addition, the AUC for this panel to identify AFP-negative patients with HBV-HCC was 0.796 (95% CI, 0.734-0.858), with a sensitivity of 52.4% and a specificity of 89.0%. Importantly, the panel in combination with AFP significantly increased the positive rate for early HBV-HCC to 84.1% (P = 0.005) and for late HBV-HCC to 96.3% (P < 0.001). Our findings suggest that AFP and the autoantibody panel may be independent but complementary serologic biomarkers for HBV-HCC detection. PREVENTION RELEVANCE: We developed a robust diagnostic panel for identifying patients with HBV-HCC from patients with CHB. This autoantibody panel provided superior diagnostic performance for HBV-HCC at an early stage and/or with negative AFP results. Our findings suggest that AFP and the autoantibody panel may be independent but complementary biomarkers for HBV-HCC detection.

The association between circulating 25-hydroxyvitamin D and pancreatic cancer: a systematic review and meta-analysis of observational studies.

PURPOSE: The relationship between circulating 25-hydroxyvitamin D [25(OH)D] and pancreatic cancer has been well studied but remains unclear. The purpose of this study was to elucidate the association between circulating 25(OH)D and pancreatic cancer by using a meta-analytic approach. METHODS: PubMed, Embase, and Wed of Science databases were searched through October 15, 2022. A random or fixed-effects model was used to estimate the pooled odds ratio (OR), risk ratio (RR), hazard ratio (HR) and their 95% confidence intervals (CIs). RESULTS: A total of 16 studies including 529,917 participants met the inclusion criteria, of which 10 reported incidence and 6 reported mortality. For the highest versus lowest categories of circulating 25(OH)D, the pooled OR of pancreatic cancer incidence in case-control studies was 0.98 (95% CI 0.69-1.27), and the pooled HRs of pancreatic cancer mortality in cohort and case-control studies were 0.64 (95% CI 0.45-0.82) and 0.78 (95% CI 0.62-0.95), respectively. The leave-one-out sensitivity analyses found no outliers and Galbraith plots indicated no substantial heterogeneity. CONCLUSION: Evidence from this meta-analysis suggested that high circulating 25(OH)D levels may be associated with decreased mortality but not incidence of pancreatic cancer. Our findings may provide some clues for the treatment of pancreatic cancer and remind us to be cautious about widespread vitamin D supplementation for the prevention of pancreatic 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/.

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 potential resistance mechanisms and therapeutic targets for the relapse of BCMA CAR-T therapy in relapsed/refractory multiple myeloma through single-cell sequencing.

BACKGROUND: BCMA CAR-T is highly effective for relapsed/refractory multiple myeloma(R/R-MM) and significantly improves the survival of patients. However, the short remission time and high relapse rate of MM patients treated with BCMA CAR-T remain bottlenecks that limit long-term survival. The immune microenvironment of the bone marrow (BM) in R/R-MM may be responsible for this. The present study aims to present an in-depth analysis of resistant mechanisms and to explore potential novel therapeutic targets for relapse of BCMA CAR-T treatment via single-cell RNA sequencing (scRNA-seq) of BM plasma cells and immune cells. METHODS: This study used 10X Genomic scRNA-seq to identify cell populations in R/R-MM CD45+ BM cells before BCMA CAR-T treatment and relapse after BCMA CAR-T treatment. Cell Ranger pipeline and CellChat were used to perform detailed analysis. RESULTS: We compared the heterogeneity of CD45+ BM cells before BCMA CAR-T treatment and relapse after BCMA CAR-T treatment. We found that the proportion of monocytes/macrophages increased, while the percentage of T cells decreased at relapse after BCMA CAR-T treatment. We then reclustered and analyzed the alterations in plasma cells, T cells, NK cells, DCs, neutrophils, and monocytes/macrophages in the BM microenvironment before BCMA CAR-T treatment and relapse after BCMA CAR-T treatment. We show here that the percentage of BCMA positive plasma cells increased at relapse after BCMA CAR-T cell therapy. Other targets such as CD38, CD24, SLAMF7, CD138, and GPRC5D were also found to be expressed in plasma cells of the R/R-MM patient at relapse after BCMA CAR-T cell therapy. Furthermore, exhausted T cells, TIGIT+NK cells, interferon-responsive DCs, and interferon-responsive neutrophils, increased in the R/R-MM patient at relapse after BCMA CAR-T cell treatment. Significantly, the proportion of IL1ßhi Mφ, S100A9hi Mφ, interferon-responsive Mφ, CD16hi Mφ, MARCO hi Mφ, and S100A11hi Mφ significantly increased in the R/R-MM patient at relapse after BCMA CAR-T cell therapy. Cell-cell communication analysis indicated that monocytes/macrophages, especially the MIF and APRIL signaling pathway are key players in R/R-MM patient at relapse after BCMA CAR-T cell therapy. CONCLUSION: Taken together, our data extend the understanding of intrinsic and extrinsic relapse of BCMA CAR-T treatment in R/R-MM patient and the potential mechanisms involved in the alterations of antigens and the induced immunosuppressive microenvironment, which may provide a basis for the optimization of BCMA CAR-T strategies. Further studies should be performed to confirm these findings.

Silk sericin as building blocks of bioactive materials for advanced therapeutics.

Silk sericin is a class of protein biopolymers produced by silkworms. Increasing attention has been paid to silk sericin for biomedical applications in the last decade, not only because of its excellent biocompatibility and biodegradability but also due to the pharmacological activities stemming from its unique amino acid compositions. In this review, the biological properties of silk sericin, including curing specific diseases and promoting tissue regeneration, as well as underlying mechanisms are summarized. We consider the antioxidant activity of silk sericin as a fundamental property, which could account for partial biological activities, despite the exact mechanisms of silk sericin's effect remaining unknown. Based on the reactive groups on silk sericin, approaches of bottom-up fabrication of silk sericin-based biomaterials are highlighted, including non-covalent interactions and chemical reactions (reduction, crosslinking, bioconjugation, and polymerization). We then briefly present the cutting-edge advances of silk sericin-based biomaterials applied in tissue engineering and drug delivery. The challenges of silk sericin-based biomaterials are proposed. With more bioactivities and underlying mechanisms of silk sericin uncovered, it is going to boost the therapeutic potential of silk sericin-based biomaterials.

Increased diagnostic yield in a cohort of hearing loss families using a comprehensive stepwise strategy of molecular testing.

Hearing loss is one of the most common sensory disorders in humans. This study proposes a stepwise strategy of deafness gene detection using multiplex PCR combined with high-throughput sequencing, Sanger sequencing, multiplex ligation-dependent probe amplification (MLPA), and whole-exome sequencing (WES) to explore its application in molecular diagnosis of hearing loss families. A total of 152 families with hearing loss were included in this study, the highest overall diagnosis rate was 73% (111/152). The diagnosis rate of multiplex PCR combined with high-throughput sequencing was 52.6% (80/152). One families was diagnosed by Sanger sequencing of GJB2 exon 1. Two families were diagnosed by MLPA analysis of the STRC gene. The diagnosis rate with additional contribution from WES was 18.4% (28/152). We identified 21 novel variants from 15 deafness genes by WES. Combining WES and deep clinical phenotyping, we diagnosed 11 patients with syndromic hearing loss (SHL). This study demonstrated improved diagnostic yield in a cohort of hearing loss families and confirmed the advantages of a stepwise strategy in the molecular diagnosis of hearing loss.

Polymorphisms of nucleotide excision repair genes associated with colorectal cancer risk: Meta-analysis and trial sequential analysis.

Background: Reduced DNA repair capacity in nucleotide excision repair (NER) pathways owing to genetic variant may influence cancer susceptibility. According to published studies, variants of NER genes associations with colorectal cancer (CRC) risk were inconclusive. Thus, this meta-analysis aimed to explore the possible association. A trial sequence analysis (TSA) analysis was performed to control the risk of false positive or false negative. Methods: PubMed, Web of Science, Embase, Cochrane Library, China National Knowledge Network (CNKI), Wanfang Database and Scientific and Technical Journal Database (VIP) were searched to identify relative studies until April 2022. The association was assessed by odds ratio (OR) in Allele, homozygous, heterozygous, dominant, recessive, and over-dominant models. In addition, Begg's and Egger's tests, sensitivity analysis, subgroup analysis and TSA analysis were performed. Results: A total of 29 studies were eventually included in the meta-analysis, including 12,153 CRC patients and 14,168 controls. It showed that excision and repair cross complementary group 1 (ERCC1) rs11615 CC genotype decreased the risk of CRC, compared with TT genotype (CC vs. TT: OR = 0.816, 95% CI = 0.673-0.990, p = 0.039). For ERCC1 rs3212986, the significant impact was detected on increased the risk of CRC in the allele (OR = 1.267, 95% CI = 1.027-1.562, p = 0.027), homozygous (OR = 1.805, 95% CI = 1.276-2.553, p = 0.001), dominant (OR = 1.214, 95% CI = 1.012-1.455, p = 0.037) and recessive (OR = 1.714, 95% CI = 1.225-2.399, p = 0.002) models, especially in the Asian population. The results revealed the association of ERCC2 rs1799793 A allele with a higher risk of CRC (A vs. G: OR = 1.163, 95% CI = 1.021-1.325, p = 0.023). It also showed that ERCC5 rs17655 increased CRC risk in the allele (OR = 1.104, 95% CI = 1.039-1.173, p = 0.001), homozygous (OR = 1.164, 95% CI = 1.018-1.329, p = 0.026), heterozygous (OR = 1.271, 95% CI = 1.018-1.329, p < 0.001), dominant (OR = 1.241, 95% CI = 1.135-1.358, p < 0.001) and over-dominant (OR = 0.828, 95% CI = 0.762-0.900, p < 0.001) models, especially among Asians. Conclusion: This meta-analysis based on current evidence suggests that the significant association was observed between ERCC1 rs11615, ERCC1 rs3212986, ERCC2 rs1799793, and ERCC5 rs17655 and CRC susceptibility. However, given the limited sample size and the influence of genetic background, studies of a larger scale and well-designed are required to confirm the results.

Identification and prognostic analysis of biomarkers to predict the progression of pancreatic cancer patients.

BACKGROUND: Pancreatic cancer (PC) is a malignancy with a poor prognosis and high mortality. Surgical resection is the only "curative" treatment. However, only a minority of patients with PC can obtain surgery. Improving the overall survival (OS) rate of patients with PC is still a major challenge. Molecular biomarkers are a significant approach for diagnostic and predictive use in PCs. Several prediction models have been developed for patients newly diagnosed with PC that is operable or patients with advanced and metastatic PC; however, these models require further validation. Therefore, precise biomarkers are urgently required to increase the efficiency of predicting a disease-free survival (DFS), OS, and sensitivity to immunotherapy in PC patients and to improve the prognosis of PC. METHODS: In the present study, we first evaluated the highly and selectively expressed targets in PC, using the GeoMxTM Digital Spatial Profiler (DSP) and then, we analyzed the roles of these targets in PCs using TCGA database. RESULTS: LAMB3, FN1, KRT17, KRT19, and ANXA1 were defined as the top five upregulated targets in PC compared with paracancer. The TCGA database results confirmed the expression pattern of LAMB3, FN1, KRT17, KRT19, and ANXA1 in PCs. Significantly, LAMB3, FN1, KRT19, and ANXA1 but not KRT17 can be considered as biomarkers for survival analysis, univariate and multivariate Cox proportional hazards model, and risk model analysis. Furthermore, in combination, LAMB3, FN1, KRT19, and ANXA1 predict the DFS and, in combination, LAMB3, KRT19, and ANXA1 predict the OS. Immunotherapy is significant for PCs that are inoperable. The immune checkpoint blockade (ICB) analysis indicated that higher expressions of FN1 or ANXA1 are correlated with lower ICB response. In contrast, there are no significant differences in the ICB response between high and low expression of LAMB3 and KRT19. CONCLUSIONS: In conclusion, LAMB3, FN1, KRT19, and ANXA1 are good predictors of PC prognosis. Furthermore, FN1 and ANXA1 can be predictors of immunotherapy in PCs.

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.

TSPAN1, TMPRSS4, SDR16C5, and CTSE as Novel Panel for Pancreatic Cancer: A Bioinformatics Analysis and Experiments Validation.

Pancreatic cancer is a lethal malignancy with a poor prognosis. This study aims to identify pancreatic cancer-related genes and develop a robust diagnostic model to detect this disease. Weighted gene co-expression network analysis (WGCNA) was used to determine potential hub genes for pancreatic cancer. Their mRNA and protein expression levels were validated through reverse transcription PCR (RT-PCR) and immunohistochemical (IHC). Diagnostic models were developed by eight machine learning algorithms and ten-fold cross-validation. Four hub genes (TSPAN1, TMPRSS4, SDR16C5, and CTSE) were identified based on bioinformatics. RT-PCR showed that the four hub genes were expressed at medium to high levels, IHC revealed that their protein expression levels were higher in pancreatic cancer tissues. For the panel of these four genes, eight models performed with 0.87-0.92 area under the curve value (AUC), 0.91-0.94 sensitivity, and 0.84-0.86 specificity in the validation cohort. In the external validation set, these models also showed good performance (0.86-0.98 AUC, 0.84-1.00 sensitivity, and 0.86-1.00 specificity). In conclusion, this study has identified four hub genes that might be closely related to pancreatic cancer: TSPAN1, TMPRSS4, SDR16C5, and CTSE. Four-gene panels might provide a theoretical basis for the diagnosis of pancreatic cancer.

The geographic alignment of primary care Health Professional Shortage Areas with markers for social determinants of health.

BACKGROUND: The Health Resources and Services Administration (HRSA), an agency within the U.S. Department of Health and Human Services (HHS), works to ensure accessible, quality, health care for the nation's underserved populations, especially those who are medically, economically, or geographically vulnerable. HRSA-designated primary care Health Professional Shortage Areas (pcHPSAs) provide a vital measure by which to identify underserved populations and prioritize locations and populations lacking access to adequate primary and preventive health care-the foundation for advancing health equity and maintaining health and wellness for individuals and populations. However, access to care is a complex, multifactorial issue that involves more than just the number of health care providers available, and pcHPSAs alone cannot fully characterize the distribution of medically, economically, and geographically vulnerable populations. METHODS AND FINDINGS: In this county-level analysis, we used descriptive statistics and multiple correspondence analysis to assess how HRSA's pcHPSA designations align geographically with other established markers of medical, economic, and geographic vulnerability. Reflecting recognized social determinants of health (SDOH), markers included demographic characteristics, race and ethnicity, rates of low birth weight births, median household income, poverty, educational attainment, and rurality. Nationally, 96 percent of U.S. counties were either classified as whole county or partial county pcHPSAs or had one or more established markers of medical, economic, or geographic vulnerability in 2017, suggesting that at-risk populations were nearly ubiquitous throughout the nation. Primary care HPSA counties in HHS Regions 4 and 6 (largely lying within the southeastern and south central United States) had the most pervasive and complex patterns in population risk. CONCLUSION: HHS Regions displayed unique signatures with respect to SDOH markers. Descriptive and analytic findings from our work may help inform health workforce and health care planning at all levels, and, by illustrating both the complexity of and differences in county-level population characteristics in pcHPSA counties, our findings may have relevance for strengthening the delivery of primary care and addressing social determinants of health in areas beset by provider shortages.

Alcohol Consumption by Beverage Type and Risk of Breast Cancer: A Dose-Response Meta-Analysis of Prospective Cohort Studies.

AIMS: Alcohol intake has been shown to increase the risk of breast cancer. However, the dose-response analysis of different alcoholic beverages (spirits, wine and beer) is not clear. Our meta-analysis aims to provide a dose-response estimation between different alcohols and breast cancer risk. METHODS: Search of PubMed and Web of Science and manual searches were conducted up to 1 December 2018, and summary relative risks (RRs) and attributable risk percentage (ARP) for alcohol intake on the development of breast cancer were calculated. Dose-response meta-analysis modeled relationships between drinking type and breast cancer risk. Sources of heterogeneity were explored, and sensitivity analyses were conducted to test the robustness of findings. RESULTS: In total, 22 cohort studies and 45,350 breast cancer cases were included. Current drinkers for ER+ had an increased risk compared with never drinkers. In dose-response analysis, there was a statistically significant linear trend with breast cancer risk increasing gradually by total alcohol and wine dose: when adding 10 g per day, the risk increased by 10.5% (RR = 1.10, 95%CI = 1.08-1.13) in total alcohol and 8.9% (RR = 1.08, 95%CI = 1.04-1.14) in wine. For postmenopausal women, the risk increases by 11.1% (RR = 1.11, 95%CI = 1.09-1.13) with every 10 g of total alcohol increase. Furthermore, the breast cancer alcohol-attributed percentage is higher in Europe than in North America and Asia. CONCLUSIONS: The effect of drinking on the incidence of breast cancer is mainly manifested in ER+ breast cancer. Quantitative analysis showed total drinking had a significant risk for breast cancer, especially for postmenopausal women. However, for different alcohols, just wine intake has the similar results.

Establishment and validation of an immunodiagnostic model for prediction of breast cancer.

Serum autoantibodies that react with tumor-associated antigens (TAAs) can be used as potential biomarkers for diagnosis of cancer. This study aims to evaluate the immunodiagnostic value of 11 anti-TAAs autoantibodies for detection of breast cancer (BC) and establish a diagnostic model for distinguishing BC from normal human controls (NHC) and benign breast diseases (BBD). Sera from 10 BC patients and 10 NHC were used to detect 11 anti-TAAs autoantibodies by western blotting. The 11 anti-TAAs autoantibodies were further assessed in 983 sera by relative quantitative enzyme-linked immunosorbent assay (ELISA). Binary logistic regression and Fisher linear discriminant analysis were conducted to establish a prediction model by using 184 BC and 184 NHC (training cohort, n = 568) and validated by leave-one-out cross-validation. Logistic regression model was selected to establish the prediction model. Results were validated using an independent validation cohort (n = 415). The five anti-TAAs (p53, cyclinB1, p16, p62, 14-3-3ξ) autoantibodies were selected to construct the model with the area under the curve (AUC) of 0.943 (95% CI, 0.919-0.967) in training cohort and 0.916 (95% CI, 0.886-0.947) in the validation cohort. In the identification of BC and BBD, AUCs were 0.881 (95% CI, 0.848-0.914) and 0.849 (95% CI, 0.803-0.894) in training and validation cohort, respectively. In summary, our study indicates that the immunodiagnostic model can distinguish BC from NHC and BC from BBD and this model may have a potential application in immunodiagnosis of breast cancer.

A dose-response meta-analysis of green tea consumption and breast cancer risk.

Whether drinking green tea (GT) could reduce the risk of breast cancer (BC) is still controversial. The search was performed using PubMed, Embase and Web of Science databases. The generalised least square method and constrained cubic spline model were performed to assess the dose-response trends between GT consumption and BC risk. The attributable risk proportion (ARP) was also calculated. A total of 16 studies were included and the pooled relative risks was 0.86 (95%CI: 0.75-0.99) for BC risk at the highest vs. lowest levels of GT consumption. GT consumption (pnonlinearity = .110), drinking GT years (pnonlinearity = .393) and BC risk were both negatively linearly correlated. Moreover, The ARP results demonstrated in China, people who drink GT do not suffer from BC, 23.5% of which may be attributed to drinking GT. In conclusion, drinking GT may have a positive effect on reducing BC risk, especially in long-term, high doses.