Rare pathogenic structural variants show potential to enhance prostate cancer germline testing for African men.

Prostate cancer (PCa) is highly heritable, with men of African ancestry at greatest risk and associated lethality. Lack of representation in genomic data means germline testing guidelines exclude for African men. Established that structural variations (SVs) are major contributors to human disease and prostate tumourigenesis, their role is under-appreciated in familial and therapeutic testing. Utilising a clinico-methodologically matched African (n = 113) versus European (n = 57) deep-sequenced PCa resource, we interrogated 42,966 high-quality germline SVs using a best-fit pathogenicity prediction workflow. We identified 15 potentially pathogenic SVs representing 12.4% African and 7.0% European patients, of which 72% and 86% met germline testing standard-of-care recommendations, respectively. Notable African-specific loss-of-function gene candidates include DNA damage repair MLH1 and BARD1 and tumour suppressors FOXP1, WASF1 and RB1. Representing only a fraction of the vast African diaspora, this study raises considerations with respect to the contribution of kilo-to-mega-base rare variants to PCa pathogenicity and African associated disparity.

Human papillomavirus infection and the risk of cancer at specific sites other than anogenital tract and oropharyngeal region: an umbrella review.

BACKGROUND: Despite numerous studies having evaluated the associations between human papillomavirus (HPV) infection and risk of specific cancers other than anogenital tract and oropharyngeal, the findings are inconsistent and the quality of evidence has not been systematically quantified. We aimed to summarise the existing evidence as well as to evaluate the strength and credibility of these associations. METHODS: We conducted an umbrella review of systematic reviews and meta-analyses of observational studies. PubMed, EMBASE, and Web of Science were searched from inception to March 2024. Studies with systematic reviews and meta-analyses that examined associations between HPV or HPV-associated genotypes infection and specific cancers were eligible for this review. The quality of the methodology was evaluated using A Measurement Tool to Assess systematic Reviews (AMSTAR). The credibility of the evidence was assessed using GRADE. The protocol was preregistered with PROSPERO (CRD42023439070). FINDINGS: The umbrella review identified 31 eligible studies reporting 87 associations with meta-analytic estimates, including 1191 individual studies with 336,195 participants. Of those, 29 (93.5%) studies were rated as over moderate quality by AMSTAR. Only one association indicating HPV-18 infection associated with an increased risk of breast cancer (odds ratio [OR] = 3.48, 95% confidence interval [CI] = 2.24-5.41) was graded as convincing evidence. There were five unique outcomes identified as highly suggestive evidence, including HPV infection increased the risk of oral squamous cell carcinoma (OR = 7.03, 95% CI = 3.87-12.76), oesophageal cancer (OR = 3.32, 95% CI = 2.54-4.34), oesophageal squamous cell carcinoma (OR = 2.69, 95% CI = 2.05-3.54), lung cancer (OR = 3.60, 95% CI = 2.59-5.01), and breast cancer (OR = 6.26, 95% CI = 4.35-9.00). According to GRADE, one association was classified as high, indicating that compared with the controls in normal tissues, HPV infection was associated with an increased risk of breast cancer. INTERPRETATION: The umbrella review synthesised up-to-date observational evidence on HPV infection with the risk of breast cancer, oral squamous cell carcinoma, oesophageal cancer, oesophageal squamous cell carcinoma, and lung cancer. Further larger prospective cohort studies are needed to verify the associations, providing public health recommendations for prevention of disease. FUNDING: National Key Research and Development Program of China, Natural Science Foundation of China, Outstanding Scientific Fund of Shengjing Hospital of China Medical University, and 345 Talent Project of Shengjing Hospital of China Medical University.

Triglyceride-glucose index and health outcomes: an umbrella review of systematic reviews with meta-analyses of observational studies.

BACKGROUND: Numerous meta-analyses have explored the association between the triglyceride-glucose (TyG) index and diverse health outcomes, yet the comprehensive assessment of the scope, validity, and quality of this evidence remains incomplete. Our aim was to systematically review and synthesise existing meta-analyses of TyG index and health outcomes and to assess the quality of the evidence. METHODS: A thorough search of PubMed, EMBASE, and Web of Science databases was conducted from their inception through to 8 April 2024. We assessed the quality of reviews using A Measurement Tool to Assess Systematic Reviews (AMSTAR) and the certainty of the evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system. This study was registered with PROSPERO (CRD: 42024518587). RESULTS: Overall, a total of 95 associations from 29 meta-analyses were included, investigating associations between TyG index and 30 health outcomes. Of these, 83 (87.4%) associations were statistically significant (P < 0.05) according to the random effects model. Based on the AMSTAR tool, 16 (55.2%) meta-analyses were high quality and none was low quality. The certainty of the evidence, assessed by the GRADE framework, showed that 6 (6.3%) associations were supported by moderate-quality evidence. When compared with the lowest category of the TyG index, the risk of contrast-induced nephropathy (CIN) [relative risk (RR) = 2.25, 95%CI 1.82, 2.77], the risk of stroke in patients with diabetes mellitus (RR = 1.26, 95%CI 1.18, 1.33) or with acute coronary syndrome disease (RR = 1.56, 95%CI 1.06, 2.28), the prognosis of coronary artery disease (CAD)-non-fatal MI (RR = 2.02, 95%CI 1.32, 3.10), and the severity of CAD including coronary artery stenosis (RR = 3.49, 95%CI 1.71, 7.12) and multi-vessel CAD (RR = 2.33, 95%CI 1.59, 3.42) increased with high TyG index. CONCLUSION: We found that the TyG index was positively associated with many diseases including the risk of CIN and stroke, the prognosis of CAD, and the severity of CAD which were supported by moderate-quality evidence. TyG index might be useful to identify people at high-risk for developing these diseases.

The Remarkable Anti-Breast Cancer Efficacy and Anti-Metastasis by Multifunctional Nanoparticles Co-Loading Squamocin, R848 and IR 780.

Background: Breast cancer is a heterogeneous disease globally accounting for approximately 1 million new cases annually. Chemotherapy remains the main therapeutic option, but the antitumor efficacy needs to be improved. Methods: Two multifunctional nanoparticles were developed in this paper using oleic acid and mPEG2k-PCL2k as the drug carriers. Squamocin (Squ) was employed as a chemotherapeutic agent. Resiquimod (R848) or ginsenoside Rh2 was co-encapsulated in the nanoparticles to remold the immunosuppressive tumor microenvironment, and IR780 was coloaded as a photosensitizer to realize photothermal therapy. Results: The obtained Squ-R848-IR780 nanoparticles and Squ-Rh2-IR780 nanoparticles were uniformly spherical and approximately (162.200 ± 2.800) nm and (157.300 ± 1.1590) nm, respectively, in average diameter, with good encapsulation efficiency (above 85% for each drug), excellent stability in various physiological media and high photothermal conversion efficiency (24.10% and 22.58%, respectively). After intravenous administration, both nanoparticles quickly accumulated in the tumor and effectively enhanced the local temperature of the tumor to over 45 °C when irradiated by an 808 nm laser. At a low dose of 0.1 mg/kg, Squ nanoparticles treatment alone displayed a tumor inhibition rate of 55.28%, pulmonary metastasis inhibition rate of 59.47% and a mean survival time of 38 days, which were all higher than those of PTX injection (8 mg/kg) (43.64%, 25 days and 37.25%), indicating that Squ was a potent and effective antitumor agent. Both multifunctional nanoparticles, Squ-Rh2-IR780 nanoparticles and Squ-R848-IR780 nanoparticles, demonstrated even better therapeutic efficacy, with tumor inhibition rates of 90.02% and 97.28%, pulmonary metastasis inhibition rates of 95.42% and 98.09, and mean survival times of 46 days and 52 days, respectively. Conclusion: The multifunctional nanoparticles coloaded with squamocin, R848 and IR 780 achieved extraordinary therapeutic efficacy and excellent antimetastasis activity and are thus promising in the future treatment of breast tumors and probably other tumors.

SMG5 Inhibition Restrains Hepatocellular Carcinoma Growth and Enhances Sorafenib Sensitivity.

Hepatocellular carcinoma (HCC) has a pathogenesis that remains elusive with restricted therapeutic strategies and efficacy. This study aimed to investigate the role of SMG5, a crucial component in nonsense-mediated mRNA decay (NMD) that degrades mRNA containing a premature termination codon (PTC), in HCC pathogenesis and therapeutic resistance. We demonstrated an elevated expression of SMG5 in HCC and scrutinized its potential as a therapeutic target. Our findings revealed that SMG5 knockdown not only inhibited the migration, invasion, and proliferation of HCC cells but also influenced sorafenib resistance. Differential gene expression analysis between the control and SMG5 knockdown groups showed an upregulation of MAT1A in the latter. High expression of MAT1A, a catalyst for S-adenosylmethionine (SAM) production, as suggested by TCGA data, was indicative of a better prognosis for HCC. Further, an enzyme-linked immunosorbent assay showed a higher concentration of SAM in SMG5 knockdown cell supernatants. Furthermore, we found that exogenous SAM supplementation enhanced the sensitivity of HCC cells to sorafenib alongside changes in the expression of Bax and Bcl 2, apoptosis-related proteins. Our findings underscore the important role of SMG5 in HCC development and its involvement in sorafenib resistance, highlighting it as a potential target for HCC treatment.

Role of artificial intelligence in digital pathology for gynecological cancers.

The diagnosis of cancer is typically based on histopathological sections or biopsies on glass slides. Artificial intelligence (AI) approaches have greatly enhanced our ability to extract quantitative information from digital histopathology images as a rapid growth in oncology data. Gynecological cancers are major diseases affecting women's health worldwide. They are characterized by high mortality and poor prognosis, underscoring the critical importance of early detection, treatment, and identification of prognostic factors. This review highlights the various clinical applications of AI in gynecological cancers using digitized histopathology slides. Particularly, deep learning models have shown promise in accurately diagnosing, classifying histopathological subtypes, and predicting treatment response and prognosis. Furthermore, the integration with transcriptomics, proteomics, and other multi-omics techniques can provide valuable insights into the molecular features of diseases. Despite the considerable potential of AI, substantial challenges remain. Further improvements in data acquisition and model optimization are required, and the exploration of broader clinical applications, such as the biomarker discovery, need to be explored.

IL-2 promotes expansion and intratumoral accumulation of tumor infiltrating dendritic cells in pancreatic cancer.

This study aims to investigate the diagnostic potential of IL-2 for PDAC and develop a method to improve the dendritic cell (DC) based vaccine against PDAC. The gene expression data and clinical characteristics information for 178 patients with PDAC were obtained from The Cancer Genome Atlas (TCGA). DCs were isolated from Human peripheral blood mononuclear cells (PBMCs) and were cultured in 4 different conditions. DCs were pulsed by tumor cell lysates or KRAS G12D1 - 23 peptide, and then used to activate T cells. The mixture of DCs and T cells were administered to xenograft mouse model through the tail vein. The infiltration of DCs and T cells were detected by immunohistochemistry. The generation of KRAS G12D mutation specific cytotoxic T cells was determined by in vitro killing assay. We observed that PDAC patients with higher IL-2 mRNA levels exhibited improved overall survival and increased infiltration of CD8 + T cells, NK cells, naïve B cells, and resting myeloid DCs in the tumor microenvironment. IL-2 alone did not enhance DC proliferation, antigen uptake, or apoptosis inhibition unless co-cultured with PBMCs. DCs co-cultured with PBMCs in IL-2-containing medium demonstrated the strongest tumor repression effect in vitro and in vivo. Compared to DCs obtained through the traditional method (cultured in medium containing GM-CSF and IL-4), DCs cultured with PBMCs, and IL-2 exhibited increased tumor infiltration capacity, potentially facilitating sustained T cell immunity. DCs cultured in the PBMCs-IL-2 condition could promote the generation of cytotoxic T cells targeting tumor cells carrying KRAS G12D mutation.

SH3RF2 contributes to cisplatin resistance in ovarian cancer cells by promoting RBPMS degradation.

Platinum-based chemotherapy remains one of the major choices for treatment of ovarian cancer (OC). However, primary or acquired drug resistance severely impairs their efficiency, thereby causing chemotherapy failure and poor prognosis. SH3 domain containing ring finger 2 (SH3RF2) has been linked to the development of cancer. Here we find higher levels of SH3RF2 in the tumor tissues from cisplatin-resistant OC patients when compared to those from cisplatin-sensitive patients. Similarly, cisplatin-resistant OC cells also express higher levels of SH3RF2 than normal OC cells. Through in vitro and in vivo loss-of-function experiments, SH3RF2 is identified as a driver of cisplatin resistance, as evidenced by increases in cisplatin-induced cell apoptosis and DNA damage and decreases in cell proliferation induced by SH3RF2 depletion. Mechanistically, SH3RF2 can directly bind to the RNA-binding protein mRNA processing factor (RBPMS). RBPMS has been reported as an inhibitor of cisplatin resistance in OC. As a E3 ligase, SH3RF2 promotes the K48-linked ubiquitination of RBPMS to increase its proteasomal degradation and activator protein 1 (AP-1) transactivation. Impairments in RBPMS function reverse the inhibitory effect of SH3RF2 depletion on cisplatin resistance. Collectively, the SH3RF2-RBPMS-AP-1 axis is an important regulator in cisplatin resistance and inhibition of SH3RF2 may be a potential target in preventing cisplatin resistance.

Proteomic profiling of ovarian clear cell carcinomas identifies prognostic biomarkers for chemotherapy.

Clear cell ovarian carcinoma (CCOC) is a relatively rare subtype of ovarian cancer (OC) with high degree of resistance to standard chemotherapy. Little is known about the underlying molecular mechanisms, and it remains a challenge to predict its prognosis after chemotherapy. Here, we first analyzed the proteome of 35 formalin-fixed paraffin-embedded (FFPE) CCOC tissue specimens from a cohort of 32 patients with CCOC (H1 cohort) and characterized 8697 proteins using data-independent acquisition mass spectrometry (DIA-MS). We then performed proteomic analysis of 28 fresh frozen (FF) CCOC tissue specimens from an independent cohort of 24 patients with CCOC (H2 cohort), leading to the identification of 9409 proteins with DIA-MS. After bioinformatics analysis, we narrowed our focus to 15 proteins significantly correlated with the recurrence free survival (RFS) in both cohorts. These proteins are mainly involved in DNA damage response, extracellular matrix (ECM), and mitochondrial metabolism. Parallel reaction monitoring (PRM)-MS was adopted to validate the prognostic potential of the 15 proteins in the H1 cohort and an independent confirmation cohort (H3 cohort). Interferon-inducible transmembrane protein 1 (IFITM1) was observed as a robust prognostic marker for CCOC in both PRM data and immunohistochemistry (IHC) data. Taken together, this study presents a CCOC proteomic data resource and a single promising protein, IFITM1, which could potentially predict the recurrence and survival of CCOC.

Adherence to Sulfur Microbial Diet and Ovarian Cancer Survival: Evidence from a Prospective Cohort Study.

SCOPE: The study aims to investigate the role of the sulfur microbial diet in the survival of ovarian cancer (OC). METHODS AND RESULTS: A prospective cohort study is conducted with 703 patients diagnosed with OC between 2015 and 2020. Diet information is collected using a validated food frequency questionnaire. Deaths are ascertained up to March 31, 2021, via the death registry linkage. During the follow-up period (median: 37.2 months, interquartile range: 24.7-50.2 months), 130 deaths are observed. A higher sulfur microbial diet score is significantly associated with an increased risk of all-cause mortality among OC patients (tertile 3 vs tertile 1: HR = 1.93, 95% CI = 1.11-3.35). Each 1-standard deviation increment in the sulfur microbial diet score increases the all-cause mortality risk by 33% (95% CI = 1.04-1.71). Stratified analysis shows that significant associations are found in OC patients diagnosed over 50 years of age, with body mass index ≥24  kg m-2 , who changed their diet after diagnosis, or without residual lesions. CONCLUSIONS: Adherence to the sulfur microbial diet, characterized by high intakes of red meats and processed meats, and low intakes of fruits, vegetables, and whole grains, is associated with poor survival in OC patients.

XYLEM CYSTEINE PEPTIDASE 1 and its inhibitor CYSTATIN 6 regulate pattern-triggered immunity by modulating the stability of the NADPH oxidase RESPIRATORY BURST OXIDASE HOMOLOG D.

Plants produce a burst of reactive oxygen species (ROS) after pathogen infection to successfully activate immune responses. During pattern-triggered immunity (PTI), ROS are primarily generated by the NADPH oxidase RESPIRATORY BURST OXIDASE HOMOLOG D (RBOHD). RBOHD is degraded in the resting state to avoid inappropriate ROS production; however, the enzyme mediating RBOHD degradation and how to prevent RBOHD degradation after pathogen infection is unclear. In this study, we identified an Arabidopsis (Arabidopsis thaliana) vacuole-localized papain-like cysteine protease, XYLEM CYSTEINE PEPTIDASE 1 (XCP1), and its inhibitor CYSTATIN 6 (CYS6). Pathogen-associated molecular pattern-induced ROS burst and resistance were enhanced in the xcp1 mutant but were compromised in the cys6 mutant, indicating that XCP1 and CYS6 oppositely regulate PTI responses. Genetic and biochemical analyses revealed that CYS6 interacts with XCP1 and depends on XCP1 to enhance PTI. Further experiments showed that XCP1 interacts with RBOHD and accelerates RBOHD degradation in a vacuole-mediated manner. CYS6 inhibited the protease activity of XCP1 toward RBOHD, which is critical for RBOHD accumulation upon pathogen infection. As CYS6, XCP1, and RBOHD are conserved in all plant species tested, our findings suggest the existence of a conserved strategy to precisely regulate ROS production under different conditions by modulating the stability of RBOHD.

Proteomic characterization of epithelial ovarian cancer delineates molecular signatures and therapeutic targets in distinct histological subtypes.

Clear cell carcinoma (CCC), endometrioid carcinoma (EC), and serous carcinoma (SC) are the major histological subtypes of epithelial ovarian cancer (EOC), whose differences in carcinogenesis are still unclear. Here, we undertake comprehensive proteomic profiling of 80 CCC, 79 EC, 80 SC, and 30 control samples. Our analysis reveals the prognostic or diagnostic value of dysregulated proteins and phosphorylation sites in important pathways. Moreover, protein co-expression network not only provides comprehensive view of biological features of each histological subtype, but also indicates potential prognostic biomarkers and progression landmarks. Notably, EOC have strong inter-tumor heterogeneity, with significantly different clinical characteristics, proteomic patterns and signaling pathway disorders in CCC, EC, and SC. Finally, we infer MPP7 protein as potential therapeutic target for SC, whose biological functions are confirmed in SC cells. Our proteomic cohort provides valuable resources for understanding molecular mechanisms and developing treatment strategies of distinct histological subtypes.

The performance of deep learning on thyroid nodule imaging predicts thyroid cancer: A systematic review and meta-analysis of epidemiological studies with independent external test sets.

BACKGROUND AND AIMS: It is still controversial whether deep learning (DL) systems add accuracy to thyroid nodule imaging classification based on the recent available evidence. We conducted this study to analyze the current evidence of DL in thyroid nodule imaging diagnosis in both internal and external test sets. METHODS: Until the end of December 2022, PubMed, IEEE, Embase, Web of Science, and the Cochrane Library were searched. We included primary epidemiological studies using externally validated DL techniques in image-based thyroid nodule appraisal. This systematic review was registered on PROSPERO (CRD42022362892). RESULTS: We evaluated evidence from 17 primary epidemiological studies using externally validated DL techniques in image-based thyroid nodule appraisal. Fourteen studies were deemed eligible for meta-analysis. The pooled sensitivity, specificity, and area under the curve (AUC) of these DL algorithms were 0.89 (95% confidence interval 0.87-0.90), 0.84 (0.82-0.86), and 0.93 (0.91-0.95), respectively. For the internal validation set, the pooled sensitivity, specificity, and AUC were 0.91 (0.89-0.93), 0.88 (0.85-0.91), and 0.96 (0.93-0.97), respectively. In the external validation set, the pooled sensitivity, specificity, and AUC were 0.87 (0.85-0.89), 0.81 (0.77-0.83), and 0.91 (0.88-0.93), respectively. Notably, in subgroup analyses, DL algorithms still demonstrated exceptional diagnostic validity. CONCLUSIONS: Current evidence suggests DL-based imaging shows diagnostic performances comparable to clinicians for differentiating thyroid nodules in both the internal and external test sets.

Association of pre-diagnosis specific color groups of fruit and vegetable intake with ovarian cancer survival: results from the ovarian cancer follow-up study (OOPS).

Background: The colors of fruits and vegetables (FV) reflect the presence of pigmented bioactive compounds. The evidence of pre-diagnosis specific FV color group intake contributing to ovarian cancer (OC) survival is limited and inconsistent. Methods: A prospective cohort study was conducted between 2015 and 2020 with 700 newly diagnosed OC patients. Pre-diagnosis dietary information was assessed by a validated food frequency questionnaire. We classified FV into five groups based on the color of their edible parts (e.g., green, red/purple, orange/yellow, white, and uncategorized groups). Cox proportional hazard models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association of specific color groups of FV before diagnosis with OC survival. Potential multiplicative and additive interactions were assessed. Results: 130 patients died during a median follow-up of 37.57 (interquartile: 24.77-50.20) months. We observed the improved survival with a higher pre-diagnosis intake of total FV (HRtertile 3 vs. tertile 1 = 0.63, 95%CI = 0.40-0.99), total vegetables (HRtertile 3 vs. tertile 1 = 0.57, 95%CI = 0.36-0.90), and red/purple FV (HRtertile 3 vs. tertile 1 = 0.52, 95%CI = 0.33-0.82). In addition, we observed significant dose-response relationships for per standard deviation increment between total vegetable intake (HR = 0.79, 95%CI = 0.65-0.96) and red/purple group intake (HR = 0.77, 95%CI = 0.60-0.99) before diagnosis with OC survival. Additionally, pre-diagnosis green FV intake was borderline associated with better OC survival (HRper standard deviation increment = 0.83; 95%CI = 0.69-1.00). In contrast, we did not observe significant associations between pre-diagnosis intake of total fruits, orange/yellow, white, and uncategorized groups and OC survival. Conclusion: Pre-diagnosis FV intake from various color groups, especially the green and red/purple ones, may improve OC survival. Further studies are needed to validate our findings.

DNA Methylation-Based Testing in Peripheral Blood Mononuclear Cells Enables Accurate and Early Detection of Colorectal Cancer.

An effective blood-based method for the diagnosis of colorectal cancer has not yet been developed. Molecular alterations of immune cells occur early in tumorigenesis, providing the theoretical underpinning for early cancer diagnosis based on immune cell profiling. Therefore, we aimed to develop an effective detection method based on peripheral blood mononuclear cells (PBMC) to improve the diagnosis of colorectal cancer. Analysis of the genome-wide methylation landscape of PBMCs from patients with colorectal cancer and healthy controls by microarray, pyrosequencing, and targeted bisulfite sequencing revealed five DNA methylation markers for colorectal cancer diagnosis, especially early-stage colorectal cancer. A single-tube multiple methylation-specific quantitative PCR assay (multi-msqPCR) for simultaneous detection of five methylation markers was established, which allowed quantitative analysis of samples with as little as 0.1% PBMC DNA and had better discriminative performance than single-molecule detection. Then, a colorectal cancer diagnostic model (CDM) based on methylation markers and the multi-msqPCR method was constructed that achieved high accuracy for early-stage colorectal cancer (AUC = 0.91; sensitivity = 81.18%; specificity = 89.39%), which was improved compared with CEA (AUC = 0.79). The CDM also enabled a high degree of discrimination for advanced adenoma cases (AUC = 0.85; sensitivity = 63.04%). Follow-up data also demonstrated that the CDM could identify colorectal cancer potential up to 2 years before currently used diagnostic methods. In conclusion, the approach constructed in this study based on PBMC-derived DNA methylation markers and a multi-msqPCR method is a promising and easily implementable diagnostic method for early-stage colorectal cancer. SIGNIFICANCE: Development of a diagnostic model for early colorectal cancer based on epigenetic analysis of PBMCs supports the utility of altered DNA methylation in immune cells for cancer diagnosis.

A multiplex blood-based assay targeting DNA methylation in PBMCs enables early detection of breast cancer.

The immune system can monitor tumor development, and DNA methylation is involved in the body's immune response to tumors. In this work, we investigate whether DNA methylation alterations in peripheral blood mononuclear cells (PBMCs) could be used as markers for early detection of breast cancer (BC) from the perspective of tumor immune alterations. We identify four BC-specific methylation markers by combining Infinium 850 K BeadChips, pyrosequencing and targeted bisulfite sequencing. Based on the four methylation markers in PBMCs of BC, we develop an efficient and convenient multiplex methylation-specific quantitative PCR assay for the detection of BC and validate its diagnostic performance in a multicenter cohort. This assay was able to distinguish early-stage BC patients from normal controls, with an AUC of 0.940, sensitivity of 93.2%, and specificity of 90.4%. More importantly, this assay outperformed existing clinical diagnostic methods, especially in the detection of early-stage and minimal tumors.

A deep learning model using hyperspectral image for EUS-FNA cytology diagnosis in pancreatic ductal adenocarcinoma.

BACKGROUND AND AIMS: Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) is considered to be a first-line procedure for the pathological diagnosis of pancreatic cancer owing to its high accuracy and low complication rate. The number of new cases of pancreatic ductal adenocarcinoma (PDAC) is increasing, and its accurate pathological diagnosis poses a challenge for cytopathologists. Our aim was to develop a hyperspectral imaging (HSI)-based convolution neural network (CNN) algorithm to aid in the diagnosis of pancreatic EUS-FNA cytology specimens. METHODS: HSI images were captured of pancreatic EUS-FNA cytological specimens from benign pancreatic tissues (n = 33) and PDAC (n = 39) prepared using a liquid-based cytology method. A CNN was established to test the diagnostic performance, and Attribution Guided Factorization Visualization (AGF-Visualization) was used to visualize the regions of important classification features identified by the model. RESULTS: A total of 1913 HSI images were obtained. Our ResNet18-SimSiam model achieved an accuracy of 0.9204, sensitivity of 0.9310 and specificity of 0.9123 (area under the curve of 0.9625) when trained on HSI images for the differentiation of PDAC cytological specimens from benign pancreatic cells. AGF-Visualization confirmed that the diagnoses were based on the features of tumor cell nuclei. CONCLUSIONS: An HSI-based model was developed to diagnose cytological PDAC specimens obtained using EUS-guided sampling. Under the supervision of experienced cytopathologists, we performed multi-staged consecutive in-depth learning of the model. Its superior diagnostic performance could be of value for cytologists when diagnosing PDAC.

DPHL v.2: An updated and comprehensive DIA pan-human assay library for quantifying more than 14,000 proteins.

A comprehensive pan-human spectral library is critical for biomarker discovery using mass spectrometry (MS)-based proteomics. DPHL v.1, a previous pan-human library built from 1,096 data-dependent acquisition (DDA) MS data of 16 human tissue types, allows quantifying of 10,943 proteins. Here, we generated DPHL v.2 from 1,608 DDA-MS data. The data included 586 DDA-MS data acquired from 18 tissue types, while 1,022 files were derived from DPHL v.1. DPHL v.2 thus comprises data from 24 sample types, including several cancer types (lung, breast, kidney, and prostate cancer, among others). We generated four variants of DPHL v.2 to include semi-tryptic peptides and protein isoforms. DPHL v.2 was then applied to two colorectal cancer cohorts. The numbers of identified and significantly dysregulated proteins increased by at least 21.7% and 14.2%, respectively, compared with DPHL v.1. Our findings show that the increased human proteome coverage of DPHL v.2 provides larger pools of potential protein biomarkers.

Diet quality and survival after ovarian cancer: results from an ovarian cancer follow-up study (OOPS).

Background and aims: Owing to the limited studies and controversial evidence, the connection between diet quality and survival of patients with ovarian cancer (OC) has been indistinct. Our study intends to first investigate this topic based on Chinese diet quality scores. Methods: Our data come from an ovarian cancer follow-up study, which includes 796 patients with OC between 2015 and 2020. Three diet quality scores, including the Chinese Healthy Eating Index (CHEI), Dietary Balance Index (DBI), and Chinese Food Pagoda Score (CFPS), were calculated using a validated 111-item food frequency questionnaire. We used the Cox proportional hazards regression model to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Potential multiplicative and additive interactions were also assessed. Results: With a median follow-up time of 37.17 months (interquartile: 24.73-50.17 months), we recorded 130 deaths. According to comparisons of the highest to lowest tertile of scores, the pre-diagnosis CHEI was linked to better overall survival (OS) in patients (HR = 0.56, 95% CI: 0.36, 0.88). A dose-response relationship between CHEI and OS was also observed (HR = 0.85, 95% CI: 0.71, 1.00, per 1 standard deviation increment). However, no evidence of significant associations between DBI and CFPS with OS was observed. Additionally, significant multiplicative and additive interactions were seen in the diet quality scores (CHEI and DBI) with the body mass index and the menopausal status. Conclusions: A high CHEI was associated with an improved OS for patients with OC, while DBI and CFPS were unrelated to OC survival.

A combined radiomic model distinguishing GISTs from leiomyomas and schwannomas in the stomach based on endoscopic ultrasonography images.

BACKGROUND: Endoscopic ultrasonography (EUS) is recommended as the best tool for evaluating gastric subepithelial lesions (SELs); nonetheless, it has difficulty distinguishing gastrointestinal stromal tumors (GISTs) from leiomyomas and schwannomas. GISTs have malignant potential, whereas leiomyomas and schwannomas are considered benign. PURPOSE: This study aimed to establish a combined radiomic model based on EUS images for distinguishing GISTs from leiomyomas and schwannomas in the stomach. METHODS: EUS images of pathologically confirmed GISTs, leiomyomas, and schwannomas were collected from five centers. Gastric SELs were divided into training and testing datasets based on random split-sample method (7:3). Radiomic features were extracted from the tumor and muscularis propria regions. Principal component analysis, least absolute shrinkage, and selection operator were used for feature selection. Support vector machine was used to construct radiomic models. Two radiomic models were built: the conventional radiomic model included tumor features alone, whereas the combined radiomic model incorporated features from the tumor and muscularis propria regions. RESULTS: A total of 3933 EUS images from 485 cases were included. For the differential diagnosis of GISTs from leiomyomas and schwannomas, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve were 74.5%, 72.2%, 78.7%, and 0.754, respectively, for the EUS experts; 76.8%, 74.4%, 81.0%, and 0.830, respectively, for the conventional radiomic model; and 90.9%, 91.0%, 90.6%, and 0.953, respectively, for the combined radiomic model. For gastric SELs <20 mm, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the combined radiomic model were 91.4%, 91.6%, 91.1%, and 0.960, respectively. CONCLUSIONS: We developed and validated a combined radiomic model to distinguish gastric GISTs from leiomyomas and schwannomas. The combined radiomic model showed better diagnostic performance than the conventional radiomic model and could assist EUS experts in non-invasively diagnosing gastric SELs, particularly gastric SELs <20 mm.