Effectiveness of colorectal cancer screening integrating non-genetic and genetic risk: a prospective study based on UK Biobank data.

OBJECTIVE: Few studies have evaluated the benefits of colorectal cancer (CRC) screening integrating both non-genetic and genetic risk factors. Here, we aimed to integrate an existing non-genetic risk model (QCancer-10) and a 139-variant polygenic risk score to evaluate the effectiveness of screening on CRC incidence and mortality. METHODS: We applied the integrated model to calculate 10-year CRC risk for 430,908 participants in the UK Biobank, and divided the participants into low-, intermediate-, and high-risk groups. We calculated the screening-associated hazard ratios (HRs) and absolute risk reductions (ARRs) for CRC incidence and mortality according to risk stratification. RESULTS: During a median follow-up of 11.03 years and 12.60 years, we observed 5,158 CRC cases and 1,487 CRC deaths, respectively. CRC incidence and mortality were significantly lower among screened than non-screened participants in both the intermediate- and high-risk groups [incidence: HR: 0.87, 95% confidence interval (CI): 0.81-0.94; 0.81, 0.73-0.90; mortality: 0.75, 0.64-0.87; 0.70, 0.58-0.85], which composed approximately 60% of the study population. The ARRs (95% CI) were 0.17 (0.11-0.24) and 0.43 (0.24-0.61), respectively, for CRC incidence, and 0.08 (0.05-0.11) and 0.24 (0.15-0.33), respectively, for mortality. Screening did not significantly reduce the relative or absolute risk of CRC incidence and mortality in the low-risk group. Further analysis revealed that screening was most effective for men and individuals with distal CRC among the intermediate to high-risk groups. CONCLUSIONS: After integrating both genetic and non-genetic factors, our findings provided priority evidence of risk-stratified CRC screening and valuable insights for the rational allocation of health resources.

Effects of joint screening for prostate, lung, colorectal, and ovarian cancer - results from a controlled trial.

Background: Although screening is widely used to reduce cancer burden, untargeted cancers are frequently missed after single cancer screening. Joint cancer screening is presumed as a more effective strategy to reduce overall cancer burden. Methods: Gender-specific screening effects on PLCO cancer incidence, PLCO cancer mortality, all-neoplasms mortality and all-cause mortality were evaluated, and meta-analyses based on gender-specific screening effects were conducted to achieve the pooled effects. The cut-off value of time-dependent receiver-operating-characteristic curve of 10-year combined PLCO cancer risk was used to reclassify participants into low- and high-risk subgroups. Further analyses were conducted to investigate screening effects stratified by risk groups and screening compliance. Results: After a median follow-up of 10.48 years for incidence and 16.85 years for mortality, a total of 5,506 PLCO cancer cases, 1,845 PLCO cancer deaths, 3,970 all-neoplasms deaths, and 14,221 all-cause deaths were documented in the screening arm, while 6,261, 2,417, 5,091, and 18,516 outcome-specific events in the control arm. Joint cancer screening did not significantly reduce PLCO cancer incidence, but significantly reduced male-specific PLCO cancer mortality (hazard ratio and 95% confidence intervals [HR(95%CIs)]: 0.88(0.82, 0.95)) and pooled mortality [0.89(0.84, 0.95)]. More importantly, joint cancer screening significantly reduced both gender-specific all-neoplasm mortality [0.91(0.86, 0.96) for males, 0.91(0.85, 0.98) for females, and 0.91(0.87, 0.95) for meta-analyses] and all-cause mortality [0.90(0.88, 0.93) for male, 0.88(0.85, 0.92) for female, and 0.89(0.87, 0.91) for meta-analyses]. Further analyses showed decreased risks of all-neoplasm mortality was observed with good compliance [0.72(0.67, 0.77) for male and 0.72(0.65, 0.80) for female] and increased risks with poor compliance [1.61(1.40, 1.85) for male and 1.30(1.13, 1.40) for female]. Conclusion: Joint cancer screening could be recommended as a potentially strategy to reduce the overall cancer burden. More compliance, more benefits. However, organizing a joint cancer screening not only requires more ingenious design, but also needs more attentions to the potential harms. Trial registration: NCT00002540 (Prostate), NCT01696968 (Lung), NCT01696981 (Colorectal), NCT01696994 (Ovarian).

Reclassified the phenotypes of cancer types and construct a nomogram for predicting bone metastasis risk: A pan-cancer analysis.

BACKGROUND: Numerous of models have been developed to predict the bone metastasis (BM) risk; however, due to the variety of cancer types, it is difficult for clinicians to use these models efficiently. We aimed to perform the pan-cancer analysis to create the cancer classification system for BM, and construct the nomogram for predicting the BM risk. METHODS: Cancer patients diagnosed between 2010 and 2018 in the Surveillance, Epidemiology, and End Results (SEER) database were included. Unsupervised hierarchical clustering analysis was performed to create the BM prevalence-based cancer classification system (BM-CCS). Multivariable logistic regression was applied to investigate the possible associated factors for BM and construct a nomogram for BM risk prediction. The patients diagnosed between 2017 and 2018 were selected for validating the performance of the BM-CCS and the nomogram, respectively. RESULTS: A total of 50 cancer types with 2,438,680 patients were included in the construction model. Unsupervised hierarchical clustering analysis classified the 50 cancer types into three main phenotypes, namely, categories A, B, and C. The pooled BM prevalence in category A (17.7%; 95% CI: 17.5%-17.8%) was significantly higher than that in category B (5.0%; 95% CI: 4.5%-5.6%), and category C (1.2%; 95% CI: 1.1%-1.4%) (p < 0.001). Advanced age, male gender, race, poorly differentiated grade, higher T, N stage, and brain, lung, liver metastasis were significantly associated with BM risk, but the results were not consistent across all cancers. Based on these factors and BM-CCS, we constructed a nomogram for predicting the BM risk. The nomogram showed good calibration and discrimination ability (AUC in validation cohort = 88%,95% CI: 87.4%-88.5%; AUC in construction cohort = 86.9%,95% CI: 86.8%-87.1%). The decision curve analysis also demonstrated the clinical usefulness. CONCLUSION: The classification system and prediction nomogram may guide the cancer management and individualized BM screening, thus allocating the medical resources to cancer patients. Moreover, it may also have important implications for studying the etiology of BM.

Predicting locus-specific DNA methylation levels in cancer and paracancer tissues.

Aim: To predict base-resolution DNA methylation in cancerous and paracancerous tissues. Material & methods: We collected six cancer DNA methylation datasets from The Cancer Genome Atlas and five cancer datasets from Gene Expression Omnibus and established machine learning models using paired cancerous and paracancerous tissues. Tenfold cross-validation and independent validation were performed to demonstrate the effectiveness of the proposed method. Results: The developed cross-tissue prediction models can substantially increase the accuracy at more than 68% of CpG sites and contribute to enhancing the statistical power of differential methylation analyses. An XGBoost model leveraging multiple correlating CpGs may elevate the prediction accuracy. Conclusion: This study provides a powerful tool for DNA methylation analysis and has the potential to gain new insights into cancer research from epigenetics.

Association between diabetes at different diagnostic ages and risk of cancer incidence and mortality: a cohort study.

Background: Different ages for diagnosis of diabetes have diverse effects on risks of cardiovascular disease, dementia, and mortality, but there is little evidence of cancer. This study investigated the relationship between diabetes at different diagnostic ages and risks of cancer incidence and mortality in people aged 37-73 years. Methods: Participants with diabetes in the UK Biobank prospective cohort were divided into four groups: ≤40, 41-50, 51-60, and >60 years according to age at diagnosis. A total of 26,318 diabetics and 105,272 controls (1:4 randomly selected for each diabetic matched by the same baseline age) were included. We calculated the incidence density, standardized incidence, and mortality rates of cancer. Cox proportional hazard model was used to examine the associations of diabetes at different diagnostic ages with cancer incidence and mortality, followed by subgroup analyses. Results: Compared to corresponding controls, standardized incidence and mortality rates of overall and digestive system cancers were higher in diabetes diagnosed at age 41-50, 51-60, and >60 years, especially at 51-60 years. Individuals diagnosed with diabetes at different ages were at higher risk to develop site-specific cancers, with a prominently increased risk of liver cancer since the diagnosis age of >40 years. Significantly, participants with diabetes diagnosed at 51-60 years were correlated with various site-specific cancer risks [hazard ratio (HR) for incidence: 1.088-2.416, HR for mortality: 1.276-3.269]. Moreover, for mortality of digestive system cancers, we observed an interaction effect between smoking and diabetes diagnosed at 51-60 years. Conclusion: Our findings highlighted that the age at diagnosis of diabetes, especially 51-60 years, was critical risks of cancer incidence and mortality and may represent a potential preventative window for cancer.

A causal relationship between antioxidants, minerals and vitamins and metabolic syndrome traits: a Mendelian randomization study.

BACKGROUND: The available evidence regarding the association of antioxidants, minerals, and vitamins with the risk of metabolic syndrome (MetS) traits is currently limited and inconsistent. Therefore, the purpose of this Mendelian randomization (MR) study was to investigate the potential causal relationship between genetically predicted antioxidants, minerals, and vitamins, and MetS. METHODS: In this study, we utilized genetic variation as instrumental variable (IV) to capture exposure data related to commonly consumed dietary nutrients, including antioxidants (ß-carotene, lycopene, and uric acid), minerals (copper, calcium, iron, magnesium, phosphorus, zinc, and selenium), and vitamins (folate, vitamin A, B6, B12, C, D, E, and K1). The outcomes of interest, namely MetS (n = 291,107), waist circumference (n = 462,166), hypertension (n = 463,010), fasting blood glucose (FBG) (n = 281,416), triglycerides (n = 441,016), and high-density lipoprotein cholesterol (HDL-C) (n = 403,943), were assessed using pooled data obtained from the most comprehensive genome-wide association study (GWAS) available. Finally, we applied the inverse variance weighting method as the result and conducted a sensitivity analysis for further validation. RESULTS: Genetically predicted higher iron (OR = 1.070, 95% CI 1.037-1.105, P = 2.91E-05) and magnesium levels (OR = 1.130, 95% CI 1.058-1.208, P = 2.80E-04) were positively associated with increased risk of MetS. For each component of MetS, higher level of genetically predicted selenium (OR = 0.971, 95% CI 0.957-0.986, P = 1.09E-04) was negatively correlated with HDL-C levels, while vitamin K1 (OR = 1.023, 95% CI 1.012-1.033, P = 2.90E-05) was positively correlated with HDL-C levels. Moreover, genetically predicted vitamin D (OR = 0.985, 95% CI 0.978-0.992, P = 5.51E-5) had a protective effect on FBG levels. Genetically predicted iron level (OR = 1.043, 95% CI 1.022-1.064, P = 4.33E-05) had a risk effect on TG level. CONCLUSIONS: Our study provides evidence that genetically predicted some specific, but not all, antioxidants, minerals, and vitamins may be causally related to the development of MetS traits.

Associations of chest X-ray trajectories, smoking, and the risk of lung cancer in two population-based cohort studies.

Objectives: Despite the increasing use of computed tomography (CT), chest X-ray (CXR) remains the first-line investigation for suspected lung cancer (LC) in primary care. However, the associations of CXR trajectories, smoking and LC risk remain unknown. Methods: A total of 52,486 participants from the PLCO and 22,194 participants from the NLST were included. The associations of CXR trajectories with LC risk were evaluated with multivariable COX regression models and pooled with meta-analyses. Further analyses were conducted to explore the stratified associations by smoking status and the factors associated with progression and regression in CXR. Results: Compared to stable negative CXR (CXRSN), HRs (95%CIs) of LC incidence were 2.88(1.50-5.52), 3.86(2.03-7.35), and 1.08(0.80-1.46) for gain of positive CXR (CXRGP), stable positive CXR (CXRSP), and loss of positive CXR (CXRLP), while the risk of LC mortality were 1.58(1.33-1.87), 2.56(1.53-4.29), and 1.05(0.89-1.25). Similar trends were observed across different smoking status. However, LC risk with CXRGP overweighed that with CXRSP among ever smokers [2.95(2.25-3.88) vs. 2.59(1.33-5.02)] and current smokers [2.33(1.70-3.18) vs. 2.26(1.06-4.83)]. Moreover, compared to CXRSN among never smokers, even no progression in CXR, the HRs(95%CIs) of LC incidence were 7.39(5.60-9.75) and 31.45(23.58-41.95) for ever and current smokers, while risks of LC mortality were 6.30(5.07-7.81) and 27.17(21.65-34.11). If participants gained positive CXR, LC incidence risk significantly climbed to 22.04(15.37-31.60) and 71.97(48.82-106.09) for ever and current smokers, while LC mortality risk climbed to 11.90(8.58-16.50) and 38.92(27.04-56.02). CXRLP was associated with decreased LC risk. However, even smokers lost their positive CXR, and the increased risks of LC incidence and mortality did not decrease to non-significant level. Additionally, smoking was significantly associated with increased risk of CXRGP but not CXRLP. Conclusion: LC risk differed across CXR trajectories and would be modified by smoking status. Comprehensive intervention incorporating CXR trajectories and smoking status should be recommended to reduce LC risk.

The transcriptional risk scores for kidney renal clear cell carcinoma using XGBoost and multiple omics data.

Most kidney cancers are kidney renal clear cell carcinoma (KIRC) that is a main cause of cancer-related deaths. Polygenic risk score (PRS) is a weighted linear combination of phenotypic related alleles on the genome that can be used to assess KIRC risk. However, standalone SNP data as input to the PRS model may not provide satisfactory result. Therefore, Transcriptional risk scores (TRS) based on multi-omics data and machine learning models were proposed to assess the risk of KIRC. First, we collected four types of multi-omics data (DNA methylation, miRNA, mRNA and lncRNA) of KIRC patients from the TCGA database. Subsequently, a novel TRS method utilizing multiple omics data and XGBoost model was developed. Finally, we performed prevalence analysis and prognosis prediction to evaluate the utility of the TRS generated by our method. Our TRS methods exhibited better predictive performance than the linear models and other machine learning models. Furthermore, the prediction accuracy of combined TRS model was higher than that of single-omics TRS model. The KM curves showed that TRS was a valid prognostic indicator for cancer staging. Our proposed method extended the current definition of TRS from standalone SNP data to multi-omics data and was superior to the linear models and other machine learning models, which may provide a useful implement for diagnostic and prognostic prediction of KIRC.

Risk-stratified multi-round PSA screening for prostate cancer integrating the screening reference level and subgroup-specific progression indicators.

BACKGROUND: Although prostate-specific antigen (PSA) is widely used in prostate cancer (PCa) screening, nearly half of PCa cases are missed and less than one-third of cases are non-lethal. Adopting diagnostic criteria in population-based screening and ignoring PSA progression are presumed leading causes. METHODS: A total of 31,942 participants with multi-round PSA tests from the PLCO trial were included. Time-dependent receiver-operating-characteristic curves and area under curves (tdAUCs) were performed to determine the screening reference level and the optimal subgroup-specific progression indicator. Effects of risk-stratified multi-round PSA screening were evaluated with multivariable Cox regression and measured with hazard ratio [HR (95%CIs)]. RESULTS: After a median follow-up of 11.6 years, a total of 3484 PCa cases and 216 PCa deaths were documented. The tdAUC of 10-year incidence PCa with PSA was 0.816, and the cut-off value was 1.61 ng/ml. Compared to subgroup with stable negative PSA in both first-round (FR) and last-round (LR) tests [FR(-)/LR(-)], HRs (95%CI) of PCa incidence were 1.66 (1.20-2.29), 8.29 (7.25-9.48), and 14.52 (12.95-16.28) for subgroups with loss of positive PSA[FR(+)/LR(-)], gain of positive PSA[FR(-)/LR(+)], and stable positive PSA[FR(+)/LR(+)]; while HRs(95%CI) of PCa mortality were 1.47 (0.52-4.15), 5.71 (3.68-8.86), and 5.01 (3.41-7.37). After excluding regressive PSA [(namely FR(+)/LR(-)], absolute velocity was the shared optimal progression indicator for subgroups with FR(-)/LR(-), FR(-)/LR(+), and FR(+)/LR(+), with tdAUCs of 0.665, 0.681 and 0.741, and cut-off values of 0.07, 0.21, and 0.33 ng/ml/year. After reclassifying participants into groups with positive and negative progression based on subgroup-specific progression indicators, incidence HR (95%CI) were 2.41 (1.87-3.10), 2.91 (2.43-3.48), and 3.16 (2.88-3.46) for positive progression compared to negative progression within subgroups of FR(-)/LR(-), FR(-)/LR(+), and FR(+)/LR(+), while mortality HR (95%CI) were 2.22 (0.91-5.38), 2.37 (1.28-4.38), and 2.98 (1.94-4.59). To improve screening performances by excluding regressive PSA and low-risk positive progression in FR(-)/LR(-), optimized screening strategy not only significantly reduce 32.4% of missed PCa (54.0% [1881/3484] vs. 21.6% [754/3484], P < 0.001), but also detected additional 8.0% of high-grade PCa (Gleason score 7-10: 36.0% [665/1849] vs. 28.0% [206/736], P < 0.001) than traditional screening strategy. CONCLUSIONS: Risk-stratified multi-round PSA screening strategy integrating the screening reference level and the optimal subgroup-specific progression indicator of PSA could be recommended as a fundamental strategy to reduce missed diagnosis and improve the detection of high-grade PCa cases.

Integrative analysis of multi-omics data for discovery of ferroptosis-related gene signature predicting immune activity in neuroblastoma.

Immunotherapy for neuroblastoma remains unsatisfactory due to heterogeneity and weak immunogenicity. Exploring powerful signatures for the evaluation of immunotherapy outcomes remain the primary purpose. We constructed a ferroptosis-related gene (FRG) signature by least absolute shrinkage and selection operator and Cox regression, identified 10 independent prognostic FRGs in a training cohort (GSE62564), and then verified them in an external validation cohort (TCGA). Associated with clinical factors, the signature accurately predicts overall survival of 3, 5, and 10 years. An independent prognostic nomogram, which included FRG risk, age, stage of the International Neuroblastoma Staging System, and an MYCN status, was constructed. The area under the curves showed satisfactory prognostic predicting performance. Through bulk RNA-seq and proteomics data, we revealed the relationship between hub genes and the key onco-promoter MYCN gene and then validated the results in MYCN-amplified and MYCN-non-amplified cell lines with qRT-PCR. The FRG signature significantly divided patients into high- and low-risk groups, and the differentially expressed genes between the two groups were enriched in immune actions, autophagy, and carcinogenesis behaviors. The low-risk group embodied higher positive immune component infiltration and a higher expression of immune checkpoints with a more favorable immune cytolytic activity (CYT). We verified the predictive power of this signature with data from melanoma patients undergoing immunotherapy, and the predictive power was satisfactory. Gene mutations were closely related to the signature and prognosis. AURKA and PRKAA2 were revealed to be nodal hub FRGs in the signature, and both were shown to have significantly different expressions between the INSS stage IV and other stages after immunohistochemical validation. With single-cell RNA-seq analysis, we found that genes related to T cells were enriched in TNFA signaling and interferon-γ hallmark. In conclusion, we constructed a ferroptosis-related gene signature that can predict the outcomes and work in evaluating the effects of immunotherapy.

Comparisons of clinical characteristics, prognosis, epidemiological factors, and genetic susceptibility between HER2-low and HER2-zero breast cancer among Chinese females.

BACKGROUND: Traditional human epidermal growth factor receptor 2 (HER2)-negative breast cancer (BC) is recommended to be divided into HER2-low and HER2-zero subtypes due to different prognosis. However, few studies investigated their differences in clinical characteristics and prognosis among Chinese HER2-negative BC and their stratified differences by hormone receptor (HR), while fewer studies investigated their differences in epidemiological factors and genetic susceptibility. METHODS: A total of 11,911 HER2-negative BC were included to compare the clinical characteristics and prognosis between HER2-zero and HER2-low BC, and 4227 of the 11,911 HER2-negative BC were further compared to 5653 controls to investigate subtype-specific epidemiological factors and single nucleotide polymorphisms(SNPs). RESULTS: Overall, 64.2% of HER2-negative BC were HER2-low BC, and the stratified proportions of HER2-low BC were 61.9% and 75.2% for HR-positive and HR-negative BC, respectively. Compared to HER2-zero BC, HER2-low BC among HR-positive BC showed younger age at diagnosis, later stage, poorer differentiation, and higher Ki-67, while elder age at diagnosis and lower mortality were observed for HER2-low BC among HR-negative BC (all p values <0.05). Compared to healthy controls, both HER2-low and HER2-zero BC are associated with similar epidemiological factors and SNPs. However, stronger interaction between epidemiological factors and polygenic risk scores were observed for HER2-zero BC than HER2-low BC among either HR-positive [odds ratios: 10.71 (7.55-15.17) and 8.84 (6.19-12.62) for the highest risk group compared to the lowest risk group] or HR-negative BC [7.00 (3.14-15.63) and 5.70 (3.26-9.98)]. CONCLUSIONS: HER2-low BC should deserve more attention than HER2-zero BC, especially in HR-negative BC, due to larger proportion, less clinical heterogeneity, better prognosis, and less susceptibility to risk factors.

The changes of subtype markers between first and second primary breast cancers.

BACKGROUND: Previous studies investigated the changes of subtype markers [estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2)] in several clinical settings, but not for second primary breast cancer (SPBC) after first primary breast cancer (FPBC). METHODS: A total of 15,390 patients with SPBC were preliminarily selected from the Surveillance, Epidemiology, and End Results Program, and 3777 patients with complete information on three subtype markers in both FPBC and SPBC were included in the final analyses. The changes of subtype markers and their prognostic implications and potential influential factors were well investigated. RESULTS: The overall change rates of ER, PR, and HER2 between FPBC and SPBC were 23.0% (867/3777), 35.0% (1322/3777), and 18.3% (691/3777), respectively. Gains of ER, PR, and HER2 after negative index markers were 48.7% (364/748), 37.9% (418/1103), and 11.5% (370/3211), while losses of markers after positive index markers were 16.6% (503/3029), 33.8%(904/2674), and 56.7%(321/566). Loss of ER was significantly associated with increased mortality (18.1% vs. 7.9%, p < 0.001), while gain of ER was significantly associated with decreased mortality (11.5% vs. 23.2%, p < 0.001). Similar results were observed for changes of PR status. However, loss of HER2 was significantly associated with decreased mortality (8.7% vs. 16.3%, p = 0.014), and no significant association was observed between the gain of HER2 and the prognosis of SPBC. Multivariate competing risk analyses showed similar results. HER2 status in FPBC, chemotherapy, and radiotherapy was significantly associated with changes of ER/PR (all p < 0.05), and no available therapies associated with HER2 change. CONCLUSION: The changes of subtype markers are observed in a considerable proportion of patients and has statistically significant prognostic implications. Biopsies should be taken as a routine procedure for better therapy management.

Development of a modified ABC method among Helicobacter pylori infected but serum pepsinogen test-negative individuals.

BACKGROUND: Although the ABC method for gastric cancer (GC) screening has been widely adopted in Japan, it may not be suitable for other countries due to population heterogeneity and different tumor histology. We aim to develop a modified ABC method to improve GC screening performance, especially among Helicobacter pylori (Hp) infected but serum pepsinogen (sPG) test-negative individuals. METHODS: A total of 4745 participants were recruited from Tianjin, China, and were classified into four groups by combined assay for Hp infection and sPG concentrations: Group A (Hp [-], PG [-]), Group B (Hp [+], PG [-]), Group C (Hp [+], PG [+]), and Group D (Hp [-], PG [+]). We used receiver-operating characteristic (ROC) curves analysis and minimum p value method to determine the optimal cutoff point for PG II in Group B. We performed logistic regressions to examine the risk of GC across different subgroups. In addition to the derivation set, the performance of the modified ABC method was also evaluated in an external set involving 16,292 participants from Liaoning, China. RESULTS: In the modified ABC method, we further classified Group B as low-risk (Group B1) and high-risk subgroups (Group B2) using optimal sPG II cutoff point (20.0 ng/mL) by ROC curves analysis and minimum p value method. Compared with Group B1, Group B2 had a significantly higher risk of GC (adjusted OR = 2.54, 95% CI = 1.94-3.33). The modified ABC method showed good discrimination for GC (AUC = 0.61, 95% CI = 0.59-0.63) and improved risk reclassification (NRI = 0.11, p < .01). Similar results were observed in the validation dataset. CONCLUSIONS: The modified ABC method can effectively identify high-risk population for GC among Hp-infected but sPG test-negative participants in China.

Higher risk of cardiovascular mortality than cancer mortality among long-term cancer survivors.

Background: Previous studies focused more on the short-term risk of cardiovascular (CV) death due to traumatic psychological stress after a cancer diagnosis and the acute cardiotoxicity of anticancer treatments than on the long-term risk of CV death. Methods: Time trends in the proportions of CV death (PCV), cancer death (PCA), and other causes in deaths from all causes were used to show preliminary relationships among the three causes of death in 4,806,064 patients with cancer from the Surveillance, Epidemiology, and End Results (SEER) program. Competing mortality risk curves were used to investigate when the cumulative CV mortality rate (CMRCV) began to outweigh the cumulative cancer mortality rate (CMRCA) for patients with cancer who survived for more than 10 years. Multivariable competing risk models were further used to investigate the potential factors associated with CV death. Results: For patients with cancer at all sites, the PCV increased from 22.8% in the 5th year after diagnosis to 31.0% in the 10th year and 35.7% in the 20th year, while the PCA decreased from 57.7% in the 5th year after diagnosis to 41.2 and 29.9% in the 10th year and 20th year, respectively. The PCV outweighed the PCA (34.6% vs. 34.1%) since the 15th year for patients with cancer at all sites, as early as the 9th year for patients with colorectal cancer (37.5% vs. 33.2%) and as late as the 22nd year for patients with breast cancer (33.5% vs. 30.6%). The CMRCV outweighed the CMRCA since the 25th year from diagnosis. Multivariate competing risk models showed that an increased risk of CV death was independently associated with older age at diagnosis [hazard ratio and 95% confidence intervals [HR (95%CI)] of 43.39 (21.33, 88.28) for ≥ 80 vs. ≤ 30 years] and local metastasis [1.07 (1.04, 1.10)] and a decreased risk among women [0.82 (0.76, 0.88)], surgery [0.90 (0.87, 0.94)], and chemotherapy [0.85 (0.81, 0.90)] among patients with cancer who survived for more than 10 years. Further analyses of patients with cancer who survived for more than 20 years and sensitivity analyses by cancer at all sites showed similar results. Conclusion: CV death gradually outweighs cancer death as survival time increases for most patients with cancer. Both the cardio-oncologist and cardio-oncology care should be involved to reduce CV deaths in long-term cancer survivors.

Perfluoroalkyl substances (PFASs) as risk factors for breast cancer: a case-control study in Chinese population.

BACKGROUND: Perfluoroalkyl substances (PFASs) are a large family of synthetic chemicals, some of which are mammary toxicants and endocrine disruptors. Recent studies have implicated exposure to PFASs as a risk factor for breast cancer in Europe and America. Little is known about the role of PFASs with respect to breast cancer in the Chinese population. METHODS: Participants who were initially diagnosed with breast cancer at Tianjin Medical University Cancer Institute and Hospital between 2012 and 2016 were recruited as cases. The controls were randomly selected from the participants with available blood samples in the Chinese National Breast Cancer Screening Program (CNBCSP) cohort. Ultimately, we enrolled 373 breast cancer patients and 657 controls. Plasma PFASs were measured by an ultra-performance liquid chromatography (UPLC) system coupled to a 5500 Q-Trap triple quadrupole mass spectrometer. A logistic regression model with least absolute shrinkage and selection operator (LASSO) regularization was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) to assess the relationships between PFASs and breast cancer. The three most predictive variables in the LASSO model were selected from 17 PFASs, which was based on the optimal penalty coefficient (λ = 0.0218) identified with the minimum criterion. Additionally, Bayesian kernel machine regression (BKMR) and quantile g-computation models were applied to evaluate the associations between separate and mixed exposure to PFASs and breast cancer. RESULTS: Perfluorooctanesulfonic acid (PFOS) exhibited the highest concentration in both the cases and controls. Perfluorooctanoic acid (PFOA) and perfluoro-n-decanoic acid (PFDA) were positively associated with breast cancer, and perfluoro-n-tridecanoic acid (PFTrDA) was negatively associated with breast cancer according to both the continuous-PFASs and the quartile-PFASs logistic regression models. Of note, PFOA was associated with the occurrence of estrogen receptor (ER)-, progesterone receptor (PR)-, and human epidermal growth factor receptor 2 (HER2)-positive breast cancer (ORER+ = 1.47, 95% CI: 1.19, 1.80; ORPR+ = 1.36, 95% CI: 1.09, 1.69; ORHER2 = 1.62, 95% CI: 1.19, 2.21). CONCLUSIONS: Overall, we observed that PFASs were associated with breast cancer in Chinese women. Prospective cohort studies and mechanistic experiments are warranted to elucidate whether these associations are causal.

The integrated landscape of eRNA in gastric cancer reveals distinct immune subtypes with prognostic and therapeutic relevance.

The comprehensive regulation effect of eRNA on tumor immune cell infiltration and the outcome remains obscure. We comprehensively identify the eRNA-mediated immune infiltration patterns of gastric cancer (GC) samples. We creatively proposed a random forest machine-learning (ML) algorithm to map eRNA to mRNA expression patterns. The eRNA score was constructed using principal component analysis algorithms and validated in an independent cohort. Three subtypes with distinct eRNA expression patterns were determined in GC. There were significant differences between the three subtypes in the overall survival rate, immune cell infiltration characteristics, and immunotherapy response indicators. The patients in the high eRNA score group have a higher overall survival rate and might benefit from immunotherapy. This work revealed that eRNA regulation might be a new prognostic index and might offer a potential biomarker in the response of immunotherapy. Evaluating the eRNA regulation manner of GC will contribute to guiding more effective immunotherapy strategies.

Deep learning to diagnose Hashimoto's thyroiditis from sonographic images.

Hashimoto's thyroiditis (HT) is the main cause of hypothyroidism. We develop a deep learning model called HTNet for diagnosis of HT by training on 106,513 thyroid ultrasound images from 17,934 patients and test its performance on 5051 patients from 2 datasets of static images and 1 dataset of video data. HTNet achieves an area under the receiver operating curve (AUC) of 0.905 (95% CI: 0.894 to 0.915), 0.888 (0.836-0.939) and 0.895 (0.862-0.927). HTNet exceeds radiologists' performance on accuracy (83.2% versus 79.8%; binomial test, p < 0.001) and sensitivity (82.6% versus 68.1%; p < 0.001). By integrating serologic markers with imaging data, the performance of HTNet was significantly and marginally improved on the video (AUC, 0.949 versus 0.888; DeLong's test, p = 0.004) and static-image (AUC, 0.914 versus 0.901; p = 0.08) testing sets, respectively. HTNet may be helpful as a tool for the management of HT.

Diagnostic classification of cancers using DNA methylation of paracancerous tissues.

The potential role of DNA methylation from paracancerous tissues in cancer diagnosis has not been explored until now. In this study, we built classification models using well-known machine learning models based on DNA methylation profiles of paracancerous tissues. We evaluated our methods on nine cancer datasets collected from The Cancer Genome Atlas (TCGA) and utilized fivefold cross-validation to assess the performance of models. Additionally, we performed gene ontology (GO) enrichment analysis on the basis of the significant CpG sites selected by feature importance scores of XGBoost model, aiming to identify biological pathways involved in cancer progression. We also exploited the XGBoost algorithm to classify cancer types using DNA methylation profiles of paracancerous tissues in external validation datasets. Comparative experiments suggested that XGBoost achieved better predictive performance than the other four machine learning methods in predicting cancer stage. GO enrichment analysis revealed key pathways involved, highlighting the importance of paracancerous tissues in cancer progression. Furthermore, XGBoost model can accurately classify nine different cancers from TCGA, and the feature sets selected by XGBoost can also effectively predict seven cancer types on independent GEO datasets. This study provided new insights into cancer diagnosis from an epigenetic perspective and may facilitate the development of personalized diagnosis and treatment strategies.

A new risk stratification strategy for fatty liver disease by incorporating MAFLD and fibrosis score in a large US population.

BACKGROUND: Metabolic dysfunction-associated fatty liver disease (MAFLD) is a newly proposed definition of fatty liver disease (FLD) independent of excessive alcohol consumption (EAC) and hepatitis viral infection. Evidence on the mortality risk in different types of FLD [nonalcoholic FLD (NAFLD), alcoholic FLD (AFLD), and MAFLD] is sparse, hindering the identification of high-risk populations for preferential clinical surveillance. METHODS: A total of 11,000 participants in the Third National Health and Nutrition Examination Survey were enrolled. Participants were categorized into three groups [FLD( - ), MAFLD( - ), and MAFLD( +)] according to FLD and MAFLD criteria, and further categorized into six groups by EAC. Multivariate Cox proportional hazard model was used to estimate the risk of all-cause, cardiovascular-related, and cancer-related mortality. RESULTS: During a median follow-up of 23.2 years, a total of 3240 deaths were identified. Compared with FLD( - )/EAC( - ) participants, MAFLD( +) individuals had higher all-cause mortality risk [hazard ratio (HR) = 1.28, 95% confidence interval (CI) = 1.18-1.39] regardless of EAC status [MAFLD( +)/NAFLD: HR = 1.22, 95%CI = 1.11-1.34; MAFLD( +)/AFLD: HR = 1.83, 95%CI = 1.46-2.28], while not for MAFLD( - ) individuals. Furthermore, diabetes-driven-MAFLD had higher mortality risk (HR = 2.00, 95%CI = 1.77-2.27) followed by metabolic dysregulation-driven-MAFLD (HR = 1.30, 95%CI = 1.06-1.60) and overweight/obesity-driven-MAFLD (HR = 1.11, 95%CI = 1.00-1.22). Additionally, MAFLD( - ) participants with elevated fibrosis score were also associated with statistically significantly higher mortality risk (HR = 3.23, 95%CI = 1.63-6.40). CONCLUSIONS: Utilizing a representative sample of the US population, we proved the validity of MAFLD subtype and fibrosis score, rather than the traditional definition (NAFLD and AFLD), in the risk stratification of FLD patients. These findings may be applied to guide the determination of surveillance options for FLD patients.