Independent Validation Cohort Research Articles

Validating genetic variants in innate immunity linked to infectious events in acute myeloid leukemia post-induction chemotherapy

Infectious events, such as sepsis and invasive fungal disease (IFD), pose significant risks in patients with acute myeloid leukemia (AML). Previous studies, including our own, have suggested a potential role of single nucleotide polymorphisms (SNPs) within the innate immune system in influencing individual infection susceptibility. However, many of these associations lack validation in independent cohorts. This study sought to validate the impact of 11 candidate SNPs across 6 genes (TLR2, TLR4, Dectin-1, DC-SIGN, PTX3, L-Ficolin) in an independent cohort of patients. Two cohorts with newly diagnosed AML patients receiving intensive induction chemotherapy were analyzed: a stratification cohort comprising 186 patients and a validation cohort consisting of 138 patients. Multiple SNPs in each cohort were found to be associated to infectious complications, notably the DC-SIGN SNP rs4804800 demonstrated a significant association with sepsis in both cohorts. SNPs within the PTX3 and Dectin-1 genes were linked to IFD development in one cohort each. This study represents the first validation study of candidate genes associated with infectious events in AML patients after intensive induction chemotherapy. Identifying genetic predispositions to infections could significantly impact the management of antimicrobial prophylaxis and treatment in AML patients.

Continuous β-Amyloid CSF/PET Imbalance Model to Capture Alzheimer Disease Heterogeneity.

Discordance between CSF and PET biomarkers of β-amyloid (Aβ) might reflect an imbalance between soluble and aggregated species, possibly reflecting disease heterogeneity. Previous studies generally used binary cutoffs to assess discrepancies in CSF/PET biomarkers, resulting in a loss of information on the extent of discordance. In this study, we (1) jointly modeled Aβ-CSF/PET data to derive a continuous measure of the imbalance between soluble and fibrillar pools of Aβ, (2) investigated factors contributing to this imbalance, and (3) examined associations with cognitive trajectories. Across 822 cognitively unimpaired (n = 261) and cognitively impaired (n = 561) Alzheimer's Disease Neuroimaging Initiative individuals (384 [46.7%] females, mean age 73.0 ± 7.4 years), we fitted baseline CSF-Aβ42 and global Aβ-PET to a hyperbolic regression model, deriving a participant-specific Aβ-aggregation score (standardized residuals); negative values represent more soluble relative to aggregated Aβ and positive values more aggregated relative to soluble Aβ. Using linear models, we investigated whether methodological factors, demographics, CSF biomarkers, and vascular burden contributed to Aβ-aggregation scores. With linear mixed models, we assessed whether Aβ-aggregation scores were predictive of cognitive functioning. Analyses were repeated in an early independent validation cohort of 383 Amyloid Imaging to Prevent Alzheimer's Disease Prognostic and Natural History Study individuals (224 [58.5%] females, mean age 65.2 ± 6.9 years). The imbalance model could be fit (pseudo-R2 = 0.94) in both cohorts, across CSF kits and PET tracers. Although no associations were observed with the main methodological factors, lower Aβ-aggregation scores were associated with larger ventricular volume (β = 0.13, p < 0.001), male sex (β = -0.18, p = 0.019), and homozygous APOE-ε4 carriership (β = -0.56, p < 0.001), whereas higher scores were associated with increased uncorrected CSF p-tau (β = 0.17, p < 0.001) and t-tau (β = 0.16, p < 0.001), better baseline executive functioning (β = 0.12, p < 0.001), and slower global cognitive decline (β = 0.14, p = 0.006). In the validation cohort, we replicated the associations with APOE-ε4, CSF t-tau, and, although modestly, with cognition. We propose a novel continuous model of Aβ CSF/PET biomarker imbalance, accurately describing heterogeneity in soluble vs aggregated Aβ pools in 2 independent cohorts across the full Aβ continuum. Aβ-aggregation scores were consistently associated with genetic and AD-associated CSF biomarkers, possibly reflecting disease heterogeneity beyond methodological influences.

Unlocking the diagnostic power of plasma extracellular vesicle miR-200 family in pancreatic ductal adenocarcinoma

BackgroundDistinguishing benign from malignant pancreaticobiliary disease is challenging because of the absence of reliable biomarkers. Circulating extracellular vesicles (EVs) have emerged as functional mediators between cells. Their cargos, including microRNAs (miRNAs), are increasingly acknowledged as an important source of potential biomarkers. This multicentric, prospective study aimed to establish a diagnostic plasma EV-derived miRNA signature to discriminate pancreatic ductal adenocarcinoma (PDAC) from benign pancreaticobiliary disease.MethodsPlasma EVs were isolated using size exclusion chromatography (SEC) and characterised using nanoparticle tracking analysis, electron microscopy and Western blotting. EV-RNAs underwent small RNA sequencing to discover differentially expressed markers for PDAC (n = 10 benign vs. 10 PDAC). Candidate EV-miRNAs were then validated in a cohort of 61 patients (n = 31 benign vs. 30 PDAC) by RT-qPCR. Logistic regression and optimal thresholds (Youden Index) were used to develop an EV-miR-200 family model to detect cancer. This model was tested in an independent cohort of 95 patients (n = 30 benign, 33 PDAC, and 32 cholangiocarcinoma).ResultsSmall RNA sequencing and RT-qPCR showed that EV-miR-200 family members were significantly overexpressed in PDAC vs. benign disease. Combined expression of the EV-miR-200 family showed an AUC of 0.823. In an independent validation cohort, application of this model showed a sensitivity, specificity and AUC of 100%, 88%, and 0.97, respectively, for diagnosing PDAC.ConclusionsThis is the first study to validate plasma EV-miR-200 members as a clinically-useful diagnostic biomarker for PDAC. Further validation in larger cohorts and clinical trials is essential. These findings also suggest the potential utility in monitoring response and/or recurrence.Graphical

Single-cell resolution characterization of myeloid-derived cell states with implication in cancer outcome

Tumor-associated myeloid-derived cells (MDCs) significantly impact cancer prognosis and treatment responses due to their remarkable plasticity and tumorigenic behaviors. Here, we integrate single-cell RNA-sequencing data from different cancer types, identifying 29 MDC subpopulations within the tumor microenvironment. Our analysis reveals abnormally expanded MDC subpopulations across various tumors and distinguishes cell states that have often been grouped together, such as TREM2+ and FOLR2+ subpopulations. Using deconvolution approaches, we identify five subpopulations as independent prognostic markers, including states co-expressing TREM2 and PD-1, and FOLR2 and PDL-2. Additionally, TREM2 alone does not reliably predict cancer prognosis, as other TREM2+ macrophages show varied associations with prognosis depending on local cues. Validation in independent cohorts confirms that FOLR2-expressing macrophages correlate with poor clinical outcomes in ovarian and triple-negative breast cancers. This comprehensive MDC atlas offers valuable insights and a foundation for futher analyses, advancing strategies for treating solid cancers.

Serum biomarkers REG1A and REG3A combined with the traditional CEA represent a novel nomogram for the screening and risk stratification of colorectal cancer.

To develop and validate a serum protein nomogram for colorectal cancer (CRC) screening. The serum protein characteristics were extracted from an independent sample containing 30 colorectal cancer and 12 polyp tissues along with their paired samples, and different serum protein expression profiles were validated using RNA microarrays. The prediction model was developed in a training cohort that included 1345 patients clinicopathologically confirmed CRC and 518 normal participants, and data were gathered from November 2011 to January 2017. The lasso logistic regression model was employed for features selection and serum nomogram building. An internal validation cohort containing 576 CRC patients and 222 normal participants was assessed. Serum signatures containing 27 secreted proteins were significantly differentially expressed in polyps and CRC compared to paired normal tissue, and REG family proteins were selected as potential predictors. The C-index of the nomogram1 (based on Lasso logistic regression model) which contains REG1A, REG3A, CEA and age was 0.913 (95% CI, 0.899 to 0.928) and was well calibrated. Addition of CA199 to the nomogram failed to show incremental prognostic value, as shown in nomogram2 (based on logistic regression model). Application of the nomogram1 in the independent validation cohort had similar discrimination (C-index, 0.912 [95% CI, 0.890 to 0.934]) and good calibration. The decision curve (DCA) and clinical impact curve (ICI) analysis demonstrated that nomogram1 was clinically useful. This study presents a serum nomogram that included REG1A, REG3A, CEA and age, which can be convenient for screening of colorectal cancer.

The Florida Geriatric Head Trauma CT Clinical Decision Rule.

Several clinical decision rules have been devised to guide head computed tomography (CT) use in patients with minor head injuries, but none have been validated in patients 65 years or older. We aimed to derive and validate a head injury clinical decision rule for older adults. We conducted a secondary analysis of an existing dataset of consecutive emergency department (ED) patients >65 years old with blunt head trauma. The main predictive outcomes were significant intracranial injury and Need for Neurosurgical Intervention on CT. The secondary outcomes also considered in the model development and validation were All Injuries and All Intracranial Injuries. Predictor variables were identified using multiple variable logistic regression, and clinical decision rule models were developed in a split-sample derivation cohort and then tested in an independent validation cohort. Of 5776 patients, 233 (4.0%) had significant intracranial injury and an additional 104 (1.8%) met CT criteria for Need for Neurosurgical Intervention. The best performing model, the Florida Geriatric Head Trauma CT Clinical Decision Rule, assigns points based on several clinical variables. If the points totaled 25 or more, a CT scan is indicated. The included predictors were arrival via Emergency Medical Services (+30 points), Glasgow Coma Scale (GCS) <15 (+20 points), GCS <14 (+50 points), antiplatelet medications (+17 points), loss of consciousness (+16 points), signs of basilar skull fracture (+50 points), and headache (+20 points). Utilizing this clinical decision rule in the validation cohort, a point total ≥25 had a sensitivity and specificity of 100.0% (95% CI: 96.0-100) and 12.3% (95% CI: 10.9-13.8), respectively, for significant intracranial injury and Need for Neurosurgical Intervention. The Florida Geriatric Head Trauma CT Clinical Decision Rule has the potential to reduce unnecessary CT scans in older adults, without compromising safe emergency medicine practice.

Rapid detection of lung cancer based on serum Raman spectroscopy and a support vector machine: a case-control study

BackgroundEarly screening and detection of lung cancer is essential for the diagnosis and prognosis of the disease. In this paper, we investigated the feasibility of serum Raman spectroscopy for rapid lung cancer screening.MethodsRaman spectra were collected from 45 patients with lung cancer, 45 with benign lung lesions, and 45 healthy volunteers. And then the support vector machine (SVM) algorithm was applied to build a diagnostic model for lung cancer. Furthermore, 15 independent individuals were sampled for external validation, including 5 lung cancer patients, 5 benign lung lesion patients, and 5 healthy controls.ResultsThe diagnostic sensitivity, specificity, and accuracy were 91.67%, 92.22%, 90.56% (lung cancer vs. healthy control), 92.22%,95.56%,93.33% (benign lung lesion vs. healthy) and 80.00%, 83.33%, 80.83% (lung cancer vs. benign lung lesion), repectively. In the independent validation cohort, our model showed that all the samples were classified correctly.ConclusionTherefore, this study demonstrates that the serum Raman spectroscopy analysis technique combined with the SVM algorithm has great potential for the noninvasive detection of lung cancer.

Integrative multi-omics analysis unravels the host response landscape and reveals a serum protein panel for early prognosis prediction for ARDS

BackgroundThe multidimensional biological mechanisms underpinning acute respiratory distress syndrome (ARDS) continue to be elucidated, and early biomarkers for predicting ARDS prognosis are yet to be identified.MethodsWe conducted a multicenter observational study, profiling the 4D-DIA proteomics and global metabolomics of serum samples collected from patients at the initial stage of ARDS, alongside samples from both disease control and healthy control groups. We identified 28-day prognosis biomarkers of ARDS in the discovery cohort using the LASSO method, fold change analysis, and the Boruta algorithm. The candidate biomarkers were validated through parallel reaction monitoring (PRM) targeted mass spectrometry in an external validation cohort. Machine learning models were applied to explore the biomarkers of ARDS prognosis.ResultsIn the discovery cohort, comprising 130 adult ARDS patients (mean age 72.5, 74.6% male), 33 disease controls, and 33 healthy controls, distinct proteomic and metabolic signatures were identified to differentiate ARDS from both control groups. Pathway analysis highlighted the upregulated sphingolipid signaling pathway as a key contributor to the pathological mechanisms underlying ARDS. MAP2K1 emerged as the hub protein, facilitating interactions with various biological functions within this pathway. Additionally, the metabolite sphingosine 1-phosphate (S1P) was closely associated with ARDS and its prognosis. Our research further highlights essential pathways contributing to the deceased ARDS, such as the downregulation of hematopoietic cell lineage and calcium signaling pathways, contrasted with the upregulation of the unfolded protein response and glycolysis. In particular, GAPDH and ENO1, critical enzymes in glycolysis, showed the highest interaction degree in the protein–protein interaction network of ARDS. In the discovery cohort, a panel of 36 proteins was identified as candidate biomarkers, with 8 proteins (VCAM1, LDHB, MSN, FLG2, TAGLN2, LMNA, MBL2, and LBP) demonstrating significant consistency in an independent validation cohort of 183 patients (mean age 72.6 years, 73.2% male), confirmed by PRM assay. The protein-based model exhibited superior predictive accuracy compared to the clinical model in both the discovery cohort (AUC: 0.893 vs. 0.784; Delong test, P < 0.001) and the validation cohort (AUC: 0.802 vs. 0.738; Delong test, P = 0.008).InterpretationOur multi-omics study demonstrated the potential biological mechanism and therapy targets in ARDS. This study unveiled several novel predictive biomarkers and established a validated prediction model for the poor prognosis of ARDS, offering valuable insights into the prognosis of individuals with ARDS.

A risk score for predicting in-stent restenosis in patients with premature acute myocardial infarction undergoing percutaneous coronary intervention with drug-eluting stent

BackgroundThis study aimed at developing and validating a risk score to predict in-stent restenosis (ISR) in patients with premature acute myocardial infarction (AMI) undergoing percutaneous coronary intervention (PCI) with drug-eluting stent (DES). MethodsThis was a two-center retrospective study. A total of 2185 patients firstly diagnosed with premature AMI (age ≥18 years and <55 years in men, <65 years in women) from Xinjiang cohort were retrospectively analyzed. After filtering by exclusion criteria, patients were randomly divided into training cohort (n = 434) and internal validation cohort (n = 186) at a 7:3 ratio. Several candidate variables associated with ISR in the training cohort were assessed by the least absolute shrinkage and selection operator and logistic regression analysis. The ISR risk nomogram score based on the superior predictors was finally developed, and then validated in the internal validation cohort and in an independent Chengdu external validation cohort (n = 192). The higher total nomogram score, the greater the ISR risk. ResultsThe eight variables in the final risk nomogram score, cardiovascular-kidney-metabolic (CKM) score included age, diabetes mellitus (DM), body mass index (BMI), systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDLC), estimated glomerular filtration rate (eGFR), stent in left anterior descending coronary artery, minimum stent diameter <3 mm. The areas under the curve (AUC) and C-statistics [training cohort: 0.834 (95%CI: 0.787 to 0.882); internal validation cohort: 0.852 (95%CI: 0.784 to 0.921); Chengdu external validation cohort: 0.787 (95%CI: 0.692 to 0.882), respectively)] demonstrated the good discrimination of the CKM score. The Hosmer-Lemeshow test (χ2 = 7.86, P = 0.448; χ2 = 5.17, P = 0.740; χ2 = 6.35, P = 0.608, respectively) and the calibration curve confirmed the good calibration of the CKM score. Decision curve analysis (DCA) testified the clinical net benefit of the CKM score in the training and validation cohort. ConclusionThis study provided a well-developed and validated risk nomogram score, the CKM score to predict ISR in patients with premature AMI undergoing PCI with DES. Given that these variables are readily available and practical, the CKM score should be widely adopted for individualized assessment and management of premature AMI.

Radiomics Models Derived From Arterial-Phase-Enhanced CT Reliably Predict Both PD-L1 Expression and Immunotherapy Prognosis in Non-small Cell Lung Cancer: A Retrospective, Multicenter Cohort Study

Rationale and objectivesImmune checkpoint inhibitors (ICIs) have revolutionized the treatment of non-small cell lung cancer (NSCLC) and programmed cell death-ligand 1 (PD-L1) is a companion biomarker. This study aims to use baseline arterial phase enhanced CT (APECT) to construct efficient radiomic models for predicting PD-L1 expression and immunotherapy prognosis in NSCLC. Materials and methodsWe extracted radiomics features from the baseline APECT images of 204 patients enrolled in a published multicenter clinical trial that commenced on August 23, 2018, and concluded on November 15, 2019 (ClinicalTrials.gov: NCT03607539). Of these patients, 146 patients from selected centers were assigned to the training cohort. The least absolute shrinkage and selection operator (LASSO) method was used to reduce dimensionality of radiomics features and calculate tumor scores. Models were created using naive bayes, decision trees, XGBoost, and random forest algorithms according to tumor scores. These models were then validated in an independent validation cohort comprising 58 patients from the remaining centers. ResultsThe random forest algorithm outperformed the other methods. In the three-classification scenario, the random forest model achieving the area under the curve (AUC) values of 0.98 and 0.94 in the training and validation cohorts, respectively. In the two-classification scenario, the random forest model achieved AUCs of 0.99 (95%CI: 0.97-1.0, P < 0.0001) and 0.93 (95%CI: 0.83-0.98, P < 0.0001) in the training and validation cohorts, respectively. Furthermore, patients classified as PD-L1 high-expression by this model can predict treatment response (AUC = 0.859, 95%CI: 0.7 – 0.96, P < 0.001) and improved survival (HR = 0.2, 95%CI: 0.08-0.53, P = 0.001) only in validation sintilimab arm. ConclusionRadiomics models based on APECT represent a potential noninvasive approach to robustly predict PD-L1 expression and ICI treatment outcomes in patients with NSCLC, which could significantly improve precision cancer immunotherapy.

Development and Validation of a Community-Based Prediction Model for Depression in Elderly Patients with Diabetes: A Cross-Sectional Study.

In elderly diabetic patients, depression is often overlooked because professional evaluation requires psychiatrists, but such specialists are lacking in the community. Therefore, we aimed to create a simple depression screening model that allows earlier detection of depressive disorders in elderly diabetic patients by community health workers. The prediction model was developed in a primary cohort that consisted of 210 patients with diabetes, and data were gathered from December 2022 to February 2023. The independent validation cohort included 99 consecutive patients from February 2023 to March 2023. Multivariable logistic regression analysis was used to develop the predictive model. We incorporated common demographic characteristics, diabetes-specific factors, family structure characteristics, the self-perceived burden scale (SPBS) score, and the family APGAR (adaptation, partnership, growth, affection, resolution) score. The performance of the nomogram was assessed with respect to its calibration (calibration curve, the Hosmer-Lemeshow test), discrimination (the area under the curve (AUC)), and clinical usefulness (Decision curve analysis (DCA)). The prediction nomogram incorporated 5 crucial factors such as glucose monitoring status, exercise status, monthly income, sleep disorder status, and the SPBS score. The model demonstrated strong discrimination in the primary cohort, with an AUC of 0.839 (95% CI, 0.781-0.897). This discriminative ability was further validated in the validation cohort, with an AUC of 0.857 (95% CI, 0.779-0.935). Moreover, the nomogram exhibited satisfactory calibration. DCA suggested that the prediction of depression in elderly patients with diabetes mellitus was of great clinical value. The prediction model provides precise and user-friendly guidance for community health workers in preliminary screenings for depression among elderly patients with diabetes.

Value analysis of ITLN1 in the diagnostic and prognostic assessment of colorectal cancer.

Colorectal cancer (CRC) remains the leading cause of cancer death worldwide. Less than half of the patients are diagnosed when the cancer is locally advanced. Several studies have shown that intelectin-1 (ITLN1) can serve as a key prognostic and therapeutic target for CRC. The purpose of this study was to investigate the clinical value of ITLN1 in CRC and to analyse its potential as a predictive biomarker for CRC. Colon adenocarcinoma (COAD) is the main type of CRC. COAD project in The Cancer Genome Atlas (TCGA) database served as the training cohort, and GSE39582 series in the Gene Expression Omnibus (GEO) database served as the external independent validation cohort. First, the difference in the expression level of ITLN1 between COAD tissue and normal tissue was analysed, and the results were verified via immunohistochemistry. The relationship between ITLN1 expression and the prognosis of COAD patients was evaluated via the heatmap and the Kaplan-Meier (KM) curve. The ITLN1 coexpressed gene set obtained by Pearson correlation analysis was used. The prognostic signatures that were significantly correlated with survival status were screened by Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. Finally, a nomogram related to ITLN1 was constructed based on the risk score of the prognostic signature and routine clinicopathological variables. ITLN1 is significantly underexpressed in tumour tissues and can be used as a valuable tool to distinguish COAD. The high-expression group of ITLN1 was verified to have a greater survival rate. ITLN1 is significantly associated with a good prognosis in COAD patients. Six candidate genes (ITLN1 and MORC2, SH2D7, LGALS4, ATOH1, and NAT2) were selected for use in the Cox-LASSO regression analysis to calculate the risk score. Finally, a nomogram was constructed with a comprehensive risk score and clinicopathologic factors to successfully predict and verify the 1-year, 3-year, and 5-year survival probability. Our study established ITLN1 as an effective tool for CRC screening, diagnosis, and prognostic assessment, provided a basis for further study of the molecular function of ITLN1, and provided new insights for the mechanistic exploration and treatment of CRC.

Development of an artificial intelligent model for pre-endoscopic screening of precancerous lesions in gastric cancer

BackgroundGiven the high cost of endoscopy in gastric cancer (GC) screening, there is an urgent need to explore cost-effective methods for the large-scale prediction of precancerous lesions of gastric cancer (PLGC). We aim to construct a hierarchical artificial intelligence-based multimodal non-invasive method for pre-endoscopic risk screening, to provide tailored recommendations for endoscopy.MethodsFrom December 2022 to December 2023, a large-scale screening study was conducted in Fujian, China. Based on traditional Chinese medicine theory, we simultaneously collected tongue images and inquiry information from 1034 participants, considering the potential of these data for PLGC screening. Then, we introduced inquiry information for the first time, forming a multimodality artificial intelligence model to integrate tongue images and inquiry information for pre-endoscopic screening. Moreover, we validated this approach in another independent external validation cohort, comprising 143 participants from the China-Japan Friendship Hospital.ResultsA multimodality artificial intelligence-assisted pre-endoscopic screening model based on tongue images and inquiry information (AITonguequiry) was constructed, adopting a hierarchical prediction strategy, achieving tailored endoscopic recommendations. Validation analysis revealed that the area under the curve (AUC) values of AITonguequiry were 0.74 for overall PLGC (95% confidence interval (CI) 0.71–0.76, p < 0.05) and 0.82 for high-risk PLGC (95% CI 0.82–0.83, p < 0.05), which were significantly and robustly better than those of the independent use of either tongue images or inquiry information alone. In addition, AITonguequiry has superior performance compared to existing PLGC screening methodologies, with the AUC value enhancing 45% in terms of PLGC screening (0.74 vs. 0.51, p < 0.05) and 52% in terms of high-risk PLGC screening (0.82 vs. 0.54, p < 0.05). In the independent external verification, the AUC values were 0.69 for PLGC and 0.76 for high-risk PLGC.ConclusionOur AITonguequiry artificial intelligence model, for the first time, incorporates inquiry information and tongue images, leading to a higher precision and finer-grained pre-endoscopic screening of PLGC. This enhances patient screening efficiency and alleviates patient burden.

Development and Validation of a Tool for Assessing Adherence to Gluten-Free Diet in Patients With Celiac Disease.

Life-long adherence to gluten-free diet (GFD) and its assessment is essential for patients with celiac disease (CeD). We have developed and validated a tool for assessing adherence to GFD which can be used by both physicians and dietitians. Phase 1: Development, content validation, and assessment of reliability of tool. Phase 2: Validation of tool against standard dietary evaluation (SDE) (gold standard), immunoglobulin A - anti-tissue transglutaminase antibodies (IgA anti-tTG Ab), and gluten immunogenic peptides in urine. Overall, 380 biopsy-confirmed patients with CeD (derivation cohort: n = 100 [phase 1], n = 210 [phase 2] and independent validation cohort, n = 70) were recruited. Of an initial 90-point questionnaire, 84 items (Celiac Disease: Compliance Assessment Test [CD-CAT.v1]) were retained after content validation and pilot testing. In phase 1, upon administering CD-CAT.v1 on 100 patients, a comprehensive 35-item tool (CD-CAT.v2; α = 0.86) was obtained after removing items with low test-retest reliability and item-rest correlation values. In phase 2, upon administering CD-CAT.v2 on 210 patients, 22 items were removed having low correlation values (R < 0.4) with SDE. Finally, a 13-item tool (CD-CAT.v3; α = 0.84) was obtained with high criterion validity with SDE ( r = 0.806, P < 0.001), moderate convergent validity with celiac disease adherence test ( r = 0.602, P = 0.007), and moderate to weak correlation with urine gluten immunogenic peptides ( r = 0.46, P = 0.001) and IgA anti-tTG Ab ( r = 0.39, P = 0.008), respectively. The final 13-item tool also strongly correlated with SDE ( r = 0.78, P < 0.001) in an independent validation cohort of 70 patients with CeD. Principal component analysis identified 3 relevant subscales with a cumulative variance of 62%. The sensitivity and specificity of CD-CAT.v3 were 80% and 91%, respectively, with an area under curve of 0.905 with SDE. The obtained cutoff score of <19 from the receiver operating characteristic curve was further categorized as 13 = excellent, 14-18 = very good, 19-28 = average, and >28 = poor adherence to GFD. CD-CAT is a new and rapid tool for monitoring dietary adherence to GFD with high sensitivity and specificity, which can be administered by both physicians and dietitians.

AI-based histopathology image analysis reveals a distinct subset of endometrial cancers

Endometrial cancer (EC) has four molecular subtypes with strong prognostic value and therapeutic implications. The most common subtype (NSMP; No Specific Molecular Profile) is assigned after exclusion of the defining features of the other three molecular subtypes and includes patients with heterogeneous clinical outcomes. In this study, we employ artificial intelligence (AI)-powered histopathology image analysis to differentiate between p53abn and NSMP EC subtypes and consequently identify a sub-group of NSMP EC patients that has markedly inferior progression-free and disease-specific survival (termed ‘p53abn-like NSMP’), in a discovery cohort of 368 patients and two independent validation cohorts of 290 and 614 from other centers. Shallow whole genome sequencing reveals a higher burden of copy number abnormalities in the ‘p53abn-like NSMP’ group compared to NSMP, suggesting that this group is biologically distinct compared to other NSMP ECs. Our work demonstrates the power of AI to detect prognostically different and otherwise unrecognizable subsets of EC where conventional and standard molecular or pathologic criteria fall short, refining image-based tumor classification. This study’s findings are applicable exclusively to females.

Development and Validation Study of the Prognostic Impact of Deep Learning-Determined Myxoid Stroma After Neoadjuvant Chemotherapy in Patients with Esophageal Squamous Cell Carcinoma.

This study was designed to investigate the prognostic significance of artificial intelligence (AI)-based quantification of myxoid stroma in patients undergoing esophageal squamous cell carcinoma (ESCC) surgery after neoadjuvant chemotherapy (NAC) and to verify its significance in an independent validation cohort from another hospital. We evaluated two datasets of patients with pathological stage II or III ESCC who underwent surgery after NAC. Cohort 1 consisted of 85 patients who underwent R0 surgery for the primary tumor after NAC. Cohort 2, the validation cohort, consisted of 80 patients who received same treatments in another hospital. AI-based myxoid stroma was evaluated in resected specimens, and its area was categorized by using the receiver operating characteristic curve for overall survival (OS) of cohort 1. The F1 scores, which are the degree of agreement between the automatically detected myxoid stroma and manual annotations, were 0.83 and 0.79 for cohorts 1 and 2. The myxoid stroma-high group had a significantly poorer prognosis than the myxoid stroma-low group in terms of OS, disease-specific survival (DSS), and recurrence-free survival (RFS) in cohort 1. Comparable results were observed in cohort 2, where OS, DSS, and RFS were significantly affected by myxoid stroma. Multivariate analysis for RFS revealed that AI-determined myxoid stroma-high was one of the independent prognostic factors in cohort 1 (hazard ratio [HR] 1.97, p = 0.037) and cohort 2 (HR 4.45, p < 0.001). AI-determined myxoid stroma may be a novel and useful prognostic factor for patients with pathological stage II or III ESCC after NAC.

Incremental value of C-reactive protein to the MEESSI acute heart failure risk score.

We hypothesized that the current gold standard for risk stratification of patients with acute heart failure (AHF), the Multiple Estimation of risk based on the Emergency department Spanish Score In patients with AHF (MEESSI-AHF) risk score, can be further improved by adding systemic inflammation as quantified by C-reactive protein (CRP). In a prospective multicentre diagnostic study (BASEL V), AHF was centrally adjudicated by two independent cardiologists. The MEESSI-AHF risk score was calculated using an established reduced and recalibrated model containing 12 independent risk factors. Model extension was performed by refitting and adding CRP in the logistic regression model with 30-day mortality as binary outcome. Discrimination, calibration and clinical usefulness were used to assess the performance of the extended Multiple Estimation of risk based on the Emergency department Spanish Score In patients (MEESSI) model. Validation was performed in an independent, retrospective and single-centre AHF cohort. Among 1208 AHF patients with complete data allowing calculation of the recalibrated MEESSI and the extended MEESSI models, the prognostic accuracy for 30-day mortality of the extended MEESSI model (c-statistic 0.83, 95% confidence interval [CI] 0.79-0.87) was significantly higher compared to the recalibrated model (c-statistic 0.79, 95% CI 0.75-0.83, p = 0.013). The extended model allowed to stratify a higher percentage of patients into the lowest risk group compared to the recalibrated model (33.1% vs. 20.3%). Demonstrating a calibration plot's slope of 1.00 (95% CI 0.81-1.19) and an intercept of 0.0 (95% CI -0.22 to 0.22), the extended MEESSI model achieved excellent and improved calibration. Results were confirmed in the independent validation cohort (n = 575). Quantifying inflammation using CRP concentration provided incremental value in AHF risk stratification using the established MEESSI model.

A comprehensive multi-omics analysis reveals unique signatures to predict Alzheimer's disease.

Complex disorders, such as Alzheimer's disease (AD), result from the combined influence of multiple biological and environmental factors. The integration of high-throughput data from multiple omics platforms can provide system overviews, improving our understanding of complex biological processes underlying human disease. In this study, integrated data from four omics platforms were used to characterise biological signatures of AD. The study cohort consists of 455 participants (Control:148, Cases:307) from the Religious Orders Study and Memory and Aging Project (ROSMAP). Genotype (SNP), methylation (CpG), RNA and proteomics data were collected, quality-controlled and pre-processed (SNP = 130; CpG = 83; RNA = 91; Proteomics = 119). Using a diagnosis of Mild Cognitive Impairment (MCI)/AD combined as the target phenotype, we first used Partial Least Squares Regression as an unsupervised classification framework to assess the prediction capabilities for each omics dataset individually. We then used a variation of the sparse generalized canonical correlation analysis (sGCCA) to assess predictions of the combined datasets and identify multi-omics signatures characterising each group of participants. Analysing datasets individually we found methylation data provided the best predictions with an accuracy of 0.63 (95%CI = [0.54-0.71]), followed by RNA, 0.61 (95%CI = [0.52-0.69]), SNP, 0.59 (95%CI = [0.51-0.68]) and proteomics, 0.58 (95%CI = [0.51-0.67]). After integration of the four datasets, predictions were dramatically improved with a resulting accuracy of 0.95 (95% CI = [0.89-0.98]). The integration of data from multiple platforms is a powerful approach to explore biological systems and better characterise the biological signatures of AD. The results suggest that integrative methods can identify biomarker panels with improved predictive performance compared to individual platforms alone. Further validation in independent cohorts is required to validate and refine the results presented in this study.

Additional impact of genetic ancestry over race/ethnicity to prevalence of KRAS mutations and allele-specific subtypes in non-small cell lung cancer

KRAS mutation is the most common oncogenic driver in patients with non-small cell lung cancer (NSCLC). However, the detailed understanding of how self-reported race and/or ethnicity (SIRE), genetically inferred ancestry (GIA), and their interaction affect KRAS mutation is largely unknown. Here, we investigated the associations between SIRE, quantitative GIA, and KRAS mutation and its allele-specific subtypes in a multi-ethnic cohort of 3918 patients from the Boston Lung Cancer Survival cohort and the Chinese OrigiMed cohort with an independent validation cohort of 1450 patients with NSCLC. This comprehensive analysis included detailed covariates including age at diagnosis, sex, clinical stage, cancer histology, and smoking status. We report that SIRE is significantly associated with KRAS mutations, modified by sex, with SIRE-Asian patients showing lower rates of KRAS mutation, transversion substitution, and the allele-specific subtype KRASG12C compared to SIRE-White patients, after adjusting for potential confounders. Moreover, GIA was found to correlate with KRAS mutations, where patients with a higher proportion of European ancestry had an increased risk of KRAS mutations, especially more transition substitutions and KRASG12D. Notably, among SIRE-White patients, an increase in European ancestry was linked to a higher likelihood of KRAS mutations, whereas an increase in Admixed American ancestry was associated with a reduced likelihood, suggesting that quantitative GIA offers additional information beyond SIRE. The association of SIRE, GIA, and their interplay with KRAS driver mutations in NSCLC highlights the importance of incorporating both into population-based cancer research, aiming to refine clinical decision-making processes and mitigate health disparities.

Identification of 1-Methylnicotinamide as a specific biomarker for the progression of cirrhosis to hepatocellular carcinoma

PurposeHepatocellular carcinoma (HCC) is a prevalent malignant tumor, often arising from hepatitis induced by the hepatitis B virus (HBV) in China. However, effective biomarkers for early diagnosis are lacking, leading to a 5-year overall survival rate of less than 20% among patients with advanced HCC. This study aims to identify serum biomarkers for early HCC diagnosis to enhance patient survival rates.MethodsWe established an independent cohort comprising 27 healthy individuals, 13 patients with HBV-induced cirrhosis, 13 patients with hepatitis B-type HCC, and 8 patients who progressed from cirrhosis to hepatocellular carcinoma during follow-up. Serum metabolic abnormalities during the progression from cirrhosis to HCC were studied using untargeted metabolomics. Liquid chromatography-mass spectrometry-based metabolomics methods characterized the subjects’ serum metabolic profiles. Partial least squares discriminant analysis (PLS-DA) was employed to elucidate metabolic profile changes during the progression from cirrhosis to HCC. Differentially expressed metabolites (DEMs) between cirrhosis and HCC groups were identified using the LIMMA package in the R language. Two machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest Classifier (RF), were used to identify key metabolic biomarkers involved in the progression from cirrhosis to HCC. Key metabolic biomarkers were further validated using targeted metabolomics in a new independent validation cohort comprising 25 healthy individuals and 25 patients with early-stage hepatocellular carcinoma.ResultsA total of 155 serum metabolites were identified, of which 21/54 metabolites exhibited significant changes in HCC patients compared with cirrhosis patients and healthy individuals, respectively. PLS-DA clustering results demonstrated a significant change trend in the serum metabolic profile of patients with HBV-induced cirrhosis during the progression to HCC. Utilizing LASSO regression and RF algorithms, we confirmed 10 key metabolic biomarkers. Notably, 1-Methylnicotinamide (1-MNAM) exhibited a persistent and significant decrease in healthy individuals, cirrhosis, and HCC patients. Moreover, 1-MNAM levels in developing patients were significantly higher during the cirrhosis stage than in the HCC stage. Targeted metabolomic validation in an external cohort further confirmed the good diagnostic performance of 1-MNAM in early HCC detection.ConclusionOur findings imply that 1-MNAM may be a specific biomarker for the progression of cirrhosis to HCC.