Interpretable machine learning model based on clinical factors for predicting muscle radiodensity loss after treatment in ovarian cancer.

PURPOSE: Muscle radiodensity loss after surgery and adjuvant chemotherapy is associated with poor outcomes in ovarian cancer. Assessing muscle radiodensity is a real-world clinical challenge owing to the requirement for computed tomography (CT) with consistent protocols and labor-intensive processes. This study aimed to use interpretable machine learning (ML) to predict muscle radiodensity loss. METHODS: This study included 723 patients with ovarian cancer who underwent primary debulking surgery and platinum-based chemotherapy between 2010 and 2019 at two tertiary centers (579 in cohort 1 and 144 in cohort 2). Muscle radiodensity was assessed from pre- and post-treatment CT acquired with consistent protocols, and a decrease in radiodensity ≥ 5% was defined as loss. Six ML models were trained, and their performances were evaluated using the area under the curve (AUC) and F1-score. The SHapley Additive exPlanations (SHAP) method was applied to interpret the ML models. RESULTS: The CatBoost model achieved the highest AUC of 0.871 (95% confidence interval, 0.870-0.874) and F1-score of 0.688 (95% confidence interval, 0.685-0.691) among the models in the training set and outperformed in the external validation set, with an AUC of 0.839 and F1-score of 0.673. Albumin change, ascites, and residual disease were the most important features associated with a higher likelihood of muscle radiodensity loss. The SHAP force plot provided an individualized interpretation of model predictions. CONCLUSION: An interpretable ML model can assist clinicians in identifying ovarian cancer patients at risk of muscle radiodensity loss after treatment and understanding the contributors of muscle radiodensity loss.

SheddomeDB 2023: A Revision of an Ectodomain Shedding Database Based on a Comprehensive Literature Review and Online Resources.

Ectodomain shedding of membrane proteins is a proteolytic event involved in several biological phenomena, including inflammation, development, diseases, and cancer progression. Though ectodomain shedding is a post-translational modification that plays an important role in cellular regulation, this biological phenomenon is seriously underannotated in public protein databases. Given the importance of the shedding events, we conducted a comprehensive literature review for membrane protein shedding and constructed the database, SheddomeDB in 2017. In response to user feedback, novel shedding findings, more associated biomedical events, and the advance in web technology, we revised SheddomeDB to a new version, SheddomeDB 2023. The revised SheddomeDB 2023 includes 481 protein entries across seven species; all the content was manually verified and curated. The content of SheddomeDB 2023 mainly came from a comprehensive literature survey by our newly developed semiautomated screening tool. We also integrated verified and updated cleavage and secretome information from other databases into the revision. In addition, SheddomeDB 2023 features a graphical presentation of cleavage information and a user-friendly interface for searching and browsing entries in the database. This revised comprehensive database of ectodomain shedding is expected to benefit biomedical researchers across different disciplines.

Explainable machine learning model for predicting skeletal muscle loss during surgery and adjuvant chemotherapy in ovarian cancer.

BACKGROUND: Skeletal muscle loss during treatment is associated with poor survival outcomes in patients with ovarian cancer. Although changes in muscle mass can be assessed on computed tomography (CT) scans, this labour-intensive process can impair its utility in clinical practice. This study aimed to develop a machine learning (ML) model to predict muscle loss based on clinical data and to interpret the ML model by applying SHapley Additive exPlanations (SHAP) method. METHODS: This study included the data of 617 patients with ovarian cancer who underwent primary debulking surgery and platinum-based chemotherapy at a tertiary centre between 2010 and 2019. The cohort data were split into training and test sets based on the treatment time. External validation was performed using 140 patients from a different tertiary centre. The skeletal muscle index (SMI) was measured from pre- and post-treatment CT scans, and a decrease in SMI ≥ 5% was defined as muscle loss. We evaluated five ML models to predict muscle loss, and their performance was determined using the area under the receiver operating characteristic curve (AUC) and F1 score. The features for analysis included demographic and disease-specific characteristics and relative changes in body mass index (BMI), albumin, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). The SHAP method was applied to determine the importance of the features and interpret the ML models. RESULTS: The median (inter-quartile range) age of the cohort was 52 (46-59) years. After treatment, 204 patients (33.1%) experienced muscle loss in the training and test datasets, while 44 (31.4%) patients experienced muscle loss in the external validation dataset. Among the five evaluated ML models, the random forest model achieved the highest AUC (0.856, 95% confidence interval: 0.854-0.859) and F1 score (0.726, 95% confidence interval: 0.722-0.730). In the external validation, the random forest model outperformed all ML models with an AUC of 0.874 and an F1 score of 0.741. The results of the SHAP method showed that the albumin change, BMI change, malignant ascites, NLR change, and PLR change were the most important factors in muscle loss. At the patient level, SHAP force plots demonstrated insightful interpretation of our random forest model to predict muscle loss. CONCLUSIONS: Explainable ML model was developed using clinical data to identify patients experiencing muscle loss after treatment and provide information of feature contribution. Using the SHAP method, clinicians may better understand the contributors to muscle loss and target interventions to counteract muscle loss.

Association of prognostic nutritional index with muscle loss and survival in patients with ovarian cancer treated with primary debulking surgery and chemotherapy.

PURPOSE: Sarcopenia is prevalent in ovarian cancer and contributes to poor survival. This study is aimed at investigating the association of prognostic nutritional index (PNI) with muscle loss and survival outcomes in patients with ovarian cancer. METHODS: This retrospective study analyzed 650 patients with ovarian cancer treated with primary debulking surgery and adjuvant platinum-based chemotherapy at a tertiary center from 2010 to 2019. PNI-low was defined as a pretreatment PNI of < 47.2. Skeletal muscle index (SMI) was measured on pre- and posttreatment computed tomography (CT) at L3. The cut-off for the SMI loss associated with all-cause mortality was calculated using maximally selected rank statistics. RESULTS: The median follow-up was 4.2 years, and 226 deaths (34.8%) were observed. With a median duration of 176 days (interquartile range: 166-187) between CT scans, patients experienced an average decrease in SMI of 1.7% (P < 0.001). The cut-off for SMI loss as a predictor of mortality was - 4.2%. PNI-low was independently associated with SMI loss (odds ratio: 1.97, P = 0.001). On multivariable analysis of all-cause mortality, PNI-low and SMI loss were independently associated with all-cause mortality (hazard ratio: 1.43, P = 0.017; hazard ratio: 2.27, P < 0.001, respectively). Patients with both SMI loss and PNI-low (vs. neither) had triple the risk of all-cause mortality (hazard ratio: 3.10, P < 0.001). CONCLUSIONS: PNI is a predictor of muscle loss during treatment for ovarian cancer. PNI and muscle loss are additively associated with poor survival. PNI can help clinicians guide multimodal interventions to preserve muscle and optimize survival outcomes.

Putative markers for the detection of early-stage bladder cancer selected by urine metabolomics.

BACKGROUND: Early detection of bladder cancer remains challenging because patients with early-stage bladder cancer usually have no incentive to take cytology or cystoscopy tests if they are asymptomatic. Our goal is to find non-invasive marker candidates that may help us gain insight into the metabolism of early-stage bladder cancer and be examined in routine health checks. RESULTS: We acquired urine samples from 124 patients diagnosed with early-stage bladder cancer or hernia (63 cancer patients and 61 controls). In which 100 samples were included in our marker discovery cohort, and the remaining 24 samples were included in our independent test cohort. We obtained metabolic profiles of 922 compounds of the samples by gas chromatography-mass spectrometry. Based on the metabolic profiles of the marker discovery cohort, we selected marker candidates using Wilcoxon rank-sum test with Bonferroni correction and leave-one-out cross-validation; we further excluded compounds detected in less than 60% of the bladder cancer samples. We finally selected eight putative markers. The abundance of all the eight markers in bladder cancer samples was high but extremely low in hernia samples. Moreover, the up-regulation of these markers might be in association with sugars and polyols metabolism. CONCLUSIONS: In the present study, comparative urine metabolomics selected putative metabolite markers for the detection of early-stage bladder cancer. The suggested relations between early-stage bladder cancer and sugars and polyols metabolism may create opportunities for improving the detection of bladder cancer.

Morpho-physiological properties and connectivity of vasoactive intestinal polypeptide-expressing interneurons in the mouse hippocampal dentate gyrus.

The hippocampus is a key brain structure for cognitive and emotional functions. Among the hippocampal subregions, the dentate gyrus (DG) is the first station that receives multimodal sensory information from the cortex. Local-circuit inhibitory GABAergic interneurons (INs) regulate the excitation-inhibition balance in the DG principal neurons (PNs) and therefore are critical for information processing. Similar to PNs, GABAergic INs also receive distinct inhibitory inputs. Among various classes of INs, vasoactive intestinal polypeptide-expressing (VIP+ ) INs preferentially target other INs in several brain regions and thereby directly modulate the GABAergic system. However, the morpho-physiological characteristics and postsynaptic targets of VIP+ INs in the DG are poorly understood. Here, we report that VIP+ INs in the mouse DG are highly heterogeneous based on their morpho-physiological characteristics. In approximately two-thirds of morphologically reconstructed cells, their axons ramify in the hilus. The remaining cells project their axons exclusively to the molecular layer (15%), to both the molecular layer and hilus (10%), or throughout the entire DG layers (8%). Generally, VIP+ INs display variable intrinsic properties and discharge patterns without clear correlation with their morphologies. Finally, VIP+ INs are recruited with a long latency in response to theta-band cortical inputs and preferentially innervate GABAergic INs over glutamatergic PNs. In summary, VIP+ INs in the DG are composed of highly diverse subpopulations and control the DG output via disinhibition.

Association between real-world home blood pressure measurement patterns and blood pressure variability among older individuals with hypertension: A community-based blood pressure variability study.

Home blood pressure (BP) monitoring is a useful tool for hypertension management. BP variability (BPV) has been associated with an increased risk of cardiovascular events. However, little is known about the correlation between BPV and different measurement patterns of long-term home BP monitoring. This longitudinal cohort study aimed to assess the associations between dynamic BP measurement patterns and BPV. A total of 1128 participants (mean age, 77.4 ± 9.3 years; male, 51%) with 23 269 behavior measuring units were included. We used sliding window sampling to classify the home BP data with a regular 6-month interval into units in a sliding manner until the data are not continuous. Three measurement patterns (stable frequent [SF], stable infrequent [SI], and unstable [US]) were assessed based on the home BP data obtained within the first 3 months of the study, and the data in the subsequent 3 months were used to assess the BPV of that unit. We used linear mixed-effects model to assess the association between BP measurement patterns and BPV with adjustment for possible confounding factors including average BP. Average real variability and coefficient variability were used as measures of the BPV. No significant differences were observed in average BP between the SF, SI, and US patterns. However, BPV in the SF group was significantly lower than that in the US and SI groups (all p-values < .05). The BPV in SI and US groups was not significantly different. A stable and frequent BP measuring pattern was independently associated with a lower BPV.

Stout camphor tree genome fills gaps in understanding of flowering plant genome evolution.

We present reference-quality genome assembly and annotation for the stout camphor tree (Cinnamomum kanehirae (Laurales, Lauraceae)), the first sequenced member of the Magnoliidae comprising four orders (Laurales, Magnoliales, Canellales and Piperales) and over 9,000 species. Phylogenomic analysis of 13 representative seed plant genomes indicates that magnoliid and eudicot lineages share more recent common ancestry than monocots. Two whole-genome duplication events were inferred within the magnoliid lineage: one before divergence of Laurales and Magnoliales and the other within the Lauraceae. Small-scale segmental duplications and tandem duplications also contributed to innovation in the evolutionary history of Cinnamomum. For example, expansion of the terpenoid synthase gene subfamilies within the Laurales spawned the diversity of Cinnamomum monoterpenes and sesquiterpenes.

Combining membrane proteomics and computational three-way pathway analysis revealed signalling pathways preferentially regulated in human iPSCs and human ESCs.

Owing to the clinical potential of human induced pluripotent stem cells (hiPSCs) in regenerative medicine, a thorough examination of the similarities and differences between hiPSCs and human embryonic stem cells (hESCs) has become indispensable. Moreover, as the important roles of membrane proteins in biological signalling, functional analyses of membrane proteome are therefore promising. In this study, a pathway analysis by the bioinformatics tool GSEA was first performed to identify significant pathways associated with the three comparative membrane proteomics experiments: hiPSCs versus precursor human foreskin fibroblasts (HFF), hESCs versus precursor HFF, and hiPSCs versus hESCs. A following three-way pathway comparison was conducted to identify the differentially regulated pathways that may contribute to the differences between hiPSCs and hESCs. Our results revealed that pathways related to oxidative phosphorylation and focal adhesion may undergo incomplete regulations during the reprogramming process. This hypothesis was supported by another public proteomics dataset to a certain degree. The identified pathways and their core enriched proteins could serve as the starting point to explore the possible ways to make hiPSCs closer to hESCs.

A knowledge-based T2-statistic to perform pathway analysis for quantitative proteomic data.

Approaches to identify significant pathways from high-throughput quantitative data have been developed in recent years. Still, the analysis of proteomic data stays difficult because of limited sample size. This limitation also leads to the practice of using a competitive null as common approach; which fundamentally implies genes or proteins as independent units. The independent assumption ignores the associations among biomolecules with similar functions or cellular localization, as well as the interactions among them manifested as changes in expression ratios. Consequently, these methods often underestimate the associations among biomolecules and cause false positives in practice. Some studies incorporate the sample covariance matrix into the calculation to address this issue. However, sample covariance may not be a precise estimation if the sample size is very limited, which is usually the case for the data produced by mass spectrometry. In this study, we introduce a multivariate test under a self-contained null to perform pathway analysis for quantitative proteomic data. The covariance matrix used in the test statistic is constructed by the confidence scores retrieved from the STRING database or the HitPredict database. We also design an integrating procedure to retain pathways of sufficient evidence as a pathway group. The performance of the proposed T2-statistic is demonstrated using five published experimental datasets: the T-cell activation, the cAMP/PKA signaling, the myoblast differentiation, and the effect of dasatinib on the BCR-ABL pathway are proteomic datasets produced by mass spectrometry; and the protective effect of myocilin via the MAPK signaling pathway is a gene expression dataset of limited sample size. Compared with other popular statistics, the proposed T2-statistic yields more accurate descriptions in agreement with the discussion of the original publication. We implemented the T2-statistic into an R package T2GA, which is available at https://github.com/roqe/T2GA.