Predictive Tool Research Articles

DNA methylation of serotonin genes as predictive biomarkers of antidepressant treatment response

Selective serotonin reuptake inhibitors (SSRI) are frequently ineffective in treating depressive episodes and biomarkers are needed to optimize antidepressant treatment outcomes. DNA methylation levels of serotonin transporter (SLC6A4) and tryptophan hydroxylase 2 genes (TPH2) have been suggested to predict antidepressant clinical outcomes but their applicability remains uncertain. In this study, we: 1) evaluated SLC6A4/TPH2 methylation biomarker potential for predicting clinical outcomes after escitalopram treatment; 2) evaluated whether changes in SLC6A4/TPH2 methylation are informative of treatment mechanisms. We used a cohort of 90 unmedicated patients with major depressive disorder that were part of a 12-week open-label longitudinal trial and compared our observations with previous findings. Depressive symptoms were measured at baseline and after 8 and 12 weeks of treatment using the Hamilton Depression Rating Scale (HAMD6/17). We found an association between baseline TPH2 methylation and both clinical response (β:3.43; p = 0.01; 95 % CI:[0.80; 6.06]) and change in depressive symptoms after 8 weeks (β:−45.44; p = 0.01; 95 %CI:[− −78.58; −12.30]). However, we found no evidence for predictive value of any gene (TPH2 AUC: 0.74 95 % CI:[0.42;0.79]; SLC6A4: AUC: 0.61; 95 % CI: [0.48–0.78]). Methylation levels changed at the trend level for CpG sites of SLC6A4 and TPH2 over the course of 12 weeks of treatment. In addition, similar to previous observations, we found a trend for an association between methylation of SLC6A4 CpG2 (chr17:30,236,083) and HAMD17 change after 12 weeks. Our findings suggest that although TPH2 and SLC6A4 methylation may be informative of antidepressant treatment outcome, they are unlikely to prove useful as clinical predictor tools.

Forecasting Cost Risks of Corn and Soybean Crops through Monte Carlo Simulation

Considering that investing in the production of corn and soybeans is conditioned by production costs and several risks, the objective of this research work was to develop a simulation model for the prediction of the production costs of these commodities, considering the variability and correlation of key variables. The descriptive analysis of the data focused on measures such as mean, standard deviation, and coefficient of variation. To evaluate the relationship between commodity and input prices, Spearman’s demonstration coefficient and the coefficient of determination (R2) were used. A Monte Carlo simulation (MCS) was used to evaluate the variation in production costs and net revenues. The Predictor tool was used to make predictions based on historical data and time series models. This study was made for the period between 2018 and 2022 based on data provided by fifty companies from the state of São Paulo, Brazil. The results showed that the production cost/ha of corn faces a high-cost risk, particularly when production and market conditions are characterized by high levels of volatility, uncertainty, complexity, and ambiguity. The model proposed forecasts prices more accurately, as it considers the variation in the costs of inputs that most significantly influence the costs of corn and soybean crops.

Minor role of TP53 and TERT promoter mutations in medullary thyroid carcinoma: report of new cases and revision of the literature.

Aims of this study were to investigate the prevalence of TP53 and TERT mutations in Medullary Thyroid carcinoma (MTC) and their role in inducing aggressiveness in positive cases. We performed a literature search in PubMed to identify studies investigating the prevalence of TERT and TP53 mutations in MTC. We also included data on MTC cases (n = 193) obtained at our center and unpublished. The in-silico pathogenicity of the TP53 mutations has been evaluated by predictor tools. We identified a total of 25 and 11 published papers: all together 1280 cases have been investigated for the presence of TP53 mutations and 974 for TERT promoter mutation. Twenty-five out of 1280 (2%) cases had a TP53 mutation while only 3/974 MTC cases (0.3%) have been found to be positive for TERT promoter mutations. Among all, we identified 19 different TP53 mutations that in 12 cases were demonstrated to have an in silico predicted high pathogenic role and a high impact on protein function. Three non-sense and 4 probably not damaging mutations were also reported. The pathogenic role of the TERT promoter mutations has been previously in vitro determined. No correlation between TP53 and/or TERT mutations and aggressiveness of MTC has been demonstrated. The prevalence of TP53 and TERT promoter mutations is very low in MTC. The reported mutations are pathogenic in the majority of cases. Because of their rarity it is not possible to clarify if they play or not a role in the pathogenesis and/or aggressiveness of this specific thyroid tumor.

Grand canonically optimized grain boundary phases in hexagonal close-packed titanium

Grain boundaries (GBs) profoundly influence the properties and performance of materials, emphasizing the importance of understanding the GB structure and phase behavior. As recent computational studies have demonstrated the existence of multiple GB phases associated with varying the atomic density at the interface, we introduce a validated, open-source GRand canonical Interface Predictor (GRIP) tool that automates high-throughput, grand canonical optimization of GB structures. While previous studies of GB phases have almost exclusively focused on cubic systems, we demonstrate the utility of GRIP in an application to hexagonal close-packed titanium. We perform a systematic high-throughput exploration of tilt GBs in titanium and discover previously unreported structures and phase transitions. In low-angle boundaries, we demonstrate a coupling between point defect absorption and the change in the GB dislocation network topology due to GB phase transformations, which has important implications for the accommodation of radiation-induced defects.

A Rare Combination of Compound Heterozygous Mutations in the PAH Gene in Three Unrelated Consanguineous Iranian Families with Classical Phenylketonuria

Abstract Background: The PAH gene mutations have been linked to the development of phenylketonuria (PKU), which is recognized as the most common inborn metabolic disorder, and is caused by a deficiency in the phenylalanine hydroxylase (PAH) enzyme. The Iranian population, known for its diversity and high consanguinity, offers a valuable sample for studying autosomal recessive disorders. Our study investigated three unrelated families with PKU from Iran, utilizing clinical, laboratory, and computational approaches. Materials and Methods: We performed direct PCR sequencing for 13 exons of the PAH gene on three Iranian patients who were diagnosed with PKU. Then, Sanger sequencing confirmed the segregation of the mutations from parents to probands. Pathogenicity predictor tools, including ACMG, CADD, SIFT, Polyphen-2, and Mutation Taster, were utilized to analyze the identified genetic variants. The three-dimensional structure of the mutant forms of the protein was predicted. We also analyzed the protein–protein interactions of PAH using the STRING database. Results: All three patients exhibited rare compound heterozygosity rearrangements in the PAH gene (NM_000277.3). These included three missense variants: [c.533A>G/c.1222C>T], [c.526C>T/c.1222C>T], and [c.533A> / c.526C>T]. Conclusion: This study adds to the body of evidence establishing the association between PAH mutations and the development of PKU. We speculated that the conjunction of a high consanguinity rate in populations such as Iran, coupled with the founder effect, can give rise to atypical genetic profiles, as observed in the rearrangement of compound heterozygosity in this study Moreover, our research underscores the significance of genetic testing in the precise diagnosis of individuals affected by inborn errors of metabolism.

Development of clinical prediction model to guide the use of CT head scans for non-traumatic Thai patient with seizure: A cross-sectional study.

The aim of this study was to develop clinical predictor tools for guiding the use of computed tomography (CT) head scans in non-traumatic Thai patients presented with seizure. A prediction model using a retrospective cross-sectional design was conducted. We recruited adult patients (aged ≥ 18 years) who had been diagnosed with seizures by their physicians and had undergone CT head scans for further investigation. Positive CT head defined as the presence of any new lesion that related to the patient's presented seizure officially reported by radiologist. A total of 9 candidate predictors were preselected. The prediction model was developed using a full multivariable logistic regression with backward stepwise elimination. We evaluated the model's predictive performance in terms of its discriminative ability and calibration via AuROC and calibration plot. The application was then constructed based on final model. A total of 362 patients were included into the analysis which comprising of 71 patients with positive CT head findings and 291 patients with normal results. Six final predictors were identified including: Glasgow coma scale, the presence of focal neurological deficit, history of malignancy, history of CVA, Epilepsy, and the presence of alcohol withdrawal symptom. In terms of discriminative ability, the final model demonstrated excellent performance (AuROC of 0.82 (95% CI: 0.76-0.87)). The calibration plot illustrated a good agreement between observed and predicted risks. This prediction model offers a reliable tool for effectively reduce unnecessary use and instill confidence in supporting physicians in determining the need for CT head scans in non-traumatic patients with seizures.

O-255 Ovarian Response Prediction Index (ORPI) as a predictor tool for ovarian response and clinical pregnancy in IVF/ICSI cycle : A Prospective Cohort Study

Abstract Study question Can incorporation of the Ovarian Response Prediction Index (ORPI), integrating AMH, AFC and age, offer a reliable prognostication of ovarian stimulation response in ART cycles? Summary answer The study affirms that ORPI, a three-variable index, emerges as a robust predictor for both ovarian response and clinical pregnancy rates within IVF/ICSI cycles. What is known already In the realm of individualized Controlled Ovarian Stimulation, precision in forecasting ovarian response proves pivotal. Ovarian reserve, influenced by age, AMH, and AFC, exhibits considerable heterogeneity among women of analogous age. While AMH and AFC function as pivotal markers, a holistic approach, exemplified by ORPI, stands out as a more comprehensive tool for predicting ovarian response. Study design, size, duration This prospective cohort study enrolled 302 subjects undergoing IVF cycles at a tertiary infertility center spanning from 1st January 2022 to 31st December 2023. Rigorous inclusion criteria mandated age ≤38 years, a BMI between 20–25 kg/m2, regular menstrual cycles, intact bilateral ovaries, no history of ovarian surgeries, absence of severe endometriosis, and no evidence of endocrine disorders. Participants/materials, setting, methods Subjects adhering to the inclusion criteria underwent venous blood sampling for AMH measurement and transvaginal ultrasound for AFC assessment. The ORPI was meticulously computed as (AMH × AFC) / patient age. Key endpoints encompassed the total oocytes retrieved, mature MII oocytes, and occurrences of clinical pregnancy. Rigorous statistical methodologies were employed for robust analysis. Main results and the role of chance The mean values for age (years), AMH (ng/mL), ORPI, AFC, and fertilized oocytes were 31.75 ± 4, 3.75 ± 3.39, 1.06 ± 1.22, 7.1 ± 2.94, and 6.11 ± 3.26, respectively. A highly significant positive correlation (p-value < 0.0001) was observed between ORPI and the numbers of oocytes and MII oocytes, with correlation coefficients of 0.714 and 0.746, respectively. Interpretation of the area under the ROC curve revealed that ORPI was a significant predictor of obtaining ≥4 oocytes at a cutoff point of > 0.2, with an area under the curve of 0.907. Similarly, for MII oocytes ≥4, ORPI was a significant predictor at a cutoff point of > 0.2, with an area under the curve of 0.937. In the case of positive clinical pregnancy, ORPI emerged as a significant predictor at a cutoff point of > 0.75, with an area under the curve of 0.822. The study underscored a substantial positive correlation between ORPI and both oocyte and MII oocyte counts, affirming ORPI’s pivotal role as a predictor for clinical pregnancy. Limitations, reasons for caution Limitations due to the constraint of sample size temper the generalizability of findings. The study did not account for live birth rates, and a comparative analysis with alternative markers was omitted. A more expansive, meticulously designed study is imperative. Wider implications of the findings ORPI, an innovative ovarian response biomarker, showcases considerable potential for predicting both ovarian response and clinical pregnancy rates. Its integration into individualized Controlled Ovarian Stimulation regimens holds promise for augmenting the counselling and prognostication process for patients embarking on IVF/ICSI cycles. Trial registration number MCDH/2023/28

Intelligent Reconfiguration-Promoted Cellular Internalization of Core-Shell DNA Nanoprobe Equipped with Successive Dual Stimuli-Responsive Protective Satellites for Amplification Fluorescence Imaging of Tumor Cells.

Although DNA probes have attracted increasing interest for precise tumor cell identification by imaging intracellular biomarkers, the requirement of commercial transfection reagents, limited targeting ligands, and/or non-biocompatible inorganic nanostructures has hampered the clinic translation. To circumvent these shortcomings, a reconfigurable ES-NC (Na+-dependent DNAzyme (E)-based substrate (S) cleavage core/shell DNA nanocluster (NC)) entirely from DNA strands is assembled for precise imaging of cancerous cells in a successive dual-stimuli-responsive manner. This nanoprobe is composed of a strung DNA tetrahedral satellites-based protective (DTP) shell, parallelly aligned target-responsive sensing (PTS) interlayer, and hydrophobic cholesterol-packed innermost layer (HCI core). Tetrahedral axial rotation-activated reconfiguration of DTP shell promotes the exposure of interior hydrophobic moieties, enabling cholesterol-mediated cellular internalization without auxiliary elements. Within cells, over-expressed glutathione triggers the disassembly of the DTP protective shell (first stimulus), facilitating target-stimulated signal transduction/amplification process (second stimuli). Target miRNA-21 is detected down to 10.6 fM without interference from coexisting miRNAs. Compared with transfection reagent-mediated counterpart, ES-NC displays a higher imaging ability, resists nuclease degradation, and has no detectable damage to healthy cells. The blindtest demonstrates that the ES-NC is suitable for the identification of cancerous cells from healthy cells, indicating a promising tool for early diagnosis and prediction of cancer.

Identifying early language predictors: A replication of Gasparini et al. (2023) confirming applicability in a general population cohort

AbstractBackgroundIdentifying language disorders earlier can help children receive the support needed to improve developmental outcomes and quality of life. Despite the prevalence and impacts of persistent language disorder, there are surprisingly no robust predictor tools available. This makes it difficult for researchers to recruit young children into early intervention trials, which in turn impedes advances in providing effective early interventions to children who need it.AimsTo validate externally a predictor set of six variables previously identified to be predictive of language at 11 years of age, using data from the Longitudinal Study of Australian Children (LSAC) birth cohort. Also, to examine whether additional LSAC variables arose as predictive of language outcome.Methods & ProceduresA total of 5107 children were recruited to LSAC with developmental measures collected from 0 to 3 years. At 11–12 years, children completed the Clinical Evaluation of Language Fundamentals, 4th Edition, Recalling Sentences subtest. We used SuperLearner to estimate the accuracy of six previously identified parent‐reported variables from ages 2–3 years in predicting low language (sentence recall score ≥ 1.5 SD below the mean) at 11–12 years. Random forests were used to identify any additional variables predictive of language outcome.Outcomes & ResultsComplete data were available for 523 participants (52.20% girls), 27 (5.16%) of whom had a low language score. The six predictors yielded fair accuracy: 78% sensitivity (95% confidence interval (CI) = [58, 91]) and 71% specificity (95% CI = [67, 75]). These predictors relate to sentence complexity, vocabulary and behaviour. The random forests analysis identified similar predictors.Conclusions & ImplicationsWe identified an ultra‐short set of variables that predicts 11–12‐year language outcome with ‘fair’ accuracy. In one of few replication studies of this scale in the field, these methods have now been conducted across two population‐based cohorts, with consistent results. An imminent practical implication of these findings is using these predictors to aid recruitment into early language intervention studies. Future research can continue to refine the accuracy of early predictors to work towards earlier identification in a clinical context.WHAT THIS PAPER ADDSWhat is already known on the subject There are no robust predictor sets of child language disorder despite its prevalence and far‐reaching impacts. A previous study identified six variables collected at age 2–3 years that predicted 11–12‐year language with 75% sensitivity and 81% specificity, which warranted replication in a separate cohort.What this study adds to the existing knowledge We used machine learning methods to identify a set of six questions asked at age 2–3 years with ≥ 71% sensitivity and specificity for predicting low language outcome at 11–12 years, now showing consistent results across two large‐scale population‐based cohort studies.What are the potential or clinical implications of this work? This predictor set is more accurate than existing feasible methods and can be translated into a low‐resource and time‐efficient recruitment tool for early language intervention studies, leading to improved clinical service provision for young children likely to have persisting language difficulties.

Subset of retinoblastoma tumours is associated with BRCA1/2 mutations

BackgroundWe investigated the potential association between pathogenic BRCA1/2 variants and retinoblastoma pathogenicity.MethodsIn this single-centre, retrospective case series, we performed hereditary cancer panel tests using blood samples for patients with retinoblastoma...

Is a Nomogram a Viable Predictive Tool for Managing Heparin Resistance in Pediatric Cardiac Surgery?

Is a Nomogram a Viable Predictive Tool for Managing Heparin Resistance in Pediatric Cardiac Surgery?

Development and validation of a clinical prediction model for glioma grade using machine learning.

Histopathological evaluation is currently the gold standard for grading gliomas; however, this technique is invasive. This study aimed to develop and validate a diagnostic prediction model for glioma by employing multiple machine learning algorithms to identify risk factors associated with high-grade glioma, facilitating the prediction of glioma grading. Data from 1114 eligible glioma patients were obtained from The Cancer Genome Atlas (TCGA) database, which was divided into a training set (n= 781) and a test set (n= 333). Fifty machine learning algorithms were employed, and the optimal algorithm was selected to construct a prediction model. The performance of the machine learning prediction model was compared to the clinical prediction model in terms of discrimination, calibration, and clinical validity to assess the performance of the prediction model. The area under the curve (AUC) values of the machine learning prediction models (training set: 0.870 vs. 0.740, test set: 0.863 vs. 0.718) were significantly improved from the clinical prediction models. Furthermore, significant improvement in discrimination was observed for the Integrated Discrimination Improvement (IDI) (training set: 0.230, test set: 0.270) and Net Reclassification Index (NRI) (training set: 0.170, test set: 0.170) from the clinical prognostic model. Both models showed a high goodness of fit and an increased net benefit. A strong prediction accuracy model can be developed using machine learning algorithms to screen for high-grade glioma risk predictors, which can serve as a non-invasive prediction tool for preoperative diagnostic grading of glioma.

Five-Axis Finish Milling Machining for an Inconel 718 Alloy Monolithic Blisk

Blisks subjected to rough machining for channel creation must undergo finishing processes, and such processes must achieve the required tolerance limits. A high-quality surface finish and predictable long tool life are critical for the finish milling of blisks. Accordingly, the aim of this study was to optimize parameters for the finish machining of an Inconel 718 alloy monolithic blisk. Ball-cone mills were used to machine the blade surface at a constant depth. A sensory tool holder was used to collect cutting force signals during machining, and a digital microscope was used to examine tool wear. The surface texture measuring instrument was used to measure blisk blade surface roughness to evaluate processing quality. This study manipulated two cutting parameters, namely cutting speed and feed per tooth, and investigated their effects. The relationship between cutting conditions and machining efficiency was analyzed. According to the experimental results, we identified a set of optimal parameters at effective cutting speeds of 46.53 m/min, feed per tooth of 0.1 mm/tooth, and depth of cut of 0.05 mm for marginal tool wear and fast cutting speeds. Then the corresponding tool life was estimated by using the derived parameters.

Bipartite networks represent causality better than simple networks: evidence, algorithms, and applications.

A network, whose nodes are genes and whose directed edges represent positive or negative influences of a regulatory gene and its targets, is often used as a representation of causality. To infer a network, researchers often develop a machine learning model and then evaluate the model based on its match with experimentally verified "gold standard" edges. The desired result of such a model is a network that may extend the gold standard edges. Since networks are a form of visual representation, one can compare their utility with architectural or machine blueprints. Blueprints are clearly useful because they provide precise guidance to builders in construction. If the primary role of gene regulatory networks is to characterize causality, then such networks should be good tools of prediction because prediction is the actionable benefit of knowing causality. But are they? In this paper, we compare prediction quality based on "gold standard" regulatory edges from previous experimental work with non-linear models inferred from time series data across four different species. We show that the same non-linear machine learning models have better predictive performance, with improvements from 5.3% to 25.3% in terms of the reduction in the root mean square error (RMSE) compared with the same models based on the gold standard edges. Having established that networks fail to characterize causality properly, we suggest that causality research should focus on four goals: (i) predictive accuracy; (ii) a parsimonious enumeration of predictive regulatory genes for each target gene g; (iii) the identification of disjoint sets of predictive regulatory genes for each target g of roughly equal accuracy; and (iv) the construction of a bipartite network (whose node types are genes and models) representation of causality. We provide algorithms for all goals.

Determination and Correlation of Finger Print Pattern and Blood Grouping in Diabetes Mellitus: An Analytical Study

BACKGROUND: To Determine and correlate between fingerprint patterns and blood group in type II diabetes mellitus. RESULTS: One hundred individuals with type II diabetes mellitus and One hundred normal individuals as controls were selected for study. Fingerprints were obtained by dactyloscopy. Various fingerprint patterns like arches, whorls, loops were studied and correlated with the respective blood group based on ABO system among the volunteer controls and diabetics. After obtaining results, the analysis was made using Chi square test and One way ANOVA which showed, the most predominant fingerprint pattern among the 100 diabetic volunteers is “Arch pattern” and the most predominant blood group was “O” positive among the diabetic volunteers. Also correlating both finger print and blood group “Whorl pattern” with “B” blood group showed higher predominance among the healthy volunteers. CONCLUSIONS: This is the first study correlating finger print pattern with blood grouping in subjects of Diabetes mellitus. Our study findings showed that O positive blood group with arch pattern type of finger print was the most common findings among diabetes. Thus correlating fingerprint pattern and blood grouping act as a early predictor tool for diagnosing Type II Diabetes mellitus.

A Novel Investigation for Early Sex Determination in Alive Adult European Seabass (Dicentrarchus labrax) Using cyp19a1a, dmrt1a, and dmrt1b Genes Expression in Tail Fin tissues

This study is the first investigation for using sex-related gene expression in tail fin tissues of seabass as early sex determination without killing the fish. The European seabass (Dicentrarchus labrax) is gonochoristic and lacks distinguishable sex chromosomes, so, sex determination is referred to molecular actions for some sex-related genes on autosomal chromosomes which are well known such as cyp19a1a, dmrt1a, and dmrt1b genes which play crucial role in gonads development and sex differentiation. cyp19a1a is expressed highly in females for ovarian development and dmrt1a and dmrt1b are for testis development in males. In this study, we evaluated the difference in the gene expression levels of studied genes by qPCR in tail fins and gonads. We then performed discriminant analysis (DA) using morphometric traits and studied gene expression parameters as predictor tools for fish sex. The results revealed that cyp19a1a gene expression was significantly higher in future females’ gonads and tail fins (p ≥ 0.05). Statistically, cyp19a1a gene expression was the best parameter to discriminate sex even the hit rate of any other variable by itself could not correctly classify 100% of the fish sex except when it was used in combination with cyp19a1a. In contrast, Dmrt1a gene expression was higher in males than females but there were difficulties in analyzing dmrt1a and dmrt1b expressions in the tail because levels were low. So, it could be used in future research to differentiate and determine the sex of adult fish using the cyp19a1a gene expression marker without killing or sacrificing fish.

Exploiting genomic tools for genetic dissection and improving the resistance to Fusarium stalk rot in tropical maize.

A stable genomic region conferring FSR resistance at ~250 Mb on chromosome 1 was identified by GWAS. Genomic prediction has the potential to improve FSR resistance. Fusarium stalk rot (FSR) is a global destructive disease in maize; the efficiency of phenotypic selection for improving FSR resistance was low. Novel genomic tools of genome-wide association study (GWAS) and genomic prediction (GP) provide an opportunity for genetic dissection and improving FSR resistance. In this study, GWAS and GP analyses were performed on 562 tropical maize inbred lines consisting of two populations. In total, 15 SNPs significantly associated with FSR resistance were identified across two populations and the combinedPOP consisting of all 562 inbred lines, with the P-values ranging from 1.99 × 10-7 to 8.27 × 10-13, and the phenotypic variance explained (PVE) values ranging from 0.94 to 8.30%. The genetic effects of the 15 favorable alleles ranged from -4.29 to -14.21% of the FSR severity. One stable genomic region at ~ 250Mb on chromosome 1 was detected across all populations, and the PVE values of the SNPs detected in this region ranged from 2.16 to 5.18%. Prediction accuracies of FSR severity estimated with the genome-wide SNPs were moderate and ranged from 0.29 to 0.51. By incorporating genotype-by-environment interaction, prediction accuracies were improved between 0.36 and 0.55 in different breeding scenarios. Considering both the genome coverage and the threshold of the P-value of SNPs to select a subset of molecular markers further improved the prediction accuracies. These findings extend the knowledge of exploiting genomic tools for genetic dissection and improving FSR resistance in tropical maize.

Involvement of FAM170B-AS1, hsa-miR-1202, and hsa-miR-146a-5p in breast cancer.

FAM170B-AS1 is usually expressed low in all organs except for testicular tissues. No study was performed to explore its role in breast cancer (BC). Contradictory results were reported about hsa-miR-1202 and hsa-miR-146a-5p in BC. The present study aimed to explore the involvement of FAM170B-AS1 in BC using bioinformatics predictive tools, followed by a practical validation besides exploring the impact of hsa-miR-1202 and hsa-miR-146a-5p in BC. This study enrolled 96 female patients with BC, 30 patients with benign breast diseases (BBD), and 25 control subjects. The expressions of circulating FAM170B-AS1, hsa-miR-1202, and hsa-miR-146a-5p were quantified using qRT-PCR. These ncRNAs' associations, predictive, and diagnostic roles in BC were statistically tested. The underlying miRNA/mRNA targets of FAM170B-AS1 in BC were bioinformatically predicted followed by confirmation based on the GEPIA and TCGA databases. The expression of FAM170B-AS1 was upregulated in sera of BC patients and hsa-miR-1202 was upregulated in sera of BBD and BC patients while that of hsa-miR-146a-5p was downregulated in BC. These FAM170B-AS1 was significantly associated with BC when compared to BBD. FAM170B-AS1 and hsa-miR-1202 were statistically associated with the BC's stage, grade, and LN metastasis. FAM170B-AS1 and hsa-miR-146a-5p gave the highest specificity and sensitivity for BC. KRAS and EGFR were predicted to be targeted by FAM170B-AS1 through interaction with hsa-miR-143-3p and hsa-miR-7-5p, respectively. Based on the TCGA database, cancer patients having mutations in FAM170B show good overall survival. The present study reported that for the first time, FAM170B-AS1 may be a potential risk factor, predictive, and diagnostic marker for BC. In addition, FAM170B-AS1 might be involved in BC by interacting with hsa-miR-143-3p/KRAS and hsa-miR-7-5p/EGFR through enhancement or repression that may present a new therapeutic option for BC.

Identification of Novel Stemness-based Subtypes and Construction of a Prognostic Risk Model for Patients with Lung Squamous Cell Carcinoma.

Although cancer stem cells (CSCs) contribute to tumorigenesis, progression, and drug resistance, stemness-based classification and prognostic signatures of lung squamous cell carcinoma (LUSC) remain unclarified. This study attempted to identify stemness-based subtypes and develop a prognostic risk model for LUSC. Based on RNA-seq data from The Cancer Genome Atlas (TCGA), Gene-Expression Omnibus (GEO) and Progenitor Cell Biology Consortium (PCBC), mRNA expression-based stemness index (mRNAsi) was calculated by one-class logistic regression (OCLR) algorithm. A weighted gene coexpression network (WGCNA) was employed to identify stemness subtypes. Differences in mutation, clinical characteristics, immune cell infiltration, and antitumor therapy responses were determined. We constructed a prognostic risk model, followed by validations in GEO cohort, pan-cancer and immunotherapy datasets. LUSC patients with subtype C2 had a better prognosis, manifested by higher mRNAsi, higher tumor protein 53 (TP53) and Titin (TTN) mutation frequencies, lower immune scores and decreased immune checkpoints. Patients with subtype C2 were more sensitive to Imatinib, Pyrimethamine, and Paclitaxel therapy, whereas those with subtype C1 were more sensitive to Sunitinib, Saracatinib, and Dasatinib. Moreover, we constructed stemness-based signatures using seven genes (BMI1, CCDC51, CTNS, EIF1AX, FAM43A, THBD, and TRIM68) and found high-risk patients had a poorer prognosis in the TCGA cohort. Similar results were found in the GEO cohort. We verified the good performance of risk scores in prognosis prediction and therapy responses. The stemness-based subtypes shed novel insights into the potential roles of LUSC-stemness in tumor heterogeneity, and our prognostic signatures offer a promising tool for prognosis prediction and guide therapeutic decisions in LUSC.

Predictive modeling for breast cancer based on machine learning algorithms and features selection methods

Breast cancer is one of the leading causes of death among women worldwide. However, early prediction of breast cancer plays a crucial role. Therefore, strong needs exist for automatic accurate early prediction of breast cancer. In this paper, machine learning (ML) classifiers combined with features selection methods are used to build an intelligent tool for breast cancer prediction. The Wisconsin diagnostic breast cancer (WDBC) dataset is used to train and test the model. Classification algorithms, including support vector machine (SVM), light gradient boosting machine (LightGBM), random forest (RF), logistic regression (LR), k-nearest neighbors (k-NN), and naïve Bayes, were employed. Performance measures for each of them were obtained, namely: accuracy, precision, recall, F-score, Kappa, Matthews correlation coefficient (MCC), and time. The results indicate that without feature selection, LightGBM achieves the highest accuracy at 95%. With minimum redundancy maximum relevance (mRMR) feature selection (15 features), LightGBM outperforms other classifiers, achieving an accuracy of 98%. For Pearson correlation coefficient feature selection (15 features), LightGBM also excels with a 95% accuracy rate. Lasso feature selection (5 features) produces varied results across classifiers, with logistic regression achieving the highest accuracy at 96%. These findings underscore the importance of feature selection in refining model performance and in improving detection for breast cancer.