Probabilistic graphical models relate immune status with response to neoadjuvant chemotherapy in breast cancer

Layer Analysis Based on RNA-Seq Reveals Molecular Complexity of Gastric Cancer

Gastric adenocarcinoma (GA) is a significant global health issue with poor prognosis, despite advancements in treatment. Although molecular classifications, such as The Cancer Genome Atlas (TCGA), provide valuable insights, their clinical utility remains limited. We performed a multi-layered functional analysis using TCGA RNA sequencing data to better define molecular subtypes and explore therapeutic implications. We reanalyzed TCGA RNA-seq data from 142 GA patients with localized disease who received adjuvant chemotherapy. Our approach included probabilistic graphical models and recurrent sparse k-means/consensus cluster algorithms for layer-based analysis. Our findings revealed survival differences among TCGA groups, with the GS subtype showing the poorest prognosis. We identified twelve functional nodes and seven biological layers, each with distinct functions. The combined molecular layer (CML) classification identified three prognostic groups that align with TCGA subtypes. CML2 (GS-like) displayed gene expression related to lipid metabolism, correlating with worse survival. Transcriptomic heterogeneity within the CIN subtype revealed clusters tied to proteolysis and lipid metabolism. We identified a subset of CIN tumors with profiles similar to MSI, termed CIN-MSI-like. Claudin-18, a key gene in proteolysis, was overexpressed across TCGA subtypes, suggesting it is a potential therapeutic target. Our study advances GA biology, enabling refined stratification and personalized treatment. Further studies are needed to translate these findings into clinical practice.

Machine learning-based models for the prediction of breast cancer recurrence risk

Breast cancer is the most common malignancy diagnosed in women worldwide. The prevalence and incidence of breast cancer is increasing every year; therefore, early diagnosis along with suitable relapse detection is an important strategy for prognosis improvement. This study aimed to compare different machine algorithms to select the best model for predicting breast cancer recurrence. The prediction model was developed by using eleven different machine learning (ML) algorithms, including logistic regression (LR), random forest (RF), support vector classification (SVC), extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), decision tree, multilayer perceptron (MLP), linear discriminant analysis (LDA), adaptive boosting (AdaBoost), Gaussian naive Bayes (GaussianNB), and light gradient boosting machine (LightGBM), to predict breast cancer recurrence. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score were used to evaluate the performance of the prognostic model. Based on performance, the optimal ML was selected, and feature importance was ranked by Shapley Additive Explanation (SHAP) values. Compared to the other 10 algorithms, the results showed that the AdaBoost algorithm had the best prediction performance for successfully predicting breast cancer recurrence and was adopted in the establishment of the prediction model. Moreover, CA125, CEA, Fbg, and tumor diameter were found to be the most important features in our dataset to predict breast cancer recurrence. More importantly, our study is the first to use the SHAP method to improve the interpretability of clinicians to predict the recurrence model of breast cancer based on the AdaBoost algorithm. The AdaBoost algorithm offers a clinical decision support model and successfully identifies the recurrence of breast cancer.

A Novel Molecular Analysis Approach in Colorectal Cancer Suggests New Treatment Opportunities

Colorectal cancer (CRC) is a molecular and clinically heterogeneous disease. In 2015, the Colorectal Cancer Subtyping Consortium classified CRC into four consensus molecular subtypes (CMS), but these CMS have had little impact on clinical practice. The purpose of this study is to deepen the molecular characterization of CRC. A novel approach, based on probabilistic graphical models (PGM) and sparse k-means-consensus cluster layer analyses, was applied in order to functionally characterize CRC tumors. First, PGM was used to functionally characterize CRC, and then sparse k-means-consensus cluster was used to explore layers of biological information and establish classifications. To this aim, gene expression and clinical data of 805 CRC samples from three databases were analyzed. Three different layers based on biological features were identified: adhesion, immune, and molecular. The adhesion layer divided patients into high and low adhesion groups, with prognostic value. The immune layer divided patients into immune-high and immune-low groups, according to the expression of immune-related genes. The molecular layer established four molecular groups related to stem cells, metabolism, the Wnt signaling pathway, and extracellular functions. Immune-high patients, with higher expression of immune-related genes and genes involved in the viral mimicry response, may benefit from immunotherapy and viral mimicry-related therapies. Additionally, several possible therapeutic targets have been identified in each molecular group. Therefore, this improved CRC classification could be useful in searching for new therapeutic targets and specific therapeutic strategies in CRC disease.

Brain Gene Expression in Systemic Hypothyroidism and Mouse Models of MCT8 Deficiency: The Mct8-Oatp1c1-Dio2 Triad

Background: The monocarboxylate transporter 8 (Mct8) protein is a primary thyroxine (T4) and triiodothyronine (T3) (thyroid hormone [TH]) transporter. Mutations of the MCT8-encoding, SLC16A2 gene alter thyroid function and TH metabolism and severely impair neurodevelopment (Allan-Herndon-Dudley syndrome [AHDS]). Mct8-deficient mice manifest thyroid alterations but lack neurological signs. It is believed that Mct8 deficiency in mice is compensated by T4 transport through the Slco1c1-encoded organic anion transporter polypeptide 1c1 (Oatp1c1). This allows local brain generation of sufficient T3 by the Dio2-encoded type 2 deiodinase, thus preventing brain hypothyroidism. The Slc16a2/Slco1c1 (MO) and Slc16a2/Dio2 (MD) double knockout (KO) mice lacking T4 and T3 transport, or T3 transport and T4 deiodination, respectively, should be appropriate models of AHDS. Our goal was to compare the cerebral hypothyroidism of systemic hypothyroidism (SH) caused by thyroid gland blockade with that present in the double KO mice. Methods: We performed RNA sequencing by using RNA from the cerebral cortex and striatum of SH mice and the double KO mice on postnatal days 21-23. Real-time polymerase chain reaction was used to confirm RNA-Seq results in replicate biological samples. Cell type involvement was assessed from cell type-enriched genes. Functional genomic differences were analyzed by functional node activity based on a probabilistic graphical model. Results: Each of the three conditions gave a different pattern of gene expression, with partial overlaps. SH gave a wider and highest variation of gene expression than MD or MO. This was partially due to secondary gene responses to hypothyroidism. The set of primary transcriptional T3 targets showed a tighter overlap, but quantitative gene responses indicated that the gene responses in SH were more severe than in MD or MO. Examination of cell type-enriched genes indicated cellular differences between the three conditions. Conclusions: The results indicate that the neurological impairment of AHDS is too severe to be fully explained by TH deprivation only.

Modeling and comparing data mining algorithms for prediction of recurrence of breast cancer

Breast cancer is the most common invasive cancer and the second leading cause of cancer death in women. and regrettably, this rate is increasing every year. One of the aspects of all cancers, including breast cancer, is the recurrence of the disease, which causes painful consequences to the patients. Moreover, the practical application of data mining in the field of breast cancer can help to provide some necessary information and knowledge required by physicians for accurate prediction of breast cancer recurrence and better decision-making. The main objective of this study is to compare different data mining algorithms to select the most accurate model for predicting breast cancer recurrence. This study is cross-sectional and data gathering of this research performed from June 2018 to June 2019 from the official statistics of Ministry of Health and Medical Education and the Iran Cancer Research Center for patients with breast cancer who had been followed for a minimum of 5 years from February 2014 to April 2019, including 5471 independent records. After initial pre-processing in dataset and variables, seven new and conventional data mining algorithms have been applied that each one represents one kind of data mining approach. Results show that the C5.0 algorithm possibly could be a helpful tool for the prediction of breast cancer recurrence at the stage of distant recurrence and nonrecurrence, especially in the first to third years. also, LN involvement rate, Her2 value, Tumor size, free or closed tumor margin were found to be the most important features in our dataset to predict breast cancer recurrence.

Biological molecular layer classification of muscle-invasive bladder cancer opens new treatment opportunities

BACKGROUND

Muscle-invasive bladder tumors are associated with a high risk of relapse and metastasis even after neoadjuvant chemotherapy and radical cystectomy. Therefore, further therapeutic options are needed and molecular characterization of the disease may help to identify new targets. The aim of this study was to characterize muscle-invasive bladder tumors at the molecular level using computational analyses.

METHODS

The TCGA cohort of muscle-invasive bladder cancer patients was used to describe these tumors. Probabilistic graphical models, layer analyses based on sparse k-means coupled with Consensus Cluster, and Flux Balance Analysis were applied to characterize muscle-invasive bladder tumors at a functional level.

RESULTS

Luminal and Basal groups were identified, and an immune molecular layer with independent value was also described. Luminal tumors showed decreased activity in the nodes of epidermis development and extracellular matrix, and increased activity in the node of steroid metabolism leading to a higher expression of the androgen receptor. This fact points to the androgen receptor as a therapeutic target in this group. Basal tumors were highly proliferative according to Flux Balance Analysis, which makes these tumors good candidates for neoadjuvant chemotherapy. The Immune-high group showed a higher degree of expression of immune biomarkers, suggesting that this group may benefit from immune therapy.

CONCLUSIONS

Our approach, based on layer analyses, established a Luminal group candidate for therapy with androgen receptor inhibitors, a proliferative Basal group which seems to be a good candidate for chemotherapy, and an immune-high group candidate for immunotherapy.

Chitosan-Based Nanogel Enhances Chemotherapeutic Efficacy of 10-Hydroxycamptothecin against Human Breast Cancer Cells

Chitosan (CS), the second most abundant polysaccharide in nature, has been widely developed as a nanoscopic drug delivery vehicle due to its intriguing characteristics. In this work, a positively charged CS-based nanogel was designed and synthesized to inhibit the proliferation of breast cancer cell lines. The model drug of 10-hydroxycamptothecin (HCPT) was entrapped into the core via a facile diffusion to form CS/HCPT. The characteristics of CS/HCPT were evaluated by assessing particle size, drug loading content, and drug loading efficiency. Furthermore, cell internalization, cytotoxicity, and apoptosis of CS/HCPT were also investigated in vitro. The present investigation indicated that the positively charged CS-based nanogel could be potentially used as a promising drug delivery system.