Machine learning analysis of TCGA cancer data

Modulatory effect of metformin and its transporters on immune infiltration in tumor microenvironment: a bioinformatic study with experimental validation

Metformin is a traditional antidiabetic drug for type 2 diabetes mellitus. However, it showed antitumor activity in many types of tumors, and it also has an influence on tumor metastasis in several types of tumors. It is transported through organic cationic transporters (OCTs), OCT1, OCT2, and OCT3, into the cells or into tumor microenvironment (TME). The complex interaction of metformin and its transporters on immune infiltration in TME of different types of tumors of The Cancer Genomic Atlas (TCGA) is not yet studied. The objective of this study is to identify the most suitable therapeutic target of tumors and immune infiltrates for metformin and its transporters in the TME. TIMER2.0, a bioinformatic tool, and other computational analysis were used to investigate this complex interaction; moreover, the identification of metformin target protein in TME is also investigated. The results revealed that the most suitable therapeutic target for metformin and OCTs among 32 types of TCGA data tumor types is Breast Invasive carcinoma (BRCA), and the most relevant immune infiltrate among 14 types of immune infiltrates that yields better prognosis and better therapeutical effect in TME is Macrophage M1. Furthermore, metformin showed a cytotoxic effect and an inhibitory effect on Urokinase Plasminogen Activator (uPA) gene expression in a concentration dependent fashion in MDA-MB-231 breast cancer cell line. This may suggest that metformin is a promising antitumor drug, stimulant for natural antitumor immune infiltrates, and inhibitor for metastasis in breast cancer.

Deep Learning-Powered Whole Slide Image Analysis in Cancer Pathology

Pathology is the cornerstone of modern cancer care. With the advancement of precision oncology, the demand for histopathologic diagnosis and stratification of patients is increasing as personalized cancer therapy relies on accurate biomarker assessment. Recently, rapid development of whole slide imaging technology has enabled digitalization of traditional histologic slides at high resolution, holding promise to improve both the precision and efficiency of histopathologic evaluation. In particular, deep learning approaches, such as Convolutional Neural Network, Graph Convolutional Network, and Transformer, have shown great promise in enhancing the sensitivity and accuracy of whole slide image (WSI) analysis in cancer pathology because of their ability to handle high-dimensional and complex image data. The integration of deep learning models with WSIs enables us to explore and mine morphologic features beyond the visual perception of pathologists, which can help automate clinical diagnosis, assess histopathologic grade, predict clinical outcomes, and even discover novel morphologic biomarkers. In this review, we present a comprehensive framework for incorporating deep learning with WSIs, highlighting how deep learning-driven WSI analysis advances clinical tasks in cancer care. Furthermore, we critically discuss the opportunities and challenges of translating deep learning-based digital pathology into clinical practice, which should be considered to support personalized treatment of cancer patients.

Interpretable multi-stage attention network to predict cancer subtype, microsatellite instability, TP53 mutation and TMB of endometrial and colorectal cancer

Mismatch repair deficiency (dMMR), also known as high-grade microsatellite instability (MSI-H), is a well-established biomarker for predicting the immunotherapy response in endometrial cancer (EC) and colorectal cancer (CRC). Tumor mutational burden (TMB) has also emerged as an important quantitative genomic biomarker for assessing the efficacy of immune checkpoint inhibitors. Although next-generation sequencing (NGS) can be used to assess MSI and TMB, the high costs, low sample throughput, and significant DNA requirements make NGS impractical for routine clinical screening. In this study, an interpretable, multi-stage attention deep learning (DL) network is introduced to predict pathological subtypes, MSI, TP53 mutations, and TMB directly from low-cost, routinely used histopathological whole slide images of EC and CRC slides. Experimental results showed that this method consistently outperformed seven state-of-the-art approaches in cancer subtyping and molecular status prediction across EC and CRC datasets. Fisher's Least Significant Difference test confirmed a strong correlation between model predictions and actual molecular statuses (MSI, TP53, and TMB) (p<0.001). Furthermore, Kaplan-Meier disease-free survival analysis revealed that CRC patients with model-predicted high TMB had significantly longer disease-free survival than those with low TMB (p<0.05). These findings demonstrate that the proposed DL-based approach holds significant potential for directly predicting immunotherapy-related pathological diagnoses and molecular statuses from routine WSIs, supporting personalized cancer immunotherapy treatment decisions in EC and CRC.

Neutrophil-related IL1R2 gene predicts the occurrence and early progression of myocardial infarction

Introduction

Myocardial infarction (MI) is a leading cause of death worldwide. Immune cells play a significant role in the MI development. This study aims to identify a marker related to neutrophil for the diagnosis and early progression of MI.

Methods

Key genes were screened using three machine learning algorithms to establish a diagnostic model. A gene associated with the early progression of MI was identified based on single cell RNA sequencing data. To further validate the predictive value of the gene, the mouse models of MI were constructed. Immunofluorescence (IF) analysis demonstrated the co-expression of the gene with neutrophils. Immunohistochemistry (IHC) was performed to validate the role of the gene in the progression of MI.

Results

Neutrophils were identified and verified as the key infiltrating immune cells (IICs) involved in the onset of MI. A diagnostic panel with superior performance was developed using five key genes related to neutrophils in MI (AUC = 0.887). Among the panel, IL1R2 was found to early phase of MI, which was further corroborated by IHC in mouse models of MI.

Conclusions

This study suggests that IL1R2, which is specific to neutrophils, can predict the diagnosis and early progression of MI, providing new insights into the clinical management of MI.

Exploring the Mechanism of Canmei Formula in Preventing and Treating Recurrence of Colorectal Adenoma Based on Data Mining and Algorithm Prediction

BACKGROUND

The high incidence of recurrence and malignant transformation of colorectal adenoma (CRA) are current issues that need to be addressed in clinical practice. Canmei Formula (CMF) has shown promising results in the prevention and treatment, however, it lacks effective clinical data support and its mechanism of action is not fully elucidated.

OBJECTIVE

The aim of this study is to evaluate the clinical efficacy and safety of CMF in preventing and treating CRA, and to explore its effective chemical components and pharmacological mechanisms.

METHOD

A randomized controlled clinical trial was conducted, with patients diagnosed with CRA within 6 months as the study subjects. After randomization, the patients were divided into a treatment group (receiving CMF granules) or a control group (receiving berberine hydrochloride tablets). The one-year recurrence rate of CRA was used as the key efficacy indicator to assess the effectiveness of CMF in preventing and treating CRA. The chemical components of CMF were identified using the UFLC-Q-TOF-MS/MS combined system. Data mining and the wSDTNBI algorithm were combined to construct a differential expression gene (DEG) - CMF prediction target interaction network for CRA. The core targets of CMF in CRA prevention and treatment were identified through topological analysis, and validated using molecular docking and in vitro experiments.

RESULT

During the period from October 1 2021 to December 31 2023, a total of 228 participants were included in the study. After block randomization, 114 patients were assigned to each group. In the treatment group, 98 patients completed follow-up examinations, with 16 patients (14.0%) exhibiting shedding, Adenoma recurrence was identified in 24 (24.5%) patients through colonoscopy. In the control group, 99 cases completed the follow-up examination, while 15 cases (13.2%) were lost to follow-up. There were 45 cases (45.5%) experienced recurrence of adenomas. During the follow-up period, no cases of colorectal cancer or severe adverse reactions were reported. UFLC-QTOF-MS/MS identification was combined with traditional Chinese medicine database mining to obtain 192 active chemical components of Canmei Formula. Using the wSDTNBI algorithm, 1044 prediction targets were predicted, and 3308 differentially expressed genes of CRA were extracted from the TCGA database. Network topology analysis and bioinformatics analysis were performed on 164 intersecting core targets. Molecular docking and qPCR analysis revealed that CMF downregulates angiotensin II type 1 receptor (AT1R) and regulated interleukin-8 (CXCL8) and matrix metalloproteinase 13 (MMP13) within the REN/Ang II/AT1R axis of the renin-angiotensin signaling pathway, thereby preventing and treating CRA.

CONCLUSION

This small-scale randomized controlled clinical trial showed that CMF granules can safely and effectively reduce the risk of CRA recurrence. CMF prevents and treats colorectal adenomas by modulating the renin-angiotensin signaling pathway and the inflammatory response.

AI for all: bridging data gaps in machine learning and health

Artificial intelligence (AI) and its subset, machine learning, have tremendous potential to transform health care, medicine, and population health through improved diagnoses, treatments, and patient care. However, the effectiveness of these technologies hinges on the quality and diversity of the data used to train them. Many datasets currently used in machine learning are inherently biased and lack diversity, leading to inaccurate predictions that may perpetuate existing health disparities. This commentary highlights the challenges of biased datasets, the impact on marginalized communities, and the critical need for strategies to address these disparities throughout the research continuum. To overcome these challenges, it is essential to adopt more inclusive data collection practices, engage collaboratively with community stakeholders, and leverage innovative approaches like federated learning. These steps can help mitigate bias and enhance the accuracy and fairness of AI-assisted or informed health care solutions. By addressing systemic biases embedded across research phases, we can build a better foundation for AI to enhance diagnostic and treatment capabilities and move society closer to the goal where improved health and health care can be a fundamental right for all, and not just for some.

Ensemble transformer-based multiple instance learning to predict pathological subtypes and tumor mutational burden from histopathological whole slide images of endometrial and colorectal cancer

In endometrial cancer (EC) and colorectal cancer (CRC), in addition to microsatellite instability, tumor mutational burden (TMB) has gradually gained attention as a genomic biomarker that can be used clinically to determine which patients may benefit from immune checkpoint inhibitors. High TMB is characterized by a large number of mutated genes, which encode aberrant tumor neoantigens, and implies a better response to immunotherapy. Hence, a part of EC and CRC patients associated with high TMB may have higher chances to receive immunotherapy. TMB measurement was mainly evaluated by whole-exome sequencing or next-generation sequencing, which was costly and difficult to be widely applied in all clinical cases. Therefore, an effective, efficient, low-cost and easily accessible tool is urgently needed to distinguish the TMB status of EC and CRC patients. In this study, we present a deep learning framework, namely Ensemble Transformer-based Multiple Instance Learning with Self-Supervised Learning Vision Transformer feature encoder (ETMIL-SSLViT), to predict pathological subtype and TMB status directly from the H&E stained whole slide images (WSIs) in EC and CRC patients, which is helpful for both pathological classification and cancer treatment planning. Our framework was evaluated on two different cancer cohorts, including an EC cohort with 918 histopathology WSIs from 529 patients and a CRC cohort with 1495 WSIs from 594 patients from The Cancer Genome Atlas. The experimental results show that the proposed methods achieved excellent performance and outperforming seven state-of-the-art (SOTA) methods in cancer subtype classification and TMB prediction on both cancer datasets. Fisher's exact test further validated that the associations between the predictions of the proposed models and the actual cancer subtype or TMB status are both extremely strong (p<0.001). These promising findings show the potential of our proposed methods to guide personalized treatment decisions by accurately predicting the EC and CRC subtype and the TMB status for effective immunotherapy planning for EC and CRC patients.

Expression of ALG8 in hepatocellular carcinoma and its diagnostic and prognostic significance

BACKGROUND

Alpha-1,3-glucosyltransferase (ALG8), a key enzyme in protein glycosylation, is implicated in the oncogenesis and progression of several human malignancies. This study aimed to define the role of ALG8 in hepatocellular carcinoma (HCC) and uncover its mechanisms of action.

METHODS

ALG8 expression in HCC and normal tissues was analyzed using the TCGA and GEO databases, validated by RT-qPCR and western blot. Survival outcomes were evaluated via Cox analyses, and ALG8's impact on HCC behavior was examined through functional assays. GO, KEGG, and GSEA identified ALG8-related pathways, validated by biochemical assays.

RESULTS

In bioinformatics analyses, ALG8 was overexpressed in HCC tissues (p < 0.05 for all comparisons) and correlated with poorer survival (p = 0.006 and p = 0.025, respectively), establishing its role as an independent prognostic factor. In vitro experiments showed that knockdown of ALG8 reduced HCC cell proliferation, migration, and invasion. Using the STRING platform and TCGA-LIHC dataset, we identified ALG8-interacting genes and their associated differentially expressed genes (DEGs). GO and KEGG analyses further linked ALG8 to genes involved in glycosylation, signal release, and other processes, as well as pathways including neuroactive ligand-receptor interaction and N-Glycan biosynthesis. GSEA, corroborated by western blot and immunofluorescence, points to the Wnt/β-catenin signaling cascade as a probable mechanistic pathway through which ALG8 may modulate HCC progression.

CONCLUSION

Elevated ALG8 expression in HCC is linked to worse outcomes and increased tumor aggressiveness, with silencing ALG8 reducing Wnt/β-catenin signaling, highlighting ALG8 as a potential therapeutic target.

The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution

Artificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.

The nexus of nuclear envelope dynamics, circular economy and cancer cell pathophysiology

The nuclear envelope (NE) is a critical component in maintaining the function and structure of the eukaryotic nucleus. The NE and lamina are disassembled during each cell cycle to enable an open mitosis. Nuclear architecture construction and deconstruction is a prime example of a circular economy, as it fulfills a highly efficient recycling program bound to continuous assessment of the quality and functionality of the building blocks. Alterations in the nuclear dynamics and lamina structure have emerged as important contributors to both oncogenic transformation and cancer progression. However, the knowledge of the NE breakdown and reassembly is still limited to a fraction of participating proteins and complexes. As cancer cells contain highly diverse nuclei in terms of DNA content, but also in terms of nuclear number, size, and shape, it is of great interest to understand the intricate relationship between these nuclear features in cancer cell pathophysiology. In this review, we provide insights into how those NE dynamics are regulated, and how lamina destabilization processes may alter the NE circular economy. Moreover, we expand the knowledge of the lamina-associated domain region by using strategic algorithms, including Artificial Intelligence, to infer protein associations, assess their function and location, and predict cancer-type specificity with implications for the future of cancer diagnosis, prognosis and treatment. Using this approach we identified NUP98 and MECP2 as potential proteins that exhibit upregulation in Acute Myeloid Leukemia (LAML) patients with implications for early diagnosis.

Deep learning to assess microsatellite instability directly from histopathological whole slide images in endometrial cancer

Molecular classification, particularly microsatellite instability-high (MSI-H), has gained attention for immunotherapy in endometrial cancer (EC). MSI-H is associated with DNA mismatch repair defects and is a crucial treatment predictor. The NCCN guidelines recommend pembrolizumab and nivolumab for advanced or recurrent MSI-H/mismatch repair deficient (dMMR) EC. However, evaluating MSI in all cases is impractical due to time and cost constraints. To overcome this challenge, we present an effective and efficient deep learning-based model designed to accurately and rapidly assess MSI status of EC using H&E-stained whole slide images. Our framework was evaluated on a comprehensive dataset of gigapixel histopathology images of 529 patients from the Cancer Genome Atlas (TCGA). The experimental results have shown that the proposed method achieved excellent performances in assessing MSI status, obtaining remarkably high results with 96%, 94%, 93% and 100% for endometrioid carcinoma G1G2, respectively, and 87%, 84%, 81% and 94% for endometrioid carcinoma G3, in terms of F-measure, accuracy, precision and sensitivity, respectively. Furthermore, the proposed deep learning framework outperforms four state-of-the-art benchmarked methods by a significant margin (p < 0.001) in terms of accuracy, precision, sensitivity and F-measure, respectively. Additionally, a run time analysis demonstrates that the proposed method achieves excellent quantitative results with high efficiency in AI inference time (1.03 seconds per slide), making the proposed framework viable for practical clinical usage. These results highlight the efficacy and efficiency of the proposed model to assess MSI status of EC directly from histopathological slides.

A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies

BACKGROUND

RNA sequencing combined with machine learning techniques has provided a modern approach to the molecular classification of cancer. Class predictors, reflecting the disease class, can be constructed for known tissue types using the gene expression measurements extracted from cancer patients. One challenge of current cancer predictors is that they often have suboptimal performance estimates when integrating molecular datasets generated from different labs. Often, the quality of the data is variable, procured differently, and contains unwanted noise hampering the ability of a predictive model to extract useful information. Data preprocessing methods can be applied in attempts to reduce these systematic variations and harmonize the datasets before they are used to build a machine learning model for resolving tissue of origins.

RESULTS

We aimed to investigate the impact of data preprocessing steps-focusing on normalization, batch effect correction, and data scaling-through trial and comparison. Our goal was to improve the cross-study predictions of tissue of origin for common cancers on large-scale RNA-Seq datasets derived from thousands of patients and over a dozen tumor types. The results showed that the choice of data preprocessing operations affected the performance of the associated classifier models constructed for tissue of origin predictions in cancer.

CONCLUSION

By using TCGA as a training set and applying data preprocessing methods, we demonstrated that batch effect correction improved performance measured by weighted F1-score in resolving tissue of origin against an independent GTEx test dataset. On the other hand, the use of data preprocessing operations worsened classification performance when the independent test dataset was aggregated from separate studies in ICGC and GEO. Therefore, based on our findings with these publicly available large-scale RNA-Seq datasets, the application of data preprocessing techniques to a machine learning pipeline is not always appropriate.

Enhancing cancer stage prediction through hybrid deep neural networks: a comparative study

Efficiently detecting and treating cancer at an early stage is crucial to improve the overall treatment process and mitigate the risk of disease progression. In the realm of research, the utilization of artificial intelligence technologies holds significant promise for enhancing advanced cancer diagnosis. Nonetheless, a notable hurdle arises when striving for precise cancer-stage diagnoses through the analysis of gene sets. Issues such as limited sample volumes, data dispersion, overfitting, and the use of linear classifiers with simple parameters hinder prediction performance. This study introduces an innovative approach for predicting early and late-stage cancers by integrating hybrid deep neural networks. A deep neural network classifier, developed using the open-source TensorFlow library and Keras network, incorporates a novel method that combines genetic algorithms, Extreme Learning Machines (ELM), and Deep Belief Networks (DBN). Specifically, two evolutionary techniques, DBN-ELM-BP and DBN-ELM-ELM, are proposed and evaluated using data from The Cancer Genome Atlas (TCGA), encompassing mRNA expression, miRNA levels, DNA methylation, and clinical information. The models demonstrate outstanding prediction accuracy (89.35%-98.75%) in distinguishing between early- and late-stage cancers. Comparative analysis against existing methods in the literature using the same cancer dataset reveals the superiority of the proposed hybrid method, highlighting its enhanced accuracy in cancer stage prediction.

Multilayered insights: a machine learning approach for personalized prognostic assessment in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a complex malignancy, and precise prognosis assessment is vital for personalized treatment decisions.

Objective

This study aimed to develop a multi-level prognostic risk model for HCC, offering individualized prognosis assessment and treatment guidance.

Methods

By utilizing data from The Cancer Genome Atlas (TCGA) and the Surveillance, Epidemiology, and End Results (SEER) database, we performed differential gene expression analysis to identify genes associated with survival in HCC patients. The HCC Differential Gene Prognostic Model (HCC-DGPM) was developed through multivariate Cox regression. Clinical indicators were incorporated into the HCC-DGPM using Cox regression, leading to the creation of the HCC Multilevel Prognostic Model (HCC-MLPM). Immune function was evaluated using single-sample Gene Set Enrichment Analysis (ssGSEA), and immune cell infiltration was assessed. Patient responsiveness to immunotherapy was evaluated using the Immunophenoscore (IPS). Clinical drug responsiveness was investigated using drug-related information from the TCGA database. Cox regression, Kaplan-Meier analysis, and trend association tests were conducted.

Results

Seven differentially expressed genes from the TCGA database were used to construct the HCC-DGPM. Additionally, four clinical indicators associated with survival were identified from the SEER database for model adjustment. The adjusted HCC-MLPM showed significantly improved discriminative capacity (AUC=0.819 vs. 0.724). External validation involving 153 HCC patients from the International Cancer Genome Consortium (ICGC) database verified the performance of the HCC-MLPM (AUC=0.776). Significantly, the HCC-MLPM exhibited predictive capacity for patient response to immunotherapy and clinical drug efficacy (P < 0.05).

Conclusion

This study offers comprehensive insights into HCC prognosis and develops predictive models to enhance patient outcomes. The evaluation of immune function, immune cell infiltration, and clinical drug responsiveness enhances our comprehension and management of HCC.

Classifying breast cancer using multi-view graph neural network based on multi-omics data

Introduction: As the evaluation indices, cancer grading and subtyping have diverse clinical, pathological, and molecular characteristics with prognostic and therapeutic implications. Although researchers have begun to study cancer differentiation and subtype prediction, most of relevant methods are based on traditional machine learning and rely on single omics data. It is necessary to explore a deep learning algorithm that integrates multi-omics data to achieve classification prediction of cancer differentiation and subtypes. Methods: This paper proposes a multi-omics data fusion algorithm based on a multi-view graph neural network (MVGNN) for predicting cancer differentiation and subtype classification. The model framework consists of a graph convolutional network (GCN) module for learning features from different omics data and an attention module for integrating multi-omics data. Three different types of omics data are used. For each type of omics data, feature selection is performed using methods such as the chi-square test and minimum redundancy maximum relevance (mRMR). Weighted patient similarity networks are constructed based on the selected omics features, and GCN is trained using omics features and corresponding similarity networks. Finally, an attention module integrates different types of omics features and performs the final cancer classification prediction. Results: To validate the cancer classification predictive performance of the MVGNN model, we conducted experimental comparisons with traditional machine learning models and currently popular methods based on integrating multi-omics data using 5-fold cross-validation. Additionally, we performed comparative experiments on cancer differentiation and its subtypes based on single omics data, two omics data, and three omics data. Discussion: This paper proposed the MVGNN model and it performed well in cancer classification prediction based on multiple omics data.

Differential gene expression of immunity and inflammation genes in colorectal cancer using targeted RNA sequencing

Introduction

Colorectal cancer (CRC) is a heterogeneous disease caused by molecular changes, as driver mutations, gene methylations, etc., and influenced by tumor microenvironment (TME) pervaded with immune cells with both pro- and anti-tumor effects. The studying of interactions between the immune system (IS) and the TME is important for developing effective immunotherapeutic strategies for CRC. In our study, we focused on the analysis of expression profiles of inflammatory and immune-relevant genes to identify aberrant signaling pathways included in carcinogenesis, metastatic potential of tumors, and association of Kirsten rat sarcoma virus (KRAS) gene mutation.

Methods

A total of 91 patients were enrolled in the study. Using NGS, differential gene expression analysis of 11 tumor samples and 11 matching non-tumor controls was carried out by applying a targeted RNA panel for inflammation and immunity genes containing 475 target genes. The obtained data were evaluated by the CLC Genomics Workbench and R library. The significantly differentially expressed genes (DEGs) were analyzed in Reactome GSA software, and some selected DEGs were used for real-time PCR validation.

Results

After prioritization, the most significant differences in gene expression were shown by the genes TNFRSF4, IRF7, IL6R, NR3CI, EIF2AK2, MIF, CCL5, TNFSF10, CCL20, CXCL11, RIPK2, and BLNK. Validation analyses on 91 samples showed a correlation between RNA-seq data and qPCR for TNFSF10, RIPK2, and BLNK gene expression. The top differently regulated signaling pathways between the studied groups (cancer vs. control, metastatic vs. primary CRC and KRAS positive and negative CRC) belong to immune system, signal transduction, disease, gene expression, DNA repair, and programmed cell death.

Conclusion

Analyzed data suggest the changes at more levels of CRC carcinogenesis, including surface receptors of epithelial or immune cells, its signal transduction pathways, programmed cell death modifications, alterations in DNA repair machinery, and cell cycle control leading to uncontrolled proliferation. This study indicates only basic molecular pathways that enabled the formation of metastatic cancer stem cells and may contribute to clarifying the function of the IS in the TME of CRC. A precise identification of signaling pathways responsible for CRC may help in the selection of personalized pharmacological treatment.

Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer

Established prognostic tests based on limited numbers of transcripts can identify high-risk breast cancer patients, yet are approved only for individuals presenting with specific clinical features or disease characteristics. Deep learning algorithms could hold potential for stratifying patient cohorts based on full transcriptome data, yet the development of robust classifiers is hampered by the number of variables in omics datasets typically far exceeding the number of patients. To overcome this hurdle, we propose a classifier based on a data augmentation pipeline consisting of a Wasserstein generative adversarial network (GAN) with gradient penalty and an embedded auxiliary classifier to obtain a trained GAN discriminator (T-GAN-D). Applied to 1244 patients of the METABRIC breast cancer cohort, this classifier outperformed established breast cancer biomarkers in separating low- from high-risk patients (disease specific death, progression or relapse within 10 years from initial diagnosis). Importantly, the T-GAN-D also performed across independent, merged transcriptome datasets (METABRIC and TCGA-BRCA cohorts), and merging data improved overall patient stratification. In conclusion, the reiterative GAN-based training process allowed generating a robust classifier capable of stratifying low- vs high-risk patients based on full transcriptome data and across independent and heterogeneous breast cancer cohorts.

Breast cancer prediction with transcriptome profiling using feature selection and machine learning methods

BACKGROUND

We used a hybrid machine learning systems (HMLS) strategy that includes the extensive search for the discovery of the most optimal HMLSs, including feature selection algorithms, a feature extraction algorithm, and classifiers for diagnosing breast cancer. Hence, this study aims to obtain a high-importance transcriptome profile linked with classification procedures that can facilitate the early detection of breast cancer.

METHODS

In the present study, 762 breast cancer patients and 138 solid tissue normal subjects were included. Three groups of machine learning (ML) algorithms were employed: (i) four feature selection procedures are employed and compared to select the most valuable feature: (1) ANOVA; (2) Mutual Information; (3) Extra Trees Classifier; and (4) Logistic Regression (LGR), (ii) a feature extraction algorithm (Principal Component Analysis), iii) we utilized 13 classification algorithms accompanied with automated ML hyperparameter tuning, including (1) LGR; (2) Support Vector Machine; (3) Bagging; (4) Gaussian Naive Bayes; (5) Decision Tree; (6) Gradient Boosting Decision Tree; (7) K Nearest Neighborhood; (8) Bernoulli Naive Bayes; (9) Random Forest; (10) AdaBoost, (11) ExtraTrees; (12) Linear Discriminant Analysis; and (13) Multilayer Perceptron (MLP). For evaluating the proposed models' performance, balance accuracy and area under the curve (AUC) were used.

RESULTS

Feature selection procedure LGR + MLP classifier achieved the highest prediction accuracy and AUC (balanced accuracy: 0.86, AUC = 0.94), followed by an LGR + LGR classifier (balanced accuracy: 0.84, AUC = 0.94). The results showed that achieved AUC for the LGR + LGR classifier belonged to the 20 biomarkers as follows: TMEM212, SNORD115-13, ATP1A4, FRG2, CFHR4, ZCCHC13, FLJ46361, LY6G6E, ZNF323, KRT28, KRT25, LPPR5, C10orf99, PRKACG, SULT2A1, GRIN2C, EN2, GBA2, CUX2, and SNORA66.

CONCLUSIONS

The best performance was achieved using the LGR feature selection procedure and MLP classifier. Results show that the 20 biomarkers had the highest score or ranking in breast cancer detection.

Machine Learning Algorithms for Prediction of Survival Curves in Breast Cancer Patients

Today, cancer is the second leading cause of death worldwide, and the number of people diagnosed with the disease is expected to rise. Breast cancer is the most commonly diagnosed cancer in women, and it has one of the highest survival rates when treated properly. Because the effectiveness and, as a result, survival of the patient are dependent on each case, it is critical to know the modelling of their survival ahead of time. Artificial intelligence is a rapidly expanding field, and its clinical applications are following suit (having surpassed humans in many evidence-based medical tasks). From the inception of since first stable risk estimator based on statistical methods appeared in survival analysis, there have been numerous versions of it created, with machine learning being used in only a few of them. Nonlinear relationships between variables and the impact they have on the variable to be predicted are very easy to evaluate using statistical methods. However, because they are just mathematical equations, they have flaws that limit the quality of their output. The main goal of this study is to find the best machine learning algorithms for predicting the individualised survival of breast cancer patients, as well as the most appropriate treatment, and to propose new numerical variable stratifications. They will still be carried out using unsupervised machine learning methods that divide patients into groups based on their risk in each dataset. We will compare it to standard groupings to see if it has more significance. Knowing that the greatest challenge in dealing with clinical data is its quantity and quality, we have gone to great lengths to ensure their quality before replicating them. We used the Cox statistical method in conjunction with other statistical methods and tests to find the best possible dataset with which to train our model, despite its ease of multivariate analysis.