Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis.

BACKGROUND: Finding correlation patterns is an important goal of analyzing biological data. Currently available methods for correlation analysis mainly use non-direct associations, such as the Pearson correlation coefficient, and focus on the interpretation of networks at the level of modules. For biological objects such as genes, their collective function depends on pairwise gene-to-gene interactions. However, a large amount of redundant results from module level methods often necessitate further detailed analysis of gene interactions. New approaches of measuring direct associations among variables, such as the part mutual information (PMI), may help us better interpret the correlation pattern of biological data at the level of variable pairs. RESULTS: We use PMI to calculate gene co-expression networks of cancer mRNA transcriptome data. Our results show that the PMI-based networks with fewer edges could represent the correlation pattern and are robust across biological conditions. The PMI-based networks recall significantly more important parts of omics defined gene-pair relationships than the Pearson Correlation Coefficient (PCC)-based networks. Based on the scores derived from PMI-recalled copy number variation or DNA methylation gene-pairs, the patients with cancer can be divided into groups with significant differences on disease specific survival. CONCLUSIONS: PMI, measuring direct associations between variables, extracts more important biological relationships at the level of gene pairs than conventional indirect association measures do. It can be used to refine module level results from other correlation methods. Particularly, PMI is beneficial to analysis of biological data of the complicated systems, for example, cancer transcriptome data.

Advancing Pan-cancer Gene Expression Survial Analysis by Inclusion of Non-coding RNA.

Non-coding RNAs occupy a significant fraction of the human genome. Their biological significance is backed up by a plethora of emerging evidence. One of the most robust approaches to demonstrate non-coding RNA's biological relevance is through their prognostic value. Using the rich gene expression data from The Cancer Genome Altas (TCGA), we designed Advanced Expression Survival Analysis (AESA), a web tool which provides several novel survival analysis approaches not offered by previous tools. In addition to the common single-gene approach, AESA computes the gene expression composite score of a set of genes for survival analysis and utilizes permutation test or cross-validation to assess the significance of log-rank statistic and the degree of over-fitting. AESA offers survival feature selection with post-selection inference and utilizes expanded TCGA clinical data including overall, disease-specific, disease-free, and progression-free survival information. Users can analyse either protein-coding or non-coding regions of the transcriptome. We demonstrated the effectiveness of AESA using several empirical examples. Our analyses showed that non-coding RNAs perform as well as messenger RNAs in predicting survival of cancer patients. These results reinforce the potential prognostic value of non-coding RNAs. AESA is developed as a module in the freely accessible analysis suite MutEx. Abbreviation: ACC: Adrenocortical Carcinoma (n = 92); BLCA: Bladder Urothelial Carcinoma (n = 412); BRCA: Breast Invasive Carcinoma (n = 1098); CESC: Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (n = 307); CHOL: Cholangiocarcinoma (n = 51); COAD: Colon Adenocarcinoma (n = 461); DLBC: Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (n = 58); ESCA: Oesophageal Carcinoma (n = 185); GBM: Glioblastoma Multiforme (n = 617); HNSC: Head and Neck Squamous Cell Carcinoma (n = 528); KICH: Kidney Chromophobe (n = 113); KIRC: Kidney Renal Clear Cell Carcinoma (n = 537); KIRP: Kidney Renal Papillary Cell Carcinoma (n = 291); LAML: Acute Myeloid Leukaemia (n = 200); LGG: Brain Lower Grade Glioma (n = 516); LIHC: Liver Hepatocellular Carcinoma (n = 377); LUAD: Lung Adenocarcinoma (n = 585); LUSC: Lung Squamous Cell Carcinoma (n = 504); MESO: Mesothelioma (n = 87); OV: Ovarian Serous Cystadenocarcinoma (n = 608) PAAD: Pancreatic Adenocarcinoma (n = 185); PCPG: Pheochromocytoma and Paraganglioma (n = 179); PRAD: Prostate Adenocarcinoma (n = 500); READ: Rectum Adenocarcinoma (n = 172); SARC: Sarcoma (n = 261); SKCM: Skin Cutaneous Melanoma (n = 470); STAD: Stomach Adenocarcinoma (n = 443); TGCT: Testicular Germ Cell Tumours (n = 150); THCA: Thyroid Carcinoma (n = 507) THYM: Thymoma (n = 124); UCEC: Uterine Corpus Endometrial Carcinoma (n = 560); UCS: Uterine Carcinosarcoma (n = 57); UVM: Uveal Melanoma (n = 80).

Smart Extraction and Analysis System for Clinical Research.

BACKGROUND: With the increasing use of electronic health records (EHRs), there is a growing need to expand the utilization of EHR data to support clinical research. The key challenge in achieving this goal is the unavailability of smart systems and methods to overcome the issue of data preparation, structuring, and sharing for smooth clinical research. MATERIALS AND METHODS: We developed a robust analysis system called the smart extraction and analysis system (SEAS) that consists of two subsystems: (1) the information extraction system (IES), for extracting information from clinical documents, and (2) the survival analysis system (SAS), for a descriptive and predictive analysis to compile the survival statistics and predict the future chance of survivability. The IES subsystem is based on a novel permutation-based pattern recognition method that extracts information from unstructured clinical documents. Similarly, the SAS subsystem is based on a classification and regression tree (CART)-based prediction model for survival analysis. RESULTS: SEAS is evaluated and validated on a real-world case study of head and neck cancer. The overall information extraction accuracy of the system for semistructured text is recorded at 99%, while that for unstructured text is 97%. Furthermore, the automated, unstructured information extraction has reduced the average time spent on manual data entry by 75%, without compromising the accuracy of the system. Moreover, around 88% of patients are found in a terminal or dead state for the highest clinical stage of disease (level IV). Similarly, there is an ∼36% probability of a patient being alive if at least one of the lifestyle risk factors was positive. CONCLUSION: We presented our work on the development of SEAS to replace costly and time-consuming manual methods with smart automatic extraction of information and survival prediction methods. SEAS has reduced the time and energy of human resources spent unnecessarily on manual tasks.

Dual-stream multi-dependency graph neural network enables precise cancer survival analysis.

Histopathology image-based survival prediction aims to provide a precise assessment of cancer prognosis and can inform personalized treatment decision-making in order to improve patient outcomes. However, existing methods cannot automatically model the complex correlations between numerous morphologically diverse patches in each whole slide image (WSI), thereby preventing them from achieving a more profound understanding and inference of the patient status. To address this, here we propose a novel deep learning framework, termed dual-stream multi-dependency graph neural network (DM-GNN), to enable precise cancer patient survival analysis. Specifically, DM-GNN is structured with the feature updating and global analysis branches to better model each WSI as two graphs based on morphological affinity and global co-activating dependencies. As these two dependencies depict each WSI from distinct but complementary perspectives, the two designed branches of DM-GNN can jointly achieve the multi-view modeling of complex correlations between the patches. Moreover, DM-GNN is also capable of boosting the utilization of dependency information during graph construction by introducing the affinity-guided attention recalibration module as the readout function. This novel module offers increased robustness against feature perturbation, thereby ensuring more reliable and stable predictions. Extensive benchmarking experiments on five TCGA datasets demonstrate that DM-GNN outperforms other state-of-the-art methods and offers interpretable prediction insights based on the morphological depiction of high-attention patches. Overall, DM-GNN represents a powerful and auxiliary tool for personalized cancer prognosis from histopathology images and has great potential to assist clinicians in making personalized treatment decisions and improving patient outcomes.

From Pixels to Prognosis: A Survey on AI-Driven Cancer Patient Survival Prediction Using Digital Histology Images.

Survival analysis is an integral part of medical statistics that is extensively utilized to establish prognostic indices for mortality or disease recurrence, assess treatment efficacy, and tailor effective treatment plans. The identification of prognostic biomarkers capable of predicting patient survival is a primary objective in the field of cancer research. With the recent integration of digital histology images into routine clinical practice, a plethora of Artificial Intelligence (AI)-based methods for digital pathology has emerged in scholarly literature, facilitating patient survival prediction. These methods have demonstrated remarkable proficiency in analyzing and interpreting whole slide images, yielding results comparable to those of expert pathologists. The complexity of AI-driven techniques is magnified by the distinctive characteristics of digital histology images, including their gigapixel size and diverse tissue appearances. Consequently, advanced patch-based methods are employed to effectively extract features that correlate with patient survival. These computational methods significantly enhance survival prediction accuracy and augment prognostic capabilities in cancer patients. The review discusses the methodologies employed in the literature, their performance metrics, ongoing challenges, and potential solutions for future advancements. This paper explains survival analysis and feature extraction methods for analyzing cancer patients. It also compiles essential acronyms related to cancer precision medicine. Furthermore, it is noteworthy that this is the inaugural review paper in the field. The target audience for this interdisciplinary review comprises AI practitioners, medical statisticians, and progressive oncologists who are enthusiastic about translating AI-driven solutions into clinical practice. We expect this comprehensive review article to guide future research directions in the field of cancer research.

Survival analysis of cancer patients in Portugal following the reference centre model implementation.

Cancer has affected around eighteen million people all over the world in 2018. In Portugal, cancer was diagnosed in sixty thousand individuals during 2018, being the second leading cause of death (one in every four deaths). Following the European Directive 2011/24/EU, the Portuguese Health System has been recognizing oncology Reference Centres (RCs), which are focused on delivering best-in-class treatment for cancer patients. This paper performs a survival analysis of cancer patients in Portugal, having hospital episodes with discharge date after the official recognition, in 2016, of the first RCs for hepatobiliary, pancreatic, sarcomas and oesophageal cancer. The aim is to assess the impact of RCs on the survival probability of these patients. For each cancer type, survival curves are estimated using the Kaplan-Meier methodology, and hazard ratios are estimated for different covariates, using multivariate Extended Cox models. The results obtained support the implementation and encourage the further extension of the RC model for oncology in Portugal, as cancer patients treated in an oncology RC, overall, have a better survival probability when compared to patients who had no episode in an RC. These results are clearer for hepatobiliary and pancreatic cancer, but also visible for sarcomas and oesophageal cancer.

A systematic review on machine learning and deep learning techniques in cancer survival prediction.

Cancer is a disease which is characterised by the unusual and uncontrollable growth of body cells. This usually happens asymptomatically and gets spread to other parts of the body. The major problem in treating cancer is that its progress is not monitored once it is diagnosed. The progress or the prognosis can be done through survival analysis. The survival analysis is the branch of statistics that deals in predicting the time of event of occurrence. In the case of cancer prognosis the event is the survival time of the patient from the onset of the disease or it can be the recurrence of the disease after undergoing a treatment. This study aims to bring out the machine learning and deep learning models involved in providing the prognosis to the cancer patients.

Body mass index and type 2 diabetes and breast cancer survival: a Mendelian randomization study.

The causal relationship between body mass index (BMI) and type 2 diabetes (T2D) and breast cancer prognosis is still ambiguous. The aim of this study was to investigate the prognostic effect of BMI and T2D on breast cancer disease-free survival (DFS) among Asian individuals. In this two-sample Mendelian randomization (MR) study, the instrumental variables (IVs) were identified using a genome-wide association study (GWAS) among 24,000 participants in the Taiwan Biobank. Importantly, the validity of these IVs was confirmed with a previous large-scale GWAS (Biobank Japan Project, BBJ). In this study, we found that a genetic predisposition toward higher BMI (as indicated by BMI IVs, F = 86.88) was associated with poor breast cancer DFS (hazard ratio [HR] = 6.11; P < 0.001). Furthermore, higher level of genetically predicted T2D (as indicated by T2D IVs) was associated with an increased risk of recurrence of and mortality from breast cancer (HR = 1.43; P < 0.001). Sensitivity analyses, including the weighted-median approach, MR-Egger regression, Radial regression and Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) supported the consistency of our findings. Finally, the causal relationship between BMI and poor breast cancer prognosis was confirmed in a prospective cohort study. Our MR analyses demonstrated the causal relationship between the genetic prediction of elevated BMI and a greater risk of T2D with poor breast cancer prognosis. BMI and T2D have important clinical implications and may be used as prognostic indicators of breast cancer.

An Integrated Deep Network for Cancer Survival Prediction Using Omics Data.

As a highly sophisticated disease that humanity faces, cancer is known to be associated with dysregulation of cellular mechanisms in different levels, which demands novel paradigms to capture informative features from different omics modalities in an integrated way. Successful stratification of patients with respect to their molecular profiles is a key step in precision medicine and in tailoring personalized treatment for critically ill patients. In this article, we use an integrated deep belief network to differentiate high-risk cancer patients from the low-risk ones in terms of the overall survival. Our study analyzes RNA, miRNA, and methylation molecular data modalities from both labeled and unlabeled samples to predict cancer survival and subsequently to provide risk stratification. To assess the robustness of our novel integrative analytics, we utilize datasets of three cancer types with 836 patients and show that our approach outperforms the most successful supervised and semi-supervised classification techniques applied to the same cancer prediction problems. In addition, despite the preconception that deep learning techniques require large size datasets for proper training, we have illustrated that our model can achieve better results for moderately sized cancer datasets.

System-Based Differential Gene Network Analysis for Characterizing a Sample-Specific Subnetwork.

Gene network estimation is a method key to understanding a fundamental cellular system from high throughput omics data. However, the existing gene network analysis relies on having a sufficient number of samples and is required to handle a huge number of nodes and estimated edges, which remain difficult to interpret, especially in discovering the clinically relevant portions of the network. Here, we propose a novel method to extract a biomedically significant subnetwork using a Bayesian network, a type of unsupervised machine learning method that can be used as an explainable and interpretable artificial intelligence algorithm. Our method quantifies sample specific networks using our proposed Edge Contribution value (ECv) based on the estimated system, which realizes condition-specific subnetwork extraction using a limited number of samples. We applied this method to the Epithelial-Mesenchymal Transition (EMT) data set that is related to the process of metastasis and thus prognosis in cancer biology. We established our method-driven EMT network representing putative gene interactions. Furthermore, we found that the sample-specific ECv patterns of this EMT network can characterize the survival of lung cancer patients. These results show that our method unveils the explainable network differences in biological and clinical features through artificial intelligence technology.

Population-Based Brain Tumor Survival Analysis via Spatial- and Temporal-Smoothing.

In cancer research, population-based survival analysis has played an important role. In this article, we conduct survival analysis on patients with brain tumors using the SEER (Surveillance, Epidemiology, and End Results) database from the NCI (National Cancer Institute). It has been recognized that cancer survival models have spatial and temporal variations which are caused by multiple factors, but such variations are usually not "abrupt" (that is, they should be smooth). As such, spatially and temporally pooling all data and analyzing each spatial/temporal point separately are either inappropriate or ineffective. In this article, we develop and implement a spatial- and temporal-smoothing technique, which can effectively accommodate spatial/temporal variations and realize information borrowing across spatial/temporal points. Simulation demonstrates effectiveness of the proposed approach in improving estimation. Data on a total of 123,571 patients with brain tumors diagnosed between 1911 and 2010 from 16 SEER sites is analyzed. Findings different from separate estimation and simple pooling are made. Overall, this study may provide a practically useful way for modeling the survival of brain tumor (and other cancers) using population data.

MethSurv: a web tool to perform multivariable survival analysis using DNA methylation data.

AIM: To develop a web tool for survival analysis based on CpG methylation patterns. MATERIALS & METHODS: We utilized methylome data from 'The Cancer Genome Atlas' and used the Cox proportional-hazards model to develop an interactive web interface for survival analysis. RESULTS: MethSurv enables survival analysis for a CpG located in or around the proximity of a query gene. For further mining, cluster analysis for a query gene to associate methylation patterns with clinical characteristics and browsing of top biomarkers for each cancer type are provided. MethSurv includes 7358 methylomes from 25 different human cancers. CONCLUSION: The MethSurv tool is a valuable platform for the researchers without programming skills to perform the initial assessment of methylation-based cancer biomarkers.