Multi-omics integration with weighted affinity and self-diffusion applied for cancer subtypes identification

With the advent of high throughput genome-wide assays it has become possible to simultaneously measure multiple types of genomic data. Several projects like TCGA, ICGC, NCI-60 has generated comprehensive, multi-dimensional maps of the key genomic changes like MiRNA, MRNA, proteomics etc. from cancer samples[2,4]. These genomic data can be used for classifying tumour types[5]. Integrative analysis of these data from multiple sources can potentially provide additional biological insights, but methods to do any such analysis are lacking. One of the widely used solutions to handle high dimension data is by removing redundant information in the integrated sample. Most of the expressed genes are overlapped and can be projected onto lower dimension, and then be used to classify different tumor types, without the loss of any/much information. Sufficient dimension reduction (SDR) [1], a supervised dimension reduction approach, can be ideal to achieve such a goal. In this paper, we propose a novel integrative SDR method that can reduce dimensions of multiple data types simultaneously while sharing common latent structures to improve prediction and interpretation. In particular, we extend the sliced inverse regression (SIR) technique, a major SDR method, to integrate multiple omits data for simultaneous dimension reduction. SIR is a supervised dimension reduction method that assumes that the outcome variable Y depends on the predictor variable X through d unknown linear combinations of the predictor[3]. The predictor variable is replaced by its projection into a lower dimension subspace of the predictor space without the loss of information. The aim is to find the intersection of all the subspaces δ called the central susbspace (CS) of the predictor space satisfying the property Y ╨ X| Pδ X. To integrate multiple types of data, we propose and implement a new integrative sufficient dimension reduction method extending SIR[3], called integrative SIR. The main idea is that we take into account all the multi-omics data information simultaneously while finding a basis matrix for each data type with some sharing latent structures. Finally, we get d dimension data which is much smaller than the original data dimension. The reduced dimension d was achieved by cross validation. To demonstrate the integrated analysis of multi-omics data, we applied and compared conventional SIR and integrative SIR to analyze MRNA, MiRNA and proteomics expression profile of a subset of cell lines from the NCI-60 panel. The data used is taken from [6]. The outcomes we have to classify are CNS, Leukemia and Melanoma tumor types. We pre-screened 400 variables from each data type with the criteria of high variance. To find classification error, we performed random forest classification after we applied to each method with leave-one-out cross-validation. As a result, we found out that integrative SIR leads to less classification error as compared to conventional SIR. To summarize, we proposed a new integrative SIR method, a supervised dimension reduction technique for integrative analysis of multi-omics data types. Unlike conventional SDR methods, the new approach can reduce the dimensions of multiple omics data simultaneously while sharing common latent structures across data types without losing any information in prediction. By efficiently capturing the common information, our numerical study shows that integrative SIR classifies tumor types more accurately as compared to conventional SDR methods.

During the past two decades, computational analysis has become paramount for biological research. Advancements in high-throughput methods and computational tools resulted in the generation of large amounts of data from different omics fields (multi-omics), such as genomics, epigenomics, transcriptomics, and metabolomics. This plethora of large scale and diverse omics data is being driven by the understanding that a single-omic type does not provide adequate information and integrative analysis of multi-omics data is optimal to gain sufficiently meaningful insights into the actual biological mechanisms. Although various open-source tools have been developed for this purpose, multi-omics data integration and analysis are still beset by a number of problems, including software compatibility, complex parameter selection and creation of functional pipelines with multiple steps of analyses. In this work we present GenOptics, a novel visual analytics platform that aims to facilitate the integration and subsequent analysis of diverse multi-omics datasets as well as meta-data (e.g., clinical data), through a fully interactive environment. The platform comprises of two separate parts. The first incorporates asynchronous analyses of Next-Generation Sequencing raw data, including RNA-, Whole exome-, and ChIP-Seq, using workflows implemented with the Common Workflow Language (doi:10.6084/m9.figshare.3115156.v2) and Docker containers to automate software installation and confer cross-platform portability. The second part constitutes the analytical platform itself, designed to facilitate the execution of robust bioinformatics analyses by life scientists with minimal or no knowledge of programming. GenOptics constitutes an open source (https://genoptics.github.io/), computational biology platform for novel pattern and biomarker detection.

Colorectal cancer (CRC) exhibits substantial heterogeneity in molecular subtypes and clinical outcomes. We performed an integrative analysis of multi-omics data from 274 CRC patients to investigate the impact of gut microbiota composition on prognosis, identify novel subtypes, and develop a machine learning-based prognostic model. Our microbiome analysis revealed significant differences between CRC and normal tissues. Multi-omics clustering identified two major CRC subtypes, CS1 and CS2, with distinct molecular characteristics and survival outcomes. We developed the Multi-Omics Integrative Clustering and Machine Learning Score (MCMLS) model, which demonstrated strong prognostic value in predicting patient survival and outperformed existing models. The MCMLS low-score group exhibited higher immune cell infiltration, increased metabolic pathway activity, and potentially better immunotherapy response. In contrast, the MCMLS high-score group showed higher mutation burden, fibroblast infiltration, and enrichment of cell adhesion and migration pathways. Bacterial analysis revealed differentially abundant bacteria associated with prognosis. Importantly, MCMLS consistently predicted immunotherapy response across six independent datasets. Our findings highlight the complex interplay between the gut microbiome, tumor microenvironment, and immune landscape in CRC, providing valuable insights for improving patient stratification and personalized treatment strategies.

The complexity of biological systems and the limitations of analyzing individual omics studies for biomarker discovery have raised the need for a holistic approach by multi-omics integration. By integrating data from multiple layers, researchers can gain insights into the entire system rather than just individual components. Also, integrative analysis can help identify molecular signatures that are more accurate in predicting disease onset, progression, and response to treatment, leading to better-targeted therapies and personalized medicine. In this paper, we explored statistical and deep learning methods for integrative analysis of metabolomics, lipidomics, peptidomics, proteomics, and glycoproteomics data acquired by LC-MS/MS analysis of serum samples from 20 hepatocellular carcinoma (HCC) cases and 20 patients with liver cirrhosis (CIRR). The goal is to identify a panel of multi-omics features that distinguish HCC cases from cirrhotic controls. A pathway analysis using these features identified biological pathways such as LXR/RXR Activation and Acute Response signaling as significantly enriched in our multi-omics datasets.

With the development and improvement of high-throughput sequencing technologies, the acquisition and processing approaches of various biological omics data on different levels are becoming more mature. Despite several new disease-associated factors have been discovered based on single omics data analysis, identification of disease targets by integrative analysis of multi-omics data is still growing. Since life is a complex regulatory system in which the regulation of gene mutations, epigenetic alterations, abnormal gene expression as well as anomalous variations in signal pathway are related with the occurrence and development of diseases, it is obvious that finding therapeutic factors using single omics data analysis has its limitation. Systematical studies of clinical and pathological mechanisms and identification of optimal therapeutic targets through integrative analysis of multi-omics data from different levels and resources have become an important research direction of precision medicine, which would provide innovative perspectives on disease study and new theoretical basis for early diagnosis, personalized treatment and medicine guide. In this review, we introduce new technologies and research progresses in screening therapeutic targets using systematic omics such as genomics, transcriptomics and epigenomics, and also discusse new strategies and advantages of integrative analysis among them.

Pharmacogenomics is the study of how genes affect a person's response to drugs. Thus, understanding the effect of drug at the molecular level can be helpful in both drug discovery and personalized medicine. Over the years, transcriptome data upon drug treatment has been collected and several databases compiled before drug treatment cancer cell multi-omics data with drug sensitivity (IC50, AUC) or time-series transcriptomic data after drug treatment. However, analyzing transcriptome data upon drug treatment is challenging since more than 20,000 genes interact in complex ways. In addition, due to the difficulty of both time-series analysis and multi-omics integration, current methods can hardly perform analysis of databases with different data characteristics. One effective way is to interpret transcriptome data in terms of well-characterized biological pathways. Another way is to leverage state-of-the-art methods for multi-omics data integration. In this paper, we developed Drug Response analysis Integrating Multi-omics and time-series data (DRIM), an integrative multi-omics and time-series data analysis framework that identifies perturbed sub-pathways and regulation mechanisms upon drug treatment. The system takes drug name and cell line identification numbers or user's drug control/treat time-series gene expression data as input. Then, analysis of multi-omics data upon drug treatment is performed in two perspectives. For the multi-omics perspective analysis, IC50-related multi-omics potential mediator genes are determined by embedding multi-omics data to gene-centric vector space using a tensor decomposition method and an autoencoder deep learning model. Then, perturbed pathway analysis of potential mediator genes is performed. For the time-series perspective analysis, time-varying perturbed sub-pathways upon drug treatment are constructed. Additionally, a network involving transcription factors (TFs), multi-omics potential mediator genes, and perturbed sub-pathways is constructed, and paths to perturbed pathways from TFs are determined by an influence maximization method. To demonstrate the utility of our system, we provide analysis results of sub-pathway regulatory mechanisms in breast cancer cell lines of different drug sensitivity. DRIM is available at: http://biohealth.snu.ac.kr/software/DRIM/.

In this study we compared patient and technique survival of 163 new hemodialysis (HD) patients (age 11.4+/-3.1 years) and 295 peritoneal dialysis patients (7.7+/-4.8 years. P< 0.001), treated in 23 dialysis centers participating in the Italian Registry of Pediatric Chronic Peritoneal Dialysis (CPD) during the years 1989-2000. Three HD (1.8%) and 17 CPD (5.8%) patients died; the overall average death rate was 9.8/1,000 patient-years in HD and 29.8/1,000 patient-years in CPD patients. No statistically significant difference in patient survival between CPD and HD was found, while the survival of 102 CPD children younger than 5 years at the start of dialysis was lower ( P=0.0001) than that of 193 CPD and 160 HD patients aged 5-15 years. We registered 12 modality failures among HD (7.4%) patients and 44 among CPD (14.9%) patients. The main causes were vascular access failure and patient choice in HD, and infection in CPD patients. Technique survival was lower ( P=0.007) in CPD than in HD patients; a statistically significant difference ( P=0.01) was also observed between both the 0- to 5- and the 5- to 15-year-old CPD patients and the HD patients aged 5-15 years. Logistic regression analysis confirmed age at initiation of dialysis to be a predictor of patient death ( P=0.0001) in the whole patient population, and of technique failure in HD ( P=0.006) but not in CPD patients ( P=0.16).

Four hundred and eighty CAPD and 373 HD patients started regular dialysis treatment between 1981 and 1987 in 6 dialysis centers. The CAPD patients were 6 years older, on average, than the HD patients and had more complicating conditions (43.3% with 3 or more coexisting risk factors versus 28.9% with coexisting complications). The 7-year patient survival rate was not significantly different. Cox's proportional hazards regression showed that age, cardiovascular disease, cerebrovascular disease, peripheral vascular disease, diabetes, malignancy and multisystem disease had significant adverse effects on patient survival. After correcting for the influence of these factors, no significant differences in patient survival were seen. However, after 53.5 years of age, the increase in the risk of death was significantly higher in HD than in CAPD patients. Technique survival was significantly different in the 6 centers and was better for HD than for CAPD. There was no statistically significant difference between CAPD and HD technique survival when peritonitis was eliminated as a cause of failure. Based on this 7 year analysis, CAPD would appear to be an excellent alternative to HD.

Background: Rare endocrine cancers such as Adrenocortical Carcinoma (ACC) present a serious diagnostic and prognostication challenge. The knowledge about ACC pathogenesis is incomplete, and patients have limited therapeutic options. Identification of molecular drivers and effective biomarkers is required for timely diagnosis of the disease and stratify patients to offer the most beneficial treatments. In this study we demonstrate how machine learning methods integrating multi-omics data, in combination with system biology tools, can contribute to the identification of new prognostic biomarkers for ACC. Methods: ACC gene expression and DNA methylation datasets were downloaded from the Xena Browser (GDC TCGA Adrenocortical Carcinoma cohort). A highly correlated multi-omics signature discriminating groups of samples was identified with the data integration analysis for biomarker discovery using latent components (DIABLO) method. Additional regulators of the identified signature were discovered using Clarivate CBDD (Computational Biology for Drug Discovery) network propagation and hidden nodes algorithms on a curated network of molecular interactions (MetaBase™). The discriminative power of the multi-omics signature and their regulators was delineated by training a random forest classifier using 55 samples, by employing a 10-fold cross validation with five iterations. The prognostic value of the identified biomarkers was further assessed on an external ACC dataset obtained from GEO (GSE49280) using the Kaplan-Meier estimator method. An optimal prognostic signature was finally derived using the stepwise Akaike Information Criterion (AIC) that allowed categorization of samples into high and low-risk groups. Results: A multi-omics signature including genes, micro RNA's and methylation sites was generated. Systems biology tools identified additional genes regulating the features included in the multi-omics signature. RNA-seq, miRNA-seq and DNA methylation sets of features revealed a high power to classify patients from stages I-II and stages III-IV, outperforming previously identified prognostic biomarkers. Using an independent dataset, associations of the genes included in the signature with Overall Survival (OS) data demonstrated that patients with differential expression levels of 8 genes and 4 micro RNA's showed a statistically significant decrease in OS. We also found an independent prognostic signature for ACC with potential use in clinical practice, combining 9-gene/micro RNA features, that successfully predicted high-risk ACC cancer patients. Conclusion: Machine learning and integrative analysis of multi-omics data, in combination with Clarivate CBDD systems biology tools, identified a set of biomarkers with high prognostic value for ACC disease. Multi-omics data is a promising resource for the identification of drivers and new prognostic biomarkers in rare diseases that could be used in clinical practice.

Recent studies suggest a correlation between endoplasmic reticulum (ER) stress and gestational diabetes mellitus (GDM). Nevertheless, the potential causal role of ER stress-related genes remains largely unexplored. This study aims to generate hypotheses regarding these connections through an integrative analysis of multiomics data. We utilized an exploratory list of genes related to ER stress and integrated quantitative trait loci (QTL) for gene expression (eQTLs), DNA methylation (mQTLs), and protein levels (pQTLs). Genome-wide association studies (GWAS) summary statistics for GDM were obtained from the publicly accessible FinnGen database, with replication attempted using data from the GWAS Catalog. The summary-data-based Mendelian Randomization (SMR) method was applied to explore the genetic ties between these genes and GDM, followed by colocalization analysis to pinpoint overlapping causal genetic variants. Placental endothelial transcriptome data (GSE103552) were used for validation. The SMR and colocalization identified potential causal links for 27 mQTLs, 8 eQTLs, and 4 pQTLs with GDM risk. Integration of evidence across mQTL and eQTL levels suggested potential causal roles for the NUP133, VHL, TAPBP, and GPX1 genes in GDM. Notably, NUP133 shows suggestive colocalization evidence at the eQTL level. Analysis relating methylation to expression suggested hypermethylation at the CpG site cg17439967 may upregulate NUP133, potentially associating with reduced GDM risk. Transcriptomic validation in placental endothelial cells further showed differential expression of these four genes between GDM and controls. Our findings provide suggestive genetic evidence linking specific ER stress-related genes, particularly NUP133, with GDM risk, highlighting potential pathways that warrant further investigation.

The recent development of high-throughput technology has allowed us to accumulate vast amounts of multi-omics data. Because even single omics data have a large number of variables, integrated analysis of multi-omics data suffers from problems such as computational instability and variable redundancy. Most multi-omics data analyses apply single supervised analysis, repeatedly, for dimensional reduction and variable selection. However, these approaches cannot avoid the problems of redundancy and collinearity of variables. In this study, we propose a novel approach using blockwise component analysis. This would solve the limitations of current methods by applying variable clustering and sparse principal component (sPC) analysis. Our approach consists of two stages. The first stage identifies homogeneous variable blocks, and then extracts sPCs, for each omics dataset. The second stage merges sPCs from each omics dataset, and then constructs a prediction model. We also propose a graphical method showing the results of sparse PCA and model fitting, simultaneously. We applied the proposed methodology to glioblastoma multiforme data from The Cancer Genome Atlas. The comparison with other existing approaches showed that our proposed methodology is more easily interpretable than other approaches, and has comparable predictive power, with a much smaller number of variables.

High-throughput 'omic' data, such as gene expression, DNA methylation, DNA copy number, has played an instrumental role in furthering our understanding of the molecular basis in states of human health and disease. As cells with similar morphological characteristics can exhibit entirely different molecular profiles and because of the potential that these discrepancies might further our understanding of patient-level variability in clinical outcomes, there is significant interest in the use of high-throughput 'omic' data for the identification of novel molecular subtypes of a disease. While numerous clustering methods have been proposed for identifying of molecular subtypes, most were developed for single "omic' data types and may not be appropriate when more than one 'omic' data type are collected on study subjects. Given that complex diseases, such as cancer, arise as a result of genomic, epigenomic, transcriptomic, and proteomic alterations, integrative clustering methods for the simultaneous clustering of multiple 'omic' data types have great potential to aid in molecular subtype discovery. Traditionally, ad hoc manual data integration has been performed using the results obtained from the clustering of individual 'omic' data types on the same set of patient samples. However, such methods often result in inconsistent assignment of subjects to the molecular cancer subtypes. Recently, several methods have been proposed in the literature that offers a rigorous framework for the simultaneous integration of multiple 'omic' data types in a single comprehensive analysis. In this paper, we present a systematic review of existing integrative clustering methods.

ObjectiveDonor hearts that have undergone cardiopulmonary resuscitation (CPR) were previously considered marginal donors. We aimed to determine how donor CPR influences recipient outcomes (CPR+ vs CPR−). Additionally, we evaluated the overall use of CPR+ donor hearts in the modern era.MethodsAll single heart organ transplants in the United States from 2014 to 2023 were obtained for adults older than age 18 years. Survival curves were built for overall and graft survival using a Kaplan-Meier method with a log-rank test used to test for differences. Multivariate Cox-regression was performed on recipient survival with CPR status and other recipient variables as covariates.ResultsFrom 2014 to 2023, there were 13 994 CPR− donors and 15 601 CPR+ donors throughout the study period. There was no significant difference in patient survival at 30 days, 1 year, and 5 years (P = .09). Graft survival over 5 years was similar between subgroups of CPR ≤15 minutes, 16 to 30 minutes, or >30 minutes (P = .94). Use of heart transplants from donors resuscitated using CPR increased throughout the study, with CPR+ donors representing >50% of all donors since 2017.ConclusionsHeart donors who are CPR+ are being used at an increased frequency and now represent most donors. CPR+ hearts have statistically equivalent patient and graft survival outcomes over 5 years. Hearts that have undergone CPR, even for periods of >30 minutes, can be safely utilized.

895 Objective: To evaluate post transplant outcomes for patients treated with human growth hormone (rhGH) during the course of chronic renal insufficiency (CRI). Outcomes evaluated include patient survival, graft survival, incidence of acute rejection episodes (ARE), frequency of adverse events, and the occurrence of "catch down" growth. Study Design: One hundred and two patients (the "cohort" group) were identified who had been enrolled in two controlled studies to determine the efficacy and safety of rhGH in growth retarded children with CRI and (b) were subsequently enrolled in the North American Pediatric Renal Transplant Cooperative Study (NAPRTCS) and received a renal transplant. Patient survival, graft survival, time to first ARE, causes of graft failure, adverse events and serial growth data from transplant to 60 months were evaluated to address the following issues: Does rhGH treatment prior to transplantation influence patient survival, graft survival or the incidence of ARE following renal transplantation? Does rhGH prior to transplantation influence the incidence of adverse events, especially the development of de novo malignancy following renal transplantation? Does "catch down" growth occur following discontinuation of rhGH at the time of transplantation in patients manifesting accelerated growth in conjunction with rhGH treatment during CRI? Data from the cohort group pf 102 patients were compared with data from 4913 primary transplants from "other NAPRTCS" recipients (the "control" group). Results: There was no significant difference in patient survival, graft survival, incidence of ARE or time to first rejection episode between the cohort and control groups. There were no specific adverse events attributable to prior rhGH treatment. Only 2 patients developed post-transplant lymphoproliferative disease (PTLD) in the cohort group with no other malignancies reported. Both recipients with PTLD were alive with functioning grafts at 7 and 53 months following the diagnosis of PTLD. The mean height Z-scores in the cohort group at baseline and 60 months post transplant were −1.92 and −1.90. The Δ Z-score at 60 months was +0.20. These results were slightly better than those described for the control group (−1.88 and −2.10). Conclusions: Treatment of growth retarded patients with CRI does not adversely effect graft function following renal transplantation. "Catch down" growth does not occur following renal transplantation.

Symptomatic cardiac involvement is the most important prognostic factor in AL amyloidosis patients. Longterm survival is limited not only by cardiac involvement condition, but also by limited choice of treatment with unsatisfactory results. The aim of the present report is to assess the effect of achieved treatment response on survival of AL amyloidosis patients with symptomatic cardiac involvement under conventional treatment. The monitored patient set consisted of 19 patients with systemic AL amyloidosis and symptomatic cardiac involvement, treated and monitored at the III. Clinic of Internal Medicine between 2004 and 2012. The male : female ratio was 17 : 2, and the age median was 64 (range 48 to 78 years). Thirteen patients died within the monitored period. Functional status was defined according to the NYHA classification, where five patients had class II involvement, 10 patients had class III involvement, and four patients had class IV involvement. Treatment response was assessed by the application of modified IMWG and ISA criteria; all patients were undergoing conventional treatment. Nine patients were treated by a combination of alkylating agents (alkeran, cyclophosphamide), six were treated by a combination treatment with thalidomide, and four were treated by a combination of bortezomib and dexamethasone. Data were analyzed with software SPSS v. 15 (SPSS, Inc., Chicago, USA). Log Rank Test was applied to survival evaluation. The statistical analysis included only 13 patients who underwent at least three months of treatment, where six patients attained complete remission (CR), four patients attained partial remission (PR), and three patients attained only stabilization of disease (SD). Significant difference in patient survival was found to be correlated with attained hematological response, where the patients who attained CR had median survival of 39 months vs 10 months in patients who attained PR or SD (p = 0.005). The results indicate that attainment of complete hematological remission is associated with significantly longer survival of AL amyloidosis patients with symptomatic cardiac involvement.