Cancer-related Datasets Research Articles

A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6

BackgroundBreast cancer is a significant public health issue worldwide, being the most prevalent cancer among women and a leading cause of death related to this disease. The molecular processes that propel breast cancer progression are not fully elucidated, highlighting the intricate nature of the underlying biology and its crucial impact on global health. The objective of this research was to perform bioinformatics analyses on breast cancer-related datasets to gain a comprehensive understanding of the molecular mechanisms at play and to identify key genes associated with the disease.MethodsThe toolkit analyses involve techniques such as differential gene expression analysis, Gene Set Enrichment Analysis (GSEA), Weighted Co-Expression Network Analysis (WGCNA), and Machine Learning algorithms. Furthermore, in vitro cell experiments have demonstrated the impact of HSPB6 on cell migration, proliferation, and apoptosis.ResultsThe study identified multiple genes that displayed differential expression in breast cancer, notably FHL1 and HSPB6. A machine learning model was developed in this study and specifically trained for breast cancer diagnosis using these genes, achieving high precision. Furthermore, analysis of immune cell infiltration revealed an enrichment of Tregs and M2 macrophages in the treated group, showcasing its significant impact on the tumor’s immunological context. A temporal analysis of breast cancer cells using single-cell RNA sequencing provided insights into cellular developmental trajectories and highlighted changes in expression patterns across key genes during disease progression. The upregulation of HSPB6 in MCF7 cells significantly inhibited both cell migration and proliferation abilities, suggesting that promoting HSPB6 expression could induce ferroptosis in breast cancer cells.ConclusionOur findings have identified compelling molecular targets and distinctive diagnostic markers for the clinical management of breast cancer. This data will serve as crucial guidance for further research in the field.

Characteristics of Oxidative Phosphorylation-Related Subtypes and Construction of a Prognostic Signature in Ovarian Cancer.

Ovarian cancer is associated with a high mortality rate. Oxidative Phosphorylation (OXPHOS) is an active metabolic pathway in cancer; nevertheless, its role in ovarian cancer continues to be ambiguous. Therefore, the objective of this study was to identify the prognostic value of OXPHOS-related genes and the immune landscape in ovarian cancer. We obtained public ovarian cancer-related datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and recognized OXPHOS-related genes from the GeneCards database and literature. Cox regression analyses were conducted to identify prognostic OXPHOS-related genes and develop a prognostic nomogram based on the OXPHOS score and clinicopathological features of patients. Functional enrichment analyses were employed to identify related processes. A 12-gene signature was identified to classify the ovarian cancer patients into high- and low-risk groups. The Immunophenoscore (IPS) was higher in the OXPHOS score-high group than in the OXPHOS score-low group, suggesting a better response to immune checkpoint inhibitors. Functional enrichment analyses unveiled that OXPHOS-related genes were considerably abundant in a series of immune processes. The calibration curves of the constructed prognostic nomograms at 1, 2, and 3 years exhibited strong concordance between the anticipated and observed survival probabilities of ovarian cancer patients. We have constructed a prognostic model containing 12 OXPHOS-related genes and demonstrated its strong predictive value in ovarian cancer patients. OXPHOS has been found to be closely linked to immune infiltration and the reaction to immunotherapy, which may contribute to improving individualized treatment and prognostic evaluation in ovarian cancer.

A hybrid machine learning feature selection model-HMLFSM to enhance gene classification applied to multiple colon cancers dataset.

Colon cancer is a significant global health problem, and early detection is critical for improving survival rates. Traditional detection methods, such as colonoscopies, can be invasive and uncomfortable for patients. Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history. One approach is to use ML to analyse genetic data, or patient demographics and medical history, to predict the likelihood of colon cancer. However, due to the challenges imposed by variable gene expression and the high dimensionality of cancer-related datasets, traditional transductive ML applications have limited accuracy and risk overfitting. In this paper, we propose a new hybrid feature selection model called HMLFSM-Hybrid Machine Learning Feature Selection Model to improve colon cancer gene classification. We developed a multifilter hybrid model including a two-phase feature selection approach, combining Information Gain (IG) and Genetic Algorithms (GA), and minimum Redundancy Maximum Relevance (mRMR) coupling with Particle Swarm Optimization (PSO). We critically tested our model on three colon cancer genetic datasets and found that the new framework outperformed other models with significant accuracy improvements (95%, ~97%, and ~94% accuracies for datasets 1, 2, and 3 respectively). The results show that our approach improves the classification accuracy of colon cancer detection by highlighting important and relevant genes, eliminating irrelevant ones, and revealing the genes that have a direct influence on the classification process. For colon cancer gene analysis, and along with our experiments and literature review, we found that selective input feature extraction prior to feature selection is essential for improving predictive performance.

Which type of dynamic indicators should be preferred to predict patent commercial potential?

The current patent value evaluations increasingly focus on serving realistic predictive scenarios, emphasizing the commercial potential of patents at the early stage from the ex-ante perspective. This requirement poses a serious challenge: those classical dynamic indicators that have been proved to be effective in the literature may not be valid for commercial patent potential prediction from the ex-ante perspective. Thereupon, this study groups the dynamic indicators into cross-sectional indicators and longitudinal indicators. Then, a patent commercial potential prediction framework is proposed from the ex-ante perspective, in which the impact of the chronological order on predictive models is investigated comprehensively. More specifically, this study collects the USPTO cancer-related dataset from 2003 to 2013 as the training set, and combines three dynamic indicators (cross-sectional, longitudinal, and mixed) with classical static indicators to test the prediction performance for the following five years (2014–2018). The biased results caused by the ex-post perspective are indeed observed, and the longitudinal indicators are more sensitive to commercial patent potential, especially in the early stage. The effect of the ex-post perspective will gradually weaken over time, and the cross-sectional indicators provide stable prediction performance three years later. These findings will be helpful for subsequent improvements of commercial patent potential prediction models.

More than Meets the Eye: Integration of Radiomics with Transcriptomics for Reconstructing the Tumor Microenvironment and Predicting Response to Therapy

For over a decade, large cancer-related datasets (big data) have continuously been produced and made publicly available to the scientific community [...].

Identification of Potential Hub Genes Related to Aflatoxin B1, Liver Fibrosis and Hepatocellular Carcinoma via Integrated Bioinformatics Analysis

The molecular mechanism of the hepatotoxicant aflatoxin B1 to induce liver fibrosis and hepatocellular carcinoma (HCC) remains unclear, to offer fresh perspectives on the molecular mechanisms underlying the onset and progression of AFB1-Fibrosis-HCC, which may offer novel targets for the detection and therapy of HCC caused by AFB1. In this study, expression profiles of AFB1, liver fibrosis and liver cancer-related datasets were downloaded from the Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) were identified by the GEO2R tool. The STRING database, CytoHubba, and Cytoscape software were used to create the protein-protein interaction and hub genes of the combined genes, and the ssGSEA score for inflammatory cells related gene sets, the signaling pathway, and immunotherapy were identified using R software and the GSEA database. The findings revealed that AFB1-associated liver fibrosis and HCC combined genes were linked to cell process disruptions, the BUB1B and RRM2 genes were identified as hub genes, and the BUB1B gene was significantly increased in JAK-STAT signaling gene sets pathways as well as having an immunotherapy-related impact. In conclusion, BUB1B and RRM2 were identified as potential biomarkers for AFB1-induced fibrosis and HCC progression.

Reporting of Surgically Removed Lymph Nodes for Breast Tumors: Recommendations From the International Collaboration on Cancer Reporting.

The International Collaboration on Cancer Reporting (ICCR), supported by major pathology and cancer organizations, aims at the standardization of evidence-based pathology reporting of different types of cancers, with the inclusion of all parameters deemed to be relevant for best patient care and future data collection. Lymph node metastasis is one of the most important prognostic factors in breast cancer. To produce a histopathology reporting guide by a panel of recognized experts from the fields of pathology and surgery with elements deemed to be core (required) and noncore (recommended) to report when assessing regional lymph nodes of patients with breast cancer. Published literature, previous guidelines/recommendations, and current cancer staging principles were the basis of the data set drafted by the expert panel. This was discussed in a series of teleconferences and email communications. The draft data set was then made available for public consultation through the ICCR Web site. After this consultation and ICCR ratification, the data set was finalized. The ICCR has published a data set for the reporting of surgically removed lymph nodes (including sentinel lymph node biopsy, axillary lymph node dissection, targeted axillary surgery, and lymph node sampling specimens) for breast tumors. This is part of a series of 4 ICCR breast cancer-related data sets. It includes 10 core elements along with 2 noncore elements. This should allow for synoptic reporting, which is more precise, uniform, and complete than nonsynoptic reporting, and leads to improved patient outcomes.

CBNplot: Bayesian network plots for enrichment analysis.

SummaryWhen investigating gene expression profiles, determining important directed edges between genes can provide valuable insights in addition to identifying differentially expressed genes. In the subsequent functional enrichment analysis (EA), understanding how enriched pathways or genes in the pathway interact with one another can help infer the gene regulatory network (GRN), important for studying the underlying molecular mechanisms. However, packages for easy inference of the GRN based on EA are scarce. Here, we developed an R package, CBNplot, which infers the Bayesian network (BN) from gene expression data, explicitly utilizing EA results obtained from curated biological pathway databases. The core features include convenient wrapping for structure learning, visualization of the BN from EA results, comparison with reference networks, and reflection of gene-related information on the plot. As an example, we demonstrate the analysis of bladder cancer-related datasets using CBNplot, including probabilistic reasoning, which is a unique aspect of BN analysis. We display the transformability of results obtained from one dataset to another, the validity of the analysis as assessed using established knowledge and literature, and the possibility of facilitating knowledge discovery from gene expression datasets.Availability and implementationThe library, documentation and web server are available at https://github.com/noriakis/CBNplot.Supplementary information Supplementary data are available at Bioinformatics online.

Investigation of the effects of the toll-like receptor 4 pathway on immune checkpoint vista in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most common malignant tumors of the pancreas. Preclinical studies show that it evades the immune system with immune checkpoints and promotes tumor development. V-domain Ig suppressor of T cell activation (VISTA) is a new immune-check point from the B7 family and is highly expressed in cancer cells. Overexpression of toll like receptor 4 (TLR4) in pancreatic adenocarcinoma is associated with induced tumorigenesis, tumor growth, resistancy to chemotherapy. Naloxone is an opioid and inhibits TLR4-ligand association. In this study, we investigated the relation of TLR4 and downstream pathways with immune-check point VISTA in pancreatic cancer proliferation. We initially collected pancreatic cancer-related datasets using the GEPIA2 and UALCAN databases. Based on this data obtained the effect of various concentrations and incubation times of naloxone were used on PANC-1 cells proliferation. A combination of naloxone and VISTA-siRNA were applied, and the effect of both naloxone and combined treatment on TLR4, Interleukin 1 receptor associated kinase 4 (IRAK4) and VISTA gene expression were analyzed in pancreatic cancer cells. As a result of analysis with Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR), gene expression levels of TLR4, IRAK4 and VISTA were significantly suppressed and cell proliferation was significantly reduced. We found that administration of naloxone and VISTA-siRNA in combination with PDAC cells suppressed signaling. Therefore, we considered that the relationship between VISTA and TLR4 signaling pathways and the other possible associated signal molecules may be an important marker in determining the response of immune checkpoint inhibitors in cancer treatment.

Tumor purity as a prognosis and immunotherapy relevant feature in cervical cancer.

Background: Tumor purity plays a vital role in the biological process of solid tumors, but its function in gynecologic cancers remains unclear. This study explored the correlation between tumor purity and immune function of gynecological cancers and its reliability as a prognostic indicator of immunotherapy.Methods: Gynecological cancer-related datasets were downloaded from The Cancer Genome Atlas (TCGA). Tumor purity was calculated by the ESTIMATE algorithm. A LASSO Cox regression analysis was performed to construct the risk score model. A Kaplan–Meier Plotter was used to explore the relationships between tumor purity and cancer prognosis. We performed the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) to explore the pathways in the subgroups. A nomogram was used to quantitatively assess the cancer prognosis.Results: Tumor purity was negatively correlated with B cell infiltration in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). Approximately 420 genes were positively associated with B cell infiltration and CESC prognosis and were enriched in immune-related signaling pathways. There were 11 key genes used to construct a risk score model. The low-risk group had a higher immune score and better prognosis than the high-risk group. A nomogram based on risk score, T stage, and clinical-stage had good predictive value in quantitatively evaluating CESC prognosis.Conclusions: This study is the first to reveal the correlation between tumor purity and immunity in CESC and suggests that low-risk patients may be more sensitive to immunotherapy. This provides a theoretical basis for the clinical treatment of CESC.

Screening and Prognostic Analysis of Immune-Related Genes in Pancreatic Cancer.

Pancreatic cancer remains to have a high mortality, which is partly due to the lack of effective treatment strategies. In this study, genes with potential associations with immunophenotyping of pancreatic cancer were screened through bioinformatics analysis and the correlation between immune-related genes and the prognosis of pancreatic cancer patients was assessed. Firstly, differentially expressed immune genes were extracted from the pancreatic cancer-related datasets obtained for purposes of this study. The samples were processed by the “Consensus Cluster Plus” R package to determine the number of immune subtypes. Then, the pancreatic cancer immunophenotyping-related gene modules were determined. Differential analysis of immune gene modules was performed, and the function of genes related to pancreatic cancer immune subtypes was identified. The number of immune cells in the samples was calculated, followed by the differential expression analysis of immune cell numbers in each immune subtype of pancreatic cancer. The immune infiltration score was also estimated, and the correlation between the immune infiltration score and the patient prognosis with different immune subtypes was determined. Gene differences between each immune subtype were identified by differential expression analysis, and key immune genes affecting immunophenotyping were obtained. Following the analysis, 426 immune-related genes were identified to have potential involvement in the occurrence and development of pancreatic cancer, of which CD19 may be the most critical gene affecting the immunophenotyping of pancreatic cancer. CD19 played a significant role in the occurrence and development of IS2 and IS3 immune subtypes of pancreatic cancer through its action on B cells and T cells. Moreover, the expression of CD19 was increased in the collected pancreatic cancer tissues. Overall, our findings uncovered the critical role of CD19 in the prognosis of pancreatic cancer patients.

MicroRNA-100 inhibits breast cancer cell proliferation, invasion and migration by targeting FOXA1.

MicroRNAs (miRNAs/miRs) are highly conserved single-stranded small non-coding RNAs, which are involved in the physiological and pathological processes of breast cancer, and affect the prognosis of patients with breast cancer. The present study used the Gene Expression Omnibus (GEO)2R tool to detect miR-100 expression in breast cancer tissues obtained from GEO breast cancer-related datasets. Bioinformatics analysis revealed that miR-100 expression was downregulated in different stages, grades and lymph node metastasis stages of breast cancer, and patients with high miR-100 expression had a more favorable prognosis. Based on these analyses, Cell Counting Kit-8, wound healing and Transwell assays were performed, and the results demonstrated that overexpression of miR-100 inhibited the proliferation, migration and invasion of breast cancer cells. To verify the tumor-suppressive effect of miR-100 in breast cancer, the LinkedOmics and PITA databases were used to assess the association between miR-100 and forkhead box A1 (FOXA1). The results demonstrated that miR-100 had binding sites within the FOXA1 gene, and FOXA1 expression was negatively associated with miR-100 expression in breast cancer tissues. Similarly, a negative association was observed between miR-100 and FOXA1 expression, using the StarBase V3.0 database. The association between miR-100 and FOXA1 was further verified via reverse transcription-quantitative PCR and western blot analyses, and the dual-luciferase reporter assay. The results demonstrated that miR-100 targeted the 3′-untranslated region of FOXA1 in breast cancer cells. Furthermore, rescue experiments were performed to confirm whether miR-100 exerts its antitumor effects by regulating FOXA1. The results demonstrated that overexpression of FOXA1 promoted the proliferation, migration and invasion of breast cancer cells; thus, the antitumor effects of miR-100 in breast cancer were reversed following overexpression of FOXA1. Taken together, the results of the present study suggest that miR-100 inhibits the proliferation, migration and invasion of breast cancer cells by targeting FOXA1 expression. These results may provide a novel insight and an experimental basis for identifying effective therapeutic targets of high specificity for breast cancer.

Abstract 2287: Detection of RNA-Seq library preparation type via random forest

Abstract Library preparation protocols for RNA sequencing (RNA-Seq) libraries vary and affect the downstream analysis of RNA-Seq data. Available RNA sequencing datasets often lack library construction protocol information in the metadata. This information is necessary to be able to compare RNA sequencing datasets appropriately. For example, non-polyadenylated transcripts are measured when a ribosomal RNA-depletion method is used (riboD library preparation), but not when transcripts are selected by poly(A) tails (polyA library preparation). Without knowing the library preparation method, it would appear that the non-polyadenylated transcripts were expressed at much higher levels in the samples prepared via riboD. In order to tackle this issue, we have developed a Random Forest classifier that can delineate between riboD and polyA RNA-Seq datasets. A grid search method was applied to the number of trees, maximum depth, and the minimum datasets included in each leaf node, which determined the best parameters to be 100, 8, and 1 respectively. However, applying the model on the Pediatric Brain Tumor Atlas (PBTA) showed strong overfitting for polyA samples. We examined the performance of the model investigating each maximum depth increment from 1 to 8, and we determined that the best performance on our validation set was achieved with maximum depth of 1. We subsequently proceeded to train our classifier on our own curated compendiums of pediatric cancer polyA and riboD samples including 188 and 264 samples respectively, after balancing the two datasets in terms of disease prevalence, and selecting the top 5,000 most variable genes as the default input dimensionality of the model. For samples whose genes do not exactly match the predetermined 5,000 genes, we substitute the expression of the missing gene with the mean expression of the gene observed in the training data. We show that it achieves 100% classification accuracy of samples to their respective library preparation protocols in GTEX (all polyA), CCLE (all polyA), and for 7 of 9 SRA projects. Five of the SRA projects contained only riboD datasets, 3 contained only all polyA datasets, and one was 50% riboD, and 50% polyA. Notably the SRA datasets are not all cancer related datasets, showing the power of our model to distinguish between library preparation protocols in vastly different settings. The model will become available on Docker so that it is readily, and easily accessible for application on new samples. Our model serves as an important step towards robust library preparation identification. Including samples in the training procedure from diverse contexts would make our model more widely applicable. Need summary statement here and/or future work statement Citation Format: Ioannis Anastopoulos, Holly Beale, Geoff Lyle, Allison Cheney, Olena M. Vaske, Joshua M. Stuart. Detection of RNA-Seq library preparation type via random forest [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2287.

Gene Regulatory Network Inference as Relaxed Graph Matching

Bipartite network inference is a ubiquitous problem across disciplines. One important example in the field molecular biology is gene regulatory network inference. Gene regulatory networks are an instrumental tool aiding in the discovery of the molecular mechanisms driving diverse diseases, including cancer. However, only noisy observations of the projections of these regulatory networks are typically assayed. In an effort to better estimate regulatory networks from their noisy projections, we formulate a non-convex but analytically tractable optimization problem called OTTER. This problem can be interpreted as relaxed graph matching between the two projections of the bipartite network. OTTER's solutions can be derived explicitly and inspire a spectral algorithm, for which we provide network recovery guarantees. We also provide an alternative approach based on gradient descent that is more robust to noise compared to the spectral algorithm. Interestingly, this gradient descent approach resembles the message passing equations of an established gene regulatory network inference method, PANDA. Using three cancer-related data sets, we show that OTTER outperforms state-of-the-art inference methods in predicting transcription factor binding to gene regulatory regions. To encourage new graph matching applications to this problem, we have made all networks and validation data publicly available.

Exploration of the Expression of Circulating miRNA in Serum of Patients with Prostate Cancer

To explore the value of circulating micro ribonucleic acid expression in serum in the diagnosis of prostate cancer, this paper used The Cancer Genome Atlas database to download prostate cancer related datasets, analyze the expression of circulating micro ribonucleic acids in serum of prostate cancer tissues, screen differentially expressed micro ribonucleic acids, and find that micro ribonucleic acid-141 is significantly different. Then select 30 patients with prostate cancer in urology, and 20 patients with benign prostatic hyperplasia in the same period as research subject. The control group was 20 healthy subjects in the same period. The expression of serum micro ribonucleic acid-141 in control group, benign prostatic hyperplasia group and prostate cancer group was detected by fluorescent quantitative Polymerase Chain Reaction, and the content of total prostate specific antigen before treatment in prostate cancer group. The results showed that the expression level of serum micro ribonucleic acid-141 in the prostate cancer group was (11.2±8.1) times higher than that in the control group, which was significantly higher than that in the benign prostatic hyperplasia group (1.65+1.73) and the control group. The difference was statistically significant (p<0.05). The expression level of serum micro ribonucleic acid-141 in prostate cancer group was positively correlated with total prostate specific antigen level, correlation coefficient r=0.753, statistically significant (p<0.05). Serum micro ribonucleic acid-141 expression level is associated with Gleason score, clinical stages and occurrence of metastasis. With the increase of clinical stage, the expression level of micro ribonucleic acid increased. It is concluded that serum circulating micro ribonucleic acid-141 can be used as a molecular indicator for the diagnosis of prostate cancer.

An enhanced chemopreventive effect of methyl donor S-adenosylmethionine in combination with 25-hydroxyvitamin D in blocking mammary tumor growth and metastasis

Therapeutic targeting of metastatic breast cancer still remains a challenge as the tumor cells are highly heterogenous and exploit multiple pathways for their growth and metastatic spread that cannot always be targeted by a single-agent monotherapy regimen. Therefore, a rational approach through simultaneous targeting of several pathways may provide a better anti-cancer therapeutic effect. We tested this hypothesis using a combination of two nutraceutical agents S-adenosylmethionine (SAM) and Vitamin D (Vit. D) prohormone [25-hydroxyvitamin D; ‘25(OH)D’] that are individually known to exert distinct changes in the expression of genes involved in tumor growth and metastasis. Our results show that both SAM and 25(OH)D monotherapy significantly reduced proliferation and clonogenic survival of a panel of breast cancer cell lines in vitro and inhibited tumor growth, lung metastasis, and breast tumor cell colonization to the skeleton in vivo. However, these effects were significantly more pronounced in the combination setting. RNA-Sequencing revealed that the transcriptomic footprint on key cancer-related signaling pathways is broader in the combination setting than any of the monotherapies. Furthermore, comparison of the differentially expressed genes from our transcriptome analyses with publicly available cancer-related dataset demonstrated that the combination treatment upregulates genes from immune-related pathways that are otherwise downregulated in bone metastasis in vivo. Since SAM and Vit. D are both approved nutraceuticals with known safety profiles, this combination treatment may serve as a novel strategy to reduce breast cancer-associated morbidity and mortality.

Cancer Risk Analysis Based on Improved Probabilistic Neural Network.

The problem of cancer risk analysis is of great importance to health-service providers and medical researchers. In this study, we propose a novel Artificial Neural Network (ANN) algorithm based on the probabilistic framework, which aims to investigate patient patterns associated with their disease development. Compared to the traditional ANN where input features are directly extracted from raw data, the proposed probabilistic ANN manipulates original inputs according to their probability distribution. More precisely, the Naïve Bayes and Markov chain models are used to approximate the posterior distribution of the raw inputs, which provides a useful estimation of subsequent disease development. Later, this distribution information is further leveraged as additional input to train ANN. Additionally, to reduce the training cost and to boost the generalization capability, a sparse training strategy is also introduced. Experimentally, one of the largest cancer-related datasets is employed in this study. Compared to state-of-the-art methods, the proposed algorithm achieves a much better outcome, in terms of the prediction accuracy of subsequent disease development. The result also reveals the potential impact of patients' disease sequence on their future risk management.

Nearest Neighbour Propensity Score Matching and Bootstrapping for Estimating Binary Patient Response in Oncology: A Monte Carlo Simulation

Nearest Neighbour (NN) propensity score (PS) matching methods are commonly used in pharmacoepidemiology to estimate treatment response using observational data. Unfortunately, there is limited evidence on the optimal approach for accurately estimating binary treatment response and, more so, to estimate its variance. Bootstrapping, although commonly used to accurately estimate variance, is rarely used together with PS matching. In this Monte Carlo simulation-based study, we examined the performance of bootstrapping used in conjunction with PS matching, as opposed to different NN matching techniques, on a simulated dataset exhibiting varying levels of real world complexity. Thus, an experimental design was set up that independently varied the proportion of patients treated, the proportion of outcomes censored and the amount of PS matches used. Simulation results were externally validated on a real observational dataset obtained from the Belgian Cancer Registry. We found all investigated PS methods to be stable and concordant, with k-NN matching to be optimally dealing with the censoring problem, typically present in chronic cancer-related datasets, whilst being the least computationally expensive. In contrast, bootstrapping used in conjunction with PS matching, being the most computationally expensive, only showed superior results in small patient populations with long-term largely unobserved treatment effects.

An integrative gene selection with association analysis for microarray data classification

The rising interest in integrative approach has shifted gene selection from purely data-centric to incorporating addi- tional biological knowledge. Integrative gene selection is viewed as a promising approach in microarray data classification that took into consideration the complex relationships among genes. However, in most of the existing methods, the selection of genes is still based on expression values alone and biological knowledge is integrated at the end of analysis to verify experimental results or to gain biological insights. Thus, this paper proposed an integrative gene selection based on filter method and asso- ciation analysis for selecting genes that are not only differentially expressed but also informative for classification. Association analysis is employed to integrate microarray data with multiple types of biological knowledge simultaneously, and to identify groups of genes that are frequently co-occurred in target samples. It has been tested on four cancer-related datasets, and two types of biological knowledge are incorporated, namely Gene Ontology (GO) and KEGG Pathways (KEGG). The experimen- tal results show that the recommended GO based models, KEGG based models, and GO-KEGG based models outperformed the expression-only models by attaining better classification accuracies with lesser number of genes. The performance of the integrative models verified the efficiency and scalability of association analysis in mining microarray data.

New Measurement Methods of Network Robustness and Response Ability via Microarray Data

“Robustness”, the network ability to maintain systematic performance in the face of intrinsic perturbations, and “response ability”, the network ability to respond to external stimuli or transduce them to downstream regulators, are two important complementary system characteristics that must be considered when discussing biological system performance. However, at present, these features cannot be measured directly for all network components in an experimental procedure. Therefore, we present two novel systematic measurement methods – Network Robustness Measurement (NRM) and Response Ability Measurement (RAM) – to estimate the network robustness and response ability of a gene regulatory network (GRN) or protein-protein interaction network (PPIN) based on the dynamic network model constructed by the corresponding microarray data. We demonstrate the efficiency of NRM and RAM in analyzing GRNs and PPINs, respectively, by considering aging- and cancer-related datasets. When applied to an aging-related GRN, our results indicate that such a network is more robust to intrinsic perturbations in the elderly than in the young, and is therefore less responsive to external stimuli. When applied to a PPIN of fibroblast and HeLa cells, we observe that the network of cancer cells possesses better robustness than that of normal cells. Moreover, the response ability of the PPIN calculated from the cancer cells is lower than that from healthy cells. Accordingly, we propose that generalized NRM and RAM methods represent effective tools for exploring and analyzing different systems-level dynamical properties via microarray data. Making use of such properties can facilitate prediction and application, providing useful information on clinical strategy, drug target selection, and design specifications of synthetic biology from a systems biology perspective.