Microarray Gene Expression Research Articles

Deep learning assisted cancer disease prediction from gene expression data using WT-GAN

Several diverse fields including the healthcare system and drug development sectors have benefited immensely through the adoption of deep learning (DL), which is a subset of artificial intelligence (AI) and machine learning (ML). Cancer makes up a significant percentage of the illnesses that cause early human mortality across the globe, and this situation is likely to rise in the coming years, especially when non-communicable illnesses are not considered. As a result, cancer patients would greatly benefit from precise and timely diagnosis and prediction. Deep learning (DL) has become a common technique in healthcare due to the abundance of computational power. Gene expression datasets are frequently used in major DL-based applications for illness detection, notably in cancer therapy. The quantity of medical data, on the other hand, is often insufficient to fulfill deep learning requirements. Microarray gene expression datasets are used for training procedures despite their extreme dimensionality, limited volume of data samples, and sparsely available information. Data augmentation is commonly used to expand the training sample size for gene data. The Wasserstein Tabular Generative Adversarial Network (WT-GAN) model is used for the data augmentation process for generating synthetic data in this proposed work. The correlation-based feature selection technique selects the most relevant characteristics based on threshold values. Deep FNN and ML algorithms train and classify the gene expression samples. The augmented data give better classification results (> 97%) when using WT-GAN for cancer diagnosis.

Gene-expression profile analysis to disclose diagnostics and therapeutics biomarkers for thyroid carcinoma

The most frequent endocrine cancer of the head and neck is thyroid carcinoma (THCA). Although there is increasing evidence linking THCA to genetic alterations, the exact molecular mechanism behind this relationship is not yet completely known to the researchers. There is still much to learn about THCA's molecular roots and genetic biomarkers. Though drug therapies are the best choice after metastasis, unfortunately, the majority of the patients progressively develop resistance against the therapeutic drugs after receiving them for a few years. Therefore, multi-targeted different variants of therapeutic drugs may be essential for effective treatment against THCA. To understand molecular mechanisms of THCA development and progression and explore multi-targeted different variants of therapeutic drugs, we detected 80 common differentially expressed genes (cDEGs) between THCA and non-THCA samples from six microarray gene expression datasets using the statistical LIMMA approach. Through protein-protein interaction (PPI) network analysis, we identified the top-ranked eight differentially expressed genes (TIMP1, FN1, THBS1, RUNX2, SHANK2, TOP2A, LRP2, and ACTN1) as the THCA-causing key genes (KGs), where 6 KGs (TIMP1, TOP2A, FN1, ACTN1, RUNX2, THBS1) are upregulated and 2 KGs (LRP2, SHANK2) are downregulated. The expression pattern analysis of KGs with the independent TCGA database by Box plots also confirmed their upregulated and downregulated patterns. The expression analysis of KGs in different stages of THCA development indicated that these KGs might be utilized as early diagnostic and prognostic biomarkers. The pan-cancer analysis of KGs indicated a substantial correlation of KGs with multiple cancers, including THCA. Some transcription factors (TFs) and microRNAs were detected as the key transcriptional and post-transcriptional regulators of KGs using gene regulatory network (GRN) analysis. The enrichment analysis of the cDEGs revealed several key molecular functions, biological processes, cellular components, and pathways significantly associated with THCA. These findings highlight critical mechanisms influenced by the identified key genes (KGs), providing deeper insight into their roles in THCA development. Then we detected 6 repurposable drug molecules (Entrectinib, Imatinib, Ponatinib, Sorafenib, Retevmo, and Pazopanib) by molecular docking with KGs-mediated receptor proteins, ADME/T analysis, and cross-validation with the independent receptors. Therefore, these findings might be useful resources for wet lab researchers and clinicians to consider an effective treatment strategy against THCA.

Gene expression microarray analysis during metamorphosis and early calcification in the scleractinian coral Acropora millepora

ABSTRACT In the face of global warming and ocean acidification, understanding the cellular mechanisms underlying coral metamorphosis and early calcification is essential, as both processes are likely to be affected by a changing environment. In this study, we used cDNA microarray expression analysis to compare transcription profiles between four distinct life stages in the ‘complexa’ coral Acropora millepora: 1) aposymbiotic non-calcifying planula, 2) aposymbiotic calcifying flat polyp, 3) aposymbiotic calcifying primary polyp and 4) symbiotic adult tip. Highly up-regulated genes in planulae were molecules involved in calcium signalling, lipid metabolism and development. Transcripts up-regulated in post-settlement juvenile stages regulate tissue morphogenesis, cell division and responses to oxidative stress. Transcripts with the highest expression in adult polyps are involved in responses to abiotic stimuli and asexual reproduction. Transcripts up-regulated in calcifying stages included carbonic anhydrases and organic matrix components. Additionally, our results suggest an overlap between intracellular secondary messengers following settlement and metamorphosis. A subset of fluorescent proteins were up-regulated at the planula stage but down-regulated after settlement, supporting the idea that these molecules might have a role in quenching reactive oxygen species. Altogether, our results suggest that Acropora developmental stages are transcriptionally distinct units in which transcription profiles reflect responses to different physiological and ecological conditions.

Screening and validating genes associated with cuproptosis in systemic lupus erythematosus by expression profiling combined with machine learning.

Cell death has long been a focal point in life sciences research, and recently, scientists have discovered a novel form of cell death induced by copper, termed cuproptosis. This paper aimed to identify genes associated with cuproptosis in Systemic Lupus Erythematosus (SLE) through machine learning, combined with single-cell sequencing (scRNA-seq), to screen and validate related genes. The analytical results were then experimentally verified. Two published microarray gene expression datasets (GSE65391 and GSE61635) from SLE and control peripheral blood samples were downloaded from the GEO database. The GSE65391 dataset was used as the training group, while the GSE61635 dataset served as the validation group. Differentially expressed genes (DEGs) from GSE65391 identified 12 differential genes. Nine diagnostic genes, considered potential biomarkers, were selected using the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) analysis. The Receiver Operating Characteristic (ROC) curves for both the training and validation groups were used to calculate the Area Under the Curve (AUC) to assess discriminatory properties. CIBERSORT was used to assess the relationship between these diagnostic genes and a reference set of infiltrating immune cells. Single-cell RNA sequencing data (GSE162577) from SLE patients were also obtained from the GEO database and analyzed. Experimental validation of the most important SLE biomarkers was performed. Twelve significantly different cuproptosis-related genes were identified in the GSE65391 training set. Immune cell analysis revealed 12 immune cell types and identified nine signature genes, including PDHB, GLS, DLAT, LIAS, MTF1, DLST, DLD, LIPT1, and FDX1. In the GSE61635 validation set, seven genes were weakly expressed, and two genes were strongly expressed in the treatment group. According to the ROC curves, PDHB and GLS demonstrated significant diagnostic value. Additionally, correlation analysis was conducted on the nine characteristic genes in relation to immune infiltration. The distribution of key genes in immune cells was determined using single-cell RNA sequencing data. Finally, the mRNA expression of the nine diagnostic genes was validated using qPCR.

Application of Gene Expression Microarray for the Classification of Ph-Like B-Cell Acute Lymphoblastic Leukemia.

Ph-like ALL has gene expression profile similar to Ph-positive ALL but without the BCR::ABL1 fusion. The disease presents higher rates of severe clinical features and is associated with unfavorable outcomes. There is still no standard pipeline for molecular characterization of the disease, and no valid predictor gene panel is available worldwide. We performed expression microarray on 25 B-cell ALL and 6 Ph-positive B-cell ALL to cluster and identify the transcriptional signature of Ph-like ALL. qRT-PCR was used to confirm the expression of candidate genes. Four out of 25 samples (16%) shared gene expression signatures related to and clustered with control Ph-positive samples. Analysis of genes differentially expressed in Ph-like B-cell ALL and evidentially functional in normal blood cell development and leukemogenesis, we selected genes as potential biomarkers for Ph-like B-cell ALL in our dataset: ADGRE2, CD9, EPHA7, FAM129C, TCL1A, and VPREB1. Those genes were filtered by Ph-like gene signatures obtained from distinct reliable data, resulting in five genes, CA6, CHN2, JAK1, JCHAIN, and PON2, selected for validation by qRT-PCR. The Ct values of genes, including CA6 (p = 0.0017), PON2 (p = 0.0210), TCL1A (p = 0.0064), and VPREB1 (p = 0.0338), were significant in Ph-like ALL. GSEA analysis identified VPREB1 as enrichment in the KRAS signaling pathway, and several genes that interact with VPREB1 were reported as critical molecules involved in the leukemogenesis of B-cell ALL. In summary, we demonstrate using a gene expression microarray for classifying Ph-like B-cell ALL and highlight VPREB1 as a potential biomarker for this disease.

PTGES is involved in myofibroblast differentiation via HIF-1α-dependent glycolysis pathway.

Lung cancer is the leading cause of cancer-related deaths worldwide. Patients with lung cancer usually exhibit poor prognoses and low 5-year survival rates. Idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are both chronic lung dysfunctions resulting in lung fibrosis and increased risk of lung cancer. Myofibroblasts contribute to the progression of asthma, COPD and IPF, leading to fibrosis in the airway and lungs. A growing body of evidence demonstrates that metabolic reprogramming is a major hallmark of fibrosis, being important in the progression of fibrosis. Using gene expression microarray, we identified and validated that the lipid metabolic pathway was upregulated in lung fibroblasts upon interleukin (IL)-4, IL-13 and tumour necrosis factor (TNF)-α treatment. In this study, we described that prostaglandin E synthase (PTGES) was upregulated in lung fibroblasts after IL-4, IL-13 and TNF-α treatments. PTGES increased α-SMA levels and promoted lung fibroblast cell migration and invasion abilities. Furthermore, PTGES was upregulated in a lung fibrosis rat model invivo. PTGES increased AKT phosphorylation, leading to activation of the HIF-1α-glycolysis pathway in lung fibroblast cells. HIF-1α inhibitor or 2-DG treatments reduced α-SMA expression in recombinant PTGES (rPTGES)-treated lung fibroblast cells. Targeting PGE2 signalling in PTGES-overexpressing cells by a PTGES inhibitor reduced α-SMA expression. In conclusion, the results of this study demonstrate that PTGES increases the expression of myofibroblast marker via HIF-1α-dependent glycolysis and contributes to myofibroblast differentiation.

Influence of budesonide and fluticasone propionate in the anti-osteoporotic potential in human bone marrow-derived mesenchymal stem cells via stimulation of osteogenic differentiation

Osteoporosis is a prevalent bone condition with adverse effects observed in patients undergoing long-term glucocorticoid therapy, resulting in bone demineralization and tissue loss. There has been limited studies on the global response to dexamethasone in terms of comparing its expression profile to other common glucocorticoids during osteogenic differentiation. This study focused on the downregulated gene expression profile of glucocorticoid compounds; dexamethasone, budesonide, and fluticasone propionate, during osteogenic differentiation to elucidate the related target genes and pathways associated with the anti-osteoporotic potential of telomerase-immortalized human bone marrow-derived mesenchymal stem cells using a bioinformatics approach. Based on gene expression microarrays experiments and bioinformatics analysis, several key genes involved in the regulation of osteogenic differentiation and osteoporosis development in mesenchymal stem cells that were targeted by these specific glucocorticoids were determined. Network analysis using GeneCards, OMIM, and CTD databases were performed and osteoporosis-related genes were identified. LIMMA and moderated Welch test R packages were performed to determine significant downregulated differentially expressed genes for each glucocorticoid treatment. A total of 479 (dexamethasone), 84 (budesonide), and 889 (fluticasone propionate) differentially expressed genes were identified for each glucocorticoid, of which 35 common genes overlapped. Enrichment pathway analysis was conducted using Metascape, and protein-protein interaction networks were constructed using the STRING database and Cytoscape software to determine potential target genes involved with osteoporosis. Enrichment pathway analysis revealed genes involved in 3 Reactome pathways namely cytokine signaling in immune system, immune system and the interferon alpha/beta signaling pathways and identified 10 hub genes based on the PPI network to determine potential target pathways associated with osteoporosis. These findings provide preliminary insights into the relationship between the key target genes of dexamethasone, budesonide, and fluticasone propionate, and the pathways associated with regulated osteoporosis metabolism during osteogenic differentiation.

Computational Insights into Irinotecan's Interaction with UBE2I in Ovarian and Endometrial Cancers

Endometrial and Ovarian cancers are two highly prevalent and fatal reproductive diseases with poor prognoses among women. Elevated estrogen levels in Ovarian Cancer (OC) stimulate the endometrium, causing Endometrial Cancer (EC). Although numerous studies have reported the crucial genes and pathways in this cancer, the pathogenesis of this disease remains unclear. In this study, used bioinformatics tools to analyse GSE63678, GSE115810, GSE36389, GSE26712, GSE36668, GSE27651, GSE6008, GSE69429, GSE69428, GSE18521, GSE185209, GSE54388 gene expression microarray datasets for both the cancers. We analyzed the differential gene expression, functional association, and structural studies. The analysis identified crucial differentially expressed genes (DEGs) in both cancers associated with DNA damage, DNA integrity, and cell-cycle checkpoint signaling pathways. CLDN7, UBE2I, WT1, JAM2, FOXL2, F11R, JAM3, ZFPM2, MEF2C, and PIAS1 are the top 10 hub genes commonly identified in both cancer types. Only CLDN7 and F11R are upregulated, whereas the remaining hub genes are downregulated in both cancers, suggesting a common framework for contributing to tumorigenesis. Molecular docking and dynamics were performed on the UBE2I protein with Irinotecan Hydrochloride, which could serve as the new approach for treating and managing both cancers. The study reveals the common molecular pathways, pointing out the role of cell cycle and DNA damage and integrity checkpoint signaling in the pathogenesis of both cancer types. This study explored the UBE2I gene as a potential biomarker in OC and EC. Further, this study concludes that the irinotecan hydrochloride drug has higher therapeutic effects on UBE2I protein through docking and dynamics studies.

Elucidating gastric cancer mechanisms and therapeutic potential of Adociaquinone A targeting EGFR: A genomic analysis and Computer Aided Drug Design (CADD) approach.

Gastric cancer predominantly adenocarcinoma, accounts for over 85% of gastric cancer diagnoses. Current therapeutic options are limited, necessitating the discovery of novel drug targets and effective treatments. The Affymetrix gene expression microarray dataset (GSE64951) was retrieved from NCBI-GEO data normalization and DEGs identification was done by using R-Bioconductor package. Gene Ontology (GO) analysis of DEGs was performed using DAVID. The protein-protein interaction network was constructed by STRING database plugin in Cytoscape. Subclusters/modules of important interacting genes in main network were extracted by using MCODE. The hub genes from in the network were identified by using Cytohubba. The miRNet tool built a hub gene/mRNA-miRNA network and Kaplan-Meier-Plotter conducted survival analysis. AutoDock Vina and GROMACS MD simulations were used for docking and stability analysis of marine compounds against the 5CNN protein. Total 734 DEGs (507 up-regulated and 228 down-regulated) were identified. Differentially expressed genes (DEGs) were enriched in processes like cell-cell adhesion and ATP binding. Eight hub genes (EGFR, HSPA90AA1, MAPK1, HSPA4, PPP2CA, CDKN2A, CDC20, and ATM) were selected for further analysis. A total of 23 miRNAs associated with hub genes were identified, with 12 of them targeting PPP2CA. EGFR displayed the highest expression and hazard rate in survival analyses. The kinase domain of EGFR (PDBID: 5CNN) was chosen as the drug target. Adociaquinone A from Petrosia alfiani, docked with 5CNN, showed the lowest binding energy with stable interactions across a 50 ns MD simulation, highlighting its potential as a lead molecule against EGFR. This study has identified crucial DEGs and hub genes in gastric cancer, proposing novel therapeutic targets. Specifically, Adociaquinone A demonstrates promising potential as a bioactive drug against EGFR in gastric cancer, warranting further investigation. The predicted miRNA against the hub gene/proteins can also be used as potential therapeutic targets.

Characterization of Molecular Subtypes of Rat Mammary Cancer and Their Association With Environmental Exposures.

Breast cancer is a heterogeneous disease with many subtypes, and the association between these subtypes and exposure to environmental factors such as radiation remains controversial. Although the rat is used widely for research into human breast cancer, the heterogeneity and subtype definitions are unclear. Here, we leveraged an archive of rat mammary cancer samples and gene expression microarray data to classify tumors and examine their association with exposures. Eighty-four mammary cancer and 12 normal mammary tissue samples were obtained from previous experiments in which rats were exposed to different types of radiation, chemical carcinogens, and diets. Tumors were then subjected to immunohistochemical (IHC) analysis of conventional biomarkers, as well as gene expression profiling; they were then classified by three approaches based on IHC results, the PAM50 classifier algorithm, and unsupervised clustering of gene expression profiles. IHC identified four subtypes (luminal A-like, luminal B-like 1, luminal B-like 2, and triple-negative), while PAM50 identified six (luminal A, luminal B, basal-like, HER2-enriched, normal-like, and claudin-low). Unsupervised clustering divided the tumors into three large, statistically significant, groups (named "luminal A", "luminal B", and "non-luminal" clusters). The results of the three approaches were significantly associated with each other. Exposure to radiation and chemical carcinogens during post-pubertal development was significantly associated with an increased risk of developing luminal A tumors, whereas exposure to a high corn-oil diet was associated with a higher likelihood of luminal B tumors. Rat mammary cancer subtypes resemble those in humans and are related to environmental factors.

Screening of differential gene expression patterns through survival analysis for diagnosis, prognosis and therapies of clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of kidney cancer. Although there is increasing evidence linking ccRCC to genetic alterations, the exact molecular mechanism behind this relationship is not yet completely known to the researchers. Though drug therapies are the best choice after the metastasis, unfortunately, the majority of the patients progressively develop resistance against the therapeutic drugs after receiving it for almost 2 years. In this case, multi-targeted different variants of therapeutic drugs are essential for effective treatment against ccRCC. To understand molecular mechanisms of ccRCC development and progression, and explore multi-targeted different variants of therapeutic drugs, it is essential to identify ccRCC-causing key genes (KGs). In order to obtain ccRCC-causing KGs, at first, we detected 133 common differentially expressed genes (cDEGs) between ccRCC and control samples based on nine (9) microarray gene-expression datasets with NCBI accession IDs GSE16441, GSE53757, GSE66270, GSE66272, GSE16449, GSE76351, GSE66271, GSE71963, and GSE36895. Then, we filtered these cDEGs through survival analysis with the independent TCGA and GTEx database and obtained 54 scDEGs having significant prognostic power. Next, we used protein-protein interaction (PPI) network analysis with the reduced set of 54 scDEGs to identify ccRCC-causing top-ranked eight KGs (PLG, ENO2, ALDOB, UMOD, ALDH6A1, SLC12A3, SLC12A1, SERPINA5). The pan-cancer analysis with KGs based on TCGA database showed the significant association with different subtypes of kidney cancers including ccRCC. The gene regulatory network (GRN) analysis revealed some crucial transcriptional and post-transcriptional regulators of KGs. The scDEGs-set enrichment analysis significantly identified some crucial ccRCC-causing molecular functions, biological processes, cellular components, and pathways that are linked to the KGs. The results of DNA methylation study indicated the hypomethylation and hyper-methylation of KGs, which may lead the development of ccRCC. The immune infiltrating cell types (CD8+ T and CD4+ T cell, B cell, neutrophil, dendritic cell and macrophage) analysis with KGs indicated their significant association in ccRCC, where KGs are positively correlated with CD4+ T cells, but negatively correlated with the majority of other immune cells, which is supported by the literature review also. Then we detected 10 repurposable drug molecules (Irinotecan, Imatinib, Telaglenastat, Olaparib, RG-4733, Sorafenib, Sitravatinib, Cabozantinib, Abemaciclib, and Dovitinib.) by molecular docking with KGs-mediated receptor proteins. Their ADME/T analysis and cross-validation with the independent receptors, also supported their potent against ccRCC. Therefore, these outputs might be useful inputs/resources to the wet-lab researchers and clinicians for considering an effective treatment strategy against ccRCC.

Identification of potential new T cell activation molecules: a Bioinformatic Approach.

T-cell activation is central for the initiation of T cell mediated adaptive immune response and is the result of the close communication between the Antigen Presenting Cell (APC) and the T lymphocyte. Although T-cell activation is currently well understood, and many intracellular pathways are well characterized, nevertheless new players are constantly identified, and this complements the known protein interactome. In this work we aimed to identify new proteins involved in T cell activation. We reviewed and analyzed results of microarray gene expression datasets reported in the public database GEO-NCBI. Using data from GSE136625, GSE50971, GSE13887, GSE11989 and GSE902 we performed different comparisons using R and other bioinformatic tools including GEO2R and we report here upregulated genes that have no previous reports in immune related functions and with potential participation upon T-cell activation. Our results indicate that RND3, SYT10, IgSF6 and PIN1 are potential new T-cell activation molecules.

High WEE1 expression is independently linked to poor survival in multiple myeloma.

Current prognostic scores in multiple myeloma (MM) currently rely on disease burden and a limited set of genomic alterations. Some studies have suggested gene expression panels may predict clinical outcomes, but none are presently utilized in clinical practice. We therefore analyzed the MMRF CoMMpass dataset (N=659) and identified a high-risk group (top tertile) and a low-risk group ( bottom tertile) based on WEE1 expression sorted in descending order. The tyrosine kinase WEE1 is a critical cell cycle regulator during the S-phase and G2M-checkpoint. Abnormal WEE1 expression has been implicated in multiple cancers including breast, ovarian, and gastric cancers, but has not until this time been implicated in MM. PFS was significantly different (p <1e-9) between the groups, which was validated in two independent microarray gene expression profiling (GEP) datasets from the Total Therapy 2 (N=341) and 3 (N=214) trials. Our results show WEE1 expression is prognostic independent of known biomarkers, differentiates outcomes associated with known markers, is upregulated independently of its interacting neighbors, and is associated with dysregulated P53 pathways. This suggests that WEE1 expression levels may have clinical utility in prognosticating outcomes in newly diagnosed MM and may support the application of WEE1 inhibitors to MM preclinical models. Determining the causes of abnormal WEE1 expression may uncover novel therapeutic pathways.

Exploring Lignin Biosynthesis Genes in Rice: Evolution, Function, and Expression.

Lignin is nature's second most abundant vascular plant biopolymer, playing significant roles in mechanical support, water transport, and stress responses. This study identified 90 lignin biosynthesis genes in rice based on phylogeny and motif constitution, and they belong to PAL, C4H, 4CL, HCT, C3H, CCoAOMT, CCR, F5H, COMT, and CAD families. Duplication events contributed largely to the expansion of these gene families, such as PAL, CCoAOMT, CCR, and CAD families, mainly attributed to tandem and segmental duplication. Microarray data of 33 tissue samples covering the entire life cycle of rice suggested fairly high PAL, HCT, C3H, CCoAOMT, CCR, COMT, and CAD gene expressions and rather variable C4H, 4CL, and F5H expressions. Some members of lignin-related genes (OsCCRL11, OsHCT1/2/5, OsCCoAOMT1/3/5, OsCOMT, OsC3H, OsCAD2, and OsPAL1/6) were expressed in all tissues examined. The expression patterns of lignin-related genes can be divided into two major groups with eight subgroups, each showing a distinct co-expression in tissues representing typically primary and secondary cell wall constitutions. Some lignin-related genes were strongly co-expressed in tissues typical of secondary cell walls. Combined HPLC analysis showed increased lignin monomer (H, G, and S) contents from young to old growth stages in five genotypes. Based on 90 genes' microarray data, 27 genes were selected for qRT-PCR gene expression analysis. Four genes (OsPAL9, OsCAD8C, OsCCR8, and OsCOMTL4) were significantly negatively correlated with lignin monomers. Furthermore, eleven genes were co-expressed in certain genotypes during secondary growth stages. Among them, six genes (OsC3H, OsCAD2, OsCCR2, OsCOMT, OsPAL2, and OsPAL8) were overlapped with microarray gene expressions, highlighting their importance in lignin biosynthesis.

Validation of a genome-based model for adjusting radiotherapy dose (GARD) in patients with locally advanced rectal cancer

Neoadjuvant radiotherapy is the standard care of locally advanced rectal cancer. Although a majority of patients received the same dose, the curative efficacy varies among individuals. In recent years, cancer treatment has entered the era of precise medical care, and how to identify patients for proper treatment by molecular signature is an important path of individualized therapy. This study aimed to establish and validate a genome-based model for adjusting radiation dose (GARD) for Chinese locally advanced rectal cancer through gene expression microarrays, and to evaluate the response of the GARD model in predicting the efficacy of neoadjuvant radiotherapy. Fresh-frozen primary tumor from 64 patients with locally advanced rectal cancer undergoing neoadjuvant radiotherapy from 2015 to 2018 were included. The gene expression profile was analyzed using Affymetrix 3000Dx gene-chip scanner. The radiosensitivity index (RSI) and GARD were calculated using the pGRT™ algorithm. Neoadjuvant rectal cancer score (NAR) was selected as efficacy evaluation indicators. Patients were divided into high and low NAR scoring groups, and two-sample t-test was used to analyze the differences in GARD values between different NAR subgroups. ROC curves were used to calculate the cut-off values and the area under the curve (AUC) for assessing the validity of the GARD models. The personalized radiation dose ( pGRT dose )can be computed using the formula nd = GARD / (α + βd). Among patients, 1.5% T2, 46.3% T3, and 52.2% T4. Wherein pCR (n = 10; 15.6%) and no pCR (n = 54; 84.4%). The median NAR is 8.43 (rang from 0 to 50.34, IQR 3.75–14.98). NAR > 8.43 (n = 27; 42.2%) and NAR ≤ 8.43 (n = 37; 57.8%), suggesting that there are significant individual differences in clinical efficacy of patients with similar tumor stages and under the same treatment conditions. The median RSI is 0.48 (rang from 0.22 to 0.92, IQR 0.41–0.55). Median GARD was 18.40 rang from (rang from 2.26 to 37.52, IQR 14.94–22.28) within tumor tissue, suggesting individual differences in the efficacy of radiation therapy. The RSI value was significantly lower in the NAR low group (NAR ≤ 8.43) than in NAR high group (NAR > 8.43) (0.44 vs. 0.54, p = 0.0003). The GARD value was significantly higher in the NAR low group (NAR ≤ 8.43) than in NAR high group (NAR > 8.43) (21.01 vs. 15.88, p = 0.0004). Using the Receiver Operating Characteristic (ROC) curve analysis, a GARD threshold of 17 was identified as optimal, covering 37.5% of the 64-patient sample, with an area under the curve (AUC) of 0.75. In the external validation cohort, the high GARD score group demonstrated superior DFS compared to the low GARD score group(p < 0.001). Only 17% of patients had pGRT dose within the guideline recommended dose (45–50 Gy). The differences in NAR values among LARC patients receiving standard neoadjuvant radiotherapy suggest significant individual differences in clinical outcomes among patients with similar tumor stage and the same treatment conditions. Patients with a GARD value exceeding 17 exhibit a more favorable prognosis. Our results suggest that the gene expression-based pGRT™ algorithm has good efficacy prediction performance in preoperative concurrent radiotherapy for locally advanced rectal cancer, suggesting the potential clinical application of this method to guide the designation of individualized radiotherapy doses.

Improving data interpretability with new differential sample variance gene set tests.

Gene set analysis methods have played a major role in generating biological interpretations from omics data such as gene expression datasets. However, most methods focus on detecting homogenous pattern changes in mean expression and methods detecting pattern changes in variance remain poorly explored. While a few studies attempted to use gene-level variance analysis, such approach remains under-utilized. When comparing two phenotypes, gene sets with distinct changes in subgroups under one phenotype are overlooked by available methods although they reflect meaningful biological differences between two phenotypes. Multivariate sample-level variance analysis methods are needed to detect such pattern changes. We use ranking schemes based on minimum spanning tree to generalize the Cramer-Von Mises and Anderson-Darling univariate statistics into multivariate gene set analysis methods to detect differential sample variance or mean. We characterize these methods in addition to two methods developed earlier using simulation results with different parameters. We apply the developed methods to microarray gene expression dataset of prednisolone-resistant and prednisolone-sensitive children diagnosed with B-lineage acute lymphoblastic leukemia and bulk RNA-sequencing gene expression dataset of benign hyperplastic polyps and potentially malignant sessile serrated adenoma/polyps. One or both of the two compared phenotypes in each of these datasets have distinct molecular subtypes that contribute to heterogeneous differences. Our results show that methods designed to detect differential sample variance are able to detect specific hallmark signaling pathways associated with the two compared phenotypes as documented in available literature. The results in this study demonstrate the usefulness of methods designed to detect differential sample variance in providing biological interpretations when biologically relevant but heterogeneous changes between two phenotypes are prevalent in specific signaling pathways. Software implementation of the developed methods is available with detailed documentation from Bioconductor package GSAR. The available methods are applicable to gene expression datasets in a normalized matrix form and could be used with other omics datasets in a normalized matrix form with available collection of feature sets.

Microarray classification using genetic algorithm and latin hypercube sampling

Cancer, the second leading cause of global death, requires advanced diagnostic technology. Microarray gene expression technology plays an important role in comprehensively analyzing the genetic aspects of cancer. However, challenges such as high-dimensional attributes, limited samples, and varying gene presence rates hinder the accurate classification of microarray data. This study proposes a model that uses latin hypercube sampling (LHS) in genetic algorithms (GA) for Feature Selection in microarray data classification. LHS makes the chromosome samples in the initial population of GAs representative and diverse. The study used three microarray datasets with different numbers of features and classes. The results reveal that first, the use of GA alone tends to limit the exploration of the resulting feature space, while the use of LHS can expand the feature selection possibilities in the context of feature selection. Secondly, this study shows that microarray classification using GA with LHS (GALHS) consistently outperforms other feature selection methods such as based correlation features (BCF), principal component analysis (PCA), relief, and lasso. Thus, this research contributes to feature selection by applying LHS and GA to optimize the performance of microarray data classification models.

A Bioinformatics Investigation of Hub Genes Involved in Treg Migration and Its Synergistic Effects, Using Immune Checkpoint Inhibitors for Immunotherapies.

This study aimed to identify hub genes involved in regulatory T cell (Treg) function and migration, offering insights into potential therapeutic targets for cancer immunotherapy. We performed a comprehensive bioinformatics analysis using three gene expression microarray datasets from the GEO database. Differentially expressed genes (DEGs) were identified to pathway enrichment analysis to explore their functional roles and potential pathways. A protein-protein interaction network was constructed to identify hub genes critical for Treg activity. We further evaluated the co-expression of these hub genes with immune checkpoint proteins (PD-1, PD-L1, CTLA4) and assessed their prognostic significance. Through this comprehensive analysis, we identified CCR8 as a key player in Treg migration and explored its potential synergistic effects with ICIs. Our findings suggest that CCR8-targeted therapies could enhance cancer immunotherapy outcomes, with breast invasive carcinoma (BRCA) emerging as a promising indication for combination therapy. This study highlights the potential of CCR8 as a biomarker and therapeutic target, contributing to the development of targeted cancer treatment strategies.

Identification and functional analysis of genes selected using different chemometric techniques on multiarray expression data of liver from high-fat diet treated mice

The prevalence of obesity is increasing, and this lifestyle disease is related to a high-fat diet, a surplus in caloric intake, and increased inflammation. This study aimed to use a publicly available dataset of microarray gene expression data from the liver of high-fat diet fed mice (GSE39549) to determine the functional importance of small subsets of the overall genes. The regulatory aspects of the chosen mice genes were extrapolated to human genes for the determination of potential diagnostic and therapeutic targets. The chemometric approaches of principal component analysis (PCA), random forest (RF), and genetic algorithm (GA) were used as data reduction techniques to select 50 genes from a total of 15,000 genes to differentiate liver samples from high-fat diet and normal diet-fed mice. A subset of 30 genes from each of the techniques were processed with classification techniques of k-nearest neighbor and support vector machines. The results showed that random forest was best at differentiating the samples and GA was the least accurate. The results of functional annotation and protein-protein interactions showed that genes selected by PCA and RF were more associated with obesity as they identified functions related to inflammatory processes, as well as lipid and cholesterol metabolic processes. The genes selected by GA identified processes related to cilium and cell projection. The proteins identified by RF, such as Msmo and Sqle, had roles in cholesterol metabolic and biosynthetic processes. The results showed that combining the genes selected by PCA and RF allowed a better understanding of the overall functional protein modules. The crosstalk genes such as Abcg5 as well as Abcg8 that relate cholesterol metabolic and biosynthetic process to glutathione metabolic process were identified. Various miRNAs-gene interactions are present in humans for most of the genes identified by PCA, RF, or GA. Some genes that showed fewer interactions with human miRNAs are CIDEA, PLIN4, and NME8. The results suggest the use of different chemometric analyses in combination with functional genomics to identify different sets of targets for diagnostic, therapeutic, and future research.

Novel biomarkers associated with oxidative stress and immune infiltration in intervertebral disc degeneration based on bioinformatics approaches

BackgroundThe etiology of intervertebral disc degeneration (IVDD), a prevalent degenerative disease in the elderly, remains to be fully elucidated. The objective of this study was to identify immune infiltration and oxidative stress (OS) biomarkers in IVDD, aiming to provide further insights into the intricate pathogenesis of IVDD. MethodsThe Gene Expression microarrays were obtained from the Gene Expression Omnibus (GEO) database. We conducted enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. Subsequently, the R language packages CIBERSORT, MCPcounter, and WGCNA were employed to compare immune infiltration levels between IVDD samples and control samples. A protein-protein interaction (PPI) network was constructed using the Search Tools for the Retrieval of Interacting Genes (STRING) database to identify significant gene clusters. To identify hub genes, we employed Cytoscape's Molecular Complex Detection (MCODE) plug-in. The mRNA levels of hub genes in the cell model were validated by qPCR, while Western blotting was used to validate their protein levels. ResultsThe GSE70362 dataset from the GEO database identified a total of 1799 genes that were differentially expressed. Among these, 43 genes were found to be differentially expressed and also associated with OS. The differentially expressed genes associated with OS and the immune-related module genes identified through WGCNA were further intersected, resulting in the identification of 10 key genes that were differentially expressed and played crucial roles in both immune response and OS. Subsequently, we validated four diagnostic markers (PPIA, MAP3K5, PXN, and JAK2) using the GSE122429 external dataset. In a cellular model of OS in NP cells, we have identified the upregulation of PPIA and PXN genes, which could serve as novel markers for IVDD. ConclusionThe study successfully identified and validated differentially expressed genes associated with oxidative stress and immune infiltration in IVDD samples compared to normal ones. Notably, the newly discovered biomarkers PPIA and PXN have not been previously reported in IVDD-related research.