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Abstract Tumor-associated macrophages (TAMs) residing in the tumor microenvironment (TME) are characterized by their pivotal roles in tumor progression, antitumor immunity, and TME remodeling. However, a thorough comparative characterization of tumor-TAM crosstalk across IDH-defined categories of glioma remains elusive, likely contributing to mixed outcomes in clinical trials. We delineated the phenotypic heterogeneity of TAMs across IDH-stratified gliomas. Notably, two TAM subsets with a mesenchymal phenotype were enriched in IDH-WT glioblastoma (GBM) and correlated with poorer patient survival and reduced response to anti-PD-1 immune checkpoint inhibitor (ICI). We proposed SLAMF9 receptor as a potential therapeutic target. Inference of gene regulatory networks identified PPARG, ELK1, and MXI1 as master transcription factors of mesenchymal BMD-TAMs. Our analyses of reciprocal tumor-TAM interactions revealed distinct crosstalk in IDH-WT tumors, including ANXA1-FPR1/3, FN1-ITGAVB1, VEGFA-NRP1, and TNFSF12-TNFRSF12A with known contribution to immunosuppression, tumor proliferation, invasion and TAM recruitment. Spatially resolved transcriptomics further elucidated the architectural organization of highlighted communications. Furthermore, we demonstrated significant upregulation of ANXA1, FN1, NRP1, and TNFRSF12A genes in IDH-WT tumors using bulk RNA-seq and RT-qPCR. Longitudinal expression analysis of candidate genes revealed no difference between primary and recurrent tumors indicating that the interactive network of malignant states with TAMs does not drastically change upon recurrence. Collectively, our study offers insights into the unique cellular composition and communication of TAMs in glioma TME, revealing novel vulnerabilities for therapeutic interventions in IDH-WT GBM.

Abstract Background The need for intelligent and effective treatment of diseases and the increase in drug design costs have raised drug repurposing as one of the effective strategies in biomedicine. There are various computational methods for drug repurposing, one of which is using transcription signatures, especially single-cell RNA sequencing (scRNA-seq) data, which show us a clear and comprehensive view of the inside of the cell to compare the state of disease and health. Methods In this study, we used 91,103 scRNA-seq samples from 29 patients with colorectal cancer (GSE144735 and GSE132465). First, differential gene expression (DGE) analysis was done using the ASAP website. Then we reached a list of drugs that can reverse the gene signature pattern from cancer to normal using the iLINCS website. Further, by searching various databases and articles, we found 12 drugs that have FDA approval, and so far, no one has reported them as a drug in the treatment of any cancer. Then, to evaluate the cytotoxicity and performance of these drugs, the MTT assay and real-time PCR were performed on two colorectal cancer cell lines (HT29 and HCT116). Results According to our approach, 12 drugs were suggested for the treatment of colorectal cancer. Four drugs were selected for biological evaluation. The results of the cytotoxicity analysis of these drugs are as follows: tezacaftor (IC10 = 19 µM for HCT-116 and IC10 = 2 µM for HT-29), fenticonazole (IC10 = 17 µM for HCT-116 and IC10 = 7 µM for HT-29), bempedoic acid (IC10 = 78 µM for HCT-116 and IC10 = 65 µM for HT-29), and famciclovir (IC10 = 422 µM for HCT-116 and IC10 = 959 µM for HT-29). Conclusions Cost, time, and effectiveness are the main challenges in finding new drugs for diseases. Computational approaches such as transcriptional signature-based drug repurposing methods open new horizons to solve these challenges. In this study, tezacaftor, fenticonazole, and bempedoic acid can be introduced as promising drug candidates for the treatment of colorectal cancer. These drugs were evaluated in silico and in vitro, but it is necessary to evaluate them in vivo.

Abstract Background It has been demonstrated in the literature that a dysbiotic microbiome could have a negative impact on the host immune system and promote disease onset or exacerbation. Co-occurrence networks have been widely adopted to identify biomarkers and keystone taxa in the pathogenesis of microbiome-related diseases. Despite the promising results that network-driven approaches have led to in various human diseases, there is a dearth of research pertaining to key taxa that contribute to the pathogenesis of lung cancer. Therefore, our primary goal in this study is to explore co-existing relationships among members of the lung microbial community and any potential gained or lost interactions in lung cancer. Results Using integrative and network-based approaches, we integrated four studies assessing the microbiome of lung biopsies of cancer patients. Differential abundance analyses showed that several bacterial taxa are different between tumor and tumor-adjacent normal tissues (FDR adjusted p-value

Abstract Background P4 medicine (predict, prevent, personalize, and participate) is a new approach to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and treatment of diseases, prediction plays a fundamental role. One of the intelligent strategies is the design of deep learning models that can predict the state of the disease using gene expression data. Results We create an autoencoder deep learning model called DeeP4med, including a Classifier and a Transferor that predicts cancer's gene expression (mRNA) matrix from its matched normal sample and vice versa. The range of the F1 score of the model, depending on tissue type in the Classifier, is from 0.935 to 0.999 and in Transferor from 0.944 to 0.999. The accuracy of DeeP4med for tissue and disease classification was 0.986 and 0.992, respectively, which performed better compared to seven classic machine learning models (Support Vector Classifier, Logistic Regression, Linear Discriminant Analysis, Naive Bayes, Decision Tree, Random Forest, K Nearest Neighbors). Conclusions Based on the idea of DeeP4med, by having the gene expression matrix of a normal tissue, we can predict its tumor gene expression matrix and, in this way, find effective genes in transforming a normal tissue into a tumor tissue. Results of Differentially Expressed Genes (DEGs) and enrichment analysis on the predicted matrices for 13 types of cancer showed a good correlation with the literature and biological databases. This led that by using the gene expression matrix, to train the model with features of each person in a normal and cancer state, this model could predict diagnosis based on gene expression data from healthy tissue and be used to identify possible therapeutic interventions for those patients.

Abstract Background Spinal muscular atrophy (SMA) could be classified as 5q and non-5q, based on the chromosomal location of causative genes. A rare form of non-5q SMA is an autosomal-recessive condition called spinal muscular atrophy with progressive myoclonic epilepsy (SMA-PME), phenotypically characterized by myoclonic and generalized seizures with progressive neurological deterioration. SMA-PME is a clinically heterogeneous disorder that arises from biallelic pathogenic variants in ASAH1 gene. Methods Following clinical and primary laboratory assessments, whole-exome sequencing was performed to detect the disease-causing variants in three cases of SMA-PME from different families. Also, Multiplex ligation-dependent probe amplification (MLPA) was employed for determining the copy numbers of SMN1 and SMN2 genes to rule out 5q SMA. Results Exome sequencing revealed two different homozygous missense mutations (c.109 C > A [p.Pro37Thr] or c.125 C > T [p.Thr42Met]) in exon 2 of the ASAH1 gene in the affected members of the families. Sanger sequencing of the other family members showed the expected heterozygous carriers. In addition, no clinically relevant variant was identified in patients by MLPA. Conclusion This study describes two different ASAH1 mutations and the clinical picture of 3 SMA-PME patients. In addition, previously reported mutations have been reviewed. This study could help to fortify the database of this rare disease with more clinical and genomic data.

Abstract During Iraq-Iran conflict, chemical weapons, particularly SM gas, were used numerous times, whose aftereffects are still present. This study aimed to compare serum proteome in the chronic ML (n = 10) and HC (n = 10). TMT label-based quantitative proteomics was used to examine serums from two groups. Among total significant proteins, 14 proteins were upregulated (log2 ≥ FC 0.5, p 0.05), and 6 proteins were downregulated (log2 ≤ FC − 0.5, p 0.05). By helping PPI network, and EA, 11 main pathways connected to significantly different protein expression levels were discovered, including inflammatory and cell adhesion signaling pathways. It may be deduced that the wounded organs of exposed individuals experience poor repair cycles of cell degeneration and regeneration because certain repair signals were elevated while other structural and adhesion molecules were downregulated. The systems biology approach can help enhance our basic knowledge of biological processes, and contribute to a deeper understanding of pathophysiological mechanisms, as well as the identification of potential biomarkers of disease.

Abstract Background asthma, chronic obstructive pulmonary disease (COPD), and idiopathic pulmonary fibrosis (IPF) are three serious pulmonary diseases that contain common and unique characteristics. Therefore, the identification of biomarkers that differentiate these diseases is of importance for preventing misdiagnosis. In this regard, the present study aimed to identify the disorders at the early stages, based on lung transcriptomics data and drug-target interactions. Methods To this end, the differentially expressed genes were found in each disease. Then, WGCNA was utilized to find specific and consensus gene modules among the three diseases. Finally, the disease-disease similarity was analyzed, followed by determining candidate drug-target interactions. Results The results confirmed that the asthma lung transcriptome was more similar to COPD than IPF. In addition, the biomarkers were found in each disease and thus were proposed for further clinical validations. These genes included RBM42, STX5, and TRIM41 in asthma, CYP27A1, GM2A, LGALS9, SPI1, and NLRC4 in COPD, ATF3, PPP1R15A, ZFP36, SOCS3, NAMPT, and GADD45B in IPF, LRRC48 and CETN2 in asthma-COPD, COL15A1, GIMAP6, and JAM2 in asthma-IPF and LMO7, TSPAN13, LAMA3, and ANXA3 in COPD-IPF. Finally, analyzing drug-target networks suggested anti-inflammatory candidate drugs for treating the above mentioned diseases. Conclusion In general, the results revealed the unique and common biomarkers among three chronic lung diseases. Eventually, some drugs were suggested for treatment purposes.

Abstract Background TEK signaling plays a very important role in folliculogenesis. It activates Ras/ERK/MYC, PI3K/AKT/mTORC1 and ovarian steroidogenesis activation pathways. These are the main pathways for cell growth, differentiation, migration, adhesion, proliferation, survival and protein synthesis. Results TEK signaling on each of the two important pathways where levels of pERK, pMYC, pAkt, pMCL1 and pEIF4EBP1 are increased in dominant follicles and pMYC is decreased in dominant follicles. Over activation of ERK and MYC which are the main cell growth and proliferation and over activation of Akt, MCl1, mTORC1 and EIF4EBP1 which are the main cell survival and protein synthesis factors act as promoting factors for folliculogenesis. In case of over expression of hsa-miR-30d-3p and hsa-miR-451a, MYC activity level is considerably increased in subordinate follicles. Our simulation results show that in the presence of has-miR-548v and bta-miR-22-3p, downstream factors of pathways are inhibited. Conclusions Our work offers insight into the design of natural biological procedures and makes predictions that can guide further experimental studies on folliculogenesis pathways. Moreover, it defines a simple signal processing unit that may be useful for engineering synthetic biology and genes circuits to carry out cell-based computation.

Abstract Exosomes which are membrane vesicles released by cells have gained significant interest in the field of cancer therapy as a novel means of intercellular communication. Their role in immune activation and their pathophysiological functions in cancer therapy have been recognized. Exosomes carry diverse bioactive components including proteins, mRNA, microRNAs, and bioactive lipids. These molecules have therapeutic potential in promoting tissue regeneration, supporting stem cell activity, preventing cell death, modulating immune responses, and promoting the growth of new blood vessels. However, the precise roles of exosomes derived from mesenchymal stem cells (MSCs) in the treatment of various cancers are still not fully understood. Consequently, cancer stem cells (CSCs) can self-renew and differentiate into various cell types. Understanding the mechanisms that sustain their persistence is crucial for developing effective therapies. Exosomes have recently gained interest as vehicles for intercellular communication between CSCs and non-CSCs, influencing cancer progression and the microenvironment. Research is ongoing on the utilization of exosomes derived from cancer stem cells (CSC-Exosome) for cancer treatment. The composition of extracellular vesicles is influenced by the specific type and condition of the cells from which they are secreted. Circulating exosomes contain stable RNA molecules such as mRNAs, microRNAs, and long non-coding RNAs (lncRNAs). In this review, we will explore the significance of exosomes and their diverse cellular combinations in the context of cancer therapy. Graphical Abstract