RNA-Seq transcriptome analysis shows anti-tumor actions of melatonin in a breast cancer xenograft model

Single-cell spatial transcriptomics reveals a dystrophic trajectory following a developmental bifurcation of myoblast cell fates in facioscapulohumeral muscular dystrophy

Facioscapulohumeral muscular dystrophy (FSHD) is linked to abnormal derepression of the transcription activator DUX4. This effect is localized to a low percentage of cells, requiring single-cell analysis. However, single-cell/nucleus RNA-seq cannot fully capture the transcriptome of multinucleated large myotubes. To circumvent these issues, we use multiplexed error-robust fluorescent in situ hybridization (MERFISH) spatial transcriptomics that allows profiling of RNA transcripts at a subcellular resolution. We simultaneously examined spatial distributions of 140 genes, including 24 direct DUX4 targets, in in vitro differentiated myotubes and unfused mononuclear cells (MNCs) of control, isogenic D4Z4 contraction mutant and FSHD patient samples, as well as the individual nuclei within them. We find myocyte nuclei segregate into two clusters defined by the expression of DUX4 target genes, which is exclusively found in patient/mutant nuclei, whereas MNCs cluster based on developmental states. Patient/mutant myotubes are found in "FSHD-hi" and "FSHD-lo" states with the former signified by high DUX4 target expression and decreased muscle gene expression. Pseudotime analyses reveal a clear bifurcation of myoblast differentiation into control and FSHD-hi myotube branches, with variable numbers of DUX4 target-expressing nuclei found in multinucleated FSHD-hi myotubes. Gene coexpression modules related to extracellular matrix and stress gene ontologies are significantly altered in patient/mutant myotubes compared with the control. We also identify distinct subpathways within the DUX4 gene network that may differentially contribute to the disease transcriptomic phenotype. Taken together, our MERFISH-based study provides effective gene network profiling of multinucleated cells and identifies FSHD-induced transcriptomic alterations during myoblast differentiation.

Transcriptomic, Proteomic, and Genomic Mutational Fraction Differences Based on HPV Status Observed in Patient-Derived Xenograft Models of Penile Squamous Cell Carcinoma

Metastatic penile squamous cell carcinoma (PSCC) has only a 50% response rate to first-line combination chemotherapies and there are currently no targeted-therapy approaches. Therefore, we have an urgent need in advanced-PSCC treatment to find novel therapies. Approximately half of all PSCC cases are positive for high-risk human papillomavirus (HR-HPV). Our objective was to generate HPV-positive (HPV+) and HPV-negative (HPV-) patient-derived xenograft (PDX) models and to determine the biological differences between HPV+ and HPV- disease. We generated four HPV+ and three HPV- PSCC PDX animal models by directly implanting resected patient tumor tissue into immunocompromised mice. PDX tumor tissue was found to be similar to patient tumor tissue (donor tissue) by histology and short tandem repeat fingerprinting. DNA mutations were mostly preserved in PDX tissues and similar APOBEC (apolipoprotein B mRNA editing catalytic polypeptide) mutational fractions in donor tissue and PDX tissues were noted. A higher APOBEC mutational fraction was found in HPV+ versus HPV- PDX tissues (p = 0.044), and significant transcriptomic and proteomic expression differences based on HPV status included p16 (CDKN2A), RRM2, and CDC25C. These models will allow for the direct testing of targeted therapies in PSCC and determine their response in correlation to HPV status.

Integrating differential expression, co-expression and gene network analysis for the identification of common genes associated with tumor angiogenesis deregulation

Angiogenesis is essential for tumor growth and cancer metastasis. Identifying the molecular pathways involved in this process is the first step in the rational design of new therapeutic strategies to improve cancer treatment. In recent years, RNA-seq data analysis has helped to determine the genetic and molecular factors associated with different types of cancer. In this work we performed integrative analysis using RNA-seq data from human umbilical vein endothelial cells (HUVEC) and patients with angiogenesis-dependent diseases to find genes that serve as potential candidates to improve the prognosis of tumor angiogenesis deregulation and understand how this process is orchestrated at the genetic and molecular level. We downloaded four RNA-seq datasets (including cellular models of tumor angiogenesis and ischaemic heart disease) from the Sequence Read Archive. Our integrative analysis includes a first step to determine differentially and co-expressed genes. For this, we used the ExpHunter Suite, an R package that performs differential expression, co-expression and functional analysis of RNA-seq data. We used both differentially and co-expressed genes to explore the human gene interaction network and determine which genes were found in the different datasets that may be key for the angiogenesis deregulation. Finally, we performed drug repositioning analysis to find potential targets related to angiogenesis inhibition. We found that that among the transcriptional alterations identified, SEMA3D and IL33 genes are deregulated in all datasets. Microenvironment remodeling, cell cycle, lipid metabolism and vesicular transport are the main molecular pathways affected. In addition to this, interacting genes are involved in intracellular signaling pathways, especially in immune system and semaphorins, respiratory electron transport and fatty acid metabolism. The methodology presented here can be used for finding common transcriptional alterations in other genetically-based diseases.

Therapeutic Potential of Melatonin Counteracting Chemotherapy-Induced Toxicity in Breast Cancer Patients: A Systematic Review

The purpose of this systematic review is to provide an overview of the existing knowledge on the therapeutic potential of melatonin to counteract the undesirable effects of chemotherapy in breast cancer patients. To this aim, we summarized and critically reviewed preclinical- and clinical-related evidence according to the PRISMA guidelines. Additionally, we developed an extrapolation of melatonin doses in animal studies to the human equivalent doses (HEDs) for randomized clinical trials (RCTs) with breast cancer patients. For the revision, 341 primary records were screened, which were reduced to 8 selected RCTs that met the inclusion criteria. We assembled the evidence drawn from these studies by analyzing the remaining gaps and treatment efficacy and suggested future translational research and clinical trials. Overall, the selected RCTs allow us to conclude that melatonin combined with standard chemotherapy lines would derive, at least, a better quality of life for breast cancer patients. Moreover, regular doses of 20 mg/day seemed to increase partial response and 1-year survival rates. Accordingly, this systematic review leads us to draw attention to the need for more RCTs to provide a comprehensive view of the promising actions of melatonin in breast cancer and, given the safety profile of this molecule, adequate translational doses should be established in further RCTs.

Antitumor effect of melatonin on breast cancer in experimental models: A systematic review

Breast cancer is the most frequent malignant neoplasm in females. While conventional treatments such as chemotherapy and radiotherapy are available, they are highly invasive and toxic to oncological patients. Melatonin is a promising molecule for the treatment of breast cancer with antitumor effects on tumorigenesis and tumor progression. The aim of this systematic review was to synthesize knowledge about the antitumor effect of melatonin on breast cancer in experimental models and propose the main mechanisms of action already described in relation to the processes regulated by melatonin. PubMed, Web of Science, and Embase databases were used. The inclusion criteria were in vitro and in vivo experimental studies that used different formulations of melatonin as a treatment for breast cancer, without year or language restrictions. Risk of bias for studies was assessed using the Systematic Review Center for Laboratory Animal Experimentation (SYRCLE) tool. Data from selected articles were presented as narrative descriptions and tables. Seventy-five articles on different breast cancer cell lines and experimental models treated with melatonin alone, or in combination with other compounds were included. Melatonin showed antitumor effects on proliferative pathways related to the cell cycle and tumorigenesis, tumor death, angiogenesis, and tumor metastasis, as well as on oxidative stress and immune regulatory pathways. These effects were either dependent or independent of melatonin receptors. Herein, we clarify the antitumor action of melatonin on different tumorigenic processes in breast cancer in experimental models. Systematic review registration: PROSPERO database (CRD42022309822/https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022309822).

Whole transcriptome profiling of liquid biopsies from tumour xenografted mouse models enables specific monitoring of tumour-derived extracellular RNA

While cell-free DNA (cfDNA) is widely being investigated, free circulating RNA (extracellular RNA, exRNA) has the potential to improve cancer therapy response monitoring and detection due to its dynamic nature. However, it remains unclear in which blood subcompartment tumour-derived exRNAs primarily reside. We developed a host-xenograft deconvolution framework, exRNAxeno, with mapping strategies to either a combined human-mouse reference genome or both species genomes in parallel, applicable to exRNA sequencing data from liquid biopsies of human xenograft mouse models. The tool enables to distinguish (human) tumoural RNA from (murine) host RNA, to specifically analyse tumour-derived exRNA. We applied the combined pipeline to total exRNA sequencing data from 95 blood-derived liquid biopsy samples from 30 mice, xenografted with 11 different tumours. Tumoural exRNA concentrations are not determined by plasma platelet levels, while host exRNA concentrations increase with platelet content. Furthermore, a large variability in exRNA abundance and transcript content across individual mice is observed. The tumoural gene detectability in plasma is largely correlated with the RNA expression levels in the tumour tissue or cell line. These findings unravel new aspects of tumour-derived exRNA biology in xenograft models and open new avenues to further investigate the role of exRNA in cancer.

Quantitative Proteogenomic Characterization of Inflamed Murine Colon Tissue Using an Integrated Discovery, Verification, and Validation Proteogenomic Workflow

Chronic inflammation of the colon causes genomic and/or transcriptomic events, which can lead to expression of non-canonical protein sequences contributing to oncogenesis. To better understand these mechanisms, Rag2^−/−Il10^−/− mice were infected with Helicobacter hepaticus to induce chronic inflammation of the cecum and the colon. Transcriptomic data from harvested proximal colon samples were used to generate a customized FASTA database containing non-canonical protein sequences. Using a proteogenomic approach, mass spectrometry data for proximal colon proteins were searched against this custom FASTA database using the Galaxy for Proteomics (Galaxy-P) platform. In addition to the increased abundance in inflammatory response proteins, we also discovered several non-canonical peptide sequences derived from unique proteoforms. We confirmed the veracity of these novel sequences using an automated bioinformatics verification workflow with targeted MS-based assays for peptide validation. Our bioinformatics discovery workflow identified 235 putative non-canonical peptide sequences, of which 58 were verified with high confidence and 39 were validated in targeted proteomics assays. This study provides insights into challenges faced when identifying non-canonical peptides using a proteogenomics approach and demonstrates an integrated workflow addressing these challenges. Our bioinformatic discovery and verification workflow is publicly available and accessible via the Galaxy platform and should be valuable in non-canonical peptide identification using proteogenomics.

Melatonin and Pathological Cell Interactions: Mitochondrial Glucose Processing in Cancer Cells

Melatonin is synthesized in the pineal gland at night. Since melatonin is produced in the mitochondria of all other cells in a non-circadian manner, the amount synthesized by the pineal gland is less than 5% of the total. Melatonin produced in mitochondria influences glucose metabolism in all cells. Many pathological cells adopt aerobic glycolysis (Warburg effect) in which pyruvate is excluded from the mitochondria and remains in the cytosol where it is metabolized to lactate. The entrance of pyruvate into the mitochondria of healthy cells allows it to be irreversibly decarboxylated by pyruvate dehydrogenase (PDH) to acetyl coenzyme A (acetyl-CoA). The exclusion of pyruvate from the mitochondria in pathological cells prevents the generation of acetyl-CoA from pyruvate. This is relevant to mitochondrial melatonin production, as acetyl-CoA is a required co-substrate/co-factor for melatonin synthesis. When PDH is inhibited during aerobic glycolysis or during intracellular hypoxia, the deficiency of acetyl-CoA likely prevents mitochondrial melatonin synthesis. When cells experiencing aerobic glycolysis or hypoxia with a diminished level of acetyl-CoA are supplemented with melatonin or receive it from another endogenous source (pineal-derived), pathological cells convert to a more normal phenotype and support the transport of pyruvate into the mitochondria, thereby re-establishing a healthier mitochondrial metabolic physiology.

3D RNA-seq: a powerful and flexible tool for rapid and accurate differential expression and alternative splicing analysis of RNA-seq data for biologists

RNA-sequencing (RNA-seq) analysis of gene expression and alternative splicing should be routine and robust but is often a bottleneck for biologists because of different and complex analysis programs and reliance on specialized bioinformatics skills. We have developed the '3D RNA-seq' App, an R shiny App and web-based pipeline for the comprehensive analysis of RNA-seq data from any organism. It represents an easy-to-use, flexible and powerful tool for analysis of both gene and transcript-level gene expression to identify differential gene/transcript expression, differential alternative splicing and differential transcript usage (3D) as well as isoform switching from RNA-seq data. 3D RNA-seq integrates state-of-the-art differential expression analysis tools and adopts best practice for RNA-seq analysis. The program is designed to be run by biologists with minimal bioinformatics experience (or by bioinformaticians) allowing lab scientists to analyse their RNA-seq data. It achieves this by operating through a user-friendly graphical interface which automates the data flow through the programs in the pipeline. The comprehensive analysis performed by 3D RNA-seq is extremely rapid and accurate, can handle complex experimental designs, allows user setting of statistical parameters, visualizes the results through graphics and tables, and generates publication quality figures such as heat-maps, expression profiles and GO enrichment plots. The utility of 3D RNA-seq is illustrated by analysis of data from a time-series of cold-treated Arabidopsis plants and from dexamethasone-treated male and female mouse cortex and hypothalamus data identifying dexamethasone-induced sex- and brain region-specific differential gene expression and alternative splicing.

3D RNA-seq: a powerful and flexible tool for rapid and accurate differential expression and alternative splicing analysis of RNA-seq data for biologists

RNA-sequencing (RNA-seq) analysis of gene expression and alternative splicing should be routine and robust but is often a bottleneck for biologists because of different and complex analysis programs and reliance on specialized bioinformatics skills. We have developed the '3D RNA-seq' App, an R shiny App and web-based pipeline for the comprehensive analysis of RNA-seq data from any organism. It represents an easy-to-use, flexible and powerful tool for analysis of both gene and transcript-level gene expression to identify differential gene/transcript expression, differential alternative splicing and differential transcript usage (3D) as well as isoform switching from RNA-seq data. 3D RNA-seq integrates state-of-the-art differential expression analysis tools and adopts best practice for RNA-seq analysis. The program is designed to be run by biologists with minimal bioinformatics experience (or by bioinformaticians) allowing lab scientists to analyse their RNA-seq data. It achieves this by operating through a user-friendly graphical interface which automates the data flow through the programs in the pipeline. The comprehensive analysis performed by 3D RNA-seq is extremely rapid and accurate, can handle complex experimental designs, allows user setting of statistical parameters, visualizes the results through graphics and tables, and generates publication quality figures such as heat-maps, expression profiles and GO enrichment plots. The utility of 3D RNA-seq is illustrated by analysis of data from a time-series of cold-treated Arabidopsis plants and from dexamethasone-treated male and female mouse cortex and hypothalamus data identifying dexamethasone-induced sex- and brain region-specific differential gene expression and alternative splicing.

Modulation of the tumor microenvironment (TME) by melatonin

The tumor microenvironment (TME) includes a number of non-cancerous cells that affect cancer cell survival. Although CD8+ T lymphocytes and natural killer (NK) cells suppress tumor growth through induction of cell death in cancer cells, there are various immunosuppressive cells such as regulatory T cells (Tregs), tumor-associated macrophages (TAMs), cancer-associated fibroblasts (CAFs), myeloid-derived suppressor cells (MDSCs), etc., which drive cancer cell proliferation. These cells may also support tumor growth and metastasis by stimulating angiogenesis, epithelial-mesenchymal transition (EMT), and resistance to apoptosis. Interactions between cancer cells and other cells, as well as molecules released into EMT, play a key role in tumor growth and suppression of antitumoral immunity. Melatonin is a natural hormone that may be found in certain foods and is also available as a drug. Melatonin has been demonstrated to modulate cell activity and the release of cytokines and growth factors in TME. The purpose of this review is to explain the cellular and molecular mechanisms of cancer cell resistance as a result of interactions with TME. Next, we explain how melatonin affects cells and interactions within the TME.

Melatonin: A regulator of the interplay between FoxO1, miR96, and miR215 signaling to diminish the growth, survival, and metastasis of murine adenocarcinoma

Melatonin (Mel.), also known as the magic hormone, is a nocturnally secreted hormone orchestrates the clearance of free radicals that have been built up and cumulated during day. This study aims to detect the impact of pineal gland removal on the incidence of tumor development and to assess the signaling pathways via which exogenous melatonin counteract cancer growth. This goal has been achieved by novel approach for pineal destruction using dental micromotor which validated by melatonin downregulation in blood plasma. Mice were injected sub-cutenously with Ehrlich cells to develop solid tumor as a murine model of breast cancer. The increase at tumor markers carcino embryonic antigen, TNFα, and nuclear factor kappa-light-chain-enhancer of activated B cells was over countered by exogenous melatonin supplementation (20 mg/kg) daily for 1 month. The anticancer effects of melatonin were significantly mediated by scavenging H₂ O₂ and NO and diminishing of lipid peroxidation marker malondialdehyde. The real-time polymerase chain Rx analyses indicated a significant effect of Melatonin in upregulating the expression of miR215, fork head box protein O1 (foxO1), and downregulation of miR96. Flowcytometric analyses indicated a significant effect of melatonin on induction of cell cycle arrest at G1 phase which was further confirmed by Ki67 downregulation. Immunohistochemical analyses indicated the role of melatonin in upregulating P53-dependent apoptosis and downregulating CD44 signaling for survivin, matrix metallo-protein kinase 2, and vascular endothelial growth factor to inhibit cell survival and metastasis. In conclusion, this study sheds the light on M./P53/miR215/CD44 with an emphasis on M./miR96//foxO1 signaling cascades, as a novel pathway of melatonin signaling in adenocarcinoma to diminish cancer cell growth, survival and metastasis.

Application of Weighted Gene Coexpression Network Analysis to Identify Key Modules and Hub Genes in Systemic Juvenile Idiopathic Arthritis

Systemic juvenile idiopathic arthritis (sJIA) is a severe autoinflammatory disorder with a still not clearly defined molecular mechanism. To better understand the disease, we used scattered datasets from public domains and performed a weighted gene coexpression network analysis (WGCNA) to identify key modules and hub genes underlying sJIA pathogenesis. Two gene expression datasets, GSE7753 and GSE13501, were used to construct the WGCNA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were applied to the genes and hub genes in the sJIA modules. Cytoscape was used to screen and visualize the hub genes. We further compared the hub genes with the genome-wide association study (GWAS) genes and used a consensus WGCNA to verify that our conclusions were conservative and reproducible across multiple independent datasets. A total of 5,414 genes were obtained for WGCNA, from which highly correlated genes were divided into 17 modules. The red module demonstrated the highest correlation with the sJIA module (r = 0.8, p = 3e⁻²⁹), whereas the green-yellow module was found to be closely related to the non-sJIA module (r = 0.62, p = 1e⁻¹⁴). Functional enrichment analysis demonstrated that the red module was mostly enriched in the activation of immune responses, infection, nucleosomes, and erythrocytes, and the green-yellow module was mostly enriched in immune responses and inflammation. Additionally, the hub genes in the red module were highly enriched in erythrocyte differentiation, including ALAS2, AHSP, TRIM10, TRIM58, and KLF1. The hub genes from the green-yellow module were mainly associated with immune responses, as exemplified by the genes KLRB1, KLRF1, CD160, and KIRs. We identified sJIA-related modules and several hub genes that might be associated with the development of sJIA. Particularly, the modules may help understand the mechanisms of sJIA, and the hub genes may become biomarkers and therapeutic targets of sJIA in the future.

Immunomodulation: An immune regulatory mechanism in carcinoma therapeutics

Cancer has been generally related to the possession of numerous mutations which interrupt important signaling pathways. Nevertheless, deregulated immunological signaling is considered as one of the key factors associated with the development and progression of cancer. The signaling pathways operate as modular network with different components interacting in a switch-like fashion with two proteins interplaying between each other leading to direct or indirect inhibition or stimulation of down-stream factors. Genetic, epigenetic, and transcriptomic alterations maintain the pathological conduit of different signaling pathways via affecting diverse mechanisms including cell destiny. At present, immunotherapy is one of the best therapies opted for cancer treatment. The cancer immunotherapy strategy includes harnessing the specificity and killing mechanisms of the immunological system to target and eradicate malignant cells. Targeted therapies utilizing several little molecules including Galunisertib, Astragaloside-IV, Melatonin, and Jervine capable of regulating key signaling pathways can effectively help in the management of different carcinomas.

Melatonin downregulates TRPC6, impairing store-operated calcium entry in triple-negative breast cancer cells

Melatonin has been reported to induce effective reduction in growth and development in a variety of tumors, including breast cancer. In triple-negative breast cancer (TNBC) cells, melatonin attenuates a variety of cancer features, such as tumor growth and apoptosis resistance, through a number of still poorly characterized mechanisms. One biological process that is important for TNBC cells is store-operated Ca2+ entry (SOCE), which is modulated by TRPC6 expression and function. We wondered whether melatonin might intersect with this pathway as part of its anticancer activity. We show that melatonin, in the nanomolar range, significantly attenuates TNBC MDA-MB-231 cell viability, proliferation, and migration in a time- and concentration-dependent manner, without having any effect on nontumoral breast epithelial MCF10A cells. Pretreatment with different concentrations of melatonin significantly reduced SOCE in MDA-MB-231 cells without altering Ca2+ release from the intracellular stores. By contrast, SOCE in MCF10A cells was unaffected by melatonin. In the TNBC MDA-MB-468 cell line, melatonin not only attenuated viability, migration, and SOCE, but also reduced TRPC6 expression in a time- and concentration-dependent manner, without altering expression or function of the Ca2+ channel Orai1. The expression of exogenous TRPC6 overcame the effect of melatonin on SOCE and cell proliferation, and silencing or inhibition of TRPC6 impaired the inhibitory effect of melatonin on SOCE. These findings indicate that TRPC6 downregulation might be involved in melatonin's inhibitory effects on Ca2+ influx and the maintenance of cancer hallmarks and point toward a novel antitumoral mechanism of melatonin in TNBC cells.

A meta-analysis of microRNA networks regulated by melatonin in cancer: Portrait of potential candidates for breast cancer treatment

Melatonin is a ubiquitous molecule with a broad spectrum of functions including widespread anti-cancer activities. Identifying how melatonin intervenes in complex molecular signaling at the gene level is essential to guide proper therapies. Using meta-analysis approach, herein we examined the role of melatonin in regulating the expression of 46 microRNAs (miRNAs) and their target genes in breast, oral, gastric, colorectal, and prostate cancers, and glioblastoma. The deregulated miRNA-associated target genes revealed their involvement in the regulation of cellular proliferation, differentiation, apoptosis, senescence, and autophagy. Melatonin changes the expression of miRNA-associated genes in breast, gastric, and oral cancers. These genes are associated with cellular senescence, the hedgehog signaling pathway, cell proliferation, p53 signaling, and the hippo signaling pathway. Conversely, colorectal and prostate cancers as well as glioblastoma and oral carcinoma present a clear pattern of less pronounced changes in the expression of miRNA-associated genes. Most notably, colorectal cancer displayed a unique molecular change in response to melatonin. Considering breast cancer network complexity, we compared the genes found during the meta-analysis with RNA-Seq data from breast cancer-bearing mice treated with melatonin. Mechanistically, melatonin upregulated genes associated with immune responses and apoptotic processes, whereas it downregulated genes involved in cellular aggressiveness/metastasis (eg, mitosis, telomerase activity, and angiogenesis). We further characterized the expression profile of our gene subsets with human breast cancer and found eight upregulated genes and 16 downregulated genes that were appositively correlated with melatonin. Our results pose a multi-dimension network of tumor-associated genes regulated by miRNAs potentially targeted by melatonin.

Crucial transcripts predict response to initial immunoglobulin treatment in acute Kawasaki disease

Although intravenous immunoglobulin (IVIG) can effectively treat Kawasaki disease (KD), 10–20% of KD patients show no beneficial clinical response. Developing reliable criteria to discriminate non-responders is important for early planning of appropriate regimens. To predict the non-responders before IVIG treatment, gene expression dataset of 110 responders and 61 non-responders was obtained from Gene Expression Omnibus. After weighted gene co-expression network analysis, we found that modules positively correlated with the non-responders were mainly associated with myeloid cell activation. Transcripts up-regulated in the non-responders, IL1R2, GK, HK3, C5orf32, CXCL16, NAMPT and EMILIN2, were proven to play key roles via interaction with other transcripts in co-expression network. The crucial transcripts may affect the clinical response to IVIG treatment in acute KD. And these transcripts may serve as biomarkers and therapeutic targets for precise diagnosis and treatment of the non-responders.

Boosting immune system against cancer by melatonin: A mechanistic viewpoint

Cancer is a disease of high complexity. Resistance to therapy is a major challenge in cancer targeted therapies. Overcoming this resistance requires a deep knowledge of the cellular interactions within tumor. Natural killer (NK) cells and cytotoxic T lymphocytes (CTLs) are the main anti-cancer immune cells, while T regulatory cells (Tregs) and cancer associated fibroblasts (CAFs) facilitate immune escape of cancer cells. Melatonin is a natural agent with anti-cancer functions that has also been suggested as an adjuvant in combination with cancer therapy modalities such as chemotherapy, radiotherapy, immunotherapy and tumor vaccination. One of the main effects of melatonin is regulation of immune responses against cancer cells. Melatonin has been shown to potentiate the activities of anti-cancer immune cells, as well as attenuating the activities of Tregs and CAFs. It also has a potent effect on the mitochondria, which may change immune responses against cancer. In this review, we explain the mechanisms of immune regulation by melatonin involved in its anti-cancer effects.

CD36 identified by weighted gene co-expression network analysis as a hub candidate gene in lupus nephritis

Lupus nephritis (LN) is a severe manifestation of systemic lupus erythematosus (SLE), which often progresses to end-stage renal disease (ESRD) and ultimately leads to death. At present, there are no definitive therapies towards LN, so that illuminating the molecular mechanism behind the disease has become an urgent task for researchers. Bioinformatics has become a widely utilized method for exploring genes related to disease. This study set out to conduct weighted gene co-expression network analysis (WGCNA) and screen the hub gene of LN. We performed WGCNA on the microarray expression profile dataset of GSE104948 from the Gene Expression Omnibus (GEO) database with 18 normal and 21 LN samples of glomerulus. A total of 5,942 genes were divided into 5 co-expression modules, one of which was significantly correlated to LN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on the LN-related module, and the module was proved to be associated mainly with the activation of inflammation, immune response, cytokines, and immune cells. Genes in the most significant GO terms were extracted for sub-networks of WGNCA. We evaluated the centrality of genes in the sub-networks by Maximal Clique Centrality (MCC) method and CD36 was ultimately screened out as a hub candidate gene of the pathogenesis of LN. The result was verified by its differentially expressed level between normal and LN in GSE104948 and the other three multi-microarray datasets of GEO. Moreover, we further demonstrated that the expression level of CD36 is related to the WHO Lupus Nephritis Class of LN patients with the help of Nephroseq database. The current study proposed CD36 as a vital candidate gene in LN for the first time and CD36 may perform as a brand-new biomarker or therapeutic target of LN in the future.