Identification of Core Genes and Pathways in Melanoma Metastasis via Bioinformatics Analysis

AS03-adjuvanted H5N1 vaccine enhances immune response by modulating NR4A1, SDC1, ID3 genes, and reducing cortisol

The AS03-adjuvanted H5N1 influenza vaccine induces significantly higher immune responses compared to the non-adjuvanted H5N1 vaccine. However, the immunological mechanisms underlying this enhancement remain unclear. We aimed to identify the key genes and pathways involved in the immune response to the AS03-adjuvanted H5N1 vaccine compared to the non-adjuvanted H5N1 vaccine. The expression profiles of GSE102012 and GSE112293 were downloaded from the Gene Expression Omnibus database to identify differentially expressed genes between AS03-adjuvanted and non-adjuvanted H5N1 vaccine groups. Subsequently, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery online tool. The protein-protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes database. Through cluster analysis of the PPI network, three hub genes, namely NR4A1, SDC1, and ID3, were identified as pivotal players in the intricate network of interactions. The ID3 was up-regulated, and the other two hub genes were down-regulated. The results of the GO analysis highlighted enrichment in seven biological processes, three cellular components, and two molecular functions among the differentially expressed genes. The KEGG pathway analysis revealed the involvement of the Cushing syndrome pathway. The AS03-adjuvanted H5N1 vaccine may enhance immune responses through suppressing the NR4A1 gene and the SDC1 gene, upregulating the ID3 gene, and reducing cortisol production compared to the non-adjuvanted H5N1 vaccine.

Targeting CDK1 and copper homeostasis in breast cancer via a nanopolymer drug delivery system

The prevalence of breast cancer (BRCA) is notable in the female population, being a commonly diagnosed malignancy, where the management of copper levels is crucial for treatment success. This research aims to explore the influence of copper homeostasis on BRCA therapy, with a specific focus on the role of Cyclin-Dependent Kinase 1 (CDK1) and its relationship to copper regulation. A novel thermosensitive hydrogel incorporating nanoparticles (NPs) was engineered to synergize with the chemotherapy drug vincristine (VCR) in inhibiting tumor growth and metastasis. Through a comprehensive approach involving bioinformatics analyses, in vitro experiments, and in vivo models, the study identified CDK1 as a significant factor in BRCA progression under copper homeostasis. MBVP-Gel, a novel thermosensitive hydrogel incorporating NPs, was developed to enhance the delivery of chemotherapy drugs and regulate copper homeostasis in breast cancer treatment. The MBVP-Gel, formulated with copper chelation and VCR NPs, effectively suppressed CDK1 expression, thereby restraining BRCA cell growth and metastasis while enhancing the therapeutic impact of VCR. This investigation offers fresh insights and experimental validation on the interaction between copper homeostasis and BRCA, providing a valuable foundation for refining future treatment strategies. These findings underscore the potential advantages of targeting copper homeostasis and CDK1 in enhancing BRCA therapy, setting the stage for individualized interventions and improved patient consequences.

Novel insights into the role of quercetin and kaempferol from Carthamus tinctorius L. in the management of nonalcoholic fatty liver disease via NR1H4-mediated pathways

This study investigates the novel therapeutic potential of quercetin and kaempferol, two bioactive compounds derived from Carthamus tinctorius L., in treating nonalcoholic fatty liver disease (NAFLD) by modulating the bile acid receptor NR1H4 (Nuclear Receptor Subfamily 1 Group H Member 4) and its associated metabolic pathways. A rat model of NAFLD was established, and RNA sequencing and proteomics were carefully employed to identify differential gene expressions associated with the disease. The active components of Carthamus tinctorius L. were screened, followed by the construction of a comprehensive network that maps the interactions between these components, NR1H4 and NAFLD-related pathways. Both in vitro (using HepG2 cells) and in vivo experiments were conducted to evaluate the effects on NR1H4 expression levels through Western blot and RT-qPCR analyses. Our findings identify NR1H4 as a pivotal target in NAFLD. Network pharmacology analysis indicates that quercetin and kaempferol play crucial roles in combating NAFLD, with in vitro and in vivo experiments confirming their ability to mitigate hepatocyte steatosis by enhancing NR1H4 expression. Notably, the protective effects of these compounds were inhibited by the NR1H4 antagonist guggulsterone, highlighting the importance of NR1H4 upregulation. This study demonstrates the novel therapeutic efficacy of quercetin and kaempferol from Carthamus tinctorius L. in treating NAFLD through NR1H4 upregulation. This mechanism contributes to the regulation of lipid metabolism, improvement of liver function, reduction of inflammation, and alleviation of oxidative stress, offering a promising direction for future NAFLD treatment strategies.

Novel machine learning model for predicting cancer drugs' susceptibilities and discovering novel treatments

BACKGROUND AND OBJECTIVE

Timely treatment is crucial for cancer patients, so it's important to administer the appropriate treatment as soon as possible. Because individuals can respond differently to a given drug due to their unique genomic profiles, we aim to use their genomic information to predict how various drugs will affect them and determine the best course of treatment.

METHODS

We present Kernelized Residual Stacking (KRS), a new multi-task learning approach, and use it to predict the responses to anti-cancer drugs based on genomic data. We demonstrate the superior predictive performance of KRS, outperforming popular competitors, by utilizing the Genomics of Drug Sensitivity in Cancer (GDSC) study and the Cancer Cell Line Encyclopedia (CCLE) study. Downstream analysis of feature genes selected by KRS is conducted to discover novel therapies.

RESULTS

We used two genomic studies to show that KRS outperforms a few popular competitors in predicting drugs' susceptibilities. Through downstream analysis of feature genes selected by KRS, we found that the PI3K-Akt pathway could alter drugs' susceptibilities, and its expression correlated positively with the hub gene ERBB2. We discovered eight novel small molecules based on these feature genes, which could be developed into novel combination therapies with anti-cancer drugs.

CONCLUSIONS

KRS outperforms competitors in prediction performance and selects feature genes highly correlated with drugs' susceptibilities. Novel biological results are found by investigating KRS's feature genes.

Elevated expression of ECT2 as a diagnostic marker and prognostic indicator in endometrial cancer

OBJECTIVES

The study aims to investigate genes associated with endometrial cancer (EC) progression to identify new biomarkers for early detection.

METHODS

Differentially expressed genes (DEGs), Series test of cluster (STC) and protein-protein interaction analyses identified hub genes in EC. Clinical samples were utilized to examine the expression pattern of ECT2, assess its prognostic value, and evaluate its diagnostic potential.

RESULTS

Upregulated DEGs were significantly enriched in cancer-related processes and pathways. Validations across databases identified ASPM, ATAD2, BUB1B, ECT2, KIF14, NUF2, NCAPG, and SPAG5 as potential hub genes, with ECT2 exhibiting the highest diagnostic efficacy. The expression levels of ECT2 varied significantly across different clinical stages, pathological grades, and metastasis statuses in UCEC. Furthermore, ECT2 mRNA was upregulated in the p53abn group, indicating a poorer prognosis, and downregulated in the MMRd and NSMP groups, suggesting a moderate prognosis. In clinical samples, ECT2 expression increased from normal endometria and endometrial hyperplasia without atypia (EH) to atypical endometrial hyperplasia (AH) and EC, effectively distinguishing between benign and malignant endometria. High ECT2 expression was associated with an unfavourable prognosis.

CONCLUSIONS

ECT2 expression significantly rises in AH and EC, showing high accuracy in distinguishing between benign and malignant endometria. ECT2 emerges as a promising biomarker for diagnosing endometrial neoplasia and as a prognostic indicator in EC.

Synthesis and antitumor evaluation of glycyrrhetinic acid-dithiocarbamate hybrids

Glycyrrhetinic acid (GA) is a naturally occurring triterpene compound. The aim of this study was to employ the pharmacophore hybrid strategy to merge GA with various dithiocarbamates and obtain novel compounds with better antitumor activities. We present a two-step synthetic protocol wherein the GA derivative underwent reaction with carbon disulfide and various secondary amines in a one-pot manner under mild conditions, facilitating the preparation of a series of structurally novel GA-dithiocarbamate derivatives. Bioassay screening revealed that the representative compound 3c demonstrated the capacity to reduce the mitochondrial membrane potential in Hep3B and Huh-7 cells, induce nuclear apoptosis, inhibit invasion and migration, and prompt both early and late apoptosis. Furthermore, our research findings indicated that this apoptotic phenomenon may be associated with the expression of Bcl-2, Bax, Bak, PARP, and cleaved-PARP proteins. Utilizing network pharmacology for predicting core targets and signaling pathways of compound 3c for hepatocellular carcinoma (HCC) treatment involved employing molecular docking models to demonstrate high affinity between compound and target protein. In conjunction with Western blot analysis, compound 3c may impact HCC through the PI3K-AKT-mTOR pathway.

Hsp90aa1/JUN/Ccl2 regulatory axis mediates migration and differentiation of NSPCs, promoting the onset and progression of early post-ischemic stroke epilepsy

Early-onset epilepsy following ischemic stroke is a severe neurological condition, the pathogenesis of which remains incompletely understood. Recent studies suggest that Neural stem/progenitor cells (NSPCs) play a crucial role in the disease process, yet the precise molecular mechanisms regulating NSPCs have not been thoroughly investigated. This study utilized single-cell transcriptome sequencing and bioinformatics analysis to identify disease-related genes, which were subsequently validated in both in vitro and in vivo experiments. The findings revealed that Hsp90aa1 (heat shock protein 90 kDa alpha, class A member 1), Jun proto-oncogene (JUN), and CC Motif Ligation 2 (Ccl2) constitute an important regulatory axis influencing the migration and differentiation of NSPCs, potentially impacting the onset and progression of early-onset epilepsy post-ischemic stroke. Additionally, the expression of Hsp90aa1 was found to influence the likelihood of seizure occurrence and the severity of brain ischemia.

Cytosolic Hmgb1 accumulation in mesangial cells aggravates diabetic kidney disease progression via NFκB signaling pathway

Diabetic kidney disease (DKD) is the predominant type of end-stage renal disease. Increasing evidence suggests thatglomerular mesangial cell (MC) inflammation is pivotal for cell proliferation and DKD progression. However, the exactmechanism of MC inflammation remains largely unknown. This study aims to elucidate the role of inflammatoryfactor high-mobility group box 1 (Hmgb1) in DKD. Inflammatory factors related to DKD progression are screened viaRNA sequencing (RNA-seq). In vivo and in vitro experiments, including db/db diabetic mice model, CCK-8 assay, EdUassay, flow cytometric analysis, Co-IP, FISH, qRT-PCR, western blot, single cell nuclear RNA sequencing (snRNA-seq),are performed to investigate the effects of Hmgb1 on the inflammatory behavior of MCs in DKD. Here, wedemonstrate that Hmgb1 is significantly upregulated in renal tissues of DKD mice and mesangial cells cultured withhigh glucose, and Hmgb1 cytopasmic accumulation promotes MC inflammation and proliferation. Mechanistically,Hmgb1 cytopasmic accumulation is two-way regulated by MC-specific cyto-lncRNA E130307A14Rik interaction andlactate-mediated acetylated and lactylated Hmgb1 nucleocytoplasmic translocation, and accelerates NFκB signalingpathway activation via directly binding to IκBα. Together, this work reveals the promoting role of Hmgb1 on MCinflammation and proliferation in DKD and helps expound the regulation of Hmgb1 cytopasmic accumulation in twoways. In particular, Hmgb1 may be a promising therapeutic target for DKD.

Multi-objective context-guided consensus of a massive array of techniques for the inference of Gene Regulatory Networks

BACKGROUND AND OBJECTIVE

Gene Regulatory Network (GRN) inference is a fundamental task in biology and medicine, as it enables a deeper understanding of the intricate mechanisms of gene expression present in organisms. This bioinformatics problem has been addressed in the literature through multiple computational approaches. Techniques developed for inferring from expression data have employed Bayesian networks, ordinary differential equations (ODEs), machine learning, information theory measures and neural networks, among others. The diversity of implementations and their respective customization have led to the emergence of many tools and multiple specialized domains derived from them, understood as subsets of networks with specific characteristics that are challenging to detect a priori. This specialization has introduced significant uncertainty when choosing the most appropriate technique for a particular dataset. This proposal, named MO-GENECI, builds upon the basic idea of the previous proposal GENECI and optimizes consensus among different inference techniques, through a carefully refined multi-objective evolutionary algorithm guided by various objective functions, linked to the biological context at hand.

METHODS

MO-GENECI has been tested on an extensive and diverse academic benchmark of 106 gene regulatory networks from multiple sources and sizes. The evaluation of MO-GENECI compared its performance to individual techniques using key metrics (AUROC and AUPR) for gene regulatory network inference. Friedman's statistical ranking provided an ordered classification, followed by non-parametric Holm tests to determine statistical significance.

RESULTS

MO-GENECI's Pareto front approximation facilitates easy selection of an appropriate solution based on generic input data characteristics. The best solution consistently emerged as the winner in all statistical tests, and in many cases, the median precision solution showed no statistically significant difference compared to the winner.

CONCLUSIONS

MO-GENECI has not only demonstrated achieving more accurate results than individual techniques, but has also overcome the uncertainty associated with the initial choice due to its flexibility and adaptability. It is shown intelligently to select the most suitable techniques for each case. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/MO-GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a Python package available at PyPI: https://pypi.org/project/geneci/.

Based on network pharmacology and molecular docking to explore the potential mechanism of shikonin in periodontitis

OBJECTIVES

To investigate the potential mechanisms of shikonin in preventing and treating periodontitis using network pharmacology and molecular docking methods.

MATERIALS AND METHODS

The targets of shikonin were obtained in TCMSP and SEA databases, and targets of periodontitis were gathered from the OMIM, GeneCards and Drugbank Databases. The intersecting targets were entered into the DAVID database to obtain the relevant biological functions and pathways by GO and KEGG enrichment analysis. The obtained targets were analysed the protein-protein interaction (PPI) in STRING platform. In Cytoscape 3.8.0, the network analysis function with the MCODE plug-in were used to obtain the key targets, of shikonin and periodontitis. Molecular docking and molecular dynamics simulation (MD) were used to assess the affinity between the shikonin and the key targets.

RESULTS

Shikonin was screened for 22 targets and periodontitis was screened for 944 targets, the intersecting targets were considered as potential therapeutic targets. The targets played important roles in cellular response to hypoxia, response to xenobiotic stimulus and positive regulates of apoptotic process by GO enrichment analysis. 10 significant pathways were analyzed by KEGG, such as human cytomegalovirus infection and PI3K-Akt signaling pathway, etc. Cytoscape software screened the key genes including AKT1, CCL5, CXCR4, PPARG, PTEN, PTGS2 and TP53. Molecular docking and MD results showed that shikonin could bind stably to the targets.

CONCLUSIONS

The present study enriched the molecular mechanisms in periodontitis with shikonin, providing potential therapeutic targets for periodontitis.

Multicenter proteome-wide Mendelian randomization study identifies causal plasma proteins in melanoma and non-melanoma skin cancers

This study addresses the diagnostic and therapeutic challenges in malignant melanoma (MM) and non-melanoma skin cancers (NMSC). We aim to identify circulating proteins causally linked to MM and NMSC traits using a multicenter Mendelian randomization (MR) framework. We utilized large-scale cis-MR to estimate the impact of numerous plasma proteins on MM, NMSC, squamous cell carcinoma (SCC), and basal cell carcinoma (BCC). To ensure robustness, additional analyses like MR Steiger and Bayesian colocalization are conducted, followed by replication through meta-analytical methods. The associations between identified proteins and outcomes are also validated at the tissue level using Transcriptome-Wide Association Study methods. Furthermore, a protein-protein interaction analysis is conducted to explore the relationship between identified proteins and existing cancer medication targets. The MR analysis has identified associations of 13 plasma proteins with BCC, 2 with SCC, and 1 with MM. Specifically, ASIP and KRT5 are associated with BCC, with ASIP also potentially targeting MM. CTSS and TNFSF8 are identified as promising druggability candidates for BCC. This multidimensional approach nominates ASIP, KRT5, CTSS, and TNFSF8 as potential diagnostic and therapeutic targets for skin cancers.

Exploring the Roles of Key Mediators IKBKE and HSPA1A in Alzheimer's Disease and Hepatocellular Carcinoma through Bioinformatics Analysis

Recent studies have hinted at a potential link between Alzheimer's Disease (AD) and cancer. Thus, our study focused on finding genes common to AD and Liver Hepatocellular Carcinoma (LIHC), assessing their promise as diagnostic indicators and guiding future treatment approaches for both conditions. Our research utilized a broad methodology, including differential gene expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), gene enrichment analysis, Receiver Operating Characteristic (ROC) curves, and Kaplan-Meier plots, supplemented with immunohistochemistry data from the Human Protein Atlas (HPA) and machine learning techniques, to identify critical genes and significant pathways shared between AD and LIHC. Through differential gene expression analysis, WGCNA, and machine learning methods, we identified nine key genes associated with AD, which served as entry points for LIHC analysis. Subsequent analyses revealed IKBKE and HSPA1A as shared pivotal genes in patients with AD and LIHC, suggesting these genes as potential targets for intervention in both conditions. Our study indicates that IKBKE and HSPA1A could influence the onset and progression of AD and LIHC by modulating the infiltration levels of immune cells. This lays a foundation for future research into targeted therapies based on their shared mechanisms.

Exploring the potential of thiophene derivatives as dual inhibitors of β-tubulin and Wnt/β-catenin pathways for gastrointestinal cancers in vitro

Background

Gastrointestinal cancer poses a considerable global health risk, encompassing a heterogeneous spectrum of malignancies that afflict the gastrointestinal tract. It is significant to develop efficacious therapeutic agents, as they are indispensable for both the treatment and prevention of this formidable disease.

Methods

In this study, we synthesized a novel thiophene derivative, designated as compound 1312. An assessment was performed to investigate its anti-proliferative activity in several cancer cell lines (GES-1, EC9706, SGC7901, and HT-29). Furthermore, we performed molecular biology techniques to investigate the inhibitory impact of compound 1312 on gastrointestinal cell lines SGC-7901 and HT-29.

Results

Our findings reveal that compound 1312 exhibits significant efficacy in suppressing colony formation of cancer cells. Notably, it triggers cell cycle arrest at the G2/M phase in gastrointestinal cell lines SGC7901 and HT-29. Compound 1312 was confirmed to exert inhibitory effects on cell migration and invasion in SGC7901. Additionally, the compound elicits apoptotic cell death through the activation of the DNA repair enzyme poly (ADP-ribose) polymerase (PARP) and the caspase signaling cascade. Furthermore, in vitro experiments revealed that compound 1312 effectively suppresses both the β-tubulin cytoskeletal network and the Wnt/β-catenin signaling pathway. These multifaceted anti-cancer activities highlight the potential of compound 1312 as a promising therapeutic agent for the treatment of gastrointestinal malignancies.

Conclusion

This study indicates the promising potential of compound 1312 as a prospective candidate agent for gastrointestinal cancer treatment. Further comprehensive investigations are needed to explore its therapeutic efficacy in greater detail.

Screening out molecular pathways and prognostic biomarkers of ultraviolet-mediated melanoma through computational techniques

PURPOSE

Ultraviolet radiation causes skin cancer, but the exact mechanism by which it occurs and the most effective methods of intervention to prevent it are yet unknown. For this purpose, our study will use bioinformatics and systems biology approaches to discover potential biomarkers of skin cancer for early diagnosis and prevention of disease with applicable clinical treatments.

METHODS

This study compared gene expression and protein levels in ultraviolet-mediated cultured keratinocytes and adjacent normal skin tissue using RNA sequencing data from the National Center for Biotechnology Information-Gene Expression Omnibus (NCBI-GEO) database. Then, pathway analysis was employed with a selection of hub genes from the protein-protein interaction (PPI) network and the survival and expression profiles. Finally, potential clinical biomarkers were validated by receiver operating characteristic (ROC) curve analysis.

RESULTS

We identified 32 shared differentially expressed genes (DEGs) by analyzing three different subsets of the GSE85443 dataset. Skin cancer development is related to the control of several DEGs through cyclin-dependent protein serine/threonine kinase activity, cell cycle regulation, and activation of the NIMA kinase pathways. The cytoHubba plugin in Cytoscape identified 12 hub genes from PPI; among these 3 DEGs, namely, AURKA, CDK4, and PLK1 were significantly associated with survival (P < 0.05) and highly expressed in skin cancer tissues. For validation purposes, ROC curve analysis indicated two biomarkers: AURKA (area under the curve (AUC) value = 0.8) and PLK1 (AUC value = 0.7), which were in an acceptable range.

CONCLUSIONS

Further translational research, including clinical experiments, teratogenicity tests, and in-vitro or in-vivo studies, will be performed to evaluate the expression of these identified biomarkers regarding the prognosis of skin cancer patients.

Identification of candidate biomarkers for GBM based on WGCNA

Glioblastoma multiforme (GBM), the most aggressive form of primary brain tumor, poses a considerable challenge in neuro-oncology. Despite advancements in therapeutic approaches, the prognosis for GBM patients remains bleak, primarily attributed to its inherent resistance to conventional treatments and a high recurrence rate. The primary goal of this study was to acquire molecular insights into GBM by constructing a gene co-expression network, aiming to identify and predict key genes and signaling pathways associated with this challenging condition. To investigate differentially expressed genes between various grades of Glioblastoma (GBM), we employed Weighted Gene Co-expression Network Analysis (WGCNA) methodology. Through this approach, we were able to identify modules with specific expression patterns in GBM. Next, genes from these modules were performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis using ClusterProfiler package. Our findings revealed a negative correlation between biological processes associated with neuronal development and functioning and GBM. Conversely, the processes related to the cell cycle, glomerular development, and ECM-receptor interaction exhibited a positive correlation with GBM. Subsequently, hub genes, including SYP, TYROBP, and ANXA5, were identified. This study offers a comprehensive overview of the existing research landscape on GBM, underscoring the challenges encountered by clinicians and researchers in devising effective therapeutic strategies.

Personalized identification and characterization of genome-wide gene expression differences between patient-matched intracranial and extracranial melanoma metastasis pairs

Melanoma is the most serious type of skin cancer that frequently spreads to other organs of the human body. Especially melanoma metastases to the brain (intracranial metastases) are hard to treat and a major cause of death of melanoma patients. Little is known about molecular alterations and altered mechanisms that distinguish intra- from extracranial melanoma metastases. So far, almost all existing studies compared intracranial metastases from one set of patients to extracranial metastases of an another set of melanoma patients. This neglects the important facts that each melanoma is highly individual and that intra- and extracranial melanoma metastases from the same patient are more similar to each other than to melanoma metastases from other patients in the same organ. To overcome this, we compared the gene expression profiles of 16 intracranial metastases to their corresponding 21 patient-matched extracranial metastases in a personalized way using a three-state Hidden Markov Model (HMM) to identify altered genes for each individual metastasis pair. This enabled three major findings by considering the predicted gene expression alterations across all patients: (i) most frequently altered pathways include cytokine-receptor interaction, calcium signaling, ECM-receptor interaction, cAMP signaling, Jak-STAT and PI3K/Akt signaling, (ii) immune-relevant signaling pathway genes were downregulated in intracranial metastases, and (iii) intracranial metastases were associated with a brain-like phenotype gene expression program. Further, the integration of all differentially expressed genes across the patient-matched melanoma metastasis pairs led to a set of 103 genes that were consistently down- or up-regulated in at least 11 of the 16 of the patients. This set of genes contained many genes involved in the regulation of immune responses, cell growth, cellular signaling and transport processes. An analysis of these genes in the TCGA melanoma cohort showed that the expression behavior of 11 genes was significantly associated with survival. Moreover, a comparison of the 103 genes to three closely related melanoma metastasis studies revealed a core set of eight genes that were consistently down- or upregulated in intra- compared to extracranial metastases in at least two of the three related studies (down: CILP, DPT, FGF7, LAMP3, MEOX2, TMEM119; up: GLDN, PMP2) including FGF7 that was also significantly associated with survival. Our findings contribute to a better characterization of genes and pathways that distinguish intra- from extracranial melanoma metastasis and provide important hints for future experimental studies to identify potential targets for new therapeutic approaches.

KRT80 Promotes Lung Adenocarcinoma Progression and Serves as a Substrate for VCP

Background: Keratin 80(KRT80) encodes a type II intermediate filament protein, known for maintaining cell integrity of cells and its involvement in the tumorigenesis and progression of various cancers. However, comprehensive research on its relevance to lung adenocarcinoma remains limited. Methods: In this study, we utilized multiple databases to investigate the transcriptional expression of KRT80 and its correlation with clinicopathological features. A range of assays, including the Cell Counting Kit 8 assay, colony formation assay, cell migration assay, and flow cytometry, were employed to elucidate the impact of KRT80 on the malignant behavior of lung adenocarcinoma. Immunoprecipitation and mass spectrometry were also used to identify putative genes interacting with KRT80. Results: The expression of KRT80 was elevated in lung adenocarcinoma and patients with high levels of KRT80 expression had poor clinical outcomes. Silencing KRT80 suppressed cell viability, and migration, while overexpression had the opposite effect. In addition, Immunoprecipitation and mass spectrometry revealed an interaction between KRT80 and valosin-containing protein (VCP), with VCP knockdown reducing the stability of KRT80 protein. Overexpression of KRT80 mitigated the inhibitory effect of VCP knockdown to some extent. Conclusion: Our findings collectively suggest that KRT80 is a promising diagnostic and prognostic indicator for lung adenocarcinoma. Additionally, the interaction between KRT80 and VCP plays a crucial role in the progression of lung adenocarcinoma, which implies that KRT80 is a promising therapeutic target.

Role of Network Pharmacology in Prediction of Mechanism of Neuroprotective Compounds

Network pharmacology is an emerging pioneering approach in the drug discovery process, which is used to predict the therapeutic mechanism of compounds using various bioinformatic tools and databases. Emerging studies have indicated the use of network pharmacological approaches in various research fields, particularly in the identification of possible mechanisms of herbal compounds/ayurvedic formulations in the management of various diseases. These techniques could also play an important role in the prediction of the possible mechanisms of neuroprotective compounds. The first part of the chapter includes an introduction on neuroprotective compounds based on literature. Further, network pharmacological approaches are briefly discussed. The use of network pharmacology in the prediction of the neuroprotective mechanism of compounds is discussed in detail with suitable examples. Finally, the chapter concludes with the current challenges and future prospectives.

Identifying the natural products in the treatment of atherosclerosis by increasing HDL-C level based on bioinformatics analysis, molecular docking, and in vitro experiment

BACKGROUND

Previous studies have demonstrated that high-density lipoprotein cholesterol (HDL-C) plays an anti-atherosclerosis role through reverse cholesterol transport. Several studies have validated the efficacy and safety of natural products in treating atherosclerosis (AS). However, the study of raising HDL-C levels through natural products to treat AS still needs to be explored.

METHODS

The gene sets associated with AS were collected and identified by differential gene analysis and database query. By constructing a protein-protein interaction (PPI) network, the core submodules in the network are screened out. At the same time, by calculating node importance (Nim) in the PPI network of AS disease and combining it with Kyoto Encyclopedia of genes and genomes (KEGG) pathways enrichment analysis, the key target proteins of AS were obtained. Molecular docking is used to screen out small natural drug molecules with potential therapeutic effects. By constructing an in vitro foam cell model, the effects of small molecules on lipid metabolism and key target expression of foam cells were investigated.

RESULTS

By differential gene analysis, 451 differential genes were obtained, and a total of 313 disease genes were obtained from 6 kind of databases, then 758 AS-related genes were obtained. The enrichment analysis of the KEGG pathway showed that the enhancement of HDL-C level against AS was related to Lipid and atherosclerosis, Cholesterol metabolism, Fluid shear stress and atherosclerosis, PPAR signaling pathway, and other pathways. Then we intersected 31 genes in the core module of the PPI network, the top 30 genes in Nims, and 32 genes in the cholesterol metabolism pathway, and finally found 3 genes. After the above analysis and literature collection, we focused on the following three related gene targets: APOA1, LIPC, and CETP. Molecular docking showed that Genistein has a good binding affinity for APOA1, CETP, and LIPC. In vitro, experiments showed that Genistein can up-regulated APOA1, LIPC, and CETP levels.

CONCLUSIONS

Based on our research, Genistein may have the effects of regulating HDL-C and anti-atherosclerosis. Its mechanism of action may be related to the regulation of LIPC, CETP, and APOA1 to improve lipid metabolism.

Antitumor Effect of Poplar Propolis on Human Cutaneous Squamous Cell Carcinoma A431 Cells

Propolis is a gelatinous substance processed by western worker bees from the resin of plant buds and mixed with the secretions of the maxillary glands and beeswax. Propolis has extensive biological activities and antitumor effects. There have been few reports about the antitumor effect of propolis against human cutaneous squamous cell carcinoma (CSCC) A431 cells and its potential mechanism. CCK-8 assays, label-free proteomics, RT-PCR, and a xenograft tumor model were employed to explore this possibility. The results showed that the inhibition rate of A431 cell proliferation by the ethanol extract of propolis (EEP) was dose-dependent, with an IC50 of 39.17 μg/mL. There were 193 differentially expressed proteins in the EEP group compared with the control group (p < 0.05), of which 103 proteins (53.37%) were upregulated, and 90 proteins (46.63%) were downregulated. The main three activated and suppressed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were extracellular matrix (ECM)-receptor interaction, amoebiasis, cell adhesion molecules (CAMs), nonalcoholic fatty liver disease (NAFLD), retrograde endocannabinoid signaling, and Alzheimer's disease. The tumor volume of the 100 mg/kg EEP group was significantly different from that of the control group (p < 0.05). These results provide a theoretical basis for the potential treatment of human CSCC A431 cell tumors using propolis.

Inhibition of CDK1 by RO-3306 Exhibits Anti-Tumorigenic Effects in Ovarian Cancer Cells and a Transgenic Mouse Model of Ovarian Cancer

Ovarian cancer is the deadliest gynecological malignancy of the reproductive organs in the United States. Cyclin-dependent kinase 1 (CDK1) is an important cell cycle regulatory protein that specifically controls the G2/M phase transition of the cell cycle. RO-3306 is a selective, ATP-competitive, and cell-permeable CDK1 inhibitor that shows potent anti-tumor activity in multiple pre-clinical models. In this study, we investigated the effect of CDK1 expression on the prognosis of patients with ovarian cancer and the anti-tumorigenic effect of RO-3306 in both ovarian cancer cell lines and a genetically engineered mouse model of high-grade serous ovarian cancer (KpB model). In 147 patients with epithelial ovarian cancer, the overexpression of CDK1 was significantly associated with poor prognosis compared with a low expression group. RO-3306 significantly inhibited cellular proliferation, induced apoptosis, caused cellular stress, and reduced cell migration. The treatment of KpB mice with RO-3306 for four weeks showed a significant decrease in tumor weight under obese and lean conditions without obvious side effects. Overall, our results demonstrate that the inhibition of CDK1 activity by RO-3306 effectively reduces cell proliferation and tumor growth, providing biological evidence for future clinical trials of CDK1 inhibitors in ovarian cancer.

Transcriptomic characterization revealed that METTL7A inhibits melanoma progression via the p53 signaling pathway and immunomodulatory pathway

METTL7A is a protein-coding gene expected to be associated with methylation, and its expression disorder is associated with a range of diseases. However, few research have been carried out to explore the relationship between METTL7A and tumor malignant phenotype as well as the involvement potential mechanism. We conducted our research via a combination of silico analysis and molecular biology techniques to investigate the biological function of METTL7A in the progression of cancer. Gene expression and clinical information were extracted from the TCGA database to explore expression variation and prognostic value of METTL7A. In vitro, CCK8, transwell, wound healing and colony formation assays were conducted to explore the biological functions of METT7A in cancer cell. GSEA was performed to explore the signaling pathway involved in METTL7A and validated via western blotting. In conclusion, METTL7A was downregulated in most cancer tissues and its low expression was associated with shorter overall survival. In melanoma, METTL7A downregulation was associated with poorer clinical staging, lower levels of TIL infiltration, higher IC50 levels of chemotherapeutic agents, and poorer immunotherapy outcomes. QPCR results confirm that METTL7A is down-regulated in melanoma cells. Cell function assays showed that METTL7A knockdown promoted proliferation, invasion, migration and clone formation of melanoma cells. Mechanistic studies showed that METTL7A inhibits tumorigenicity through the p53 signaling pathway. Meanwhile, METTL7A is also a potential immune regulatory factor.

Dysbindin Domain-Containing 1 in Prostate Cancer: New Insights into Bioinformatic Validation of Molecular and Immunological Features

Prostate cancer (PCa) is one of the most prevalent cancers in men, yet its pathogenic pathways remain poorly understood. Transcriptomics and high-throughput sequencing can help uncover cancer diagnostic targets and understand biological circuits. Using prostate adenocarcinoma (PRAD) datasets of various web-based applications (GEPIA, UALCAN, cBioPortal, SR Plot, hTFtarget, Genome Browser, and MetaCore), we found that upregulated dysbindin domain-containing 1 (DBNDD1) expression in primary prostate tumors was strongly correlated with pathways involving the cell cycle, mitotic in KEGG, WIKI, and REACTOME database, and transcription factor-binding sites with the DBNDD1 gene in prostate samples. DBNDD1 gene expression was influenced by sample type, cancer stage, and promoter methylation levels of different cancers, such as PRAD, liver hepatocellular carcinoma (LIHC), and lung adenocarcinoma (LUAD). Regulation of glycogen synthase kinase (GSK)-3β in bipolar disorder and ATP/ITP/GTP/XTP/TTP/CTP/UTP metabolic pathways was closely correlated with the DBNDD1 gene and its co-expressed genes in PCa. DBNDD1 gene expression was positively associated with immune infiltration of B cells, Myeloid-derived suppressor cell (MDSC), M2 macrophages, andneutrophil, whereas negatively correlated with CD8+ T cells, T follicular helper cells, M1 macrophages, and NK cells in PCa. These findings suggest that DBNDD1 may serve as a viable prognostic marker not only for early-stage PCa but also for immunotherapies.

miR-455-3p ameliorates pancreatic acinar cell injury by targeting Slc2a1

Objective

With the number of patients with acute pancreatitis (AP) increasing year by year, it is pressing to explore new key genes and markers for the treatment of AP. miR-455-3p/solute carrier family 2 member 1 (Slc2a1) obtained through bioinformatics analysis may participate in the progression of AP.

Materials and Methods

The C57BL/6 mouse model of AP was constructed for subsequent studies. Through bioinformatics analysis, the differentially expressed genes related to AP were screened and hub genes were identified. A caerulein-induced AP animal model was constructed to detect the pathological changes of mouse pancreas by HE staining. The concentrations of amylase and lipase were measured. Primary mouse pancreatic acinar cells were isolated and subjected to microscopy to observe their morphology. The enzymatic activities of trypsin and amylase were detected. The secretion of inflammatory cytokines in mouse were measured with the ELISA kits of TNF-α, IL-6 and IL-1β to determine pancreatic acinar cell damage. A binding site between the Slc2a1 3' UTR region and the miR-455-3p sequence was verified by dual-luciferase reporter assay. The expression of miR-455-3p was quantified by qRT-PCR, and Slc2a1 were detected by western blot.

Results

A total of five (Fyn, Gadd45a, Sdc1, Slc2a1, and Src) were identified by bioinformatics analysis, and miR-455-3p/Slc2a1 were further studied. HE staining results showed that the AP models were successfully established by caerulein induction. In mice with AP, the expression of miR-455-3p was reduced, while that of Slc2a1 was increased. In the caerulein-induced cell model, the expression of Slc2a1 was significantly reduced after intervention of miR-455-3p mimics, whereas increased after miR-455-3p inhibitor treatment. miR-455-3p decreased the secretion of inflammatory cytokines in the cell supernatant, reduced the activity of trypsin and amylase, and alleviated the cell damage induced by caerulein. In addition, Slc2a1 3'UTR region was bound by miR-455-3p, and its protein expression was also regulated.

Conclusion

miR-455-3p alleviated caerulein-induced mouse pancreatic acinar cell damage by regulating the expression of Slc2a1.

Identification of a novel survival predictor, CSF2RB, for female lung cancer in never smokers (LCNS) by a bioinformatics analysis

Lung cancer in never smokers (LCNS) has been considered as a separate disease and the 7th cause of cancer-related death worldwide. However, limited research has focused on "female" cohorts, which have presented a higher incidence rate. In this study, the microarray data of lung cancer tissues derived from 54 female lung cancer patients, consisting of 43 nonsmokers and 11 smokers, were selected from GSE2109 dataset. A total of 249 differentially expressed genes (DEGs) including 102 up- and 147 down-regulated genes were identified and further analyzed for gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment. By constructing protein-protein interaction (PPI) network and calculating key modules, 10 hub genes were screened out. The module analysis of the PPI network presented that the progression of female LCNS was significantly associated with immune response as chemokine activity and lipopolysaccharide response, and these biological processes (BP) might be mediated by chemokine signaling pathway and cytokine-cytokine receptor interaction. Then, survival analysis by Kaplan-Meier (K-M) Plotter online platform presented down-regulated gene colony stimulating factor 2 receptor beta common subunit (CSF2RB) of female LCNS might be involved in poor clinical outcome. Female LCNS with high expression of CSF2RB might be relevant with relative risk reduction of mortality, longer median survival time and higher 5-year survival rate, while female LCNS with low expression of CSF2RB might be implicated in a poor clinical outcome. In short, our results support CSF2RB to be a candidate survival predictor for female LCNS.

The elucidation of the anti-inflammatory mechanism of EMO in rheumatoid arthritis through an integrative approach combining bioinformatics and experimental verification

Introduction: Emodin (EMO), a natural derivative of the anthraquinone family mainly extracted from rhubarb (Rheum palmatum), has previously been demonstrated to possess superior anti-inflammatory properties from a single target or pathway. In order to explore the underlying mechanism of action of EMO against rheumatoid arthritis (RA), a network pharmacology approach was employed. Methods: A gene expression profile from GSE55457 available from the Gene Expression Omnibus (GEO) database was used to identify the targets of EMO action. Further, single cell RNA sequencing data from GEO database of RA patients (GSE159117) were downloaded and analysed. To further investigate the anti-RA effect of EMO on MH7A cells, the expression of IL-6 and IL-1β were monitored. Finally, RNA-seq analyses were conducted on synovial fibroblasts from EMO-treated. Result: We screened the key targets of EMO against RA using network pharmacology methods, including HMGB1, STAT1, EGR1, NR3C1, EGFR, MAPK14, CASP3, CASP1, IL4, IL13, IKBKB and FN1, and their reliability was verified using ROC curve. Single-cell RNA sequencing data analysis showed that these core target proteins mainly played a role by modulating monocytes. The anti-RA effect of EMO was further verified with MH7A cells, which showed that EMO could block cell differentiation and reduce the expression of IL-6 and IL-1β. WB experiments confirmed that EMO could affect the expression of COX2, HMBG1 and the phosphorylation of p38. Finally, sequencing of synovial fibroblasts from rats treated with EMO showed consistent results with those predicted and verified, further proving the anti-inflammatory effect of EMO. Conclusion: Our research shows that EMO inhibits inflammatory response of rheumatoid arthritis (RA) by targeting HMGB1, STAT1, EGR1, NR3C1, EGFR, MAPK14, CASP3, CASP1, IL4, IL13, IKBKB, FN1 and Monocytes/macrophages.

Identification of the hub genes associated with prostate cancer tumorigenesis

Introduction

Prostate cancer (PCa) is one of the most common malignant tumors of the male urogenital system; however, the underlying mechanisms remain largely unclear. This study integrated two cohort profile datasets to elucidate the potential hub genes and mechanisms in PCa.

Methods and Results

Gene expression profiles GSE55945 and GSE6919 were filtered from the Gene Expression Omnibus (GEO) database to obtain 134 differentially expressed genes (DEGs) (14 upregulated and 120 downregulated) in PCa. Gene Ontology and pathway enrichment were performed using the Database for Annotation, Visualization, and Integrated Discovery, showing that these DEGs were mainly involved in biological functions such as cell adhesion, extracellular matrix, migration, focal adhesion, and vascular smooth muscle contraction. The STRING database and Cytoscape tools were used to analyze protein-protein interactions and identify 15 hub candidate genes. Violin plot, boxplot, and prognostic curve analyses were performed using Gene Expression Profiling Interactive Analysis, which identified seven hub genes, including upregulated expressed SPP1 and downregulated expressed MYLK, MYL9, MYH11, CALD1, ACTA2, and CNN1 in PCa compared with normal tissue. Correlation analysis was performed using the OmicStudio tools, which showed that these hub genes were moderately to strongly correlated with each other. Finally, quantitative reverse transcription PCR and western blotting were performed to validate the hub genes, showing that the abnormal expression of the seven hub genes in PCa was consistent with the analysis results of the GEO database.

Discussion

Taken together, MYLK, MYL9, MYH11, CALD1, ACTA2, SPP1, and CNN1 are hub genes significantly associated with PCa occurrence. These genes are abnormally expressed, leading to the formation, proliferation, invasion, and migration of PCa cells and promoting tumor neovascularization. These genes may serve as potential biomarkers and therapeutic targets in patients with PCa.

Identification of hub genes and pathways in lung metastatic colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is one of the most prevalent types of malignant tumours. Metastasis is the leading cause of cancer-related mortality, with lung metastases accounting for 32.9% of all metastatic CRCs. However, since the biological mechanism of lung metastatic CRC is poorly understood, limited therapeutic targets are available. In the present study, we aimed to identify the key genes and molecular processes involved in CRC lung metastasis.

METHODS

The differentially expressed genes (DEGs) between primary and lung metastatic CRC patients were obtained from the Gene Expression Omnibus (GEO) database via the GEO2R tool. The enriched biological processes and pathways modulated by the DEGs were determined with Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome Gene Sets analyses. The search tool Retrieval of Interacting Genes (STRING) and Cytoscape were used to construct a protein-protein interaction (PPI) network among DEGs.

RESULTS

The DEGs were enriched in surfactant metabolism, cell-cell communication and chemokine signaling pathways. The defined hub genes were included CLU, SFTPD, CCL18, SPP1, APOE, BGN and MMP3. Among them, CLU, SFTPD and CCL18 might be associated with the specific lung tropism metastasis in CRC. In addition, the expression and prognostic values of the hub genes in CRC patients were verified in database of The Cancer Genome Atlas (TCGA) and GEO. Moreover, the protein levels of the hub genes were detected in primary and lung metastatic CRC cells, serum or tissues. Furthermore, SFTPD was confirmed to facilitate cellular proliferation and lung metastasis in CRC.

CONCLUSION

This bioinformatics study may provide a better understanding of the candidate therapeutic targets and molecular mechanisms for CRC lung metastasis.

GENECI: A novel evolutionary machine learning consensus-based approach for the inference of gene regulatory networks

Gene regulatory networks define the interactions between DNA products and other substances in cells. Increasing knowledge of these networks improves the level of detail with which the processes that trigger different diseases are described and fosters the development of new therapeutic targets. These networks are usually represented by graphs, and the primary sources for their correct construction are usually time series from differential expression data. The inference of networks from this data type has been approached differently in the literature. Mostly, computational learning techniques have been implemented, which have finally shown some specialization in specific datasets. For this reason, the need arises to create new and more robust strategies for reaching a consensus based on previous results to gain a particular capacity for generalization. This paper presents GENECI (GEne NEtwork Consensus Inference), an evolutionary machine learning approach that acts as an organizer for constructing ensembles to process the results of the main inference techniques reported in the literature and to optimize the consensus network derived from them, according to their confidence levels and topological characteristics. After its design, the proposal was confronted with datasets collected from academic benchmarks (DREAM challenges and IRMA network) to quantify its accuracy. Subsequently, it was applied to a real-world biological network of melanoma patients whose results could be contrasted with medical research collected in the literature. Finally, it has been proved that its ability to optimize the consensus of several networks leads to outstanding robustness and accuracy, gaining a certain generalization capacity after facing the inference of multiple datasets. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a python package available at PyPI: https://pypi.org/project/geneci/.

Identification of biomarkers and prediction of upstream miRNAs in diabetic nephropathy

OBJECTIVE

To explore biomarkers of diabetic nephropathy (DN) and predict upstream miRNAs.

METHODS

The data sets GSE142025 and GSE96804 were obtained from Gene Expression Omnibus database. Subsequently, common differentially expressed genes (DEGs) of renal tissue in DN and control group were identified and protein-protein interaction network (PPI) was constructed. Hub genes were screened from in DEGs and made an investigation on functional enrichment and pathway research. Finally, the target gene was selected for further study. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficiency of target gene and predicted its upstream miRNAs.

RESULTS

130 common DEGs were obtained through analysis, and 10 Hub genes were further identified. The function of Hub genes was mainly related to extracellular matrix (ECM), collagen fibrous tissue, transforming growth factor (TGF) -β, advanced glycosylation end product (AGE) -receptor (RAGE) and so on. Research showed that the expression level of Hub genes in DN group was significantly higher than that in control group. (all P<0.05). The target gene matrix metalloproteinase 2 (MMP2) was selected for further study, and it was found to be related to the fibrosis process and the genes regulating fibrosis. Meanwhile, ROC curve analysis showed that MMP2 had a good predictive value for DN. miRNA prediction suggested that miR-106b-5p and miR-93-5p could regulate the expression of MMP2.

CONCLUSION

MMP2 can be used as a biomarker for DN to participate in the pathogenesis of fibrosis, and miR-106b-5p and miR-93-5p may regulate the expression of MMP2 as upstream signals.

A Novel multi-fruit acids formula design on molecular basis for skin brightening via a system biology approach

BACKGROUND

Fruit acids have long been recognized as highly effective actives with world-wide popularity, covering skin peeling, anti-acne, anti-wrinkle applications, and skin depigmentation.

AIMS

There are complicated interconnections between the fruit acid formula and skin pigmentation behaviors. However, the lack of systematic researches on multi-ingredient formula restricted our understanding on its mechanism. Therefore, it is of great necessity to study the interactions and cascades among components, potential gene targets, signaling pathways, and biological processes via a system biology approach.

METHODS

We used system biology, molecular fingerprint, and structural biology to calculate and collect target information of the functional formula. Gene Ontology (GO) enrichment analysis was further applied to study the biological processes and pathways of the formula, and a multi-level network model of "component - molecular target - signaling pathway - skin disease" was established. Besides, the zebrafish model was utilized to verify the formula.

RESULTS

We obtained 69 hub targets by constructing a protein-protein interaction (PPI) network based on the intersection between multi-fruit acids formula (mandelic acid, lactobionic acid, niacinamide, and hydroxytyrosol) and skin indications targets (whitening-sebum balance). In vitro zebrafish models, including pigmentation, antioxidant, and radiation protection models, showed that the current formula significantly alleviated pigmentation intensity and intracellular free radical content, thus proving the efficacy of skin brightening and UV irradiation protection.

CONCLUSIONS

This research uncovered the underlying mechanism of multi-fruit acids formula and predicted its function in skin brightening and UV irradiation prevention.

Role of extracellular matrix architecture and signaling in melanoma therapeutic resistance

The extracellular matrix (ECM) is critical for maintaining tissue homeostasis therefore its production, assembly and mechanical stiffness are highly regulated in normal tissues. However, in solid tumors, increased stiffness resulting from abnormal ECM structural changes is associated with disease progression, an increased risk of metastasis and poor survival. As a dynamic and key component of the tumor microenvironment, the ECM is becoming increasingly recognized as an important feature of tumors, as it has been shown to promote several hallmarks of cancer via biochemical and biomechanical signaling. In this regard, melanoma cells are highly sensitive to ECM composition, stiffness and fiber alignment because they interact directly with the ECM in the tumor microenvironment via cell surface receptors, secreted factors or enzymes. Importantly, seeing as the ECM is predominantly deposited and remodeled by myofibroblastic stromal fibroblasts, it is a key avenue facilitating their paracrine interactions with melanoma cells. This review gives an overview of melanoma and further describes the critical roles that ECM properties such as ECM remodeling, ECM-related proteins and stiffness play in cutaneous melanoma progression, tumor cell plasticity and therapeutic resistance. Finally, given the emerging importance of ECM dynamics in melanoma, future perspectives on therapeutic strategies to normalize the ECM in tumors are discussed.

Bispecific Antibodies Progression in Malignant Melanoma

The discovery of oncogenes and immune checkpoints has revolutionized the treatment of melanoma in the past 10 years. However, the current PD-L1 checkpoints lack specificity for tumors and target normal cells expressing PD-L1, thus reducing the efficacy on malignant melanoma and increasing the side effects. In addition, the treatment options for primary or secondary drug-resistant melanoma are limited. Bispecific antibodies bind tumor cells and immune cells by simultaneously targeting two antigens, enhancing the anti-tumor targeting effect and cytotoxicity and reducing drug-resistance in malignant melanoma, thus representing an emerging strategy to improve the clinical efficacy. This review focused on the treatment of malignant melanoma by bispecific antibodies and summarized the effective results of the experiments that have been conducted, also discussing the different aspects of these therapies. The role of the melanoma epitopes, immune cell activation, cell death and cytotoxicity induced by bispecific antibodies were evaluated in the clinical or preclinical stage, as these therapies appear to be the most suitable in the treatment of malignant melanoma.