Network models of primary melanoma microenvironments identify key melanoma regulators underlying prognosis

A comprehensive review of PRAME and BAP1 in melanoma: Genomic instability and immunotherapy targets

In a thorough review of the literature, the complex roles of PRAME (preferentially expressed Antigen of Melanoma) and BAP1 (BRCA1-associated protein 1) have been investigated in uveal melanoma (UM) and cutaneous melanoma. High PRAME expression in UM is associated with poor outcomes and correlated with extraocular extension and chromosome 8q alterations. BAP1 mutations in the UM indicate genomic instability and a poor prognosis. Combining PRAME and BAP1 immunohistochemical staining facilitates effective risk stratification. Mechanistically, both genes are associated with genomic instability, making them promising targets for cancer immunotherapy. Hypomethylation of PRAME, specifically in its promoter regions, is critical for UM progression and contributes to epigenetic reprogramming. Additionally, miR-211 regulation is crucial in melanoma and has therapeutic potential. The way PRAME changes signaling pathways provides clues about the cause of cancer due to genomic instability related to modifications in DNA repair. Inhibition of poly(ADP-ribose) polymerase-1 (PARP-1) and PARP-2 in cells expressing PRAME could lead to potential therapeutic applications. Pathway enrichment analysis underscores the significance of PRAME and BAP1 in melanoma pathogenesis.

Landscape of multiple tissues' gene expression pattern associated with severe sepsis: Genetic insights from Mendelian randomization and trans-omics analysis

BACKGROUND

Sepsis, a systemic syndrome often culminating in multiple organ failure (MOF), poses a substantial global health threat. However, the gene expression pattern of various tissues associated with severe sepsis remains elusive.

METHODS

Applying the summary data-based Mendelian randomization (SMR) method, we integrated sepsis genome-wide association study (GWAS) data and expression quantitative trait loci (eQTLs) summaries. This facilitated the investigation of gene causality across 12 tissue types within 26 cohorts linked to adverse sepsis outcomes, including critical care and 28-day mortality. Additionally, trans-omics analyses, including blood transcriptome and single-cell RNA sequencing, were conducted to examine cellular origins and gene functions. The effects of ST7L on sepsis were validated in vivo and in vitro.

RESULTS

We identified 127 genes associated with severe sepsis across diverse tissues. Cross-tissue analysis highlighted ST7L as a significant pan-tissue risk factor for severe sepsis, displaying significance across 11 tissues for both critical care sepsis (meta OR 1.19, 95 % CI: 1.14-1.25, meta p < 0.0001) and 28-day-death sepsis (meta OR: 1.22, 95 % CI: 1.17-1.27, meta p < 0.0001). Notably, independent blood single-cell RNA sequencing data showed specific expression of ST7L in dendritic cells (DCs). ST7L+ DCs were elevated in non-surviving sepsis patients and exhibited an augmented inflammatory molecular pattern compared to ST7L- DCs. Both transcription and translation level of ST7L in DCs exhibited a dose-dependent pattern with LPS. Knocking down ST7L by siRNA was sufficient to alleviate the inflammation phenotype of DCs, including inhibiting p65/NF-kB pathway and inflammatory factors.

CONCLUSION

Our findings underscore ST7L as a pan-tissue risk factor for severe sepsis, specifically manifested in DCs and associated with an inflammatory phenotype. These results offer essential insights into the gene expression profiles across multiple tissues in severe sepsis, potentially identifying therapeutic targets for effective sepsis management.

Zinc finger protein 180 induces an apoptotic phenotype by activating METTL14 transcriptional activity in colorectal cancer

Zinc finger protein 180 (ZNF180) is a multifunctional protein that interacts with nucleic acids and regulates various cellular processes; however, the function of ZNF180 in colorectal cancer (CRC) remains unclear. The present study investigated the role and function of ZNF180 in CRC, and aimed to reveal the underlying molecular mechanism. The results revealed that ZNF180 was downregulated in CRC tissues and was associated with a good prognosis in patients with CRC. Additionally, the expression of ZNF180 was downregulated by methylation in CRC. In vivo and in vitro experiments revealed that ZNF180 overexpression was functionally associated with the inhibition of cell proliferation and the induction of apoptosis. Mechanistically, chromatin immunoprecipitation‑PCR and luciferase assays demonstrated that ZNF180 markedly regulated the transcriptional activity of methyltransferase 14, N6‑adenosine‑methyltransferase non‑catalytic subunit (METTL14) by directly binding to and activating its promoter region. Simultaneous overexpression of ZNF180 and knockdown of METTL14 indicated that the reduction of METTL14 could suppress the effects of ZNF180 on the induction of apoptosis. Clinically, the present study observed a significant positive correlation between ZNF180 and METTL14 expression levels, and low expression of ZNF180 and METTL14 predicted a poor prognosis in CRC. Overall, these findings revealed a novel mechanism by which the ZNF180/METTL14 axis may modulate apoptosis and cell proliferation in CRC. This evidence suggests that this axis may serve as a prognostic biomarker and therapeutic target in patients with CRC.

Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts

Introduction

While Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy.

Methods

Evaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders.

Results

Six proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort.

Discussion

Our study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making.

Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts

While Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy. Evaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders. Six proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort. Our study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making.

Abstract Figure

Roles and mechanisms of aberrant alternative splicing in melanoma - implications for targeted therapy and immunotherapy resistance

BACKGROUND

Despite advances in therapeutic strategies, resistance to immunotherapy and the off-target effects of targeted therapy have significantly weakened the benefits for patients with melanoma.

MAIN BODY

Alternative splicing plays a crucial role in transcriptional reprogramming during melanoma development. In particular, aberrant alternative splicing is involved in the efficacy of immunotherapy, targeted therapy, and melanoma metastasis. Abnormal expression of splicing factors and variants may serve as biomarkers or therapeutic targets for the diagnosis and prognosis of melanoma. Therefore, comprehensively integrating their roles and related mechanisms is essential. This review provides the first detailed summary of the splicing process in melanoma and the changes occurring in this pathway.

CONCLUSION

The focus of this review is to provide strategies for developing novel diagnostic biomarkers and summarize their potential to alter resistance to targeted therapies and immunotherapy.

Nano-drug delivery system targeting tumor microenvironment: A prospective strategy for melanoma treatment

Melanoma, the most aggressive form of cutaneous malignancy arising from melanocytes, is frequently characterized by metastasis. Despite considerable progress in melanoma therapies, patients with advanced-stage disease often have a poor prognosis due to the limited efficacy, off-target effects, and toxicity associated with conventional drugs. Nanotechnology has emerged as a promising approach to address these challenges with nanoparticles capable of delivering therapeutic agents specifically to the tumor microenvironment (TME). However, the clinical approval of nanomedicines for melanoma treatment remains limited, necessitating further research to develop nanoparticles with improved biocompatibility and precise targeting capabilities. This comprehensive review provides an overview of the current research on nano-drug delivery systems for melanoma treatment, focusing on liposomes, polymeric nanoparticles, and inorganic nanoparticles. It discusses the potential of these nanoparticles for targeted drug delivery, as well as their ability to enhance the efficacy of conventional drugs while minimizing toxicity. Furthermore, this review emphasizes the significance of interdisciplinary collaboration between researchers from various fields to advance the development of nanomedicines. Overall, this review serves as a valuable resource for researchers and clinicians interested in the potential of nano-drug delivery systems for melanoma treatment and offers insights into future directions for research in this field.

Effects of clopidogrel bisulfate on B16-F10 cells and tumor development in a murine model of melanoma

Metastatic melanoma is a very aggressive skin cancer. Platelets are constituents of the tumor microenvironment and, when activated, contribute to cancer progression, especially metastasis and inflammation. P2Y12 is an adenosine diphosphate receptor that triggers platelet activation. Inhibition of P2Y12 by clopidogrel bisulfate (CB) decreases platelet activation, which is also controlled by the extracellular concentration and the metabolism of purines by purinergic enzymes. We evaluated the effects of CB on the viability and proliferation of cultured B16-F10 cells. We also used a metastatic melanoma model with C57BL-6 mice to evaluate cancer development and purine metabolism modulation in platelets. B16-F10 cells were administered intraperitoneally to the mice. Two days later, the animals underwent a 12-day treatment with CB (30 mg/kg by gavage). We have found that CB reduced cell viability and proliferation in B16-F10 culture in 72 h at concentrations above 30 µm. In vivo, CB decreased tumor nodule counts and lactate dehydrogenase levels and increased platelet purine metabolism. Our results showed that CB has significant effects on melanoma progression.

Multiscale network modeling reveals the gene regulatory landscape driving cancer prognosis in 32 cancer types

Cancer is a complex disease with diverse molecular mechanisms that affect patient prognosis. Network-based approaches are effective in revealing a holistic picture of cancer prognosis and gene interactions. However, a comprehensive landscape of coexpression networks and prognostic gene modules across multiple cancer types remains elusive. In this study, we performed a systematic analysis of coexpression networks in 32 cancer types. Our analysis identified 4749 prognostic modules that play a vital role in regulating cancer progression. Integrative epigenomic analyses revealed that these modules were regulated by interactions between gene expression and methylation. Coregulated genes of network modules were enriched in chromosome cytobands and preferentially localized in open chromatin regions. The preserved network modules formed 330 module clusters that resided in chromosome hot spots. The cancer-type-specific prognostic modules participated in unique essential biological processes in different cancer types. Overall, our study provides rich resources of prevalent gene networks and underlying multiscale regulatory mechanisms driving cancer prognosis, which lay a foundation for biomarker discovery and therapeutic target development.

Evaluation of drug sensitivity, immunological characteristics, and prognosis in melanoma patients using an endoplasmic reticulum stress-associated signature based on bioinformatics and pan-cancer analysis

We aimed to develop endoplasmic reticulum (ER) stress-related risk signature to predict the prognosis of melanoma and elucidate the immune characteristics and benefit of immunotherapy in ER-related risk score-defined subgroups of melanoma based on a machine learning algorithm. Based on The Cancer Genome Atlas (TCGA) melanoma dataset (n = 471) and GTEx database (n = 813), 365 differentially expressed ER-associated genes were selected using the univariate Cox model and LASSO penalty Cox model. Ten genes impacting OS were identified to construct an ER-related signature by using the multivariate Cox regression method and validated with the Gene Expression Omnibus (GEO) dataset. Thereafter, the immune features, CNV, methylation, drug sensitivity, and the clinical benefit of anticancer immune checkpoint inhibitor (ICI) therapy in risk score subgroups, were analyzed. We further validated the gene signature using pan-cancer analysis by comparing it to other tumor types. The ER-related risk score was constructed based on the ARNTL, AGO1, TXN, SORL1, CHD7, EGFR, KIT, HLA-DRB1 KCNA2, and EDNRB genes. The high ER stress-related risk score group patients had a poorer overall survival (OS) than the low-risk score group patients, consistent with the results in the GEO cohort. The combined results suggested that a high ER stress-related risk score was associated with cell adhesion, gamma phagocytosis, cation transport, cell surface cell adhesion, KRAS signalling, CD4 T cells, M1 macrophages, naive B cells, natural killer (NK) cells, and eosinophils and less benefitted from ICI therapy. Based on the expression patterns of ER stress-related genes, we created an appropriate predictive model, which can also help distinguish the immune characteristics, CNV, methylation, and the clinical benefit of ICI therapy. KEY MESSAGES: Melanoma is the cutaneous tumor with a high degree of malignancy, the highest fatality rate, and extremely poor prognosis. Model usefulness should be considered when using models that contained more features. We constructed the Endoplasmic Reticulum stress-associated signature using TCGA and GEO database based on machine learning algorithm. ER stress-associated signature has excellent ability for predicting prognosis for melanoma.

Sex specific molecular networks and key drivers of Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a progressive and age-associated neurodegenerative disorder that affects women disproportionally. However, the underlying mechanisms are poorly characterized. Moreover, while the interplay between sex and ApoE genotype in AD has been investigated, multi-omics studies to understand this interaction are limited. Therefore, we applied systems biology approaches to investigate sex-specific molecular networks of AD.

METHODS

We integrated large-scale human postmortem brain transcriptomic data of AD from two cohorts (MSBB and ROSMAP) via multiscale network analysis and identified key drivers with sexually dimorphic expression patterns and/or different responses to APOE genotypes between sexes. The expression patterns and functional relevance of the top sex-specific network driver of AD were further investigated using postmortem human brain samples and gene perturbation experiments in AD mouse models.

RESULTS

Gene expression changes in AD versus control were identified for each sex. Gene co-expression networks were constructed for each sex to identify AD-associated co-expressed gene modules shared by males and females or specific to each sex. Key network regulators were further identified as potential drivers of sex differences in AD development. LRP10 was identified as a top driver of the sex differences in AD pathogenesis and manifestation. Changes of LRP10 expression at the mRNA and protein levels were further validated in human AD brain samples. Gene perturbation experiments in EFAD mouse models demonstrated that LRP10 differentially affected cognitive function and AD pathology in sex- and APOE genotype-specific manners. A comprehensive mapping of brain cells in LRP10 over-expressed (OE) female E4FAD mice suggested neurons and microglia as the most affected cell populations. The female-specific targets of LRP10 identified from the single cell RNA-sequencing (scRNA-seq) data of the LRP10 OE E4FAD mouse brains were significantly enriched in the LRP10-centered subnetworks in female AD subjects, validating LRP10 as a key network regulator of AD in females. Eight LRP10 binding partners were identified by the yeast two-hybrid system screening, and LRP10 over-expression reduced the association of LRP10 with one binding partner CD34.

CONCLUSIONS

These findings provide insights into key mechanisms mediating sex differences in AD pathogenesis and will facilitate the development of sex- and APOE genotype-specific therapies for AD.

KIF2C is a prognostic biomarker associated with immune cell infiltration in breast cancer

BACKGROUND

The kinesin-13 family member 2C (KIF2C) is a versatile protein participating in many biological processes. KIF2C is frequently up-regulated in multiple types of cancer and is associated with cancer development. However, the role of KIF2C in immune cell infiltration of tumor microenvironment and immunotherapy in breast cancer remains unclear.

METHODS

The expression of KIF2C was analyzed using Tumor Immune Estimation Resource (TIMER) database and further verified by immunohistochemical staining in human breast cancer tissues. The correlation between KIF2C expression and clinical parameters, the impact of KIF2C on clinical prognosis and independent prognostic factors were analyzed by using TCGA database, the Kaplan-Meier plotter, and Univariate and multivariate Cox analyses, respectively. The nomograms were constructed according to independent prognostic factors and validated with C-index, calibration curves, ROC curves, and decision curve analysis. A gene set enrichment analysis (GSEA) was performed to explore the underlying molecular mechanisms of KIF2C. The degree of immune infiltration was assessed by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using the Expression (ESTIMATE) algorithm and the single sample GSEA (ssGSEA). The Tumor mutational burden and Tumor Immune Dysfunction and Rejection (TIDE) were used to analyze immunotherapeutic efficiency. Finally, the KIF2C-related competing endogenous RNA (ceRNA) network was constructed to predict the putative regulatory mechanisms of KIF2C.

RESULTS

KIF2C was remarkably up-regulated in 18 different types of cancers, including breast cancer. Kaplan-Meier survival analysis showed that high KIF2C expression was associated with poor overall survival (OS). KIF2C expression was associated with clinical parameters such as age, TMN stage, T status, and molecular subtypes. We identified age, stage, estrogen receptor (ER) and KIF2C expression as OS-related independent prognosis factors for breast cancer. An OS-related nomogram was developed based on these independent prognosis factors and displayed good predicting ability for OS of breast cancer patients. Finally, our results revealed that KIF2C was significantly related to immune cell infiltration, tumor mutational burden, and immunotherapy in patients with breast cancer.

CONCLUSION

KIF2C was overexpressed in breast cancer and was positively correlated with immune cell infiltration and immunotherapy response. Therefore, KIF2C can serve as a potential biomarker for prognosis and immunotherapy in breast cancer.

SMG7-AS1 as a prognostic biomarker and predictor of immunotherapy responses for skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) is the most life-threatening skin cancer and lacks early detection and effective treatment strategies. Many long noncoding RNAs are associated with the development of tumors and may serve as potential immunotherapeutic targets. In this study, microarray analysis was performed to screen for differentially expressed lncRNAs between SKCM and normal tissues, and SMG7-AS1 was identified as an upregulated lncRNA in SKCM. Subsequently, bioinformatic analysis revealed that dysregulation of SMG7-AS1 influences metastasis and immune infiltration. qRT-PCR of clinical samples demonstrated that the expression of SMG7-AS1 was higher in melanoma tissues. Flow cytometry showed that SMG7-AS1 plays a vital role in the cell cycle. Additionally, SMG7-AS1 was found to be associated with immunotherapy responses. To the best of our knowledge, this study is the first to report that SMG7-AS1 is associated with SKCM and may serve as a prognostic biomarker and predictor of immunotherapy responses in SKCM.

Tumor cell plasticity in targeted therapy-induced resistance: mechanisms and new strategies

Despite the success of targeted therapies in cancer treatment, therapy-induced resistance remains a major obstacle to a complete cure. Tumor cells evade treatments and relapse via phenotypic switching driven by intrinsic or induced cell plasticity. Several reversible mechanisms have been proposed to circumvent tumor cell plasticity, including epigenetic modifications, regulation of transcription factors, activation or suppression of key signaling pathways, as well as modification of the tumor environment. Epithelial-to-mesenchymal transition, tumor cell and cancer stem cell formation also serve as roads towards tumor cell plasticity. Corresponding treatment strategies have recently been developed that either target plasticity-related mechanisms or employ combination treatments. In this review, we delineate the formation of tumor cell plasticity and its manipulation of tumor evasion from targeted therapy. We discuss the non-genetic mechanisms of targeted drug-induced tumor cell plasticity in various types of tumors and provide insights into the contribution of tumor cell plasticity to acquired drug resistance. New therapeutic strategies such as inhibition or reversal of tumor cell plasticity are also presented. We also discuss the multitude of clinical trials that are ongoing worldwide with the intention of improving clinical outcomes. These advances provide a direction for developing novel therapeutic strategies and combination therapy regimens that target tumor cell plasticity.

Multi-omic integration reveals cell-type-specific regulatory networks of insulin resistance in distinct ancestry populations

Our knowledge of the cell-type-specific mechanisms of insulin resistance remains limited. To dissect the cell-type-specific molecular signatures of insulin resistance, we performed a multiscale gene network analysis of adipose and muscle tissues in African and European ancestry populations. In adipose tissues, a comparative analysis revealed ethnically conserved cell-type signatures and two adipocyte subtype-enriched modules with opposite insulin sensitivity responses. The modules enriched for adipose stem and progenitor cells as well as immune cells showed negative correlations with insulin sensitivity. In muscle tissues, the modules enriched for stem cells and fibro-adipogenic progenitors responded to insulin sensitivity oppositely. The adipocyte and muscle fiber-enriched modules shared cellular-respiration-related genes but had tissue-specific rearrangements of gene regulations in response to insulin sensitivity. Integration of the gene co-expression and causal networks further pinpointed key drivers of insulin resistance. Together, this study revealed the cell-type-specific transcriptomic networks and signaling maps underlying insulin resistance in major glucose-responsive tissues. A record of this paper's transparent peer review process is included in the supplemental information.

Expressional and functional characteristics of checkpoint kinase 1 as a prognostic biomarker in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is the most common pathological subtype of liver cancer and is the third leading cause of cancer death worldwide. Checkpoint kinase 1 (CHEK1), an essential serine/threonine kinase that regulates the cell cycle, is reported to be associated with carcinogenesis. However, the biological role and clinical significance of CHEK1 in HCC are still incompletely known.

Methods

In this research, CHEK1 messenger RNA (mRNA) levels in various liver hepatocellular carcinoma (LIHC) cohorts from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were evaluated. The Kaplan-Meier database was applied to identify the correlation between survival time and CHEK1 expression in patients with HCC. Gene set enrichment analysis (GSEA) was performed to explore the potential mechanism of CHEK1 in HCC, and NetworkAnalyst v. 3.0 (https://www.networkanalyst.ca/) was used to construct the regulatory networks of CHEK1 in HCC. Discriminant Regulon Expression Analysis (DoRothEA) was used to detect the activity of transcriptional factors (TFs) in gene-enriched cells (EC) with CHEK1 coexpression. In vitro experiments were conducted to investigate the effects of CHEK1 on the biological function of HCC cells.

Results

The CHEK1 mRNA level was overexpressed in HCC, and increased CHEK1 expression correlated with poor survival outcomes. The homo sapiens-microRNA-195 (hsa-miR-195) may have contributed to the upregulation of CHEK1 in HCC. GSEA and NetworkAnalyst v. 3.0 showed that CHEK1 played a crucial part in tumor proliferation of HCC and may be regulated by TF E2F1. DoRothEA showed increased transcriptional activity of E2F1 in gene-EC with CHEK1 coexpression. Moreover, experiments of cell function showed that the knockdown of CHEK1 weakened the aggressive behavior and proliferation of HCC cells. Overexpression of E2F1 increased the proliferation and invasion of HCC cells in vitro, while the silencing of CHEK1 dampened cell invasion induced by E2F1 overexpression.

Conclusions

These results identified the prognostic significance and expression characteristics of CHEK1 in HCC through bioinformatics analysis and experimental verification. This lays the foundation for further research on the diagnosis and treatment of HCC.

Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence

We live in an unprecedented time in oncology. We have accumulated samples and cases in cohorts larger and more complex than ever before. New technologies are available for quantifying solid or liquid samples at the molecular level. At the same time, we are now equipped with the computational power necessary to handle this enormous amount of quantitative data. Computational models are widely used helping us to substantiate and interpret data. Under the label of systems and precision medicine, we are putting all these developments together to improve and personalize the therapy of cancer. In this review, we use melanoma as a paradigm to present the successful application of these technologies but also to discuss possible future developments in patient care linked to them. Melanoma is a paradigmatic case for disruptive improvements in therapies, with a considerable number of metastatic melanoma patients benefiting from novel therapies. Nevertheless, a large proportion of patients does not respond to therapy or suffers from adverse events. Melanoma is an ideal case study to deploy advanced technologies not only due to the medical need but also to some intrinsic features of melanoma as a disease and the skin as an organ. From the perspective of data acquisition, the skin is the ideal organ due to its accessibility and suitability for many kinds of advanced imaging techniques. We put special emphasis on the necessity of computational strategies to integrate multiple sources of quantitative data describing the tumour at different scales and levels.

Advances in the Application of Nanomaterials to the Treatment of Melanoma

Melanoma can be divided into cutaneous melanoma, uveal melanoma, mucosal melanoma, etc. It is a very aggressive tumor that is prone to metastasis. Patients with metastatic melanoma have a poor prognosis and shorter survival. Although current melanoma treatments have been dramatically improved, there are still many problems such as systemic toxicity and the off-target effects of drugs. The use of nanoparticles may overcome some inadequacies of current melanoma treatments. In this review, we summarize the limitations of current therapies for cutaneous melanoma, uveal melanoma, and mucosal melanoma, as well as the adjunct role of nanoparticles in different treatment modalities. We suggest that nanomaterials may have an effective intervention in melanoma treatment in the future.

The Associated of the Risk of IVIG Resistance in Kawasaki Disease with ZNF112 Gene and ZNF180 Gene in a Southern Chinese Population

Background

Kawasaki disease (KD) was one of the most common primary vasculitis. IVIG resistance was associated with an increased risk of coronary artery aneurysm. Accumulating evidences demonstrated that inflammatory gene polymorphisms might play important roles in IVIG resistance, and zinc finger proteins were closely related to immune inflammation regulation, but the effect of ZNF112/rs8113807 and ZNF180/rs2571051 on IVIG resistance in KD patients has not been reported.

Methods

A total of 996 KD patients were recruited, and the assay of TaqMan-real-time polymerase chain reaction was used for ZNF112/rs8113807 and ZNF180/rs2571051 genotyping. Odds ratio (OR) and 95% confidence interval (CI) were calculated for estimating the relationship between the polymorphisms of the both SNPs (ZNF112/rs8113807 and ZNF180/rs2571051) and the risk of IVIG resistance.

Results

Both of the ZNF112/rs8113807 CC/TC genotype and the ZNF180/rs2571051 TT/CT genotype increased the risk of IVIG resistance in KD (rs8113807: CC vs TT: adjusted OR = 1.83, 95% CI = 1.06–3.16, p = 0.0293; CC/TC vs TT adjusted: OR = 1.49, 95% CI = 1.10–2.02, p = 0.0094. rs2571051: TT vs CC adjusted: OR = 2.64, 95% CI = 1.62–4.29, p < 0.0001; TT/CT vs CC adjusted: OR = 2.14, 95% CI = 1.37–3.37, p = 0.0009; TT vs CC/CT adjusted: OR = 1.66, 95% CI = 1.22–2.27, p = 0.0014). Furthermore, the combinative analysis of risk genotypes in ZNF112/rs8113807 and ZNF180/rs2571051 showed that patients with two unfavorable genotypes were more likely to increase risk of IVIG resistance than those who carried with zero or one unfavorable genotypes (adjusted: OR = 1.68, 95% CI = 1.24–2.27, p = 0.0008).

Conclusion

Our findings enriched the genetic background of IVIG resistance risk in the KD development and suggested that the ZNF112/rs8113807 C-carrier and the ZNF180/rs2571051 T-carrier were associated with increased risk of IVIG resistance in KD patients in Chinese southern population.

Investigation of the mechanism of the anti-cancer effects of Astragalus propinquus Schischkin and Pinellia pedatisecta Schott (A&P) on melanoma via network pharmacology and experimental verification

Melanoma is a commonly malignant cutaneous tumor in China. Astragalus propinquus Schischkin and Pinellia pedatisecta Schott (A&P) have been clinically used as adjunctive drugs in the treatment of malignant melanoma. However, the effect and mechanism of A&P on melanoma have yet to be explored. The current investigation seeks to characterize the active components of A&P and their potential roles in treating malignant melanoma using network pharmacology and in vitro and in vivo experiments. We first used the traditional Chinese medicine systems pharmacology (TCMSP) database and high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS) to identify a total of 13 effective compounds within A&P. 70 common genes were obtained by matching 487 potential genes of A&P with 464 melanoma-related genes, and then we built up protein-protein interaction (PPI) network of these 70 genes, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The results revealed that A&P might influence the pathobiology of melanoma through the PI3K/Akt pathway. Molecular docking also confirmed that higher content of ingredients in A&P, including hederagenin, quercetin, beta-sitosterol and stigmasterol, had a strong binding activity (affinity < −5 kcal/mol) with the core targets AKT1, MAPK3 and ESR1. Furthermore, we confirmed A&P could inhibit melanoma cells proliferation and induce cells apoptosis through suppressing the PI3K/Akt signaling pathway by in vitro and in vivo xenograft model experiments. These findings indicate that A&P may function as a useful therapy for melanoma through the PI3K/Akt pathway.

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

The landscape of human tissue and cell type specific expression and co-regulation of senescence genes

BACKGROUND

Cellular senescence is a complex stress response that impacts cellular function and organismal health. Multiple developmental and environmental factors, such as intrinsic cellular cues, radiation, oxidative stress, oncogenes, and protein accumulation, activate genes and pathways that can lead to senescence. Enormous efforts have been made to identify and characterize senescence genes (SnGs) in stress and disease systems. However, the prevalence of senescent cells in healthy human tissues and the global SnG expression signature in different cell types are poorly understood.

METHODS

This study performed an integrative gene network analysis of bulk and single-cell RNA-seq data in non-diseased human tissues to investigate SnG co-expression signatures and their cell-type specificity.

RESULTS

Through a comprehensive transcriptomic network analysis of 50 human tissues in the Genotype-Tissue Expression Project (GTEx) cohort, we identified SnG-enriched gene modules, characterized SnG co-expression patterns, and constructed aggregated SnG networks across primary tissues of the human body. Our network approaches identified 51 SnGs highly conserved across the human tissues, including CDKN1A (p21)-centered regulators that control cell cycle progression and the senescence-associated secretory phenotype (SASP). The SnG-enriched modules showed remarkable cell-type specificity, especially in fibroblasts, endothelial cells, and immune cells. Further analyses of single-cell RNA-seq and spatial transcriptomic data independently validated the cell-type specific SnG signatures predicted by the network analysis.

CONCLUSIONS

This study systematically revealed the co-regulated organizations and cell type specificity of SnGs in major human tissues, which can serve as a blueprint for future studies to map senescent cells and their cellular interactions in human tissues.

Genome editing of PD-L1 mediated by nucleobase-modified polyamidoamine for cancer immunotherapy

Immune checkpoint blockade therapy against programmed death protein-1 and its ligand (PD-1/PD-L1) has been accepted as a promising approach to activate the immune system's anti-tumor response. Although small interfering RNA...

A Machine learning model trained on dual-energy CT radiomics significantly improves immunotherapy response prediction for patients with stage IV melanoma

BACKGROUND

To assess the additive value of dual-energy CT (DECT) over single-energy CT (SECT) to radiomics-based response prediction in patients with metastatic melanoma preceding immunotherapy.

MATERIAL AND METHODS

A total of 140 consecutive patients with melanoma (58 female, 63±16 years) for whom baseline DECT tumor load assessment revealed stage IV and who were subsequently treated with immunotherapy were included. Best response was determined using the clinical reports (81 responders: 27 complete response, 45 partial response, 9 stable disease). Individual lesion response was classified manually analogous to RECIST 1.1 through 1291 follow-up examinations on a total of 776 lesions (6.7±7.2 per patient). The patients were sorted chronologically into a study and a validation cohort (each n=70). The baseline DECT was examined using specialized tumor segmentation prototype software, and radiomic features were analyzed for response predictors. Significant features were selected using univariate statistics with Bonferroni correction and multiple logistic regression. The area under the receiver operating characteristic curve of the best subset was computed (AUROC). For each combination (SECT/DECT and patient response/lesion response), an individual random forest classifier with 10-fold internal cross-validation was trained on the study cohort and tested on the validation cohort to confirm the predictive performance.

RESULTS

We performed manual RECIST 1.1 response analysis on a total of 6533 lesions. Multivariate statistics selected significant features for patient response in SECT (min. brightness, R²=0.112, padj. ≤0.001) and DECT (textural coarseness, R²=0.121, padj. ≤0.001), as well as lesion response in SECT (mean absolute voxel intensity deviation, R²=0.115, padj. ≤0.001) and DECT (iodine uptake metrics, R²≥0.12, padj. ≤0.001). Applying the machine learning models to the validation cohort confirmed the additive predictive power of DECT (patient response AUROC SECT=0.5, DECT=0.75; lesion response AUROC SECT=0.61, DECT=0.85; p<0.001).

CONCLUSION

The new method of DECT-specific radiomic analysis provides a significant additive value over SECT radiomics approaches for response prediction in patients with metastatic melanoma preceding immunotherapy, especially on a lesion-based level. As mixed tumor response is not uncommon in metastatic melanoma, this lends a powerful tool for clinical decision-making and may potentially be an essential step toward individualized medicine.

Application of Single-Cell Multi-Omics in Dissecting Cancer Cell Plasticity and Tumor Heterogeneity

Tumor heterogeneity, a hallmark of cancer, impairs the efficacy of cancer therapy and drives tumor progression. Exploring inter- and intra-tumoral heterogeneity not only provides insights into tumor development and progression, but also guides the design of personalized therapies. Previously, high-throughput sequencing techniques have been used to investigate the heterogeneity of tumor ecosystems. However, they could not provide a high-resolution landscape of cellular components in tumor ecosystem. Recently, advance in single-cell technologies has provided an unprecedented resolution to uncover the intra-tumoral heterogeneity by profiling the transcriptomes, genomes, proteomes and epigenomes of the cellular components and also their spatial distribution, which greatly accelerated the process of basic and translational cancer research. Importantly, it has been demonstrated that some cancer cells are able to transit between different states in order to adapt to the changing tumor microenvironment, which led to increased cellular plasticity and tumor heterogeneity. Understanding the molecular mechanisms driving cancer cell plasticity is critical for developing precision therapies. In this review, we summarize the recent progress in dissecting the cancer cell plasticity and tumor heterogeneity by use of single-cell multi-omics techniques.

Uncovering potential single nucleotide polymorphisms, copy number variations and related signaling pathways in primary Sjogren's syndrome

Primary Sjogren’s syndrome (pSS) is a complex systemic autoimmune disease, which is difficult to accurately diagnose due to symptom diversity in patients, especially at earlier stages. We tried to find potential single nucleotide polymorphisms (SNPs), copy number variations (CNVs) and related signaling pathways. Genomic DNA was extracted from peripheral blood of 12 individuals (7 individuals from 3 pSS pedigrees and 5 sporadic cases) for whole-exome sequencing (WES) analysis. SNPs and CNVs were identified, followed by functional annotation of genes with SNPs and CNVs. Gene expression profile (involving 64 normal controls and 166 cases) was downloaded from the Gene Expression Omnibus database (GEO) dataset for differentially expression analysis. Sanger sequencing and in vitro validation was used to validate the identified SNPs and differentially expressed genes, respectively. A total of 5 SNPs were identified in both pedigrees and sporadic cases, such as FES, PPM1J, and TRAPPC9. A total of 3402 and 19 CNVs were identified in pedigrees and sporadic cases, respectively. Fifty-one differentially expressed genes were associated with immunity, such as BATF3, LAP3, BATF2, PARP9, and IL15RA. AMPK signaling pathway and cell adhesion molecules (CAMs) were the most significantly enriched signaling pathways of identified SNPs. Identified CNVs were associated with systemic lupus erythematosus, mineral absorption, and HTLV-I infection. IL2-STAT5 signaling, interferon-gamma response, and interferon-alpha response were significantly enriched immune related signaling pathways of identified differentially expressed genes. In conclusion, our study found some potential SNPs, CNVs, and related signaling pathways, which could be useful in understanding the pathological mechanism of pSS.

KIF2C Is a Novel Prognostic Biomarker and Correlated with Immune Infiltration in Endometrial Cancer

Endometrial cancer (EC) is commonly diagnosed cancer in women, and the prognosis of advanced types of EC is extremely poor. Kinesin family member 2C (KIF2C) has been reported as an oncogene in cancers. However, its pathophysiological roles and the correlation with tumor-infiltrating lymphocytes in EC remain unclear. The mRNA and protein levels of KIF2C in EC tissues were detected by qRT-PCR, Western blot (WB), and IHC. CCK8, Transwell, and colony formation assay were applied to assess the effects of KIF2C on cell proliferation, migration, and invasion. Cell apoptosis and cell cycle were analyzed by flow cytometry. The antitumor effect was further validated in the nude mouse xenograft cancer model and humanized mouse model. KIF2C expression was higher in EC. Knockdown of KIF2C prolonged the G1 phases and inhibited EC cell proliferation, migration, and invasion in vitro. Bioinformatics analysis indicated that KIF2C is negatively correlated with the infiltration level of CD8⁺ T cells but positively with the poor prognosis of EC patients. The apoptosis of CD8⁺ T cell was inhibited after the knockdown of KIF2C and was further inhibited when it is combined with anti-PD1. Conversely, compared to the knockdown of KIF2C expression alone, the combination of anti-PD1 further promoted the apoptosis of Ishikawa and RL95-2 cells. Moreover, the knockdown of KIF2C inhibited the expression of Ki-67 and the growth of tumors in the nude mouse xenograft cancer model. Our study found that the antitumor efficacy was further evaluated by the combination of anti-PD1 and KIF2C knockdown in a humanized mouse model. This study indicated that KIF2C is a novel prognostic biomarker that determines cancer progression and also a target for the therapy of EC and correlated with tumor immune cells infiltration in EC.