Advancing diabetes treatment: the role of mesenchymal stem cells in islet transplantation.

Diabetes mellitus, a prevalent global health challenge, significantly impacts societal and economic well-being. Islet transplantation is increasingly recognized as a viable treatment for type 1 diabetes that aims to restore endogenous insulin production and mitigate complications associated with exogenous insulin dependence. We review the role of mesenchymal stem cells (MSCs) in enhancing the efficacy of islet transplantation. MSCs, characterized by their immunomodulatory properties and differentiation potential, are increasingly seen as valuable in enhancing islet graft survival, reducing immune-mediated rejection, and supporting angiogenesis and tissue repair. The utilization of MSC-derived extracellular vesicles further exemplifies innovative approaches to improve transplantation outcomes. However, challenges such as MSC heterogeneity and the optimization of therapeutic applications persist. Advanced methodologies, including artificial intelligence (AI) and single-cell RNA sequencing (scRNA-seq), are highlighted as potential technologies for addressing these challenges, potentially steering MSC therapy toward more effective, personalized treatment modalities for diabetes. This review revealed that MSCs are important for advancing diabetes treatment strategies, particularly through islet transplantation. This highlights the importance of MSCs in the field of regenerative medicine, acknowledging both their potential and the challenges that must be navigated to fully realize their therapeutic promise.

Revolutionizing cancer immunotherapy in solid tumor: CAR engineering and single-cell sequencing insights.

The global increase in cancer incidence presents significant economic and societal challenges. While chimeric antigen receptor-modified T cell (CAR-T) therapy has demonstrated remarkable success in hematologic malignancies and has earned FDA approval, its translation to solid tumors encounters faces significant obstacles, primarily centered around identifying reliable tumor-associated antigens and navigating the complexities of the tumor microenvironment. Recent developments in single-cell RNA sequencing (scRNA-seq) have greatly enhanced our understanding of tumors by offering high-resolution, unbiased analysis of cellular heterogeneity and molecular patterns. These technologies have revolutionized our comprehension of tumor immunology and have led to notable progress in cancer immunotherapy. This mini-review explores the progress of chimeric antigen receptor (CAR) cell therapy in solid tumor treatment and the application of scRNA-seq at various stages following the administration of CAR cell products into the body. The advantages of scRNA-seq are poised to further advance the investigation of the biological characteristics of CAR cells in vivo, tumor immune evasion, the impact of different cellular components on clinical efficacy, the development of clinically relevant biomarkers, and the creation of new targeted drugs and combination therapy approaches. The integration of scRNA-seq with CAR therapy represents a promising avenue for future innovations in cancer immunotherapy. This synergy holds the potential to enhance the precision and efficacy of CAR cell therapies while expanding their applications to a broader range of malignancies.

Corrigendum: Single-cell RNA sequencing reveals the role of phosphorylation-related genes in hepatocellular carcinoma stem cells.

[This corrects the article DOI: 10.3389/fcell.2021.734287.].

Construction of a hepatocytes-related and protein kinase-related gene signature in HCC based on ScRNA-Seq analysis and machine learning algorithm.

With recent advancements in single-cell sequencing and machine learning methods, new insights into hepatocellular carcinoma (HCC) progression have been provided. Protein kinase-related genes (PKRGs) affect cell growth, differentiation, apoptosis, and signaling during HCC progression, making the predictive relevance of PKRGs in HCC highly necessary for personalized medicine. In this study, we analyzed single-cell data of HCC and used the machine learning method of LASSO regression to construct PKRG prediction models in six major cell types. CDK4 and AURKB were found to be the best PKRG prognostic signature for predicting the overall survival of HCC patients (including TCGA, ICGC, and GEO datasets) in hepatocytes. Independent clinical factors were further screened out using the COX regression method, and a nomogram combining PKRGs and cancer status was created. Treatment with Palbociclib (CDK4 Inhibitor) and Barasertib (AURKB Inhibitor) inhibited HCC cell migration. Patients classified as PKRG high- or low-risk groups showed different tumor mutation burdens, immune infiltrations, and gene enrichment. The PKRG high-risk group showed higher tumor mutation burdens and gene set enrichment analysis indicated that cell cycle, base excision repair, and RNA degradation pathways were more enriched in these patients. Additionally, the PKRG high-risk group demonstrated higher infiltration levels of Naïve CD8+ T cells, Endothelial cells, M2 macrophage, and Tregs than the low-risk group. In summary, this study established the hepatocytes-related PKRG signature for prognostic stratification at the single-cell level by using machine learning algorithms in HCC and identified potential HCC treatment targets based on the PKRG signature.

Single-cell RNA analysis to identify five cytokines signaling in immune-related genes for melanoma survival prognosis.

Melanoma is one of the deadliest skin cancers. Recently, developed single-cell sequencing has revealed fresh insights into melanoma. Cytokine signaling in the immune system is crucial for tumor development in melanoma. To evaluate melanoma patient diagnosis and treatment, the prediction value of cytokine signaling in immune-related genes (CSIRGs) is needed. In this study, the machine learning method of least absolute selection and shrinkage operator (LASSO) regression was used to establish a CSIRG prognostic signature of melanoma at the single-cell level. We discovered a 5-CSIRG signature that was substantially related to the overall survival of melanoma patients. We also constructed a nomogram that combined CSIRGs and clinical features. Overall survival of melanoma patients can be consistently predicted with good performance as well as accuracy by both the 5-CSIRG signature and nomograms. We compared the melanoma patients in the CSIRG high- and low-risk groups in terms of tumor mutation burden, infiltration of the immune system, and gene enrichment. High CSIRG-risk patients had a lower tumor mutational burden than low CSIRG-risk patients. The CSIRG high-risk patients had a higher infiltration of monocytes. Signaling pathways including oxidative phosphorylation, DNA replication, and aminoacyl tRNA biosynthesis were enriched in the high-risk group. For the first time, we constructed and validated a machine-learning model by single-cell RNA-sequencing datasets that have the potential to be a novel treatment target and might serve as a prognostic biomarker panel for melanoma. The 5-CSIRG signature may assist in predicting melanoma patient prognosis, biological characteristics, and appropriate therapy.

Construction of a lipid metabolism-related risk model for hepatocellular carcinoma by single cell and machine learning analysis.

One of the most common cancers is hepatocellular carcinoma (HCC). Numerous studies have shown the relationship between abnormal lipid metabolism-related genes (LMRGs) and malignancies. In most studies, the single LMRG was studied and has limited clinical application value. This study aims to develop a novel LMRG prognostic model for HCC patients and to study its utility for predictive, preventive, and personalized medicine. We used the single-cell RNA sequencing (scRNA-seq) dataset and TCGA dataset of HCC samples and discovered differentially expressed LMRGs between primary and metastatic HCC patients. By using the least absolute selection and shrinkage operator (LASSO) regression machine learning algorithm, we constructed a risk prognosis model with six LMRGs (AKR1C1, CYP27A1, CYP2C9, GLB1, HMGCS2, and PLPP1). The risk prognosis model was further validated in an external cohort of ICGC. We also constructed a nomogram that could accurately predict overall survival in HCC patients based on cancer status and LMRGs. Further investigation of the association between the LMRG model and somatic tumor mutational burden (TMB), tumor immune infiltration, and biological function was performed. We found that the most frequent somatic mutations in the LMRG high-risk group were CTNNB1, TTN, TP53, ALB, MUC16, and PCLO. Moreover, naïve CD8+ T cells, common myeloid progenitors, endothelial cells, granulocyte-monocyte progenitors, hematopoietic stem cells, M2 macrophages, and plasmacytoid dendritic cells were significantly correlated with the LMRG high-risk group. Finally, gene set enrichment analysis showed that RNA degradation, spliceosome, and lysosome pathways were associated with the LMRG high-risk group. For the first time, we used scRNA-seq and bulk RNA-seq to construct an LMRG-related risk score model, which may provide insights into more effective treatment strategies for predictive, preventive, and personalized medicine of HCC patients.

Construction of endothelial cell-related and autophagy-related prognostic models for hepatocellular carcinoma based on single-cell data.

BACKGROUND: We aimed to develop an autophagy-related prognostic model with single-cell RNA sequencing (ScRNA-Seq) data for hepatocellular carcinoma (HCC) patients. METHODS: ScRNA-Seq datasets of HCC patients were analyzed by Seurat. The expression of genes involved in canonical and noncanonical autophagy pathways in scRNA-seq data was also compared. Cox regression was applied to construct an AutRG risk prediction model. Subsequently, we examined the characteristics of AutRG high-risk and low-risk group patients. RESULTS: Six major cell types (hepatocytes, myeloid cells, T/NK cells, B cells, fibroblast cells, and endothelial cells) were identified in the scRNA-Seq dataset. The results showed that most of the canonical and noncanonical autophagy genes were highly expressed in hepatocytes, with the exception of MAP 1LC3B, SQSTM1, MAP 1LC3A, CYBB, and ATG3. Six AutRG risk prediction models originating from different cell types were constructed and compared. The AutRG prognostic signature (GAPDH, HSP90AA1, and TUBA1C) in endothelial cells had the best overall performance for predicting the overall survival of HCC patients, with 1-year, 3-year, and 5-year AUCs equal to 0.758, 0.68, and 0.651 in the training cohort and 0.760, 0.796, and 0.840 in the validation cohort, respectively. The different tumor mutation burden, immune infiltration, and gene set enrichment characteristics of the AutRG high-risk and low-risk group patients were identified. CONCLUSION: We constructed an endothelial cell-related and autophagy-related prognostic model of HCC patients using the ScRNA-Seq dataset for the first time. This model demonstrated the good calibration ability of HCC patients and provided a new understanding of the evaluation of prognosis.

Clinical and Prognostic Value of PPIA, SQSTM1, and CCL20 in Hepatocellular Carcinoma Patients by Single-Cell Transcriptome Analysis.

Hepatocellular carcinoma (HCC) is the most malignant and poor-prognosis subtype of primary liver cancer. The scRNA-seq approach provides unique insight into tumor cell behavior at the single-cell level. Cytokine signaling in the immune system plays an important role in tumorigenesis and has both pro-tumorigenic and anti-tumorigenic functions. A biomarker of cytokine signaling in immune-related genes (CSIRG) is urgently required to assess HCC patient diagnosis and treatment. By analyzing the expression profiles of HCC single cells, TCGA, and ICGC data, we discovered that three important CSIRG (PPIA, SQSTM1, and CCL20) were linked to the overall survival of HCC patients. Cancer status and three hub CSIRG were taken into account while creating a risk nomogram. The nomogram had a high level of predictability and accuracy. Based on the CSIRG risk score, a distinct pattern of somatic tumor mutational burden (TMB) was detected between the two groups. The enrichment of the pyrimidine metabolism pathway, purine metabolism pathway, and lysosome pathway in HCC was linked to the CSIRG high-risk scores. Overall, scRNA-seq and bulk RNA-seq were used to create a strong CSIRG signature for HCC diagnosis.

Current Topics of Relevance to the Xenotransplantation of Free Pig Islets.

Pig islet xenotransplantation is a potential treatment for patients with type 1 diabetes. Current efforts are focused on identifying the optimal pig islet source and overcoming the immunological barrier. The optimal age of the pig donors remains controversial since both adult and neonatal pig islets have advantages. Isolation of adult islets using GMP grade collagenase has significantly improved the quantity and quality of adult islets, but neonatal islets can be isolated at a much lower cost. Certain culture media and coculture with mesenchymal stromal cells facilitate neonatal islet maturation and function. Genetic modification in pigs affords a promising strategy to prevent rejection. Deletion of expression of the three known carbohydrate xenoantigens (Gal, Neu5Gc, Sda) will certainly be beneficial in pig organ transplantation in humans, but this is not yet proven in islet transplantation, though the challenge of the '4th xenoantigen' may prove problematic in nonhuman primate models. Blockade of the CD40/CD154 costimulation pathway leads to long-term islet graft survival (of up to 965 days). Anti-CD40mAbs have already been applied in phase II clinical trials of islet allotransplantation. Fc region-modified anti-CD154mAbs successfully prevent the thrombotic complications reported previously. In this review, we discuss (I) the optimal age of the islet-source pig, (ii) progress in genetic modification of pigs, (iii) the immunosuppressive regimen for pig islet xenotransplantation, and (iv) the reduction in the instant blood-mediated inflammatory reaction.

LncRNAs Target Ferroptosis-Related Genes and Impair Activation of CD4+ T Cell in Gastric Cancer.

Gastric cancer (GC) is a malignant disease of the digestive tract and a life-threatening disease worldwide. Ferroptosis, an iron-dependent cell death caused by lipid peroxidation, is reported to be highly correlated with gastric tumorigenesis and immune cell activity. However, the underlying relationship between ferroptosis and the tumor microenvironment in GC and potential intervention strategies have not been unveiled. In this study, we profiled the transcriptome and prognosis data of ferroptosis-related genes (FRGs) in GC samples of the TCGA-STAD dataset. The infiltrating immune cells in GC were estimated using the CIBERSORT and XCELL algorithms. We found that the high expression of the hub FRGs (MYB, PSAT1, TP53, and LONP1) was positively correlated with poor overall survival in GC patients. The results were validated in an external GC cohort (GSE62254). Further immune cell infiltration analysis revealed that CD4+ T cells were the major infiltrated cells in the tumor microenvironment of GC. Moreover, the hub FRGs were significantly positively correlated with activated CD4+ T cell infiltration, especially Th cells. The gene features in the high-FRG score group were enriched in cell division, DNA repair, protein folding, T cell receptor, Wnt and NIK/NF-kappaB signaling pathways, indicating that the hub FRGs may mediate CD4+ T cell activation by these pathways. In addition, an upstream transcriptional regulation network of the hub FRGs by lncRNAs was also developed. Three lncRNAs (A2M-AS1, C2orf27A, and ZNF667-AS1) were identified to be related to the expression of the hub FRGs. Collectively, these results showed that lncRNA A2M-AS1, C2orf27A, and ZNF667-AS1 may target the hub FRGs and impair CD4+ T cell activation, which finally leads to poor prognosis of GC. Effective interventions for the above lncRNAs and the hub FRGs can help promote CD4+ T cell activation in GC patients and improve the efficacy of immunotherapy. These findings provide a novel idea of GC immunotherapy and hold promise for future clinical application.

Improving Outcomes of Tyrosine Kinase Inhibitors in Hepatocellular Carcinoma: New Data and Ongoing Trials.

Targeted therapies such as oral tyrosine kinase inhibitors (TKIs) are the main therapeutic strategy effective for advanced hepatocellular carcinoma (HCC). Currently six tyrosine kinase inhibitors for HCC therapy have been approved. The newly approved first-line drug donafenib represent the major milestones in HCC therapeutics in recent years. However, drug resistance in HCC remains challenging due to random mutations in target receptors as well as downstream pathways. TKIs-based combinatorial therapies with immune checkpoint inhibitors such as PD-1/PD-L1 antibodies afford a promising strategy to further clinical application. Recent developments of nanoparticle-based TKI delivery techniques improve drug absorption and bioavailability, enhance efficient targeting delivery, prolonged circulation time, and reduce harmful side effects on normal tissues, which may improve the therapeutic efficacy of the TKIs. In this review, we summarize the milestones and recent progress in clinical trials of TKIs for HCC therapy. We also provide an overview of the novel nanoparticle-based TKI delivery techniques that enable efficient therapy.

The Immune Atlas of Human Deciduas With Unexplained Recurrent Pregnancy Loss.

Recurrent pregnancy loss (RPL) is a common fertility problem that affects 1%-2% of couples all over the world. Despite exciting discoveries regarding the important roles of the decidual natural killer cell (dNK) and regulatory T cell in pregnancy, the immune heterogeneity in patients with unexplained recurrent pregnancy loss (URPL) remains elusive. Here, we profiled the transcriptomes of 13,953 CD45+ cells from three normal and three URPL deciduas. Based on our data, the cellular composition revealed three major populations of immune cells including dNK cell, T cell, and macrophage, and four minor populations including monocytes, dendritic cell (DC), mast cell, and B cell. Especially, we identified a subpopulation of CSF1+ CD59+ KIRs-expressing dNK cells in normal deciduas, while the proportion of this subpopulation was decreased in URPL deciduas. We also identified a small subpopulation of activated dDCs that were accumulated mainly in URPL deciduas. Furthermore, our data revealed that in decidua at early pregnancy, CD8+ T cells exhibited cytotoxic properties. The decidual macrophages expressed high levels of both M1 and M2 feature genes, which made them unique to the conventional M1/M2 classification. Our single-cell data revealed the immune heterogeneity in decidua and the potentially pathogenic immune variations in URPL.

Potential roles of mesenchymal stromal cells in islet allo- and xenotransplantation for type 1 diabetes mellitus.

Islet transplantation is poised to play an important role in the treatment of type 1 diabetes mellitus (T1DM). However, there are several challenges limiting its widespread use, including the instant blood-mediated inflammatory reaction, hypoxic/ischemic injury, and the immune response. Mesenchymal stem/stromal cells (MSCs) are known to exert regenerative, immunoregulatory, angiogenic, and metabolic properties. Here, we review recent reports on the application of MSCs in islet allo- and xenotransplantation. We also document the clinical trials that have been undertaken or are currently underway, relating to the co-transplantation of islets and MSCs. Increasing evidence indicates that co-transplantation of MSCs prolongs islet graft survival by locally secreted protective factors that reduce immune reactivity and promote vascularization, cell survival, and regeneration. MSC therapy may be a promising option for islet transplantation in patients with T1DM.

Multi-View Mammographic Density Classification by Dilated and Attention-Guided Residual Learning.

Breast density is widely adopted to reflect the likelihood of early breast cancer development. Existing methods of mammographic density classification either require steps of manual operations or achieve only moderate classification accuracy due to the limited model capacity. In this study, we present a radiomics approach based on dilated and attention-guided residual learning for the task of mammographic density classification. The proposed method was instantiated with two datasets, one clinical dataset and one publicly available dataset, and classification accuracies of 88.7 and 70.0 percent were obtained, respectively. Although the classification accuracy of the public dataset was lower than the clinical dataset, which was very likely related to the dataset size, our proposed model still achieved a better performance than the naive residual networks and several recently published deep learning-based approaches. Furthermore, we designed a multi-stream network architecture specifically targeting at analyzing the multi-view mammograms. Utilizing the clinical dataset, we validated that multi-view inputs were beneficial to the breast density classification task with an increase of at least 2.0 percent in accuracy and the different views lead to different model classification capacities. Our method has a great potential to be further developed and applied in computer-aided diagnosis systems. Our code is available at https://github.com/lich0031/Mammographic_Density_Classification.

Single-Cell RNA Sequencing Reveals the Role of Phosphorylation-Related Genes in Hepatocellular Carcinoma Stem Cells.

Abnormal activation of protein kinases and phosphatases is implicated in various tumorigenesis, including hepatocellular carcinoma (HCC). Advanced HCC patients are treated with systemic therapy, including tyrosine kinase inhibitors, which extend overall survival. Investigation of the underlying mechanism of protein kinase signaling will help to improve the efficacy of HCC therapy. Combining single-cell RNA sequencing data and TCGA RNA-seq data, we profiled the protein kinases, phosphatases, and other phosphorylation-related genes (PRGs) of HCC patients in this study. We found nine protein kinases and PRGs with high expression levels that were mainly detected in HCC cancer stem cells, including POLR2G, PPP2R1A, POLR2L, PRC1, ITBG1BP1, MARCKSL1, EZH2, DTYMK, and AURKA. Survival analysis with the TCGA dataset showed that these genes were associated with poor prognosis of HCC patients. Further correlation analysis showed that these genes were involved in cell cycle-related pathways that may contribute to the development of HCC. Among them, AURKA and EZH2 were identified as two hub genes by Ingenuity Pathway Analysis. Treatment with an AURKA inhibitor (alisertib) and an EZH2 inhibitor (gambogenic) inhibited HCC cell proliferation, migration, and invasion. We also found that both AURKA and EZH2 were highly expressed in TP53-mutant HCC samples. Our comprehensive analysis of PRGs contributes to illustrating the mechanisms underlying HCC progression and identifying potential therapeutic targets for future clinical trials.

Diagnostic accuracy of serum dickkopf-1 protein in diagnosis hepatocellular carcinoma: An updated meta-analysis.

BACKGROUND: To verify the accuracy of serum dickkopf-1 protein (DKK-1) in the diagnosis of hepatocellular carcinoma (HCC) by an updated meta-analysis. METHODS: We searched potential eligible studies in PubMed and Embase before July 8, 2018. Sensitivity (SN), specificity (SP), positive likelihood ratio (PLR), negative likelihood ratio (NLR), summary receiver operating characteristics curve (sROC), and diagnostic odds ratio (DOR) were pooled with their 95% confidence intervals CIs) using a bivariate random-effects model. RESULTS: A total of 8 articles contained 10 studies on diagnosis of HCC with DKK-1 alone,7 articles contained 9 studies on diagnosis of HCC with a-fetoprotein (AFP) alone and 5 articles contained 7 studies on diagnosis of HCC with DKK-1 + AFP were identified. The pooled SN, SP, PLR, NLR, and DOR of DKK-1 alone, AFP alone and DKK-1 + AFP were 0.72 (95% CI: 0.70-0.75), 0.62 (95% CI:0.59-0.64) and 0.80 (95% CI:0.78-0.83), 0.86 (95% CI: 0.84-0.87), 0.82 (95% CI:0.80-0.84) and 0.87 (95% CI: 0.85-0.88), 4.91 (95% CI: 2.73-8.83), 3.60 (95% CI:2.01-6.44) and 6.18 (95% CI: 4.68-8.16), 0.32 (95% CI: 0.22-0.47), 0.49 (95% CI:0.40-0.60) and 0.20 (95% CI: 0.15-0.26), and 17.21 (95% CI: 9.10-32.57), 7.45 (95% CI:3.69-15.01) and 31.39 (95% CI: 23.59-43.20), respectively. The area under the sROC was 0.88, 0.70, and 0.92 for the 3 diagnostic methods. CONCLUSIONS: Serum DKK-1 + AFP showed a high accuracy for diagnosis of HCC, and serum DKK-1 alone had moderate accuracy as compared to a previous meta-analysis, while AFP alone owned an unsatisfied diagnostic behavior for HCC. Due to the limitations of the current analysis, further well-designed studies are needed to confirm the diagnostic value of DKK-1 and DKK-1 + AFP in HCC diagnosis.

Single-Field Non-Mydriatic Fundus Photography for Diabetic Retinopathy Screening: A Systematic Review and Meta-Analysis.

PURPOSE: Single-field non-mydriatic fundus photography (NMFP) has been used to detect diabetic retinopathy (DR) in many studies; however, its value in a general clinical setting has not been established. Here we performed a meta-analysis to evaluate its diagnostic effectiveness. METHOD: We systematically searched PubMed, EMBASE, and Cochrane databases for candidate studies published through May 19, 2018. A random-effect model was used to calculate the diagnostic indicators including the sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), area under the curve (AUC), and 95% confidence intervals. RESULTS: Ten prospective studies were ultimately included. The pooled sensitivity, specificity, PLR, NLR, and DOR were 0.68, 0.94, 11.2, 0.34 and 33, respectively. The AUC was 0.88. Subgroup analysis showed that single-field NMFP had a respective sensitivity and specificity of 0.73 and 0.91 when compared to standard 7-field mydriatic stereoscopic photography (7SF), and 0.54 and 0.98 when compared to slit-lamp biomicroscopy as reference standard. CONCLUSIONS: Single-field NMFP is inadequate to detect DR. Additionally, it showed higher sensitivity and lower specificity when 7SF was used as reference standard, as compared to slit-lamp biomicroscopy, suggesting that different reference standards used in DR screening might have affected the diagnostic results.