Diagnosis of hepatocellular carcinoma using liquid biopsy-based biomarkers: a systematic review and network meta-analysis

Introduction

The diagnostic performance of liquid biopsy-based biomarkers for HCC was comprehensively compared in this network meta-analysis (NMA).

Methods

A thorough literature search was conducted to identify all comparative studies from January 1, 2000, to January 11, 2024. The QUADAS-2 tool was utilized to appraise the quality of studies involving diagnostic performance. R (v4.3.3) and an ANOVA model-based NMA were used to assess the diagnostic accuracy of each biomarker.

Results

This study included 82 studies comprising a total of 15,024 patients.CircRNA demonstrated significantly superior performance in distinguishing HCC from healthy populations (superiority index: 3.550 (95% CI [0.143-3])) compared to other diagnostic biomarkers for HCC. "mRNA exhibited significantly superior performance in distinguishing HCC from liver disease patients (superiority index:10.621 (95% CI [7-11])) compared to other diagnostic biomarkers for HCC. Further subgroup analysis of the top-ranking liquid biopsy-based diagnostic biomarkers revealed that hsa_circ_000224 (superiority index: 3.091 (95% CI[0.143-9]) ranked remarkably higher in distinguishing HCC from both healthy populations and liver disease patients. Subgroup analysis of mRNA demonstrated that KIAA0101 mRNA (superiority index: 2.434 (95% CI [0.2-5]) ranked remarkably higher in distinguishing HCC from healthy populations and liver disease patients, respectively.

Discussion

The results of this meta-analysis show that circRNA and mRNA are the first choice for HCC diagnosis. Subsequent analysis of circRNA and mRNA highlighted hsa_circ_000224, hsa_circ_0003998, KIAA0101 mRNA and GPC-3mRNA as the optimal diagnostic biomarkers for distinguishing HCC from healthy populations and liver disease patients, respectively. Well-structured prospective studies are crucial to comprehensively validate these findings.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/,identifier CRD42024521299.

Single-cell RNA sequencing revealed PPARG promoted osteosarcoma progression: based on osteoclast proliferation

Background

Osteosarcoma (OS) is one of the most common primary malignant bone tumors, primarily originating from mesenchymal tissue. It is notorious for its high invasiveness, high disability rate, high mortality rate, and poor prognosis. In most primary and metastatic malignant tumors, bone destruction can promote cancer progression, which is closely related to osteoclast activation and the imbalance between osteoblasts and osteoclasts. A large number of studies confirmed that osteoclasts are an important part of OS, which play an active role in destroying bone homeostasis and promoting the progress of OS. Therefore, we conducted a detailed study of osteoclasts at the single cell level, aiming to find new OS therapeutic targets to prevent tumor progression and local spread.

Methods

We analyzed the single-cell sequencing data of OS patients and usedMonocle2, Cytotrace, and Slingshot software to analyze the pseudo-sequential trajectory during OS progression. CellChat was used to reveal the communication between cells. PySCENIC was used to identify active transcription factors in osteoclasts. Finally, we further demonstrated the results by RT-qPCR analysis, CCK-8 assay, wound healing assay, Transwell assay, etc.

Results

Through the analysis of single-cell sequencing data in OS, we identified a highly specific subgroup, C2MKI67+ Osteoclast. The key signaling pathway APP and the top 1 transcription factor PPARG in this subgroup played essential roles in osteoclast proliferation and differentiation. Given the pivotal role of osteoclasts in OS progression, we speculated that these signaling pathways and transcription factors could emerge as novel therapeutic targets, offering innovative strategies for OS treatment.

Conclusion

This study enhanced our understanding of OS and osteoclasts through scRNA-seq. Furthermore, we discovered that PPARG amplifies osteoclast activation and proliferation, resulting in excessive bone resorption and degradation of the bone matrix, thereby creating a favorable environment for tumor cell proliferation and growth. By innovatively targeting PPARG, it affected osteoclast proliferation and thus affected tumor progression; this work offered new insights and directions for the clinical treatment of OS patients.

Neoadjuvant immunotherapy and oncolytic virotherapy in HPV positive and HPV negative skin cancer: A comprehensive review

Skin cancer is the most common new cancer among Caucasians. This cancer has different types, of which non-melanoma skin cancer is the most common type. Various factors affect this disease, one of which is viral infections, including HPV. This virus plays an important role in skin cancer, especially cSCCs. There are various options for the treatment of skin cancer, and today special attention has been paid to treatments based on therapeutic goals, immunotherapy and combination therapy. In this study, we have investigated treatments based on immunotherapy and virotherapy and the effect of HPV virus on the effectiveness of these treatments in skin cancer. Treatments based on virotherapy are performed for a long time in combination with other common treatments such as radiotherapy and chemotherapy in order to have a greater effect and lower its side effects, which include: shortness of breath, tachycardia, lowering blood pressure in the patient. Also, the most important axis of immunotherapy is to focus on PD1-PDL1, despite abundant evidence on the importance of immunotherapy, many studies investigate the use of immunotherapy inhibitors in the adjuvant and neoadjuvant setting in various cancers. Also, previous findings show conflicting evidence of the effect of HPV status on the response to immunotherapy.

The role of SASP in ischemic stroke: a deep dive into cellular mechanisms

Background

The escalating incidence of ischemic stroke (IS) exerts a heavy toll on global health. Aging, a prominent risk factor, implicates the senescence-associated secretory phenotype (SASP) in IS pathogenesis. We postulated that alterations in SASP-related factor expression during IS correlate with remodeling of intercellular interaction networks and disease advancement. The present study endeavored to preliminarily dissect the SASP-IS nexus via combined bulk and single-cell transcriptome analysis.

Methods

Aggregated expression profiles from human peripheral blood bulk chips and MCAO mouse single-cell sequencing data, followed by SASP gene analysis. Executed protein interaction network and enrichment assays. Investigated immune infiltration in stroke patients, managed quality control and annotation of single-cell data, cherry-picked central cells based on SASP scores, unearthed essential genes via enrichment analysis, conducted pseudo-time and intercellular communication studies, and prognosticated drugs for hub genes. Finally, authenticated core gene expression in serum of MCAO and Sham rats using real-time fluorescent polymerase chain reaction (RT-qPCR).

Results

Fourteen hub genes were discerned. Seven cell types were annotated in MCAO mouse peripheral blood single-cell data. Basophils exhibited the highest SASP scores, with Lcp1 upregulated and Ccl3 downregulated in basophils of the MCAO group. Enrichment analysis divulged a significant association of Ccl3 with the cell apoptosis pathway and Lcp1 with immune responses. The Ccl3 gene is pivotal in basophils and basophil-neutrophil crosstalk. Additionally, we forecasted nagrestipen's regulatory function on Ccl3. RT-qPCR demonstrated a marked elevation in Lcp1 mRNA and a pronounced reduction in Ccl3 in the MCAO group relative to the Sham group.

Conclusion

The Ccl3 gene in basophils and its immune cell interaction is a linchpin in the IS immune microenvironment. Ccl3 and Lcp1 might potentially modulate IS progression by influencing SASP, proffering novel prospects for IS clinical diagnosis and treatment.

Machine learning and deep learning to improve prevention of anastomotic leak after rectal cancer surgery

Anastomotic leakage (AL) is a significant complication following rectal cancer surgery, adversely affecting both quality of life and oncological outcomes. Recent advancements in artificial intelligence (AI), particularly machine learning and deep learning, offer promising avenues for predicting and preventing AL. These technologies can analyze extensive clinical datasets to identify preoperative and perioperative risk factors such as malnutrition, body composition, and radiological features. AI-based models have demonstrated superior predictive power compared to traditional statistical methods, potentially guiding clinical decision-making and improving patient outcomes. Additionally, AI can provide surgeons with intraoperative feedback on blood supply and anatomical dissection planes, minimizing the risk of intraoperative complications and reducing the likelihood of AL development.

Pan-cancer and experimental analyses reveal the immunotherapeutic significance of CST2 and its association with stomach adenocarcinoma proliferation and metastasis

Purpose

Cystatin 2 (CST2) is a cysteine protease inhibitor, and recent research suggests its potential involvement in cancer development. However, its role in the occurrence, progression, and prognosis of pan-cancer has not been systematically investigated.

Materials and methods

This study comprehensively analyzes the differential expression of CST2 in pan-cancer. The expression distribution patterns of CST2 were examined using single-cell datasets. Furthermore, we conducted a comprehensive evaluation of the correlation between CST2 expression and various factors. These factors include prognosis, immune cell infiltration, immune-related genes, mutations, methylation, tumor mutation burden (TMB), and microsatellite instability (MSI). In addition, we analyzed the sensitivity of drugs dependent on CST2 expression. We utilized gene set enrichment analysis (GSEA) analysis to explore the biological functions of CST2 across different cancer types. Finally, in gastric cancer cell lines, we will investigate the impact of CST2 knockout on expression levels, clonal proliferation, cell apoptosis, and cell migration.

Results

CST2 exhibits abnormal overexpression in multiple tumors. Single-cell analysis reveals high expression of CST2 in fibroblasts. CST2 is closely associated with prognosis, immune cell infiltration, immune-related genes, mutations, methylation, TMB, and MSI. Enrichment analysis demonstrated a significant correlation between CST2 and immune-related pathways. In stomach adenocarcinoma (STAD), CST2-related risk models are associated with prognosis and demonstrate strong predictive capabilities, while also being closely linked to the immune microenvironment. Drug sensitivity analysis indicates the correlation between CST2 and 21 chemotherapy drugs. Finally, experimental validation revealed significantly elevated expression of CST2 in STAD, indicating its role as a driver gene in regulating malignant cell proliferation and migration.

Conclusion

CST2 serves as a potential tumor immune biomarker, playing a critical facilitating role in the proliferation and migration processes of STAD.

Heterogeneity analysis and prognostic model construction of HPV negative oral squamous cell carcinoma T cells using ScRNA-seq and bulk-RNA analysis

BACKGROUND

T cells are involved in every stage of tumor development and significantly influence the tumor microenvironment (TME). Our objective was to assess T-cell marker gene expression profiles, develop a predictive risk model for human papilloma virus (HPV)-negative oral squamous cell carcinoma (OSCC) utilizing these genes, and examine the correlation between the risk score and the immunotherapy response.

METHODS

We acquired scRNA-seq data for HPV-negative OSCC from the GEO datasets. We performed cell‒cell communication, trajectory, and pathway enrichment analyses of T-cell-associated genes. In addition, we constructed and validated a T-cell-associated gene prognostic model for HPV-negative OSCC patients using TCGA and GEO data and assessed the immune infiltration status of HPV-negative OSCC patients .qRT-PCR was used to detect the expression level of prognosis-related genes in different risk groups.

RESULTS

ScRNA-seq was conducted on 28,000 cells derived from 14 HPV-negative OSCC samples and 6 normal samples. We identified 4,635 T cells from these cells and identified 774 differentially expressed genes(DEGs) associated with T cells across five distinct T-cell subtypes. Through the integration of bulk-RNAseq data, we established a prognostic model based on DEGs related to T cells. By separating patients into high-risk and low-risk groups according to these prognostic related genes, we can accurately predict their survival rates and the immune infiltration status of the TME.qRT-PCR results showed that compared with the patients of low risk group, the expression of PMEPA1, SH2D2A, SMS and PRDX4 were significantly up-regulated in high risk group.

CONCLUSION

This study provides a resource for understanding the heterogeneity of T cells in HPV-negative OSCC patients and associated prognostic risk models. It provides new insights for predicting survival and level of immune infiltration in patients with HPV-negative OSCC.

Progress in immune microenvironment, immunotherapy and prognostic biomarkers in pediatric osteosarcoma

Pediatric osteosarcoma, the most prevalent primary malignant bone tumor in children, is marked by aggressive progression and a generally poor prognosis. Despite advances in treatment, including multi-agent chemotherapy, survival rates remain suboptimal, with metastasis, particularly to the lungs, contributing significantly to mortality. The tumor microenvironment plays a crucial role in osteosarcoma progression, with immune cells such as tumor-associated macrophages and T lymphocytes significantly influencing tumor behavior. The immunosuppressive environment, dominated by M2 macrophages, contributes to immune evasion and poor therapeutic outcomes, though recent findings suggest the potential for reprogramming these cells to enhance immune responses. This review provides a comprehensive overview of the immune landscape in pediatric osteosarcoma, with a focus on the role of immune cells and their interactions within the tumor microenvironment (TME). It examines the impact of immune checkpoints, genetic mutations, and inflammatory pathways on osteosarcoma progression, highlighting their contribution to tumor immune evasion and disease advancement. Additionally, emerging immunotherapeutic strategies, such as immune checkpoint inhibitors, macrophage reprogramming, and antibody-based therapies, are summarized in detail, showcasing their potential to improve therapeutic outcomes.

Bridging Gaps in Migraine Management: A Comprehensive Review of Conventional Treatments, Natural Supplements, Complementary Therapies, and Lifestyle Modifications

Background: Migraine, a complex neurological condition, poses significant challenges for both sufferers and healthcare providers. While prescription medications play a vital role in managing migraine attacks, the quest for natural, non-pharmacological alternatives has garnered increasing interest. This review explores the efficacy and safety of natural supplements as treatments for migraine relief, comparing them with conventional prescription medications. Methods: The review delves into herbal supplements, clinical studies on natural remedies, aromatherapy, dietary influences, and lifestyle modifications in the context of migraine management in several databases. Results: The findings shed light on the potential of natural supplements as complementary or alternative approaches to traditional migraine therapies, offering insights into a holistic and personalized treatment paradigm for migraine sufferers. Conclusions: Natural supplements have gained attention as potential treatments for migraine relief, often perceived as safer alternatives to conventional medications.

Overcoming immune evasion with innovative multi-target approaches for glioblastoma

Glioblastoma (GBM) cells leverage complex endogenous and environmental regulatory mechanisms to drive proliferation, invasion, and metastasis. Tumor immune evasion, facilitated by a multifactorial network, poses a significant challenge to effective therapy, as evidenced by the limited clinical benefits of monotherapies, highlighting the adaptive nature of immune evasion. This review explores glioblastoma's immune evasion mechanisms, the role of ICIs in the tumor microenvironment, and recent clinical advancements, offering theoretical insights and directions for monotherapy and combination therapy in glioblastoma management.

Reversing NK cell exhaustion: a novel strategy combining immune checkpoint blockade with drug sensitivity enhancement in the treatment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common lethal cancers worldwide. Natural killer cells (NK cells) play a key role in liver immunosurveillance, but in the tumor microenvironment, NK cells are readily depleted, as evidenced by down-regulation of activating receptors, reduced cytokine secretion, and attenuated killing function. The up-regulation of inhibitory receptors, such as PD-1, TIM-3, and LAG-3, further exacerbates the depletion of NK cells. Combined blockade strategies targeting these immunosuppressive mechanisms, such as the combination of PD-1 inhibitors with other inhibitory pathways (eg. TIM-3 and LAG-3), have shown potential to reverse NK cell exhaustion in preclinical studies. This article explores the promise of these innovative strategies in HCC immunotherapy, providing new therapeutic directions for optimizing NK cell function and improving drug sensitivity.

Predicting Response to Treatment and Survival in Advanced Ovarian Cancer Using Machine Learning and Radiomics: A Systematic Review

Background and Objective: Machine learning and radiomics (ML/RM) are gaining interest in ovarian cancer (OC) but only a few studies have used these methods to predict treatment response. The objective of this study was to review the literature on the applications of ML/RM in OC assessments, specifically focusing on studies describing algorithms to predict treatment response and survival. Methods: This is a systematic review of the published literature from January 1985 to December 2023 on the use of ML/RM in OC An extensive search of electronic library databases was conducted. Two independent reviewers screened the articles initially by title then by full text. Quality was assessed using the MINORS criteria. p-values were generated using the Pearson's Chi-squared (x2) test to compare the performances of ML/RM models with traditional statistics. Results: Of the 5576 screened articles, 225 studies were included. Between 2021 and 2023, 49 studies were published, highlighting the rapidly growing interest in ML/RM. Median-quality scores using the MINORS scale were similar between studies published between 1985-2021 and 2021-2023 (both 8). Neural Networks (22.6%) and LASSO (15.3%) were the most common ML/RM algorithms in OC. Among these studies, 13 focused specifically on prediction of treatment response using radiomics. A total of 5113 patients were analyzed. The most common algorithms were Random Forest (4/13) followed by Neural Networks (3/13) and Support Vectors (3/13). Radiomic analysis was used to predict response to neoadjuvant chemotherapy in seven studies, with a median AUC of 0.77 (range 0.72-0.93), while the median AUC was 0.82 (range 0.77-0.89) in the six studies assessing the prediction of optimal or complete cytoreduction. Median model accuracy reported in 7/13 studies was 73% (range 66-98%). Additionally, four studies investigated the use of ML/RM for survival prediction for OC. The XGBoost model had 80.9% accuracy in predicting 5-year survival compared to linear regression, which achieved 79% accuracy. The Random Forest model has 93.7% accuracy in predicting 12-month progression-free survival, compared to 82% for linear regression. Conclusions: In conclusion, we found that the use of ML/RM algorithms is becoming a more frequent method to predict responses to treatment of OC. These models should be validated in a prospective multicenter trial prior to integration into clinical use.

N6-Methylandenosine-related lncRNAs as potential biomarkers for predicting prognosis and the immunotherapy response in pancreatic cancer

Emerging evidence has shown that the N6-methyladenosine (m6A) modification of RNA plays key roles in tumorigenesis and the progression of various cancers. However, the potential roles of the m6A modification of long noncoding RNAs (lncRNAs) in pancreatic cancer (PaCa) are still unknown. To analyze the prognostic value of m6A-related lncRNAs in PaCa, an m6A-related lncRNA signature was constructed as a risk model via Pearson's correlation and univariate Cox regression analyses in The Cancer Genome Atlas (TCGA) database. The tumor microenvironment (TME), tumor mutation burden, and drug sensitivity of PaCa were investigated by m6A-related lncRNA risk score analyses. We established an m6A-related risk prognostic model consisting of five lncRNAs, namely, LINC01091, AC096733.2, AC092171.5, AC015660.1, and AC005332.6, which not only revealed significant differences in immune cell infiltration associated with the TME between the high-risk and low-risk groups but also predicted the potential benefit of immunotherapy for patients with PaCa. Drugs such as WZ8040, selumetinib, and bortezomib were also identified as more effective for high-risk patients. Our results indicate that the m6A-related lncRNA risk model could be an independent prognostic indicator, which may provide valuable insights for identifying therapeutic approaches for PaCa.

Integrative single-cell and multi-omics analyses reveal ferroptosis-associated gene expression and immune microenvironment heterogeneity in gastric cancer

Gastric cancer (GC), a prevalent malignancy worldwide, encompasses a multitude of biological processes in its progression. Recently, ferroptosis, a novel mode of cell demise, has become a focal point in cancer research. The microenvironment of gastric cancer is composed of diverse cell populations, yet the specific gene expression profiles and their association with ferroptosis are not well understood. Our study employed single-cell RNA sequencing to thoroughly investigate the transcriptomic profiles and identify differential gene expression in gastric cancer, offering fresh insights into the cellular diversity and underlying molecular mechanisms of this disease. We discovered a set of significantly differentially expressed genes in GC, which may serve as valuable leads for future functional investigations. Subsequent analyses, including gene set intersection and functional enrichment, pinpointed genes implicated in ferroptosis and conducted comprehensive Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to elucidate their biological roles. In the gene selection and model validation section, critical genes were identified using machine learning algorithms, constructing a model with high predictive accuracy. Besides, distorted immune landscapes were further identified in RBL using ssGSEA analysis such that the complex association of gene expression features and its interaction networks as well as infiltration by various types of immune cells can be more clearly understood. Correlation analysis with different immune cell subtypes showed CTSB as an important regulator in the distributions of cancer infiltrating cells. Single-cell RNA sequencing analysis was utilized to map the cellular composition and gene expression profiles of cells in the gastric cancer microenvironment, which provide critical information for elucidating cellular heterogeneity as well as tumor microenvironment regulation in GC. Moreover, the distribution of FTH1, ZFP36 and CIRBP at different expression levels show new research prospects for functional information of these promoters in tumor microenvironment. In summary, the present study augments our knowledge of molecular mechanisms underlying gastric tumorigenesisa and provide scientific basis for identifing new targets and biomarkers in therapeutic diagnosis.

A novel basement membrane-related gene signature for predicting prognosis of HNSCC

In recent years, a notably heterogeneous malignant tumor, squamous cell carcinoma of the head and neck (HNSCC), has received increasing attention, with no significant improvement in its survival rate. The rapid increase in the number of prognostic models associated with HNSCC has been observed due to its accuracy, which offers crucial clinical benefits. The 10 genes were selected from 222 human genes associated with the basement membrane in the analysis of this article. The gene pool was narrowed through different classifications and intersections, followed by univariate Cox regression analysis. Genes with statistical significance underwent further Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, resulting in the final selection of 10 genes. The data and images extracted from the Human Protein Atlas database were utilized to confirm the differential expression of the corresponding genes. Multivariate Cox regression analysis was employed to develop a nomogram, and the nomogram was assessed by additional decision curve analysis (DCA). The Gene Expression Omnibus validation set was used to validate the established model. Finally, between the high- and low-risk score groups, Gene Set Enrichment Analysis, immune correlation analysis, and drug sensitivity analysis were conducted in this paper. ITGA5, SPOCK1, EVA1C, TINAGL1, LAMB4, ADAMTS1, EGFL6, GPC2, BGN, and ITGA2B were successfully developed as basement membrane-associated risk models. The time-dependent receiver operating characteristic (timeROC) curve illustrated that the risk score prediction accuracy outperformed indicators, which were commonly adopted in clinical practice, consisting of age, stage, gender, T-staging, and N-staging. The 3-year risk score timeROC area under the curve value was 0.679. This model demonstrates a reliable ability to assess the prognosis of HNSCC patients. In addition, the specific potential biomarkers associated with the basement membrane were explored in this research.

Artificial intelligence applied to 'omics data in liver disease: towards a personalised approach for diagnosis, prognosis and treatment

Advancements in omics technologies and artificial intelligence (AI) methodologies are fuelling our progress towards personalised diagnosis, prognosis and treatment strategies in hepatology. This review provides a comprehensive overview of the current landscape of AI methods used for analysis of omics data in liver diseases. We present an overview of the prevalence of different omics levels across various liver diseases, as well as categorise the AI methodology used across the studies. Specifically, we highlight the predominance of transcriptomic and genomic profiling and the relatively sparse exploration of other levels such as the proteome and methylome, which represent untapped potential for novel insights. Publicly available database initiatives such as The Cancer Genome Atlas and The International Cancer Genome Consortium have paved the way for advancements in the diagnosis and treatment of hepatocellular carcinoma. However, the same availability of large omics datasets remains limited for other liver diseases. Furthermore, the application of sophisticated AI methods to handle the complexities of multiomics datasets requires substantial data to train and validate the models and faces challenges in achieving bias-free results with clinical utility. Strategies to address the paucity of data and capitalise on opportunities are discussed. Given the substantial global burden of chronic liver diseases, it is imperative that multicentre collaborations be established to generate large-scale omics data for early disease recognition and intervention. Exploring advanced AI methods is also necessary to maximise the potential of these datasets and improve early detection and personalised treatment strategies.

Comprehensive analysis of disulfidptosis-related genes and the immune microenvironment in heart failure

Background

Heart failure (HF) is a prevalent cardiovascular disease that currently lacks effective treatment options due to its intricate pathogenesis. A recent study has linked disulfidoptosis, a novel form of cell demise, with the development of a range of diseases. Nonetheless, the effect of disulfidoptosis on the immune microenvironment of HF is not well comprehended. In this paper, bioinformatics analysis was performed to investigate how disulfidptosis-related genes (DRGs) affect the immune microenvironment of HF.

Methods

The expression of four DRGs was initially examined using bulk RNA-Seq and single-cell RNA sequencing data. A predictive model was subsequently developed. Consensus clustering was used to distinguish between the two clusters of DRGs. The effect of these DRGs on the characteristics of the immune microenvironment was further explored, such as infiltrating immune cells, immune response gene sets, and HLAs genes.

Results

All four DRGs were dysregulated in HF samples. The predictive model based on these four DRGs effectively differentiated between HF patients and healthy individuals, which was validated in the experiment. These four DRGs were strongly associated with the abundance of infiltrating monocytes. Moreover, our analysis identified two distinct clusters of DRGs and these clusters exhibited differences in terms of immune cell abundance, immune response, and HLA gene expression. The biological functions associated with these differences were also revealed.

Conclusion

Our discovery underscores the pivotal role of DRGs in shaping the diversity and intricacy of the immune microenvironment in HF.

New insights into the mechanisms of the immune microenvironment and immunotherapy in osteosarcoma

Osteosarcoma, a malignant bone tumor primarily affecting adolescents, is highly invasive with a poor prognosis. While surgery and chemotherapy have improved survival for localized cases, pulmonary metastasis significantly reduces survival to approximately 20%, highlighting the need for novel treatments. Immunotherapy, which leverages the immune system to target osteosarcoma cells, shows promise. This review summarizes the biological characteristics of osteosarcoma, mechanisms of pulmonary metastasis, and the tumor immune microenvironment (TME). It involves recent immunotherapy advances, including monoclonal antibodies, tumor vaccines, immune cell therapies, checkpoint inhibitors, and oncolytic viruses, and discusses combining these with standard treatments.

Characterizing tumor biology and immune microenvironment in high-grade serous ovarian cancer via single-cell RNA sequencing: insights for targeted and personalized immunotherapy strategies

Background

High-grade serous ovarian cancer (HGSOC), the predominant subtype of epithelial ovarian cancer, is frequently diagnosed at an advanced stage due to its nonspecific early symptoms. Despite standard treatments, including cytoreductive surgery and platinum-based chemotherapy, significant improvements in survival have been limited. Understanding the molecular mechanisms, immune landscape, and drug sensitivity of HGSOC is crucial for developing more effective and personalized therapies. This study integrates insights from cancer immunology, molecular profiling, and drug sensitivity analysis to identify novel therapeutic targets and improve treatment outcomes. Utilizing single-cell RNA sequencing (scRNA-seq), the study systematically examines tumor heterogeneity and immune microenvironment, focusing on biomarkers influencing drug response and immune activity, aiming to enhance patient outcomes and quality of life.

Methods

scRNA-seq data was obtained from the GEO database in this study. Differential gene expression was analyzed using gene ontology and gene set enrichment methods. InferCNV identified malignant epithelial cells, while Monocle, Cytotrace, and Slingshot software inferred subtype differentiation trajectories. The CellChat software package predicted cellular communication between malignant cell subtypes and other cells, while pySCENIC analysis was utilized to identify transcription factor regulatory networks within malignant cell subtypes. Finally, the analysis results were validated through functional experiments, and a prognostic model was developed to assess prognosis, immune infiltration, and drug sensitivity across various risk groups.

Results

This study investigated the cellular heterogeneity of HGSOC using scRNA-seq, focusing on tumor cell subtypes and their interactions within the tumor microenvironment. We confirmed the key role of the C2 IGF2+ tumor cell subtype in HGSOC, which was significantly associated with poor prognosis and high levels of chromosomal copy number variations. This subtype was located at the terminal differentiation of the tumor, displaying a higher degree of malignancy and close association with stage IIIC tissue types. The C2 subtype was also associated with various metabolic pathways, such as glycolysis and riboflavin metabolism, as well as programmed cell death processes. The study highlighted the complex interactions between the C2 subtype and fibroblasts through the MK signaling pathway, which may be closely related to tumor-associated fibroblasts and tumor progression. Elevated expression of PRRX1 was significantly connected to the C2 subtype and may impact disease progression by modulating gene transcription. A prognostic model based on the C2 subtype demonstrated its association with adverse prognosis outcomes, emphasizing the importance of immune infiltration and drug sensitivity analysis in clinical intervention strategies.

Conclusion

This study integrates molecular oncology, immunotherapy, and drug sensitivity analysis to reveal the mechanisms driving HGSOC progression and treatment resistance. The C2 IGF2+ tumor subtype, linked to poor prognosis, offers a promising target for future therapies. Emphasizing immune infiltration and drug sensitivity, the research highlights personalized strategies to improve survival and quality of life for HGSOC patients.

Predictive value of a constructed artificial neural network model for microvascular invasion in hepatocellular carcinoma: A retrospective study

BACKGROUND

Microvascular invasion (MVI) is a significant risk factor for recurrence and metastasis following hepatocellular carcinoma (HCC) surgery. Currently, there is a paucity of preoperative evaluation approaches for MVI.

AIM

To investigate the predictive value of texture features and radiological signs based on multiparametric magnetic resonance imaging in the non-invasive preoperative prediction of MVI in HCC.

METHODS

Clinical data from 97 HCC patients were retrospectively collected from January 2019 to July 2022 at our hospital. Patients were classified into two groups: MVI-positive (n = 57) and MVI-negative (n = 40), based on postoperative pathological results. The correlation between relevant radiological signs and MVI status was analyzed. MaZda4.6 software and the mutual information method were employed to identify the top 10 dominant texture features, which were combined with radiological signs to construct artificial neural network (ANN) models for MVI prediction. The predictive performance of the ANN models was evaluated using area under the curve, sensitivity, and specificity. ANN models with relatively high predictive performance were screened using the DeLong test, and the regression model of multilayer feedforward ANN with backpropagation and error backpropagation learning method was used to evaluate the models' stability.

RESULTS

The absence of a pseudocapsule, an incomplete pseudocapsule, and the presence of tumor blood vessels were identified as independent predictors of HCC MVI. The ANN model constructed using the dominant features of the combined group (pseudocapsule status + tumor blood vessels + arterial phase + venous phase) demonstrated the best predictive performance for MVI status and was found to be automated, highly operable, and very stable.

CONCLUSION

The ANN model constructed using the dominant features of the combined group can be recommended as a non-invasive method for preoperative prediction of HCC MVI status.

Characterization of NOD-like receptor-based molecular heterogeneity in glioma and its association with immune micro-environment and metabolism reprogramming

Background and purpose

The characteristics and role of NOD-like receptor (NLR) signaling pathway in high-grade gliomas were still unclear. This study aimed to reveal the association of NLR with clinical heterogeneity of glioblastoma (GBM) patients, and to explore the role of NLR pathway hub genes in the occurrence and development of GBM.

Methods

Transcriptomic data from 496 GBM patients with complete prognostic information were obtained from the TCGA, GEO, and CGGA databases. Using the NMF clustering algorithm and the expression profiles of NLR genes, these 496 GBM patients were classified into different clinical subtypes. The pathway activity of NLR and the immune micro-environment characteristics were then compared between these subtypes. A novel and accurate NLR expression profile-based prognostic marker for GBM was developed using LASSO and COX regression analysis.

Results

Based on the NLR gene expression profile, GBM patients were accurately divided into two clinical subtypes (C1 and C2) with different clinical outcomes. The two groups of patients showed different immune microenvironment characteristics and metabolic characteristics, which might be the potential reason for the difference in prognosis. Differential expression and enrichment analyzes revealed intrinsic gene signature differences between C1 and C2 subtypes. Based on the differential expression profiles of C1 and C2, prognostic molecular markers related to NLR were developed. The AUC value of the 3-year ROC curve ranged from 0.601 to 0.846, suggesting its potential clinical significance. Single-cell sequencing analysis showed that the NLR gene was mainly active in myeloid cells within GBM. The random forest algorithm identified the crucial role of TRIP6 gene in NLR pathway. Molecular biology experiments confirmed that TRIP6 was abnormally overexpressed in GBM. Knockdown of TRIP6 gene can significantly inhibit the proliferation and migration ability of GBM cells.

Conclusion

The NLR signaling pathway plays a critical role in regulating immune microenvironment and metabolism reprogramming of GBM. TRIP6 is a potential hub gene within the NLR pathway and affects the malignant biological behavior of GBM cells.

Elucidating stearoyl metabolism and NCOA4-mediated ferroptosis in gastric cancer liver metastasis through multi-omics single-cell integrative mendelian analysis: advancing personalized immunotherapy strategies

BACKGROUND

The metabolism of stearoyl-GPE plays a key role in the liver metastasis of gastric cancer. This investigation delves into the mechanisms underlying the intricate tumor microenvironment (TME) heterogeneity triggered by stearoyl metabolism in gastric cancer with liver metastasis (LMGC), offering novel perspectives for LMGC.

OBJECTIVE

Utilizing Mendelian randomization, we determined that stearoyl metabolism significantly contributes to the progression of gastric cancer (GC). Following this, bulk transcriptome analyses and single-cell multiomics techniques to investigate the roles of stearoyl-GPE metabolism-related genes, particularly NCOA4, in regulating LMGC TME.

RESULTS

Our analysis highlights the crucial role of stearoyl metabolism in modulating the complex microenvironment of LMGC, particularly impacting monocyte cells. Through single-cell sequencing and spatial transcriptomics, we have identified key metabolic genes specific to stearoyl metabolism within the monocyte cell population, including NCOA4. Regarding the relationship between ferroptosis, stearoyl metabolism, and LMGC findings, it is plausible that stearoyl metabolism and LMGC pathways intersect with mechanisms involved in ferroptosis. Ferroptosis, characterized by iron-dependent lipid peroxidation, represents a regulated form of cell death. The activity of Stearoyl-CoA desaturase (SCD), a critical enzyme in stearoyl metabolism, has been associated with the modulation of lipid composition and susceptibility to ferroptosis. Furthermore, the LMGC is integral to cellular processes related to oxidative stress and lipid metabolism, both of which are significant factors in the context of ferroptosis.

CONCLUSION

This study enhances the understanding of the relationship between stearoyl metabolism and ferroptosis in promoting liver metastasis of gastric cancer and its role in the regulation of tumor heterogeneity. In addition, this study contributes to a deeper understanding of the dynamics of gastric cancer tumor microenvironment (TME) and provides a basis for the development of better interventions to combat cancer metastasis.

Crosstalk between non-coding RNAs and programmed cell death in colorectal cancer: implications for targeted therapy

BACKGROUND

Colorectal cancer (CRC) remains one of the most common causes of cancer-related mortality worldwide. Its progression is influenced by complex interactions involving genetic, epigenetic, and environmental factors. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), have been identified as key regulators of gene expression, affecting diverse biological processes, notably programmed cell death (PCD).

OBJECTIVE

This review aims to explore the relationship between ncRNAs and PCD in CRC, focusing on how ncRNAs influence cancer cell survival, proliferation, and treatment resistance.

METHODS

A comprehensive literature analysis was conducted to examine recent findings on the role of ncRNAs in modulating various PCD mechanisms, including apoptosis, autophagy, necroptosis, and pyroptosis, and their impact on CRC development and therapeutic response.

RESULTS

ncRNAs were found to significantly regulate PCD pathways, impacting tumor growth, metastasis, and treatment sensitivity in CRC. Their influence on these pathways highlights the potential of ncRNAs as biomarkers for early CRC detection and as targets for innovative therapeutic interventions.

CONCLUSION

Understanding the involvement of ncRNAs in PCD regulation offers new insights into CRC biology. The targeted modulation of ncRNA-PCD interactions presents promising avenues for personalized cancer treatment, which may improve patient outcomes by enhancing therapeutic effectiveness and reducing resistance.

DAMPs prognostic signature predicts tumor immunotherapy, and identifies immunosuppressive mechanism of pannexin 1 channels in pancreatic ductal adenocarcinoma

Background

Damage-associated molecular patterns (DAMPs) induced by immunogenic cell death (ICD) may be useful for the immunotherapy to patients undergoing pancreatic ductal adenocarcinoma (PDAC). The aim of this study is to predict the prognosis and immunotherapy responsiveness of PDAC patients using DAMPs-related genes.

Methods

K-means analysis was used to identify the DAMPs-related subtypes of 175 PDAC cases. The significance of gene mutation and immune status in different subtypes was detected. LASSO regression was used to construct a DAMPs-related prognostic signature to predict the immunotherapy responsiveness of PDAC. Subsequently, in vivo and in vitro experiments and Bulk-RNA seq were used to verify the effect of hub gene pannexin 1 (PANX1) on PDAC.

Results

Two subtypes were clustered based on the expression levels of DAMPs genes from 175 PDAC patients. Besides, the prognosis and immune landscape in up-regulated DAMPs expression subtypes was poor. In addition, we constructed a DAMPs-related prognostic signature that correlated with immune cell infiltration and predicted immunotherapy or chemotherapy responsiveness of patients with PDAC. Mechanically, through Bulk-RNA sequencing and experiments, we found that PANX1 promoted tumor progression and immune regulation via the ATP release to active NOD1/NFκB signaling pathway in PDAC.

Conclusion

Our in silico analyses established a classification system based on ICD-related DAMPs genes in PDAC, and constructed a DAMPs-related prognostic model to predict the efficacy of immunotherapy. This study will provide a new perspective for targeting the DAMPs-related molecule PANX1 in the treatment of PDAC.

Advancements in targeted and immunotherapy strategies for glioma: toward precision treatment

In recent years, significant breakthroughs have been made in cancer therapy, particularly with the development of molecular targeted therapies and immunotherapies, owing to advances in tumor molecular biology and molecular immunology. High-grade gliomas (HGGs), characterized by their high malignancy, remain challenging to treat despite standard treatment regimens, including surgery, radiotherapy, chemotherapy, and tumor treating fields (TTF). These therapies provide limited efficacy, highlighting the need for novel treatment strategies. Molecular targeted therapies and immunotherapy have emerged as promising avenues for improving treatment outcomes in high-grade gliomas. This review explores the current status and recent advancements in targeted and immunotherapeutic approaches for high-grade gliomas.

Integrative Analysis of Whole-Genome and Transcriptomic Data Reveals Novel Variants in Differentially Expressed Long Noncoding RNAs Associated with Asthenozoospermia

Background/Objectives: Asthenozoospermia, characterized by reduced sperm motility, is a common cause of male infertility. Emerging evidence suggests that noncoding RNAs, particularly long noncoding RNAs (lncRNAs), play a critical role in the regulation of spermatogenesis and sperm function. Coding regions have a well-characterized role and established predictive value in asthenozoospermia. However, this study was designed to complement previous findings and provide a more holistic understanding of asthenozoospermia, this time focusing on noncoding regions. This study aimed to identify and prioritize variants in differentially expressed (DE) lncRNAs found exclusively in asthenozoospermic men, focusing on their impact on lncRNA structure and lncRNA-miRNA-mRNA interactions. Methods: Whole-genome sequencing (WGS) was performed on samples from asthenozoospermic and normozoospermic men. Additionally, an RNA-seq dataset from normozoospermic and asthenozoospermic individuals was analyzed to identify DE lncRNAs. Bioinformatics analyses were conducted to map unique variants on DE lncRNAs, followed by prioritization based on predicted functional impact. The structural impact of the variants and their effects on lncRNA-miRNA interactions were assessed using computational tools. Gene ontology (GO) and KEGG pathway analyses were employed to investigate the affected biological processes and pathways. Results: We identified 4173 unique variants mapped to 258 DE lncRNAs. After prioritization, 5 unique variants in 5 lncRNAs were found to affect lncRNA structure, while 20 variants in 17 lncRNAs were predicted to disrupt miRNA-lncRNA interactions. Enriched pathways included Wnt signaling, phosphatase binding, and cell proliferation, all previously implicated in reproductive health. Conclusions: This study identifies specific variants in DE lncRNAs that may play a role in asthenozoospermia. Given the limited research utilizing WGS to explore the role of noncoding RNAs in male infertility, our findings provide valuable insights and a foundation for future studies.

Exploring the influence of age on the causes of death in advanced nasopharyngeal carcinoma patients undergoing chemoradiotherapy using machine learning methods

The present study analyzed the impact of age on the causes of death (CODs) in patients with nasopharyngeal carcinoma (NPC) undergoing chemoradiotherapy (CRT) using machine learning approaches. A total of 2841 patients (1037 classified as older, ≥ 60 years and 1804 as younger, < 60 years) were enrolled. Variations in the CODs between the two age groups were analyzed before and after applying inverse probability of treatment weighting (IPTW). Additionally, seven different machine learning models were employed as predictive tools to identify key variables and assess the therapeutic outcomes in NPC patients receiving CRT. The younger group exhibited a significantly longer overall survival (OS) than the older group, both before the IPTW adjustment (140 vs. 50 months, P < 0.001) and after the adjustment (137 vs. 53 months, P < 0.001). After IPTW, the older group was associated with worse 5-, 10-, and 15-year cumulative incidences in terms of NPC-related deaths (30, 34, and 38% vs. 21, 27, and 30%; P < 0.001), cardiovascular disease (CVD; 4.1, 7.2, and 8.8% vs. 0.5, 1.8, and 3.0%; P < 0.001), and other causes (8.3, 17, and 24% vs. 4.1, 8.7, and 12%; P < 0.001). However, cumulative incidences of secondary malignant neoplasms were comparable between the two groups (P = 0.100). The random forest (RF) model demonstrated the highest concordance index of 0.701 among all models. Time-dependent variable importance plots indicated that age was the most influential factor affecting 3-, 5-, and 10-year survival, followed by metastasis and tumor stage. Younger patients had significantly longer OS than their older counterparts. Older patients had a higher likelihood of dying from non-NPC-related causes, particularly CVDs. The RF model showed the best predictive accuracy, identifying age as the most critical factor influencing OS in NPC patients undergoing CRT.

Harnessing the tumor microenvironment: targeted cancer therapies through modulation of epithelial-mesenchymal transition

The tumor microenvironment (TME) is integral to cancer progression, impacting metastasis and treatment response. It consists of diverse cell types, extracellular matrix components, and signaling molecules that interact to promote tumor growth and therapeutic resistance. Elucidating the intricate interactions between cancer cells and the TME is crucial in understanding cancer progression and therapeutic challenges. A critical process induced by TME signaling is the epithelial-mesenchymal transition (EMT), wherein epithelial cells acquire mesenchymal traits, which enhance their motility and invasiveness and promote metastasis and cancer progression. By targeting various components of the TME, novel investigational strategies aim to disrupt the TME's contribution to the EMT, thereby improving treatment efficacy, addressing therapeutic resistance, and offering a nuanced approach to cancer therapy. This review scrutinizes the key players in the TME and the TME's contribution to the EMT, emphasizing avenues to therapeutically disrupt the interactions between the various TME components. Moreover, the article discusses the TME's implications for resistance mechanisms and highlights the current therapeutic strategies toward TME modulation along with potential caveats.

The clinical impact of galectin-8 in drug resistant breast cancer

Breast cancer remains the leading cause of cancer-related mortality among women globally. A significant challenge in lowering breast cancer death rates is multidrug resistance. This resistance arises through various mechanisms, such as heightened drug efflux, improved DNA repair, escape from senescence, epigenetic modifications, tumor heterogeneity, alterations in the tumor microenvironment (TME), and the epithelial-to-mesenchymal transition (EMT). These factors collectively make overcoming drug resistance particularly difficult. Therefore, in this study, we analyzed data from The Cancer Genome Atlas (TCGA) and identified a novel gene, galectin-8, which plays a critical regulatory role in breast cancer progression. Gene Set Enrichment Analysis (GSEA) further revealed that galectin-8 is involved in modulating drug resistance in breast cancer. To validate this finding, we conducted a mass assay comparing drug-resistant triple-negative breast cancer (TNBC) cell lines with control groups. Our results demonstrated a significant increase in galectin-8 expression in the drug-resistant cells, with statistically significant differences observed. In addition, we found that reducing galectin-8 expression in drug-resistant cell lines not only reinstated the effectiveness of anticancer drugs but also suppressed tumor cell proliferation and migration. Therefore, our findings highlight the significant prognostic and therapeutic potential of galectin-8, emphasizing the importance of future research to explore targeted therapeutic strategies in breast cancer.

Identification of mitophagy-related key genes and their correlation with immune cell infiltration in acute myocardial infarction via bioinformatics analysis

Background

Acute myocardial infarction (AMI), a subset of acute coronary syndrome, remains the major cause of mortality worldwide. Mitochondrial dysfunction is critically involved in AMI progression, and mitophagy plays a vital role in eliminating damaged mitochondria. This study aimed to explore mitophagy-related biomarkers and their potential molecular basis in AMI.

Methods

AMI datasets (GSE24519 and GSE34198) from the Gene Expression Omnibus database were combined and the batch effects were removed. Differentially expressed genes (DEGs) in AMI were selected, intersected with mitophagy-related genes for mitophagy-related DEGs (MRDEGs), and then subjected to enrichment analyses. Next, the MRDEGs were screened using machine learning methods (logistic regression analysis, RandomForest, least absolute shrinkage and selection operator) to construct a diagnostic risk model and select the key genes in AMI. The diagnostic efficacy of the model was evaluated using a nomogram. Moreover, the infiltration patterns of different immune cells in two risk groups were compared. We also explored the interactions between the key genes themselves or with miRNAs/transcription factors (TFs) and drug compounds and visualized the protein structure of the key genes. Finally, we explored and validated the expression of key genes in plasma samples of patients with an AMI and healthy individuals.

Results

We screened 28 MRDEGs in AMI. Based on machine learning methods, 12 key genes were screened for the diagnostic risk model, including AGPS, CA2, CAT, LTA4H, MYO9B, PRDX6, PYGB, SIRT3, TFEB, TOM1, UBA52, and UBB. The nomogram further revealed the accuracy of the model for AMI diagnosis. Moreover, we found a lower abundance of immune cells such as gamma delta T and natural killer cells in the high-risk group, and the expression of key genes showed a significant correlation with immune infiltration levels in both groups. Finally, 64 miRNA-mRNA pairs, 75 TF-mRNA pairs, 119 RNA-binding protein-mRNA pairs, and 32 drug-mRNA pairs were obtained in the interaction networks.

Conclusions

In total, 12 key MRDEGs were identified and a risk model was constructed for AMI diagnosis. The findings of this study might provide novel biomarkers for improving the detection of AMI.

Multi-omics data integration identifies novel biomarkers and patient subgroups in inflammatory bowel disease

BACKGROUND

Inflammatory bowel disease (IBD), comprising Crohn's disease (CD) and ulcerative colitis (UC), is a complex condition with diverse manifestations; recent advances in multi-omics technologies are helping researchers unravel its molecular characteristics to develop targeted treatments.

OBJECTIVES

In this work, we explored one of the largest multi-omics cohorts in IBD, the Study of a Prospective Adult Research Cohort (SPARC IBD), with the goal of identifying predictive biomarkers for CD and UC and elucidating patient subtypes.

DESIGN

We analyzed genomics, transcriptomics (gut biopsy samples), and proteomics (blood plasma) from hundreds of patients from SPARC IBD. We trained a machine learning model that classifies UC versus CD samples. In parallel, we integrated multi-omics data to unveil patient subgroups in each of the 2 indications independently and analyzed the molecular phenotypes of these patient subpopulations.

RESULTS

The high performance of the model showed that multi-omics signatures are able to discriminate between the 2 indications. The most predictive features of the model, both known and novel omics signatures for IBD, can potentially be used as diagnostic biomarkers. Patient subgroup analysis in each indication uncovered omics features associated with disease severity in UC patients and with tissue inflammation in CD patients. This culminates with the observation of 2 CD subpopulations characterized by distinct inflammation profiles.

CONCLUSIONS

Our work unveiled potential biomarkers to discriminate between CD and UC and to stratify each population into well-defined subgroups, offering promising avenues for the application of precision medicine strategies.

Leveraging Single-Cell Multi-Omics to Decode Tumor Microenvironment Diversity and Therapeutic Resistance

Recent developments in single-cell multi-omics technologies have provided the ability to identify diverse cell types and decipher key components of the tumor microenvironment (TME), leading to important advancements toward a much deeper understanding of how tumor microenvironment heterogeneity contributes to cancer progression and therapeutic resistance. These technologies are able to integrate data from molecular genomic, transcriptomic, proteomics, and metabolomics studies of cells at a single-cell resolution scale that give rise to the full cellular and molecular complexity in the TME. Understanding the complex and sometimes reciprocal relationships among cancer cells, CAFs, immune cells, and ECs has led to novel insights into their immense heterogeneity in functions, which can have important consequences on tumor behavior. In-depth studies have uncovered immune evasion mechanisms, including the exhaustion of T cells and metabolic reprogramming in response to hypoxia from cancer cells. Single-cell multi-omics also revealed resistance mechanisms, such as stromal cell-secreted factors and physical barriers in the extracellular matrix. Future studies examining specific metabolic pathways and targeting approaches to reduce the heterogeneity in the TME will likely lead to better outcomes with immunotherapies, drug delivery, etc., for cancer treatments. Future studies will incorporate multi-omics data, spatial relationships in tumor micro-environments, and their translation into personalized cancer therapies. This review emphasizes how single-cell multi-omics can provide insights into the cellular and molecular heterogeneity of the TME, revealing immune evasion mechanisms, metabolic reprogramming, and stromal cell influences. These insights aim to guide the development of personalized and targeted cancer therapies, highlighting the role of TME diversity in shaping tumor behavior and treatment outcomes.

Developing and validating a machine learning model to predict multidrug-resistant Klebsiella pneumoniae-related septic shock

Background

Multidrug-resistant Klebsiella pneumoniae (MDR-KP) infections pose a significant global healthcare challenge, particularly due to the high mortality risk associated with septic shock. This study aimed to develop and validate a machine learning-based model to predict the risk of MDR-KP-associated septic shock, enabling early risk stratification and targeted interventions.

Methods

A retrospective analysis was conducted on 1,385 patients with MDR-KP infections admitted between January 2019 and June 2024. The cohort was randomly divided into a training set (n = 969) and a validation set (n = 416). Feature selection was performed using LASSO regression and the Boruta algorithm. Seven machine learning algorithms were evaluated, with logistic regression chosen for its optimal balance between performance and robustness against overfitting.

Results

The overall incidence of MDR-KP-associated septic shock was 16.32% (226/1,385). The predictive model identified seven key risk factors: procalcitonin (PCT), sepsis, acute kidney injury, intra-abdominal infection, use of vasoactive medications, ventilator weaning failure, and mechanical ventilation. The logistic regression model demonstrated excellent predictive performance, with an area under the receiver operating characteristic curve (AUC) of 0.906 in the training set and 0.865 in the validation set. Calibration was robust, with Hosmer-Lemeshow test results of P = 0.065 (training) and P = 0.069 (validation). Decision curve analysis indicated substantial clinical net benefit.

Conclusion

This study presents a validated, high-performing predictive model for MDR-KP-associated septic shock, offering a valuable tool for early clinical decision-making. Prospective, multi-center studies are recommended to further evaluate its clinical applicability and effectiveness in diverse settings.

Integrated single-cell RNA sequencing reveals the tumor heterogeneity and microenvironment landscape during liver metastasis in adenocarcinoma of esophagogastric junction

Background

Adenocarcinoma of the esophagogastric junction (AEGJ) is a highly aggressive tumor that frequently metastasizes to the liver. Understanding the cellular and molecular mechanisms that drive this process is essential for developing effective therapies.

Methods

We employed single-cell RNA sequencing to analyze the tumor heterogeneity and microenvironmental landscape in patients with AEGJ liver metastases. This approach enabled us to characterize the diverse cell populations involved in the liver metastatic process.

Results

Our analysis revealed a significant involvement of fibroblasts and mural cells in AEGJ liver metastasis. We identified a specific fibroblast type in AEGJ liver metastasis and observed distinct gene expression patterns between adenocarcinoma of the esophagogastric junction and other stomach adenocarcinomas. Our study demonstrated high expression of the SFRP2 gene in pericyte cells during the liver metastasis of AEGJ. The incorporation of GEO, TCGA, and immunofluorescence staining of SFRP2 expression enhanced our study. High expression of SFRP2 in pericytes may influence vascular stability and angiogenesis through the Wnt pathway.

Conclusion

Our study provides novel insights into the cellular interactions and molecular mechanisms that underlie AEGJ liver metastasis. Targeting the identified subtype of fibroblasts or influencing SFRP2 gene expression in pericytes may offer new therapeutic strategies for combating this aggressive tumor.

The Crucial Role of the Blood-Brain Barrier in Neurodegenerative Diseases: Mechanisms of Disruption and Therapeutic Implications

The blood-brain barrier (BBB) is a crucial structure that maintains brain homeostasis by regulating the entry of molecules and cells from the bloodstream into the central nervous system (CNS). Neurodegenerative diseases such as Alzheimer's and Parkinson's disease, as well as ischemic stroke, compromise the integrity of the BBB. This leads to increased permeability and the infiltration of harmful substances, thereby accelerating neurodegeneration. In this review, we explore the mechanisms underlying BBB disruption, including oxidative stress, neuroinflammation, vascular dysfunction, and the loss of tight junction integrity, in patients with neurodegenerative diseases. We discuss how BBB breakdown contributes to neuroinflammation, neurotoxicity, and the abnormal accumulation of pathological proteins, all of which exacerbate neuronal damage and facilitate disease progression. Furthermore, we discuss potential therapeutic strategies aimed at preserving or restoring BBB function, such as anti-inflammatory treatments, antioxidant therapies, and approaches to enhance tight junction integrity. Given the central role of the BBB in neurodegeneration, maintaining its integrity represents a promising therapeutic approach to slow or prevent the progression of neurodegenerative diseases.

Combining single-cell analysis and molecular docking techniques to construct a prognostic model for colon adenocarcinoma and uncovering inhibin subunit βb as a novel therapeutic target

Background

Colon adenocarcinoma (COAD) is a malignancy with a high mortality rate and complex biological characteristics and heterogeneity, which poses challenges for clinical treatment. Anoikis is a type of programmed cell death that occurs when cells lose their attachment to the extracellular matrix (ECM), and it plays a crucial role in tumor metastasis. However, the specific biological link between anoikis and COAD, as well as its mechanisms in tumor progression, remains unclear, making it a potential new direction for therapeutic strategy research.

Methods

We employed transcriptomic data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to pinpoint differentially expressed anoikis-related genes (ARGs) in COAD. Using Cox proportional hazards models and Lasso regression analysis, we developed a prognostic signature derived from these ARGs. We also investigated the roles and interactions of these genes in the tumor microenvironment by analyzing single-cell RNA sequencing data. Additionally, we employed molecular docking techniques to evaluate the potential of inhibin subunit beta B (INHBB) as therapeutic targets and to assess the binding affinity of candidate drugs. Finally, we used gene knockout techniques to silence the key gene INHBB and explored its biological functions in vitro.

Results

In our study, by analyzing the expression differences of ARGs, we successfully classified patients with COAD. Kaplan-Meier survival analysis demonstrated that patients with elevated risk scores experienced poorer prognosis, a finding that was confirmed in both the training and validation cohorts. Additionally, immune infiltration analysis revealed a notable increase in immune cell presence within the tumor microenvironment of high-risk patients. Molecular docking identified potential drug candidates with high binding affinity to INHBB, including risperidone. Furthermore, in vitro experiments with INHBB showed that downregulation of its expression in COAD cell lines significantly reduced cellular viability and migration capacity.

Conclusion

In summary, our research, based on the expression characteristics of ARGs, provides new insights into the precise classification, prognosis assessment, and identification of potential therapeutic targets in COAD. It also validates the key role of INHBB in the progression of COAD, establishing the foundation for future personalized treatment strategies.

MUC5B regulates alterations in the immune microenvironment in nasopharyngeal carcinoma via the Wnt/β-catenin signaling pathway

OBJECTIVE

To screen potential differentially expressed genes related to immune function in nasopharyngeal carcinoma through an online database, and to verify their mechanism of action, so as to provide a reference for the diagnosis and treatment of nasopharyngeal carcinoma in the future.

METHODS

Differentially expressed genes were analyzed from the GSE227541 dataset, and functional enrichment analysis was conducted. With mucin 5B, oligomeric mucus/gel-forming as the focus, the correlation between its expression and immune indexes was analyzed by using the TIMER database. The expressions of mucin 5B, oligomeric mucus/gel-forming in clinical NPC tissues and adjacent tissue samples were detected Furthermore, mucin 5B, oligomeric mucus/gel-forming abnormal expression vectors were constructed and transfected into human NPC cell CNE-2Z to detect alterations in cell activity, ferroptosis and the immune microenvironment. In addition, the role of the Wnt/β-catenin signaling pathway in nasopharyngeal carcinoma was observed, and the influence of mucin 5B, oligomeric mucus/gel-forming on this pathway was discussed.

RESULTS

A total of 42 differentially expressed genes were found in the GSE227541 dataset, among which mucin 5B, oligomeric mucus/gel-forming was obviously at the core of the entire network. In nasopharyngeal carcinoma tissues, the research team observed the upregulated expression of mucin 5B, oligomeric mucus/gel-forming (P < 0.05). In vitro, increased expression of elevated mucin 5B, oligomeric mucus/gel-forming activates nasopharyngeal carcinoma cell activity and immune escape and inhibits ferroptosis. In terms of pathways, upregulating mucin 5B, oligomeric mucus/gel-forming expression could activate the Wnt/β-catenin pathway.

CONCLUSIONS

Mucin 5B, oligomeric mucus/gel-forming regulates the immune escape of nasopharyngeal carcinoma cells and participates in tumor progression by mediating the Wnt/β-catenin signaling pathway.

DET induces apoptosis and suppresses tumor invasion in glioma cells via PI3K/AKT pathway

Introduction

Gliomas, particularly glioblastomas (GBM), are highly aggressive with a poor prognosis and low survival rate. Currently, deoxyelephantopin (DET) has shown promising anti-inflammatory and anti-tumor effects. Using clinical prognostic analysis, molecular docking, and network pharmacology, this study aims to explore the primary targets and signaling pathways to identify novel GBM treatment approaches.

Methods

Using PharmMapper, the chemical structure of DET was examined for possible targets after being acquired from PubChem. GBM-related targets were obtained through multi-omics approaches. A protein-protein interaction (PPI) network was constructed using Cytoscape and STRING, and target binding was evaluated through molecular docking. Enrichment analysis was conducted using Metascape. The effects of DET on GBM cell invasion, apoptosis, and proliferation were assessed through in vitro assays, including Transwell, EDU, CCK8, and flow cytometry. Western blot analysis was performed to examine the components of the PI3K/AKT signaling pathway.

Results

Among the sixty-four shared targets identified, JUN and CCND1 were the most frequently observed. Enrichment analysis demonstrated that DET influenced the MAPK and PI3K/AKT signaling pathways. In Transwell assays, DET significantly inhibited the invasive behavior of glioma cells. Western blot analysis further confirmed the downregulation of EGFR, JUN, and PI3K/AKT.

Conclusion

DET inhibits GBM cell invasion, proliferation, and apoptosis via modulating the PI3K/AKT signaling pathway, highlighting its potential as a novel therapeutic strategy for GBM treatment.

Application progress of artificial intelligence in tumor diagnosis and treatment

The rapid advancement of artificial intelligence (AI) has introduced transformative opportunities in oncology, enhancing the precision and efficiency of tumor diagnosis and treatment. This review examines recent advancements in AI applications across tumor imaging diagnostics, pathological analysis, and treatment optimization, with a particular focus on breast cancer, lung cancer, and liver cancer. By synthesizing findings from peer-reviewed studies published over the past decade, this paper analyzes the role of AI in enhancing diagnostic accuracy, streamlining therapeutic decision-making, and personalizing treatment strategies. Additionally, this paper addresses challenges related to AI integration into clinical workflows and regulatory compliance. As AI continues to evolve, its applications in oncology promise further improvements in patient outcomes, though additional research is needed to address its limitations and ensure ethical and effective deployment.

Spatial heterogeneity of the hepatocellular carcinoma microenvironment determines the efficacy of immunotherapy

Hepatocellular carcinoma (HCC) remains a global health challenge owing to its widespread incidence and high mortality. HCC has a specific immune tolerance function because of its unique physiological structure, which limits the efficacy of chemotherapy, radiotherapy, and molecular targeting. In recent years, new immune approaches, including adoptive cell therapy, tumor vaccines, and oncolytic virus therapy, have shown great potential. As the efficacy of immunotherapy mainly depends on the spatial heterogeneity of the tumor immune microenvironment, it is necessary to elucidate the crosstalk between the composition of the liver cancer immune environment, from which potential therapeutic targets can be selected to provide more appropriate individualized treatment programs. The role of spatial heterogeneity of immune cells in the microenvironment of HCC in the progression and influence of immunotherapy on improving the treatment and prognosis of HCC were comprehensively analyzed, providing new inspiration for the subsequent clinical treatment of liver cancer.

Development and evaluation of a CRISPR/Cas12a-based diagnostic test for rapid detection and genotyping of HR-HPV in clinical specimens

Persistent infection with high-risk human papillomavirus (HR-HPV) is the principal etiological factor of cervical cancer. Considering the gradual progression of cervical cancer, the early, rapid, sensitive, and specific identification of HPV, particularly HR-HPV types, is crucial in halting the advancement of the illness. Here, we established a rapid, highly sensitive, and specific HR-HPV detection platform, leveraging the CRISPR/Cas12a assay in conjunction with multienzyme isothermal rapid amplification. Our platform enables the detection and genotyping of 14 types of HR-HPV by using type-specific crRNAs. The outcomes of the detection can be interpreted either through a fluorescence reader or visually. Furthermore, we achieved one-tube multiplex detection of 14 HR-HPV types through the use of multiple amplifications and a crRNA pool. The detection sensitivity of this method is 2 copies/μL with no cross-reactivity, and the results can be obtained within 30 minutes. This method exhibited 100% clinical sensitivity and 100% clinical specificity when applied to 258 clinical specimens. Based on these findings, our CRISPR/Cas-based HR-HPV detection platform holds promise as a novel clinical detection tool, offering a visually intuitive and expedited alternative to existing HPV infection diagnostics and providing fresh perspectives for clinical cervical cancer screening.IMPORTANCEThis study developed a novel high-risk human papillomavirus (HR-HPV) detection platform based on CRISPR/Cas12a technology. This platform not only enables the rapid, highly sensitive, and specific detection and genotyping of 14 types of HR-HPV but also achieves single-tube multiplex detection of 14 HR-HPV types through ingenious design. The outcomes of the detection can be interpreted either through a fluorescence reader or visually. To the best of our knowledge, this is the first paper to utilize CRISPR/Cas diagnostic technology for the simultaneous detection of 14 types of HPV and to evaluate its feasibility in clinical sample detection using a large number of clinical samples. We hope that this work will facilitate the rapid and accurate detection of HPV and promote the broader application of CRISPR/Cas diagnostic technology.

Effects of exosomes from human dental pulp stem cells on the biological behavior of human fibroblasts

The aim of this study was to investigate the effect of dental pulp stem cell-derived exosomes (DPSCs-Exos) on the biological behaviour of fibroblasts, particularly on keloid fibroblasts (KFs) and normal skin fibroblasts (NFs), with a view to providing new insights into cellular regenerative medicine. We obtained DPSCs-Exos by ultracentrifugation and co-cultured it with KFs and NFs. We detected its effect on cell proliferation using the CCK-8 assay; cell migration ability by cell scratch and Transwell assays; extracellular matrix synthesis using the hydroxyproline content assay; the expression levels of genes associated with fibrosis by PCR assay; and the expression levels of proteins related to fibrosis in the cells using the Western Blot method. DPSCs-Exos was able to be taken up by fibroblasts after addition to the culture medium and affected the biological behavior of NFs and KFs. DPSCs-Exos promoted the proliferation of NFs, inhibited the migration and extracellular matrix synthesis of KFs. In addition, DPSCs-Exos was able to inhibit the expression of fibrosis-related genes and proteins in KFs. This study highlights the role of DPSCs-Exos in regulating the biological behaviour of fibroblasts, providing new insights for future applications in the field of cell-free regenerative medicine.

Targeting autophagy to enhance chemotherapy and immunotherapy in oral cancer

Oral cancer is a highly malignant disease characterized by recurrence, metastasis, and poor prognosis. Autophagy, a catabolic process induced under stress conditions, has been shown to play a dual role in oral cancer development and therapy. Recent studies have identified that autophagy activation in oral epithelial cells suppresses cancer cell survival by inhibiting key pathways such as the mammalian target of rapamycin (mTOR) and mitogen-activated protein kinase (MAPK), while activating the adenosine monophosphate-activated protein kinase (AMPK) pathway. Inducing autophagy promotes degradation of eukaryotic initiation factor 4E, thus reducing metastasis and enhancing the efficacy of chemotherapy, radiotherapy, and immunotherapy. Furthermore, autophagy induction can modulate the tumor immune microenvironment and enhance antitumor immunity. This review comprehensively summarizes the relationship between autophagy and oral cancer, focusing on its mechanisms and therapeutic potential when combined with conventional treatments. While promising, the precise mechanisms and clinical applications of autophagy inducers in oral cancer therapy remain to be elucidated, offering new directions for future research to improve treatment outcomes and reduce recurrence.

Targeting immune checkpoints on myeloid cells: current status and future directions

Myeloid cells accumulate extensively in most tumors and play a critical role in immunosuppression of the tumor microenvironment (TME). Like T cells, myeloid cells also express immune checkpoint molecules, which induce the immunosuppressive phenotype of these cells. In this review, we summarize the tumor-promoting function and immune checkpoint expression of four types of myeloid cells: macrophages, neutrophils, dendritic cells, and myeloid-derived suppressor cells, which are the main components of the TME. By summarizing the research status of myeloid checkpoints, we propose that blocking immune checkpoints on myeloid cells might be an effective strategy to reverse the immunosuppressive status of the TME. Moreover, combining nanotechnology, cellular therapy, and bispecific antibodies to achieve precise targeting of myeloid immune checkpoints can help to avoid the adverse effects of systemic administration, ultimately achieving a balance between efficacy and safety in cancer therapy.

Soluble TREM2 drives triple-negative breast cancer progression via activation of the AKT pathway

Triggering receptor expressed on myeloid cells 2 (TREM2) plays a key role in immune regulation, particularly within tumor-associated macrophages (TAMs). In triple-negative breast cancer (TNBC), TREM2+ TAMs have been shown to modulate the tumor microenvironment, but the role of its soluble form: soluble triggering receptor expressed on myeloid cells 2 (sTREM2), produced through proteolytic cleavage, remains unclear. In this study, we investigated the effects of sTREM2 on TNBC progression. In vitro, treatment of TNBC cells with recombinant sTREM2 or sTREM2-containing culture supernatant significantly enhanced cell proliferation, invasion, and migration. These effects were further confirmed by the use of TREM2-neutralizing antibodies, which abrogated sTREM2's tumor-promoting activities. In vivo, peri-tumoral injections of recombinant sTREM2 led to a notable acceleration of tumor growth in mouse models. Mechanistically, we found that the effects of sTREM2 were mediated through its binding to the TG2 protein in 4T1 cells, thereby activating the AKT signaling pathway. Collectively, our findings suggest that sTREM2 drives TNBC progression by enhancing critical tumor cell functions, positioning it as a potential therapeutic target for TNBC treatment.

Network Medicine-Based Strategy Identifies Maprotiline as a Repurposable Drug by Inhibiting PD-L1 Expression via Targeting SPOP in Cancer

Immune checkpoint inhibitors (ICIs) are drugs that inhibit immune checkpoint (ICP) molecules to restore the antitumor activity of immune cells and eliminate tumor cells. Due to the limitations and certain side effects of current ICIs, such as programmed death protein-1, programmed cell death-ligand 1, and cytotoxic T lymphocyte-associated antigen 4 (CTLA4) antibodies, there is an urgent need to find new drugs with ICP inhibitory effects. In this study, a network-based computational framework called multi-network algorithm-driven drug repositioning targeting ICP (Mnet-DRI) is developed to accurately repurpose novel ICIs from ≈3000 Food and Drug Administration-approved or investigational drugs. By applying Mnet-DRI to PD-L1, maprotiline (MAP), an antidepressant drug is repurposed, as a potential PD-L1 modifier for colorectal and lung cancers. Experimental validation revealed that MAP reduced PD-L1 expression by targeting E3 ubiquitin ligase speckle-type zinc finger structural protein (SPOP), and the combination of MAP and anti-CTLA4 in vivo significantly enhanced the antitumor effect, providing a new alternative for the clinical treatment of colorectal and lung cancer.

Cuproplasia-related gene signature: Prognostic insights for glioma therapy

Background

Adult-type diffuse gliomas encompass nearly a quarter of all primary tumors found in the CNS, including astrocytoma, oligodendroglioma, and glioblastoma. Histopathological tumor grade and molecular profile distinctly impact patient survival. Despite treatment advancements, patients with recurrent glioma have a very poor clinical outcome, warranting improved risk stratification to determine therapeutic interventions. Various studies have shown that copper is a notable trace element that is crucial for biological processes and has been shown to display pro-tumorigenic functions in cancer, particularly gliomas.

Methods

Differential gene expression, Cox regression, and least absolute shrinkage and selection operator regression were used to identify 19 copper-homeostasis-related gene signatures using TCGA lower-grade glioma and glioblastoma (GBM) cohorts. The GLASS Consortium dataset was used as an independent validation cohort. Enrichment analysis revealed the involvement of the signature in various cancer-related pathways and biological processes. Using this CHRG signature, a risk score model and a nomogram were developed to predict survival in glioma patients.

Results

Our prognostic CHRG signature stratified patients into high- and low-risk groups, demonstrating robust predictive performance. High-risk groups showed poorer survival outcomes. The nomogram model integrating CHRG signature and clinical features accurately predicted 1-, 3-, and 5-year survival rates in both training and test sets.

Conclusions

The identified 19-gene CHRG signature holds promise as a prognostic tool, enabling accurate risk stratification and survival prediction in glioma patients. Integrating this signature with clinical characteristics enhances prognostic accuracy, underscoring its potential clinical utility in optimizing therapeutic strategies and patient care in glioma management.

Adverse skin reactions secondary to sintilimab for advanced gastric adenocarcinoma: A case report and literature review

Immune checkpoint inhibitors, a class of anticancer drugs, which act via enhancing T cell responses against tumor cells, are associated with immune-related adverse events. The skin is one of the most commonly affected organs. In the present study, a case of a 78-year-old man, who developed systemic eczema dermatitis due to neoadjuvant treatment of locally advanced gastric adenocarcinoma with sintilimab combined with Tigio plus oxaliplatin regimen, was reported. The eczema dermatitis completely subsided after treatment with methylprednisolone. The patient and his family strongly requested surgical intervention. Postoperative pathology revealed a pathological complete response.

Comparative epigenetics of domestic animals: focusing on DNA accessibility and its impact on gene regulation and traits

IMPORTANCE

Chromatin accessibility is vital for gene regulation, determining the ability of DNA-binding proteins to access the genomic regions and drive transcriptional activity, reflecting environmental changes. Although human and murine studies have advanced the understanding of chromatin dynamics, domestic animals remain comparatively underexplored despite their importance in agriculture and veterinary medicine. Investigating the accessibility of chromatin in these species is crucial for improving traits such as productivity, disease resistance, and environmental adaptation. This review assessed chromatin accessibility research in domestic animals, highlighting its significance in understanding and improving livestock traits.

OBSERVATIONS

This review outlines chromatin accessibility research in domestic animals, focusing on critical developmental processes, tissue-specific regulation, and economically significant traits. Advances in techniques, such as Assay for Transposase-Accessible Chromatin using sequencing, have enabled detailed mapping of regulatory elements, shedding light on epigenetic regulation of traits, such as muscle development and productivity. Comparative studies have uncovered conserved and species-specific cis-regulatory elements across multiple species. These findings offer insights into regulatory mechanisms that can enhance breeding strategies and animal management. In addition, high-throughput techniques, such as single-cell analysis and deep-learning models, have advanced the study of chromatin accessibility in lesser-studied species.

CONCLUSIONS AND RELEVANCE

Chromatin accessibility is crucial in gene regulation in domestic animals, influencing development, immune response, and productivity. Despite the progress, more comprehensive epigenomic datasets and cross-species analytical tools are needed to harness chromatin accessibility in domestic animal research. Understanding these mechanisms has practical applications in improving livestock traits, advancing breeding programs, and developing disease-resistant animals, highlighting the importance of integrating epigenetic and genomic tools for enhancing animal health and productivity.

Exosome-Derived Cargos in Immune Microenvironment in Esophageal Carcinoma: A Mini-Review

Esophageal carcinoma, a lethal malignancy with limited treatment options and poor prognosis, necessitates understanding its underlying mechanisms and identifying novel therapeutic targets. Recent studies have highlighted the critical role of the immune microenvironment in esophageal carcinoma, particularly the interplay between tumor cells and immune cells mediated by exosomes and their cargos. Exosomes, small extracellular vesicles secreted by various cells, including tumor cells, facilitate intercellular communication by transferring bioactive molecules such as proteins, nucleic acids, and lipids to recipient cells. In the context of esophageal carcinoma, tumor-derived exosomes have been shown to play a significant role in shaping the immune microenvironment. In esophageal carcinoma, exosomal cargos have been found to modulate immune cell function and impact tumor progression. These cargos can carry immune inhibitory molecules, such as programmed death-ligand 1 (PD-L1), to suppress T-cell activity and promote immune evasion by tumor cells. Furthermore, exosomal cargos can activate antigen- presenting cells, enhancing their ability to present tumor-specific antigens to T cells and thereby promoting anti-tumor immune responses. Additionally, the cargos of exosomes have been implicated in the induction of immune regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs) within the esophageal carcinoma microenvironment. These immunosuppressive effectors inhibit the activity of T cells, contributing to tumor immune evasion and resistance to immune therapies. In summary, exosomes and their cargo play a crucial role in the immune microenvironment of esophageal carcinoma. Understanding the mechanisms by which exosomal cargos regulate immune cell function and tumor progression may reveal novel therapeutic targets for this devastating disease.