Integrated Machine Learning and Bioinformatic Analyses Constructed a Novel Stemness-Related Classifier to Predict Prognosis and Immunotherapy Responses for Hepatocellular Carcinoma Patients

Relevance of proteomics and metabolomics approaches to overview the tumorigenesis and better management of cancer

Proteomics and metabolomics, integral combination of OMICs platform are gaining prominence in cancer research to enhance scientific knowledge of bio-molecular interactions occurs in the cellular processes during cancer progression. This approach designed to identify potential tools for addressing the complexities of this multifaceted disease. This analysis focussed on the intricate interplay between proteins and metabolites within cancer cells and their surrounding microenvironment. By reviewing current proteomics and metabolomics studies, we aim to gain invaluable insights into tumour biology, progression, and its implication in therapeutic responses. This study highlights the importance of proteomics and metabolomics in discovering therapeutic targets and diagnostic biomarkers for targeted cancer treatment. Proteomics facilitates the analysis of protein expression, modifications and interactions, exemplified by the identification of HER2 mutations leads to development of breast cancer hence targeted therapies like trastuzumab could be initiated. Metabolomics reveals metabolic alternations such as elevated 2-hydroxyglutarate levels in gliomas linked to cancer progression and treatment resistance. The integration of these approaches clarifies complex signalling network driving oncogenesis and paves the way for innovative cancer therapies, including immune cheque point inhibitors. Proteomics and metabolomics have revolutionised cancer biology by revealing intricate signalling networks, metabolic dysregulations, and unique molecular alterations. This information is crucial for early cancer identification and prognosis, and for designing personalized therapeutic strategies. Innovative technologies like artificial intelligence and high-throughput mass spectrometry further enhance the potential of these studies. Fostering multidisciplinary collaboration and data-sharing is essential for maximising the impact of these approaches to cure as well as better management of the cancer.

Strategies for discovering novel hepatocellular carcinoma biomarkers

Liver cancer, particularly hepatocellular carcinoma (HCC), remains a significant global health challenge due to its high mortality rate and late-stage diagnosis. The discovery of reliable biomarkers is crucial for improving early detection and patient outcomes. This review provides a comprehensive overview of current and emerging biomarkers for HCC, including alpha-fetoprotein, des-gamma-carboxy prothrombin, glypican-3, Golgi protein 73, osteopontin, and microRNAs. Despite advancements, the diagnostic limitations of existing biomarkers underscore the urgent need for novel markers that can detect HCC in its early stages. The review emphasizes the importance of integrating multi-omics approaches, combining genomics, proteomics, and metabolomics, to develop more robust biomarker panels. Such integrative methods have the potential to capture the complex molecular landscape of HCC, offering insights into disease mechanisms and identifying targets for personalized therapies. The significance of large-scale validation studies, collaboration between research institutions and clinical settings, and consideration of regulatory pathways for clinical implementation is also discussed. In conclusion, while substantial progress has been made in biomarker discovery, continued research and innovation are essential to address the remaining challenges. The successful translation of these discoveries into clinical practice will require rigorous validation, standardization of protocols, and cross-disciplinary collaboration. By advancing the development and application of novel biomarkers, we can improve the early detection and management of HCC, ultimately enhancing patient survival and quality of life.

Molecular subtyping combined with multiomics analysis to study correlation between TACE refractoriness and tumor stemness in hepatocellular carcinoma

BACKGROUND

Transarterial chemoembolization (TACE) refractoriness is a significant challenge in treating intermediate to advanced-stage hepatocellular carcinoma (HCC). A few studies suggest that liver cancer stem cells (LCSCs) may be associated with TACE refractoriness. This study aims to explore the potential correlation between TACE refractoriness and HCC stemness, highlighting its clinical significance.

METHODS

This research encompassed the analysis of diverse HCC datasets, including RNA-sequencing, microarray, single-cell RNA-sequencing, and clinical cohorts. We identified common genes between TACE refractoriness and tumor stemness (TSGs). Unsupervised clustering was employed to classify HCC patients into different clusters based on TSGs (TRS clusters). The study explored the differences in clinical prognosis, biological characteristics, genomic variations, immune landscapes, and treatment responses among the TRS clusters.

RESULTS

Patients with TACE-refractoriness demonstrated significantly higher tumor stemness. Our study identified 33 TSGs and established two TRS clusters, including C1 and C2. C1 was associated with TACE refractoriness, elevated tumor stemness, and poorer prognosis. Genomic alterations were found to be significantly different between the TRS clusters. The C1 exhibited signs of immunosuppression and lower activity of immune effector cells, while the C2 had a more robust immune response and higher level of immune cell presence. Single-cell RNA-seq revealed distinct cell type characteristics in each subtypes, with the C1 showing a higher proportion of stem cells and malignant cells.

CONCLUSION

Our findings establish a connection between TACE refractoriness and tumor stemness in HCC, proposing a novel subtype classification to guide personalized treatment. Insights gained may facilitate overcoming TACE refractoriness and the development of innovative therapies.

Transcriptomics reveals preterm birth risk: identification and validation of key genes in monocytes

BACKGROUND

Preterm birth (PTB) is a leading cause of neonatal mortality and long-term disability worldwide. However, the molecular mechanisms underlying PTB remain incompletely understood, and the etiology of many PTB cases is still largely unexplained. Due to their close association with PTB, monocytes serve as an ideal matrix for identifying peripheral biomarkers predictive of preterm birth risk.

OBJECTIVE

This study aims to identify and validate biomarkers that could predict PTB, improving clinical diagnostic accuracy and enhancing preventive measures against PTB.

METHODS

This study conducted a comprehensive transcriptomic analysis of monocytes obtained from PTB patients (gestational age = 28-36 weeks) and age-matched healthy controls (HC, gestational age = 37+ 1-41+ 4 weeks). Blood samples were collected within 30 min of hospital admission and prior to labor initiation to ensure consistency. We further validated the findings after screening for potential biomarkers using quantitative real-time PCR (qPCR). While the sample size was relatively small, this study provides foundational evidence supporting the role of CXCL3 and IL-6 as biomarkers for PTB, laying a framework for future prospective research.

RESULTS

We identified 295 significantly differentially expressed genes compared to the control group, and Weighted Gene Co-expression Network Analysis (WGCNA) further revealed genes significantly associated with PTB. These genes are involved in immune pathways such as rheumatoid arthritis, influenza A, and the MAPK signaling pathway. Machine learning analysis and qPCR validation identified two essential genes-CXCL3 and IL-6. Based on these two genes, the diagnostic model achieved an AUC value of 1 in the discovery cohort, distinguishing PTB patients from healthy controls.

CONCLUSION

The immune responses observed in peripheral blood mononuclear cells (PBMCs) may be closely related to the mechanisms underlying PTB. Monocyte-derived genes CXCL3 and IL-6 are promising biomarkers for predicting PTB risk, offering new diagnostic tools for clinical practice. These findings have the potential to enhance PTB prevention and management strategies.

Integrated genomic analysis of the stemness index signature of mRNA expression predicts lung adenocarcinoma prognosis and immune landscape

mRNA expression-based stemness index (mRNAsi) has been used for prognostic assessment in various cancers, but its application in lung adenocarcinoma (LUAD) is limited, which is the focus of this study. Low mRNAsi in LUAD predicted a better prognosis. Eight genes (GNG7, EIF5A, ANLN, FKBP4, GAPDH, GNPNAT1, E2F7, CISH) associated with mRNAsi were screened to establish a risk model. The differentially expressed genes between the high and low risk groups were mainly enriched in the metabolism, cell cycle functions pathway. The low risk score group had higher immune cell scores. Patients with lower TIDE scores in the low risk group had better immunotherapy outcomes. In addition, risk score was effective in assessing drug sensitivity of LUAD. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) data showed that eight genes were differentially expressed in LUAD cell lines, and knockdown of EIF5A reduced the invasion and migration ability of LUAD cells. This study designed a risk model based on the eight mRNAsi-related genes for predicting LUAD prognosis. The model accurately predicted the prognosis and survival of LUAD patients, facilitating the assessment of the sensitivity of patients to immunotherapy and chemotherapy.

Antiviral therapy can effectively suppress irAEs in HBV positive hepatocellular carcinoma treated with ICIs: validation based on multi machine learning

Background

Immune checkpoint inhibitors have proven efficacy against hepatitis B-virus positive hepatocellular. However, Immunotherapy-related adverse reactions are still a major challenge faced by tumor immunotherapy, so it is urgent to establish new methods to effectively predict immunotherapy-related adverse reactions.

Objective

Multi-machine learning model were constructed to screen the risk factors for irAEs in ICIs for the treatment of HBV-related hepatocellular and build a prediction model for the occurrence of clinical IRAEs.

Methods

Data from 274 hepatitis B virus positive tumor patients who received PD-1 or/and CTLA4 inhibitor treatment and had immune cell detection results were collected from Henan Cancer Hospital for retrospective analysis. Models were established using Lasso, RSF (RandomForest), and xgBoost, with ten-fold cross-validation and resampling methods used to ensure model reliability. The impact of influencing factors on irAEs (immune-related adverse events) was validated using Decision Curve Analysis (DCA). Both uni/multivariable analysis were accomplished by Chi-square/Fisher's exact tests. The accuracy of the model is verified in the DCA curve.

Results

A total of 274 HBV-related liver cancer patients were enrolled in the study. Predictive models were constructed using three machine learning algorithms to analyze and statistically evaluate clinical characteristics, including immune cell data. The accuracy of the Lasso regression model was 0.864, XGBoost achieved 0.903, and RandomForest reached 0.961. Resampling internal validation revealed that RandomForest had the highest recall rate (AUC = 0.892). Based on machine learning-selected indicators, antiviral therapy and The HBV DNA copy number showed a significant correlation with both the occurrence and severity of irAEs. Antiviral therapy notably reduced the incidence of IRAEs and may modulate these events through regulation of B cells. The DCA model also demonstrated strong predictive performance. Effective control of viral load through antiviral therapy significantly mitigates the occurrence of irAEs.

Conclusion

ICIs show therapeutic potential in the treatment of HBV-HCC. Following antiviral therapy, the incidence of severe irAEs decreases. Even in cases where viral load control is incomplete, continuous antiviral treatment can still mitigate the occurrence of irAEs.

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.

Developing and validating a drug recommendation system based on tumor microenvironment and drug fingerprint

Introduction

Tumor heterogeneity significantly complicates the selection of effective cancer treatments, as patient responses to drugs can vary widely. Personalized cancer therapy has emerged as a promising strategy to enhance treatment effectiveness and precision. This study aimed to develop a personalized drug recommendation model leveraging genomic profiles to optimize therapeutic outcomes.

Methods

A content-based filtering algorithm was implemented to predict drug sensitivity. Patient features were characterized by the tumor microenvironment (TME), and drug features were represented by drug fingerprints. The model was trained and validated using the Genomics of Drug Sensitivity in Cancer (GDSC) database, followed by independent validation with the Cancer Cell Line Encyclopedia (CCLE) dataset. Clinical application was assessed using The Cancer Genome Atlas (TCGA) dataset, with Best Overall Response (BOR) serving as the clinical efficacy measure. Two multilayer perceptron (MLP) models were built to predict IC50 values for 542 tumor cell lines across 18 drugs.

Results

The model exhibited high predictive accuracy, with correlation coefficients (R) of 0.914 in the training set and 0.902 in the test set. Predictions for cytotoxic drugs, including Docetaxel (R = 0.72) and Cisplatin (R = 0.71), were particularly robust, whereas predictions for targeted therapies were less accurate (R < 0.3). Validation with CCLE (MFI as the endpoint) showed strong correlations (R = 0.67). Application to TCGA data successfully predicted clinical outcomes, including a significant association with 6-month progression-free survival (PFS, P = 0.007, AUC = 0.793).

Discussion

The model demonstrates strong performance across preclinical datasets, showing its potential for real-world application in personalized cancer therapy. By bridging preclinical IC50 and clinical BOR endpoints, this approach provides a promising tool for optimizing patient-specific treatments.

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.

Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunotherapy

Background

Hepatocellular carcinoma (LIHC) poses a significant health challenge worldwide, primarily due to late-stage diagnosis and the limited effectiveness of current therapies. Cancer stem cells are known to play a role in tumor development, metastasis, and resistance to treatment. A thorough understanding of genes associated with stem cells is crucial for improving the diagnostic precision of LIHC and for the advancement of effective immunotherapy approaches.

Method

This research combines single-cell RNA sequencing with machine learning techniques to identify vital stem cell-associated genes that could act as prognostic biomarkers and therapeutic targets for LIHC. We analyzed various datasets, applying negative matrix factorization alongside machine learning algorithms to reveal gene expression patterns and construct diagnostic models. The XGBoost algorithm was specifically utilized to identify key regulatory genes related to stem cells in LIHC, and the expression levels and prognostic significance of these genes were validated experimentally.

Results

Our single-cell analysis identified 16 differential prognostic genes associated with liver cancer stem cells. Cluster analysis and diagnostic models constructed using various machine learning techniques confirmed the significance of these 16 genes in the diagnosis and immunotherapy of LIHC. Notably, the XGBoost algorithm identified S100A10 as the stem cell-related gene most relevant to the prognosis of LIHC patients. Experimental validation further supports S100A10 as a potential prognostic marker for this cancer type. Additionally, S100A10 shows a positive correlation with the stem cell marker POU5F1.

Conclusion

The results of this study highlight S100A10 as an essential predictor for liver cancer diagnosis and treatment response, particularly regarding immunotherapy. This research offers valuable insights into the molecular mechanisms underlying LIHC and suggests S100A10 as a promising target for enhancing treatment outcomes in liver cancer patients.

Single-cell and Bulk Transcriptomic Analyses Reveal a Stemness and Circadian Rhythm Disturbance-related Signature Predicting Clinical Outcome and Immunotherapy Response in Hepatocellular Carcinoma

AIMS

Investigating the impact of stemness-related circadian rhythm disruption (SCRD) on hepatocellular carcinoma (HCC) prognosis and its potential as a predictor for immunotherapy response.

BACKGROUND

Circadian disruption has been linked to tumor progression through its effect on the stemness of cancer cells.

OBJECTIVE

Develop a novel signature for SCRD to accurately predict clinical outcomes and immune therapy response in patients with HCC.

METHODS

The stemness degree of patients with HCC was assessed based on the stemness index (mRNAsi). The co-expression circadian genes significantly correlated with mRNAsi were identified and defined as stemness- and circadian-related genes (SCRGs). The SCRD scores of samples and cells were calculated based on the SCRGs. Differentially expressed genes with a prognostic value between distinct SCRD groups were identified in bulk and single-cell datasets to develop an SCRD signature.

RESULTS

A higher SCRD score indicates a worse patient survival rate. Analysis of the tumor microenvironment revealed a significant correlation between SCRD and infiltrating immune cells. Heterogeneous expression patterns, functional states, genomic variants, and cell-cell interactions between two SCRD populations were revealed by transcriptomic, genomic, and interaction analyses. The robust SCRD signature for predicting immunotherapy response and prognosis in patients with HCC was developed and validated in multiple independent cohorts.

CONCLUSIONS

In summary, distinct tumor immune microenvironment patterns were confirmed under SCRD in bulk and single-cell transcriptomic, and SCRD signature associated with clinical outcomes and immunotherapy response was developed and validated in HCC.

Integrated machine learning to predict the prognosis of lung adenocarcinoma patients based on SARS-COV-2 and lung adenocarcinoma crosstalk genes

Viruses are widely recognized to be intricately associated with both solid and hematological malignancies in humans. The primary goal of this research is to elucidate the interplay of genes between SARS-CoV-2 infection and lung adenocarcinoma (LUAD), with a preliminary investigation into their clinical significance and underlying molecular mechanisms. Transcriptome data for SARS-CoV-2 infection and LUAD were sourced from public databases. Differentially expressed genes (DEGs) associated with SARS-CoV-2 infection were identified and subsequently overlapped with TCGA-LUAD DEGs to discern the crosstalk genes (CGs). In addition, CGs pertaining to both diseases were further refined using LUAD TCGA and GEO datasets. Univariate Cox regression was conducted to identify genes associated with LUAD prognosis, and these genes were subsequently incorporated into the construction of a prognosis signature using 10 different machine learning algorithms. Additional investigations, including tumor mutation burden assessment, TME landscape, immunotherapy response assessment, as well as analysis of sensitivity to antitumor drugs, were also undertaken. We discovered the risk stratification based on the prognostic signature revealed that the low-risk group demonstrated superior clinical outcomes (p < 0.001). Gene set enrichment analysis results predominantly exhibited enrichment in pathways related to cell cycle. Our analyses also indicated that the low-risk group displayed elevated levels of infiltration by immunocytes (p < 0.001) and superior immunotherapy response (p < 0.001). In our study, we reveal a close association between CGs and the immune microenvironment of LUAD. This provides preliminary insight for further exploring the mechanism and interaction between the two diseases.

Lasso-Cox interpretable model of AFP-negative hepatocellular carcinoma

BACKGROUND

In AFP-negative hepatocellular carcinoma patients, markers for predicting tumor progression or prognosis are limited. Therefore, our objective is to establish an optimal predicet model for this subset of patients, utilizing interpretable methods to enhance the accuracy of HCC prognosis prediction.

METHODS

We recruited a total of 508 AFP-negative HCC patients in this study, modeling with randomly divided training set and validated with validation set. At the same time, 86 patients treated in different time periods were used as internal validation. After comparing the cox model with the random forest model based on Lasso regression, we have chosen the former to build our model. This model has been interpreted with SHAP values and validated using ROC, DCA. Additionally, we have reconfirmed the model's effectiveness by employing an internal validation set of independent periods. Subsequently, we have established a risk stratification system.

RESULTS

The AUC values of the Lasso-Cox model at 1, 2, and 3 years were 0.807, 0.846, and 0.803, and the AUC values of the Lasso-RSF model at 1, 2, and 3 years were 0.783, 0.829, and 0.776. Lasso-Cox model was finally used to predict the prognosis of AFP-negative HCC patients in this study. And BCLC stage, gamma-glutamyl transferase (GGT), diameter of tumor, lung metastases (LM), albumin (ALB), alkaline phosphatase (ALP), and the number of tumors were included in the model. The validation set and the separate internal validation set both indicate that the model is stable and accurate. Using risk factors to establish risk stratification, we observed that the survival time of the low-risk group, the middle-risk group, and the high-risk group decreased gradually, with significant differences among the three groups.

CONCLUSION

The Lasso-Cox model based on AFP-negative HCC showed good predictive performance for liver cancer. SHAP explained the model for further clinical application.

ABCA1 promote tumor environment heterogeneity via epithelial mesenchymal transition in Huh7 and HepG2 liver cancer cell

In this study, we delve into the intrinsic mechanisms of cell communication in hepatocellular carcinoma (HCC). Initially, employing single-cell sequencing, we analyze multiple malignant cell subpopulations and cancer-associated fibroblast (CAF) subpopulations, revealing their interplay through receptor-ligand interactions, with a particular focus on SPP1. Subsequently, employing unsupervised clustering analysis, we delineate two clusters, C1 and C2, and compare their infiltration characteristics using various tools and metrics, uncovering heightened cytotoxicity and overall invasion abundance in C1. Furthermore, our gene risk scoring model indicates heightened activity of the immune therapeutic pathway in C1. Lastly, employing a formulated scoring system, we stratify patients into high and low-risk groups, revealing notably poorer outcomes in the high-risk cohort on Kaplan-Meier curves. Risk scores exhibit a negative correlation with model genes and immune cell infiltration scores, indicating poor prognosis in the high-risk group. Further characterization elucidates the regulatory landscape of the high and low-risk groups across various signaling pathways. In addition, we used wet lab experiments to prove that ABCA1 plays a pro-oncogenic role in hepatocellular carcinoma cells by promoting proliferation, invasion, migration, and reducing apoptosis. In summary, these findings provide crucial insights, offering valuable clues and references for understanding HCC pathogenesis and patient prognosis.

Oviduct Glycoprotein 1 (OVGP1) Diagnoses Polycystic Ovary Syndrome (PCOS) Based on Machine Learning Algorithms

Aims: To investigate the diagnostic value of oviduct glycoprotein 1 (OVGP1) levels for polycystic ovary syndrome (PCOS). Materials and Methods: Serum OVGP1 concentrations were measured by enzyme-linked immunosorbent assay (ELISA). Associations between OVGP1 and endocrine parameters were evaluated by Spearman's correlation analysis. Diagnostic capacity was assessed by utilizing machine learning algorithms and receiver operating characteristic (ROC) curves. Results: OVGP1 levels were significantly decreased in PCOS patients and correlated with the serum follicle-stimulating hormone (FSH) concentration and the luteinizing hormone/follicle-stimulating hormone (LH/FSH) ratio, which are predictors of PCOS occurrence. The diagnostic value of OVGP1 combined with six signatures (LH/FSH, progesterone, total cholesterol, triglyceride, high-density lipoprotein cholesterol, and anti-Müllerian hormone) or three clinical indicators has the potential to significantly improve the accuracy of diagnosing PCOS patients. Conclusion: OVGP1 enhances the ability to diagnose when combined with clinical indicators.

Cancer molecular subtyping using limited multi-omics data with missingness

Diagnosing cancer subtypes is a prerequisite for precise treatment. Existing multi-omics data fusion-based diagnostic solutions build on the requisite of sufficient samples with complete multi-omics data, which is challenging to obtain in clinical applications. To address the bottleneck of collecting sufficient samples with complete data in clinical applications, we proposed a flexible integrative model (CancerSD) to diagnose cancer subtype using limited samples with incomplete multi-omics data. CancerSD designs contrastive learning tasks and masking-and-reconstruction tasks to reliably impute missing omics, and fuses available omics data with the imputed ones to accurately diagnose cancer subtypes. To address the issue of limited clinical samples, it introduces a category-level contrastive loss to extend the meta-learning framework, effectively transferring knowledge from external datasets to pretrain the diagnostic model. Experiments on benchmark datasets show that CancerSD not only gives accurate diagnosis, but also maintains a high authenticity and good interpretability. In addition, CancerSD identifies important molecular characteristics associated with cancer subtypes, and it defines the Integrated CancerSD Score that can serve as an independent predictive factor for patient prognosis.

KRT80 in hepatocellular carcinoma plays oncogenic role via epithelial-mesenchymal transition and PI3K/AKT pathway

Hepatocellular carcinoma (HCC), a globally prevalent form of cancer, is featured by aggressive growth and early metastasis. Elucidating the underlying mechanism and identifying the effective therapy are critical for advanced HCC patients. In the study, we detect that KRT80 was upregulated in HCC samples. HCC patients with higher KRT80 are associated with worse overall survival after surgery. Gain-of and loss-of function studies show that KRT80 enhanced HCC cells proliferation, migration, invasion, and angiogenesis, whereas its silencing abolishes the effects in vivo and in vitro. Mechanistic investigation shows that KRT80 may function as an independent prognostic risk factor and act as an oncogene by influencing EMT and modulating the PI3K/AKT signaling pathway. Together, these findings suggest that KRT80 may be a potential oncogene and a good indicator in predicting prognosis. Targeting KRT80 can offer new insights into the prevention and treatment of HCC.

The global evolution and impact of systems biology and artificial intelligence in stem cell research and therapeutics development: a scoping review

Advanced bioinformatics analysis, such as systems biology (SysBio) and artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL), is increasingly present in stem cell (SC) research. An approximate timeline on these developments and their global impact is still lacking. We conducted a scoping review on the contribution of SysBio and AI analysis to SC research and therapy development based on literature published in PubMed between 2000 and 2024. We identified an 8 to 10-fold increase in research output related to all 3 search terms between 2000 and 2021, with a 10-fold increase in AI-related production since 2010. Use of SysBio and AI still predominates in preclinical basic research with increasing use in clinically oriented translational medicine since 2010. SysBio- and AI-related research was found all over the globe, with SysBio output led by the (US, n = 1487), (UK, n = 1094), Germany (n = 355), The Netherlands (n = 339), Russia (n = 215), and France (n = 149), while for AI-related research the US (n = 853) and UK (n = 258) take a strong lead, followed by Switzerland (n = 69), The Netherlands (n = 37), and Germany (n = 19). The US and UK are most active in SCs publications related to AI/ML and AI/DL. The prominent use of SysBio in ESC research was recently overtaken by prominent use of AI in iPSC and MSC research. This study reveals the global evolution and growing intersection among AI, SysBio, and SC research over the past 2 decades, with substantial growth in all 3 fields and exponential increases in AI-related research in the past decade.

Analysis and identification of mRNAsi‑related expression signatures via RNA sequencing in lung cancer

High stemness index scores are associated with poor survival in patients with lung cancer. Studies on the mRNA expression-based stemness index (mRNAsi) are typically conducted using tumor tissues; however, mRNAsi-related expression signatures based on cell-free RNA (cfRNA) are yet to be comprehensively investigated. The present study aimed to elucidate the gene expression profiles of tumor stemness in lung cancer tissues and corresponding cfRNAs in blood, and to assess their links with immune infiltration. Tumor tissue, paracancerous tissue, peripheral blood and lymph node samples were collected from patients with stage I-III non-small cell lung cancer and RNA sequencing was performed. The TCGAbiolinks package was used to calculate the mRNAsi for each of these four types of sample. Weighted gene co-expression network analysis and differentially expressed gene analyses were performed to investigate mRNAsi-related genes, and pathway enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology-based annotation system. In addition, the STAR-Fusion tool was used to detect fusion variants, and CIBERSORT was used to analyze the correlations of stemness signatures in tissues and blood with immune cell infiltration. The mRNAsi values in peripheral blood and lymph nodes were found to be higher than those in cancer tissues. 'Hematopoietic cell lineage' was the only KEGG pathway enriched in mRNAsi-related genes in both lung cancer tissues and peripheral blood. In addition, the protein tyrosine phosphatase receptor type C associated protein gene was the only gene commonly associated with the mRNAsi in these two types of sample. The expression of mRNAsi-related genes was increased in the dendritic and Treg cells in tumor tissues, but was elevated in Treg and CD8 cells in the blood. In conclusion, cfRNAs in the blood exhibit unique stemness signatures that have potential for use in the diagnosis of lung cancer.

Characterization of stem cell landscape and assessing the stemness degree to aid clinical therapeutics in hematologic malignancies

Hematological malignancies are a group of cancers that affect the blood, bone marrow, and lymphatic system. Cancer stem cells (CSCs) are believed to be responsible for the initiation, progression, and relapse of hematological malignancies. However, identifying and targeting CSCs presents many challenges. We aimed to develop a stemness index (HSCsi) to identify and guide the therapy targeting CSCs in hematological malignancies. We developed a novel one-class logistic regression (OCLR) algorithm to identify transcriptomic feature sets related to stemness in hematologic malignancies. We used the HSCsi to measure the stemness degree of leukemia stem cells (LSCs) and correlate it with clinical outcomes.We analyze the correlation of HSCsi with genes and pathways involved in drug resistance and immune microenvironment of acute myeloid leukemia (AML). HSCsi revealed stemness-related biological mechanisms in hematologic malignancies and effectively identify LSCs. The index also predicted survival and relapse rates of various hematologic malignancies. We also identified potential drugs and interventions targeting cancer stem cells (CSCs) of hematologic malignancies by the index. Moreover, we found a correlation between stemness and bone marrow immune microenvironment in AML. Our study provides a novel method and tool to assess the stemness degree of hematologic malignancies and its implications for clinical outcomes and therapeutic strategies. Our HSC stemness index can facilitate the precise stratification of hematologic malignancies, suggest possible targeted and immunotherapy options, and guide the selection of patients.

Multiomics Analysis Identifies Prognostic Signatures for Sepsis-Associated Hepatocellular Carcinoma in Emergency Medicine

Objectives

Sepsis, caused by the body's response to infection, poses a life-threatening condition and represents a significant global health challenge. Characterized by dysregulated immune response to infection, sepsis may lead to organ dysfunction and failure, ultimately resulting in high mortality rates. The liver plays a crucial role in sepsis, yet the role of differentially expressed genes in septic patients remains unclear in hepatocellular carcinoma (HCC). In this study, we aim to investigate the significance of differentially expressed genes related to sepsis in the occurrence and prognosis of tumors in HCC.

Methods

We conducted analyses by obtaining gene transcriptome data and clinical data of HCC cases from The Cancer Genome Atlas (TCGA). Furthermore, we obtained transcriptomic sequencing results of septic patients from the Gene Expression Omnibus (GEO) database, identified intersecting differentially expressed genes between the two, and performed survival analysis on the samples using LASSO and Cox regression analysis. Combining analyses of tumor mutation burden (TMB) and immune function, we further elucidated the mechanisms of sepsis-related genes in the prognosis and treatment of HCC.

Results

We established a prognostic model consisting of four sepsis-related genes: KRT20, PAEP, CCR3, and ANLN. Both the training and validation sets showed excellent outcomes in the prognosis of tumor patients, with significantly longer survival times observed in the low-risk group based on this model compared to the high-risk group. Furthermore, analyses, such as differential analysis of tumor mutation burden, immune function analysis, GO/KEGG pathway enrichment analysis, and drug sensitivity analysis, also demonstrated the potential mechanisms of action of sepsis-related genes.

Conclusions

Models constructed based on sepsis-related genes have shown excellent predictive ability in prognosis and differential analysis of drug sensitivity among tumor patients. These predictive models can enhance patient prognosis and inform the creation of early treatment protocols for sepsis, consequently aiding in the prevention of sepsis-induced HCC development through the modulation of the overall immune status. This may play a crucial role in patient management and immunotherapy, providing valuable reference for subsequent research.

Interpretable machine learning based on CT-derived extracellular volume fraction to predict pathological grading of hepatocellular carcinoma

PURPOSE

To develop a non-invasive auxiliary assessment method based on CT-derived extracellular volume (ECV) to predict the pathological grading (PG) of hepatocellular carcinoma (HCC).

METHODS

The study retrospectively analyzed 238 patients who underwent HCC resection surgery between January 2013 and April 2023. Six machine learning algorithms were employed to construct predictive models for HCC PG: logistic regression, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), random forest, adaptive boosting, and Gaussian naive Bayes. Model performance was evaluated using receiver operating characteristic curve analysis, including area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1 score. Calibration plots were used for visual evaluation of model calibration. Clinical decision curve analysis was performed to assess potential clinical utility by calculating net benefit.

RESULTS

166 patients from Hospital A were allocated to the training set, while 72 patients from Hospital B (constituting 30.25% of the total sample) were assigned to the test set. The model achieved an AUC of 1.000 (95%CI: 1.000-1.000) in the training set and 0.927 (95%CI: 0.837-0.999) in the validation set, respectively. Ultimately, the model achieved an AUC of 0.909 (95%CI: 0.837-0.980) in the test set, with an accuracy of 0.778, sensitivity of 0.906, specificity of 0.789, negative predictive value of 0.556, and F1 score of 0.908.

CONCLUSION

This study successfully developed and validated a non-invasive auxiliary assessment method based on CT-derived ECV to predict the HCC PG, providing important supplementary information for clinical decision-making.

Novel Insights Into DLAT's Role in Alzheimer's Disease-Related Copper Toxicity Through Microglial Exosome Dynamics

BACKGROUND

Alzheimer's disease (AD) is a complex neurodegenerative disorder, with recent research emphasizing the roles of microglia and their secreted extracellular vesicles in AD pathology. However, the involvement of specific molecular pathways contributing to neuronal death in the context of copper toxicity remains largely unexplored.

OBJECTIVE

This study investigates the interaction between pyruvate kinase M2 (PKM2) and dihydrolipoamide S-acetyltransferase (DLAT), particularly focusing on copper-induced neuronal death in Alzheimer's disease.

METHODS

Gene expression datasets were analyzed to identify key factors involved in AD-related copper toxicity. The role of DLAT was validated using 5xFAD transgenic mice, while in vitro experiments were conducted to assess the impact of microglial exosomes on neuronal PKM2 transfer and DLAT expression. The effects of inhibiting the PKM2 transfer via microglial exosomes on DLAT expression and copper-induced neuronal death were also evaluated.

RESULTS

DLAT was identified as a critical factor in the pathology of AD, particularly in copper toxicity. In 5xFAD mice, increased DLAT expression was linked to hippocampal damage and cognitive decline. In vitro, microglial exosomes were shown to facilitate the transfer of PKM2 to neurons, leading to upregulation of DLAT expression and increased copper-induced neuronal death. Inhibition of PKM2 transfer via exosomes resulted in a significant reduction in DLAT expression, mitigating neuronal death and slowing AD progression.

CONCLUSION

This study uncovers a novel pathway involving microglial exosomes and the PKM2-DLAT interaction in copper-induced neuronal death, providing potential therapeutic targets for Alzheimer's disease. Blocking PKM2 transfer could offer new strategies for reducing neuronal damage and slowing disease progression in AD.

Machine learning and single-cell analysis identify the mitophagy-associated gene TOMM22 as a potential diagnostic biomarker for intervertebral disc degeneration

Background

Mitophagy selectively eliminates potentially cytotoxic and damaged mitochondria and effectively prevents excessive cytotoxicity from damaged mitochondria, thereby attenuating inflammatory and oxidative responses. However, the potential role of mitophagy in intervertebral disc degeneration remains to be elucidated.

Methods

The GSVA method, two machine learning methods (SVM-RFE algorithm and random forest), the CIBERSORT and MCPcounter methods, as well as the consensus clustering method and the WGCNA algorithm were used to analyze the involvement of mitophagy in intervertebral disc degeneration, the diagnostic value of mitophagy-associated genes in intervertebral disc degeneration, and the infiltration of immune cells, and identify the gene modules that were closely related to mitophagy. Single-cell analysis was used to detect mitophagy scores and TOMM22 expression, and pseudo-temporal analysis was used to explore the function of TOMM22 in nucleus pulposus cells. In addition, TOMM22 expression was compared between human normal and degenerated intervertebral disc tissue samples by immunohistochemistry and PCR.

Results

This study identified that the mitophagy pathway score was elevated in intervertebral disc degeneration compared with the normal condition. A strong link was present between mitophagy genes and immune cells, which may be used to typify intervertebral disc degeneration. The single-cell level showed that mitophagy-associated gene TOMM22 was highly expressed in medullary cells of the disease group. Further investigations indicated the upregulation of TOMM22 expression in late-stage nucleus pulposus cells and its role in cellular communication. In addition, human intervertebral disc tissue samples established that TOMM22 levels were higher in disc degeneration samples than in normal samples.

Conclusions

Our findings revealed that mitophagy may be used in the diagnosis of intervertebral disc degeneration and its typing, and TOMM22 is a molecule in this regard and may act as a potential diagnostic marker in intervertebral disc degeneration.

Construction of a pathomics model for predicting mRNAsi in lung adenocarcinoma and exploration of biological mechanism

Objective

This study aimed to predict the level of stemness index (mRNAsi) and survival prognosis of lung adenocarcinoma (LUAD) using pathomics model.

Methods

From The Cancer Genome Atlas (TCGA) database, 327 LUAD patients were randomly assigned to a training set (n = 229) and a validation set (n = 98) for pathomics model development and evaluation. PyRadiomics was used to extract pathomics features, followed by feature selection using the mRMR-RFE algorithm. In the training set, Gradient Boosting Machine (GBM) was utilized to establish a model for predicting mRNAsi in LUAD. The model's predictive performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). Prognostic analysis was conducted using Kaplan-Meier curves and cox regression. Additionally, gene enrichment analysis, tumor microenvironment analysis, and tumor mutational burden (TMB) analysis were performed to explore the biological mechanisms underlying the pathomics prediction model.

Results

Multivariable cox analysis (HR = 1.488, 95 % CI 1.012-2.187, P = 0.043) identified mRNAsi as a prognostic risk factor for LUAD. A total of 465 pathomics features were extracted from TCGA-LUAD histopathological images, and ultimately, the most representative 8 features were selected to construct the predictive model. ROC curves demonstrated the significant predictive value of the model for mRNAsi in both the training set (AUC = 0.769) and the validation set (AUC = 0.757). Calibration curves and Hosmer-Lemeshow goodness-of-fit test showed good consistency between the model's prediction of mRNAsi levels and the actual values. DCA indicated a good net benefit of the model. The prediction of mRNAsi levels by the pathomics model is represented using the pathomics score (PS). PS was strongly associated with the prognosis of LUAD (HR = 1.496, 95 % CI 1.008-2.222, P = 0.046). Signaling pathways related to DNA replication and damage repair were significantly enriched in the high PS group. Prediction of immune therapy response indicated significantly reduced Dysfunction in the high PS group (P < 0.001). The high PS group exhibited higher TMB values (P < 0.001).

Conclusions

The predictive model constructed based on pathomics features can forecast the mRNAsi and survival risk of LUAD. This model holds promise to aid clinical practitioners in identifying high-risk patients and devising more optimized treatment plans for patients by jointly employing therapeutic strategies targeting cancer stem cells (CSCs).

Integrated machine learning screened glutamine metabolism-associated biomarker SLC1A5 to predict immunotherapy response in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) stands as one of the most prevalent malignancies. While PD-1 immune checkpoint inhibitors have demonstrated promising therapeutic efficacy in HCC, not all patients exhibit a favorable response to these treatments. Glutamine is a crucial immune cell regulatory factor, and tumor cells exhibit glutamine dependence. In this study, HCC patients were divided into two subtypes (C1 and C2) based on glutamine metabolism-related genes via consensus clustering. The C1 pattern, in contrast to C2, was associated with a lower survival probability among HCC patients. Additionally, the C1 pattern exhibited higher proportions of patients with advanced tumor stages. The activity of C1 in glutamine metabolism and transport is significantly enhanced, while its oxidative phosphorylation activity is reduced. And, C1 was mainly involved in the progression-related pathway of HCC. Furthermore, C1 exhibited high levels of immunosuppressive cells, cytokine-receptor interactions and immune checkpoint genes, suggesting C1 as an immunosuppressive subtype. After stepwise selection based on integrated four machine learning methods, SLC1A5 was finally identified as the pivotal gene that distinguishes the subtypes. The expression of SLC1A5 was significantly positively correlated with immunosuppressive status. SLC1A5 showed the most significant correlation with macrophage infiltration, and this correlation was confirmed through the RNA-seq data of CLCA project and our cohort. Low-SLC1A5-expression samples had better immunogenicity and responsiveness to immunotherapy. As expected, SubMap and survival analysis indicated that individuals with low SLC1A5 expression were more responsive to anti-PD1 therapy. Collectively, this study categorized HCC patients based on glutamine metabolism-related genes and proposed two subclasses with different clinical traits, biological behavior, and immune status. Machine learning was utilized to identify the hub gene SLC1A5 for HCC classification, which also could predict immunotherapy response.

Identification of effective diagnostic genes and immune cell infiltration characteristics in small cell lung cancer by integrating bioinformatics analysis and machine learning algorithms

OBJECTIVES

To identify potential diagnostic markers for small cell lung cancer (SCLC) and investigate the correlation with immune cell infiltration.

METHODS

GSE149507 and GSE6044 were used as the training group, while GSE108055 served as validation group A and GSE73160 served as validation group B. Differentially expressed genes (DEGs) were identified and analyzed for functional enrichment. Machine learning (ML) was used to identify candidate diagnostic genes for SCLC. The area under the receiver operating characteristic curves was applied to assess diagnostic efficacy. Immune cell infiltration analyses were carried out.

RESULTS

There were 181 DEGs identified. The gene ontology analysis showed that DEGs were enriched in 455 functional annotations, some of which were associated with immunity. The kyoto encyclopedia of genes and genomes analysis revealed that there were 9 signaling pathways enriched. The disease ontology analysis indicated that DEGs were related to 116 diseases. The gene set enrichment analysis results displayed multiple items closely related to immunity. ZWINT and NRCAM were screened using ML and further validated as diagnostic genes. Significant differences were observed in SCLC with normal lung tissue samples among immune cell infiltration characteristics. Strong associations were found between the diagnostic genes and immune cell infiltration.

CONCLUSION

This study identified 2 diagnostic genes, ZWINT and NRCAM, that were related to immune cell infiltration by integrating bioinformatics analysis and ML algorithms. These genes could serve as potential diagnostic biomarkers and provide possible molecular targets for immunotherapy in SCLC.

Potential therapeutic targets for pelvic organ prolapse: insights from key genes related to blood vessel development

Objective

Pelvic organ prolapse (POP) is a disease in which pelvic floor support structures are dysfunctional due to disruption of the extracellular matrix (ECM). The vascular system is essential for maintaining ECM homeostasis. Therefore, this study explored the potential mechanism of blood vessel development-related genes (BVDRGs) in POP.

Methods

POP-related datasets and BVDRGs were included in this study. Differentially expressed genes (DEGs) between the POP and control groups were first identified in the GSE12852 and GSE208271 datasets, and DE-BVDRGs were identified by determining the intersection of these DEGs and BVDRGs. Subsequently, the feature genes were evaluated by machine learning. Feature genes with consistent expression trends in the GSE12852 and GSE208271 datasets were considered key genes. Afterward, the overall diagnostic efficacy of key genes in POP was evaluated through receiver operating characteristic (ROC) curve analysis. Based on the key genes, enrichment analysis, immune infiltration analysis and regulatory network construction were performed to elucidate the molecular mechanisms underlying the functions of the key genes in POP.

Results

A total of 888 DEGs1 and 643 DEGs2 were identified in the GSE12852 and GSE208271 datasets, and 26 candidate genes and 4 DE-BVDRGs were identified. Furthermore, Hyaluronan synthase 2 (HAS2), Matrix metalloproteinase 19 (MMP19) and Plexin Domain Containing 1 (PLXDC1) were identified as key genes in POP and had promising value for diagnosing POP (AUC > 0.8). Additional research revealed that the key genes were predominantly implicated in immune cell activation, chemotaxis, and cytokine release via the chemokine signaling pathway, the Nod-like receptor signaling pathway, and the Toll-like receptor signaling pathway. Analysis of immune cell infiltration confirmed a decrease in the proportion of plasma cells in POP, and MMP19 expression showed a significant negative correlation with plasma cell numbers. In addition, regulatory network analysis revealed that MALAT1 (a lncRNA) targeted hsa-miR-503-5p, hsa-miR-23a-3p and hsa-miR-129-5p to simultaneously regulate three key genes.

Conclusion

We identified three key BVDRGs (HAS2, MMP19 and PLXDC1) related to the ECM in POP, providing markers for diagnostic studies and investigations of the molecular mechanism of POP.

Screening and experimental validation of diagnostic gene in ulcerative colitis with anti-TNF-α therapy

In clinical practice, the diagnosis of ulcerative colitis (UC) mainly relies on a comprehensive analysis of a series of signs and symptoms of patients. The current biomarkers for diagnosis of UC and prognostic prediction of anti-TNF-α therapy are inaccurate. The present study aimed to perform an integrative analysis of gene expression profiles in patients with UC. A total of seven datasets from the GEO database that met our strict inclusion criteria were included. After identifying differentially expressed genes (DEGs) between UC patients and healthy individuals, the diagnostic and prognostic utility of the DEGs were then analyzed via least absolute shrinkage and selection operator and support-vector machine recursive feature elimination. Subgroup analyses of the treated and untreated groups, as well as the treatment-response group and non-response group, were also performed. Furthermore, the relationship between the expressions of UC-related genes and infiltration of immune cells in the course of treatment was also investigated. Immunohistochemical (IHC) assay was used to verify the gene expression in inflamed UC tissues. When considering all the applied methods, DUOX2, PI3, S100P, MMP7, and S100A8 had priority to be defined as the characteristic genes among DEGs. The area under curve (AUC) of the five genes, which were all consistently over-expressed, based on an external validation dataset, were all above 0.94 for UC diagnosis. Four of the five genes (DUOX2, PI3, MMP7, and S100A8) were down-regulated between treatment-responsive and nonresponsive patients. A significant difference was also observed concerning the infiltration of immune cells, including macrophage and neutrophil, between the two groups (treatment responsive and nonresponsive). The changes in the expression of DUOX2 and MMP7 based on the IHC assay were highly consistent with the results obtained in the current study. This confirmed the mild to moderate diagnostic and predictive value of DUOX2 and MMP7 in patients with UC. The conducted analyses showed that the expression profile of the five identified biomarkers accurately detects UC, whereas four of the five genes evidently predicted the response to anti-TNF-α therapy.

Systemic analysis of the prognostic significance and interaction network of miR-26b-3p in cholangiocarcinoma

MicroRNAs (miRNAs) reportedly play significant roles in the progression of various cancers and hold huge potential as both diagnostic tools and therapeutic targets. Given the ongoing uncertainty surrounding the precise functions of several miRNAs in cholangiocarcinoma (CCA), this research undertakes a comprehensive analysis of CCA data sourced from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The present study identified a novel miRNA, specifically miR-26b-3p, which exhibited prognostic value for individuals with CCA. Notably, miR-26b-3p was upregulated within CCA samples, with an inverse correlation established with patient prognosis (Hazard Ratio = 8.19, p = 0.018). Through a combination of functional enrichment analysis, analysis of the LncRNA-miR-26b-3p-mRNA interaction network, and validation by qRT PCR and western blotting, this study uncovered the potential of miR-26b-3p in potentiating the malignant progression of CCA via regulation of essential genes (including PSMD14, XAB2, SLC4A4) implicated in processes such as endoplasmic reticulum (ER) stress and responses to misfolded proteins. Our findings introduce novel and valuable insights that position miR-26b-3p-associated genes as promising biomarkers for the diagnosis and treatment of CCA.

CDKL3 is a promising biomarker for diagnosis and prognosis prediction in patients with hepatocellular carcinoma

Cyclin-dependent kinase-like 3 (CDKL3) has been identified as an oncogene in certain types of tumors. Nonetheless, its function in hepatocellular carcinoma (HCC) is poorly understood. In this study, we conducted a comprehensive analysis of CDKL3 based on data from the HCC cohort of The Cancer Genome Atlas (TCGA). Our analysis included gene expression, diagnosis, prognosis, functional enrichment, tumor microenvironment and metabolic characteristics, tumor burden, mRNA expression-based stemness, alternative splicing, and prediction of therapy response. Additionally, we performed a cell counting kit-8 assay, TdT-mediated dUTP nick-end Labeling staining, migration assay, wound healing assay, colony formation assay, and nude mouse experiments to confirm the functional relevance of CDKL3 in HCC. Our findings showed that CDKL3 was significantly upregulated in HCC patients compared to controls. Various bioinformatic analyses suggested that CDKL3 could serve as a potential marker for HCC diagnosis and prognosis. Furthermore, CDKL3 was found to be involved in various mechanisms linked to the development of HCC, including copy number variation, tumor burden, genomic heterogeneity, cancer stemness, and alternative splicing of CDKL3. Notably, CDKL3 was also closely correlated with tumor immune cell infiltration and the expression of immune checkpoint markers. Additionally, CDKL3 was shown to independently function as a risk predictor for overall survival in HCC patients by multivariate Cox regression analysis. Furthermore, the knockdown of CDKL3 significantly inhibited cell proliferation in vitro and in vivo, indicating its role as an oncogene in HCC. Taken together, our findings suggest that CDKL3 shows promise as a biomarker for the detection and treatment outcome prediction of HCC patients.

Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer

BACKGROUND

Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models for these reoperations lack precision in integrating complex clinical data.

AIM

To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients.

METHODS

Data of patients treated for colorectal cancer (n = 2044) at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected. Patients were divided into an experimental group (n = 60) and a control group (n = 1984) according to unplanned reoperation occurrence. Patients were also divided into a training group and a validation group (7:3 ratio). We used three different machine learning methods to screen characteristic variables. A nomogram was created based on multifactor logistic regression, and the model performance was assessed using receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test, and decision curve analysis. The risk scores of the two groups were calculated and compared to validate the model.

RESULTS

More patients in the experimental group were ≥ 60 years old, male, and had a history of hypertension, laparotomy, and hypoproteinemia, compared to the control group. Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation (P < 0.05): Prognostic Nutritional Index value, history of laparotomy, hypertension, or stroke, hypoproteinemia, age, tumor-node-metastasis staging, surgical time, gender, and American Society of Anesthesiologists classification. Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility.

CONCLUSION

This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer, which can improve treatment decisions and prognosis.

Histamine-related genes participate in the establishment of an immunosuppressive microenvironment and impact the immunotherapy response in hepatocellular carcinoma

Chronic inflammation is pivotal in the pathogenesis of hepatocellular carcinoma (HCC). Histamine is a biologically active substance that amplifies the inflammatory and immune response and serves as a neurotransmitter. However, knowledge of histamine's role in HCC and its effects on immunotherapy remains lacking. We focused on histamine-related genes to investigate their potential role in HCC. The RNA-seq data and clinical information regarding HCC were obtained from The Cancer Genome Atlas (TCGA). After identifying the differentially expressed genes, we constructed a signature using the univariate Cox proportional hazard regression and least absolute shrinkage and selection operator (LASSO) analyses. The signature's predictive performance was evaluated using a receiver operating characteristic curve (ROC) analysis. Furthermore, drug sensitivity, immunotherapy effects, and enrichment analyses were conducted. Histamine-related gene expression in HCC was confirmed using quantitative real-time polymerase chain reaction (qRT-PCR). A histamine-related gene prognostic signature (HRGPS) was developed in TCGA. Time-dependent ROC and Kaplan-Meier survival analyses demonstrated the signature's strong predictive power. Importantly, patients in high-risk groups exhibited a higher frequency of TP53 mutations, elevated immune checkpoint-related gene expression, and increased infiltration of immunosuppressive cells-indicating a potentially favorable response to immunotherapy. In addition, drug sensitivity analysis revealed that the signature could effectively predict chemotherapy efficacy and sensitivity. qRT-PCR results validated histamine-related gene overexpression in HCC. Our findings demonstrate that inhibiting histamine-related genes and signaling pathways can impact the therapeutic effect of anti-PD-1/PD-L1. The precise predictive ability of our signature in determining the response to different therapeutic options highlights its potential clinical significance.

Identification and validation of immune-related gene signature models for predicting prognosis and immunotherapy response in hepatocellular carcinoma

Background

This study seeks to enhance the accuracy and efficiency of clinical diagnosis and therapeutic decision-making in hepatocellular carcinoma (HCC), as well as to optimize the assessment of immunotherapy response.

Methods

A training set comprising 305 HCC cases was obtained from The Cancer Genome Atlas (TCGA) database. Initially, a screening process was undertaken to identify prognostically significant immune-related genes (IRGs), followed by the application of logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods for gene modeling. Subsequently, the final model was constructed using support vector machines-recursive feature elimination (SVM-RFE). Following model evaluation, quantitative polymerase chain reaction (qPCR) was employed to examine the gene expression profiles in tissue samples obtained from our cohort of 54 patients with HCC and an independent cohort of 231 patients, and the prognostic relevance of the model was substantiated. Thereafter, the association of the model with the immune responses was examined, and its predictive value regarding the efficacy of immunotherapy was corroborated through studies involving three cohorts undergoing immunotherapy. Finally, the study uncovered the potential mechanism by which the model contributed to prognosticating HCC outcomes and assessing immunotherapy effectiveness.

Results

SVM-RFE modeling was applied to develop an OS prognostic model based on six IRGs (CMTM7, HDAC1, HRAS, PSMD1, RAET1E, and TXLNA). The performance of the model was assessed by AUC values on the ROC curves, resulting in values of 0.83, 0.73, and 0.75 for the predictions at 1, 3, and 5 years, respectively. A marked difference in OS outcomes was noted when comparing the high-risk group (HRG) with the low-risk group (LRG), as demonstrated in both the initial training set (P <0.0001) and the subsequent validation cohort (P <0.0001). Additionally, the SVMRS in the HRG demonstrated a notable positive correlation with key immune checkpoint genes (CTLA-4, PD-1, and PD-L1). The results obtained from the examination of three cohorts undergoing immunotherapy affirmed the potential capability of this model in predicting immunotherapy effectiveness.

Conclusions

The HCC predictive model developed in this study, comprising six genes, demonstrates a robust capability to predict the OS of patients with HCC and immunotherapy effectiveness in tumor management.

Bioinformatics Analysis Reveals HIST1H2BH as a Novel Diagnostic Biomarker for Atrial Fibrillation-Related Cardiogenic Thromboembolic Stroke

Atrial fibrillation (AF) is a significant precursor to cerebral embolism. Our study sought to unearth new diagnostic biomarkers for atrial fibrillation-related cerebral embolism (AF-CE) by meticulously examining multiple GEO datasets and meta-analysis. The gene expression omnibus (GEO) database provided RNA sequencing data associated with AF and stroke. We began by pinpointing genes with varied expressions in AF-CE patient blood samples. A meta-analysis was subsequently undertaken using several RNA sequencing datasets to verify these genes. LASSO regression discerned key genes for AF-CE, with their diagnostic prowess verified through ROC curve examination. Active signaling pathways within stroke patients were discerned via GO and KEGG enrichment, with PPI interactions detailing gene interplay. Differential gene analysis revealed an upregulation of sixteen genes and a downregulation of four in stroke patient blood samples. Eight genes showcased varied expression in the meta-analysis. LASSO regression zeroed in on five of these, culminating in HIST1H2BH's identification as a characteristic gene. HIST1H2BH's prowess in predicting AF-CE was confirmed through ROC. Integrin signaling, platelet activation, ECM interactions, and the PI3K-Akt pathway were found active in stroke victims. HIST1H2BH's interaction with the notably upregulated ITGA2B was spotlighted by PPI. Additionally, HIST1H2BH exhibited links with NK cells and eosinophils. HIST1H2BH emerges as an insightful diagnostic beacon for AF-CE. Its presence, post AF, potentially modulates pathways, accentuating platelet activation and consequent thrombus generation, leading to cerebral embolism.

Letter: Presence of progression or absence of response? Alternative trial designs for immunotherapy of advanced hepatocellular carcinoma. Authors' reply

This article is linked to Jost‐Brinkmann et al papers. To view these articles, visit https://doi.org/10.1111/apt.17441 and https://doi.org/10.1111/apt.17985

Novel candidate genes for environmental stresses response in Synechocystis sp. PCC 6803 revealed by machine learning algorithms

Cyanobacteria have developed acclimation strategies to adapt to harsh environments, making them a model organism. Understanding the molecular mechanisms of tolerance to abiotic stresses can help elucidate how cells change their gene expression patterns in response to stress. Recent advances in sequencing techniques and bioinformatics analysis methods have led to the discovery of many genes involved in stress response in organisms. The Synechocystis sp. PCC 6803 is a suitable microorganism for studying transcriptome response under environmental stress. Therefore, for the first time, we employed two effective feature selection techniques namely and support vector machine recursive feature elimination (SVM-RFE) and LASSO (Least Absolute Shrinkage Selector Operator) to pinpoint the crucial genes responsive to environmental stresses in Synechocystis sp. PCC 6803. We applied these algorithms of machine learning to analyze the transcriptomic data of Synechocystis sp. PCC 6803 under distinct conditions, encompassing light, salt and iron stress conditions. Seven candidate genes namely sll1862, slr0650, sll0760, slr0091, ssl3044, slr1285, and slr1687 were selected by both LASSO and SVM-RFE algorithms. RNA-seq analysis was performed to validate the efficiency of our feature selection approach in selecting the most important genes. The RNA-seq analysis revealed significantly high expression for five genes namely sll1862, slr1687, ssl3044, slr1285, and slr0650 under ion stress condition. Among these five genes, ssl3044 and slr0650 could be introduced as new potential candidate genes for further confirmatory genetic studies, to determine their roles in their response to abiotic stresses.

Cellular senescence gene TACC3 associated with colorectal cancer risk via genetic and DNA methylated alteration

Cell senescence genes play a vital role in the pathogenesis of colorectal cancer, a process that may involve the triggering of genetic variations and reversible phenotypes caused by epigenetic modifications. However, the specific regulatory mechanisms remain unclear. Using CellAge and The Cancer Genome Atlas databases and in-house RNA-seq data, DNA methylation-modified cellular senescence genes (DMCSGs) were validated by Support Vector Machine and correlation analyses. In 1150 cases and 1342 controls, we identified colorectal cancer risk variants in DMCSGs. The regulatory effects of gene, variant, and DNA methylation were explored through dual-luciferase and 5-azacytidine treatment experiments, complemented by multiple database analyses. Biological functions of key gene were evaluated via cell proliferation assays, SA-β-gal staining, senescence marker detection, and immune infiltration analyses. The genetic variant rs4558926 in the downstream of TACC3 was significantly associated with colorectal cancer risk (OR = 1.35, P = 3.22 × 10-4). TACC3 mRNA expression increased due to rs4558926 C > G and decreased DNA methylation levels. The CpG sites in the TACC3 promoter region were regulated by rs4558926. TACC3 knockdown decreased proliferation and senescence in colorectal cancer cells. In addition, subjects with high-TACC3 expression presented an immunosuppressive microenvironment. These findings provide insights into the involvement of genetic variants of cellular senescence genes in the development and progression of colorectal cancer.

Application of artificial intelligence in the prediction of immunotherapy efficacy in hepatocellular carcinoma: Current status and prospects

Artificial Intelligence (AI) has increased as a potent tool in medicine, with promising oncology applications. The emergence of immunotherapy has transformed the treatment terrain for hepatocellular carcinoma (HCC), offering new hope to patients with this challenging malignancy. This article examines the role and future of AI in forecasting the effectiveness of immunotherapy in HCC. We highlight the potential of AI to revolutionize the prediction of therapy response, thus improving patient selection and clinical outcomes. The article further outlines the challenges and future research directions in this emerging field.

Up-regulation of RAN by MYBL2 maintains osteosarcoma cancer stem-like cells population during heterogeneous tumor generation

Intratumor heterogeneity is one of the major features of cancers, leading to aggressive disease and treatment failure. Cancer stem-like cells (CSCs) are believed to give rise to the heterogeneous cell types within tumors. Hence, understanding the regulatory mechanism underlying the recurrence process of heterogeneous tumor by CSCs could facilitate the development of CSC-targeted therapies. Here, utilizing single-cell transcriptomics, we present the molecular profile of osteosarcoma CSCs-derived heterogeneous tumors consisting of CSC clusters, osteoprogenitor and differentiated cell types, such as pre-osteoblasts, osteoblasts and chondroblasts. Furthermore, by constructing the comprehensive map of modulated genes during CSCs self-renewal and differentiation, we identify RAN exhibiting specific peak expression in osteosarcoma CSCs clusters which is transcriptionally up-regulated by MYBL2. Functionality, MYBL2-RAN pathway promotes the CSCs self-renewal by enhancing the nuclear accumulation of MYC protein, which in turn boosts the overexpression of RAN as a positive feedback. Importantly, blockage of MYBL2-RAN pathway sensitizes CSCs to cisplatin treatment and synergistically enhanced the cisplatin-induced cytotoxicity. Both MYBL2 and RAN are highly expressed in clinical osteosarcoma tissues which indicate poor prognosis. Collectively, our study provides advanced insights into the regeneration process of heterogeneous tumor originating from CSCs and highlights the MYBL2-RAN pathway as a promising target for CSC-based therapy in osteosarcoma.

Creation of a machine learning-based prognostic prediction model for various subtypes of laryngeal cancer

Depending on the source of the blastophore, there are various subtypes of laryngeal cancer, each with a unique metastatic risk and prognosis. The forecasting of their prognosis is a pressing issue that needs to be resolved. This study comprised 5953 patients with glottic carcinoma and 4465 individuals with non-glottic type (supraglottic and subglottic). Five clinicopathological characteristics of glottic and non-glottic carcinoma were screened using univariate and multivariate regression for CoxPH (Cox proportional hazards); for other models, 10 (glottic) and 11 (non-glottic) clinicopathological characteristics were selected using least absolute shrinkage and selection operator (LASSO) regression analysis, respectively; the corresponding survival models were established; and the best model was evaluated. We discovered that RSF (Random survival forest) was a superior model for both glottic and non-glottic carcinoma, with a projected concordance index (C-index) of 0.687 for glottic and 0.657 for non-glottic, respectively. The integrated Brier score (IBS) of their 1-year, 3-year, and 5-year time points is, respectively, 0.116, 0.182, 0.195 (glottic), and 0.130, 0.215, 0.220 (non-glottic), demonstrating the model's effective correction. We represented significant variables in a Shapley Additive Explanations (SHAP) plot. The two models are then combined to predict the prognosis for two distinct individuals, which has some effectiveness in predicting prognosis. For our investigation, we established separate models for glottic carcinoma and non-glottic carcinoma that were most effective at predicting survival. RSF is used to evaluate both glottic and non-glottic cancer, and it has a considerable impact on patient prognosis and risk factor prediction.

Fine particulate matter (PM2.5) induces the stem cell-like properties of hepatocellular carcinoma by activating ROS/Nrf2/Keap1-mediated autophagy

Exposure to fine particulate matter (PM2.5) has been linked to an increased incidence and mortality of hepatocellular carcinoma (HCC). However, the impact of PM2.5 exposure on HCC progression and the underlying mechanisms remain largely unknown. This study aimed to investigate the effects of PM2.5 exposure on the stem cell-like properties of HCC cells. Our findings indicate that PM2.5 exposure significantly enhances the stemness of HCC cells (p < 0.01). Subsequently, male nude mice were divided into two groups (n = 8/group for tumor-bearing assay, n = 5/group for metastasis assay) for control and PM2.5 exposure. In vivo assays revealed that exposure to PM2.5 promoted the growth, metastasis, and epithelial-mesenchymal transition (EMT) of HCC cells (p < 0.01). Further exploration demonstrated that PM2.5 enhances the stemness of HCC cells by inducing cellular reactive oxygen species (ROS) generation (p < 0.05). Mechanistic investigation indicated that elevated intracellular ROS inhibited kelch-like ECH-associated protein 1 (Keap1) levels, promoting the upregulation and nucleus translocation of NFE2-like bZIP transcription factor 2 (Nrf2). This, in turn, induced autophagy activation, thereby promoting the stemness of HCC cells (p < 0.01). Our present study demonstrates the adverse effects of PM2.5 exposure on HCC development and highlights the mechanism of ROS/Nrf2/Keap1-mediated autophagy. For the first time, we reveal the impact of PM2.5 exposure on the poor prognosis-associated cellular phenotype of HCC and its underlying mechanism, which is expected to provide new theoretical basis for the improvement of public health.

Long non-coding RNAFOXD1-AS1 modulated CTCs epithelial-mesenchymal transition and immune escape in hepatocellular carcinoma in vitro by sponging miR-615-3p

BACKGROUND

Hepatocellular carcinoma (HCC) is widely recognized as a globally prevalent malignancy. Immunotherapy is a promising therapy for HCC patients. Increasing evidence suggests that lncRNAs are involved in HCC progression and immunotherapy.

AIM

The study reveals the mechanistic role of long non-coding RNA (lncRNA) FOXD1-AS1 in regulating migration, invasion, circulating tumor cells (CTCs), epithelial-mesenchymal transition (EMT), and immune escape in HCC in vitro.

METHODS

This study employed real-time PCR (RT-qPCR) to measure FOXD1-AS1, miR-615-3p, and programmed death-ligand 1 (PD-L1). The interactions of FOXD1-AS1, miR-615-3p, and PD-L1 were validated via dual-luciferase reporter gene and ribonucleoprotein immunoprecipitation (RIP) assay. In vivo experimentation involves BALB/c mice and BALB/c nude mice to investigate the impact of HCC metastasis.

RESULTS

The upregulation of lncRNA FOXD1-AS1 in malignant tissues significantly correlates with poor prognosis. The investigation was implemented on the impact of lncRNA FOXD1-AS1 on the migratory, invasive, and EMT of HCC cells. It has been observed that the lncRNA FOXD1-AS1 significantly influences the generation and metastasis of MCTC in vivo analysis. In mechanistic analysis, lncRNA FOXD1-AS1 enhanced immune escape in HCC via upregulation of PD-L1, which acted as a ceRNA by sequestering miR-615-3p. Additionally, lncRNA FOXD1-AS1 was found to modulate the EMT of CTCs through the activation of the PI3K/AKT pathway.

CONCLUSION

This study presents compelling evidence supporting the role of lncRNA FOXD1-AS1 as a miRNA sponge that sequesters miR-655-3p and protects PD-L1 from suppression.

Identification and validation of CCR5 linking keloid with atopic dermatitis through comprehensive bioinformatics analysis and machine learning

There is sufficient evidence indicating that keloid is strongly associated with atopic dermatitis (AD) across ethnic groups. However, the molecular mechanism underlying the association is not fully understood. The aim of this study is to discover the underlying mechanism of the association between keloid and AD by integrating comprehensive bioinformatics techniques and machine learning methods. The gene expression profiles of keloid and AD were downloaded from the Gene Expression Omnibus (GEO) database. A total of 449 differentially expressed genes (DEGs) were found to be shared in keloid and AD using the training datasets of GEO (GSE158395 and GSE121212). The hub genes were identified using the protein-protein interaction network and Cytoscape software. 20 of the most significant hub genes were selected, which were mainly involved in the regulation of the inflammatory and immune response. Through two machine learning algorithms of LASSO and SVM-RFE, CCR5 was identified as the most important key gene. Subsequently, upregulated CCR5 gene expression was confirmed in validation GEO datasets (GSE188952 and GSE32924) and clinical samples of keloid and AD. Immune infiltration analysis showed that T helper (Th) 1, 2 and 17 cells were significantly enriched in the microenvironment of both keloid and AD. Positive correlations were found between CCR5 and Th1, Th2 and Th17 cells. Finally, two TFs of CCR5, NR3C2 and YY1, were identified, both of which were downregulated in keloid and AD tissues. Our study firstly reveals that keloid and AD shared common inflammatory and immune pathways. Moreover, CCR5 plays a key role in the pathogenesis association between keloid and AD. The common pathways and key genes may shed light on further mechanism research and targeted therapy, and may provide therapeutic interventions of keloid with AD.

Signature construction and molecular subtype identification based on liver-specific genes for prediction of prognosis, immune activity, and anti-cancer drug sensitivity in hepatocellular carcinoma

BACKGROUND

Liver specific genes (LSGs) are crucial for hepatocyte differentiation and maintaining normal liver function. A deep understanding of LSGs and their heterogeneity in hepatocellular carcinoma (HCC) is necessary to provide clues for HCC diagnosis, prognosis, and treatment.

METHODS

The bulk and single-cell RNA-seq data of HCC were downloaded from TCGA, ICGC, and GEO databases. Through unsupervised cluster analysis, LSGs-based HCC subtypes were identified in TCGA-HCC samples. The prognostic effects of the subtypes were investigated with survival analyses. With GSVA and Wilcoxon test, the LSGs score, stemness score, aging score, immune score and stromal score of the samples were estimated and compared. The HCC subtype-specific genes were identified. The subtypes and their differences were validated in ICGC-HCC samples. LASSO regression analysis was used for key gene selection and risk model construction for HCC overall survival. The model performance was estimated and validated. The key genes were validated for their heterogeneities in HCC cell lines with quantitative real-time PCR and at single-cell level. Their dysregulations were investigated at protein level. Their correlations with HCC response to anti-cancer drugs were estimated in HCC cell lines.

RESULTS

We identified three LSGs-based HCC subtypes with different prognosis, tumor stemness, and aging level. The C1 subtype with low LSGs score and high immune score presented a poor survival, while the C2 subtype with high LSGs score and immune score indicated an enduring survival. Although no significant survival difference between C2 and C3 HCCs was shown, the C2 HCCs presented higher immune score and stroma score. The HCC subtypes and their differences were confirmed in ICGC-HCC dataset. A five-gene prognostic signature for HCC survival was constructed. Its good performance was shown in both the training and validation datasets. The five genes presented significant heterogeneities in different HCC cell lines and hepatocyte subclusters. Their dysregulations were confirmed at protein level. Furthermore, their significant associations with HCC sensitivities to anti-cancer drugs were shown.

CONCLUSIONS

LSGs-based HCC subtype classification and the five-gene risk model might provide useful clues not only for HCC stratification and risk prediction, but also for the development of more personalized therapies for effective HCC treatment.

Prognostic Factors for Mortality in Hepatocellular Carcinoma at Diagnosis: Development of a Predictive Model Using Artificial Intelligence

BACKGROUND

Hepatocellular carcinoma (HCC) accounts for 75% of primary liver tumors. Controlling risk factors associated with its development and implementing screenings in risk populations does not seem sufficient to improve the prognosis of these patients at diagnosis. The development of a predictive prognostic model for mortality at the diagnosis of HCC is proposed.

METHODS

In this retrospective multicenter study, the analysis of data from 191 HCC patients was conducted using machine learning (ML) techniques to analyze the prognostic factors of mortality that are significant at the time of diagnosis. Clinical and analytical data of interest in patients with HCC were gathered.

RESULTS

Meeting Milan criteria, Barcelona Clinic Liver Cancer (BCLC) classification and albumin levels were the variables with the greatest impact on the prognosis of HCC patients. The ML algorithm that achieved the best results was random forest (RF).

CONCLUSIONS

The development of a predictive prognostic model at the diagnosis is a valuable tool for patients with HCC and for application in clinical practice. RF is useful and reliable in the analysis of prognostic factors in the diagnosis of HCC. The search for new prognostic factors is still necessary in patients with HCC.

Diagnostic potential of NRG1 in benign nerve sheath tumors and its influence on the PI3K-Akt signaling and tumor immunity

OBJECTIVE

Benign nerve sheath tumors (BNSTs) present diagnostic challenges due to their heterogeneous nature. This study aimed to determine the significance of NRG1 as a novel diagnostic biomarker in BNST, emphasizing its involvement in the PI3K-Akt pathway and tumor immune regulation.

METHODS

Differential genes related to BNST were identified from the GEO database. Gene co-expression networks, protein-protein interaction networks, and LASSO regression were utilized to pinpoint key genes. The CIBERSORT algorithm assessed immune cell infiltration differences, and functional enrichment analyses explored BNST signaling pathways. Clinical samples helped establish PDX models, and in vitro cell lines to validate NRG1's role via the PI3K-Akt pathway.

RESULTS

Nine hundred eighty-two genes were upregulated, and 375 downregulated in BNST samples. WGCNA revealed the brown module with the most significant difference. Top hub genes included NRG1, which was also determined as a pivotal gene in disease characterization. Immune infiltration showed significant variances in neutrophils and M2 macrophages, with NRG1 playing a central role. Functional analyses confirmed NRG1's involvement in key pathways. Validation experiments using PDX models and cell lines further solidified NRG1's role in BNST.

CONCLUSION

NRG1 emerges as a potential diagnostic biomarker for BNST, influencing the PI3K-Akt pathway, and shaping the tumor immune microenvironment.

Integrated bioinformatics analysis of SEMA3C in tongue squamous cell carcinoma using machine-learning strategies

Tongue squamous cell carcinoma (TSCC) is an aggressive oral cancer with a high incidence of metastasis and poor prognosis. We aim to identify and verify potential biomarkers for TSCC using bioinformatics analysis. To begin with, we examined clinical and RNA expression information of individuals with TSCC from the Gene Expression Omnibus (GEO) database. Differential expression analysis and functional analysis were conducted. Multiple machine-learning strategies were next employed to screen and determine the hub gene, and receiver operating characteristic (ROC) analysis was used to assess diagnostic value. Semaphorin3C (SEMA3C) was identified as a critical biomarker, presenting high diagnostic accuracy for TSCC. In the validation cohorts, SEMA3C exhibited high expression levels in TSCC. The high expression of SEMA3C was a poor prognostic factor in TSCC by the Kaplan-Meier curve. Based on the Gene Ontology (GO) analysis, SEMA3C was mapped in terms related to cell adhesion, positive regulation of JAK-STAT, positive regulation of stem cell maintenance, and positive regulation of NF-κB activity. Single-cell RNA sequencing (ScRNA-seq) analysis showed cells expressing SEMA3C were predominantly tumor cells. Then, we further verified that SEMA3C had high expression in TSCC clinical samples. In addition, the knockdown of SEMA3C suppressed the proliferation, migration, and invasion of TSCC cells in vitro. This study is the first to report the involvement of SEMA3C in TSCC, suggesting that upregulated SEMA3C could be a novel and critical potential biomarker for future predictive diagnostics, prevention, prognostic assessment, and personalized medical services in TSCC.

Development and validation of a novel stemness-related prognostic model for neuroblastoma using integrated machine learning and bioinformatics analyses

Background

Neuroblastoma (NB) is a common solid tumor in children, with a dismal prognosis in high-risk cases. Despite advancements in NB treatment, the clinical need for precise prognostic models remains critical, particularly to address the heterogeneity of cancer stemness which plays a pivotal role in tumor aggressiveness and patient outcomes. By utilizing machine learning (ML) techniques, we aimed to explore the cancer stemness features in NB and identify stemness-related hub genes for future investigation and potential targeted therapy.

Methods

The public dataset GSE49710 was employed as the training set for acquire gene expression data and NB sample information, including age, stage, and MYCN amplification status and survival. The messenger RNA (mRNA) expression-based stemness index (mRNAsi) was calculated and patients were grouped according to their mRNAsi value. Stemness-related hub genes were identified from the differentially expressed genes (DEGs) to construct a gene signature. This was followed by evaluating the relationship between cancer stemness and the NB immune microenvironment, and the development of a predictive nomogram. We assessed the prognostic outcomes including overall survival (OS) and event-free survival, employing machine learning methods to measure predictive accuracy through concordance indices and validation in an independent cohort E-MTAB-8248.

Results

Based on mRNAsi, we categorized NB patients into two groups to explore the association between varying levels of stemness and their clinical outcomes. High mRNAsi was linked to the advanced International Neuroblastoma Staging System (INSS) stage, amplified MYCN, and elder age. High mRNAsi patients had a significantly poorer prognosis than low mRNAsi cases. According to the multivariate Cox analysis, the mRNAsi was an independent risk factor of prognosis in NB patients. After least absolute shrinkage and selection operator (LASSO) regression analysis, four key genes (ERCC6L, DUXAP10, NCAN, DIRAS3) most related to mRNAsi scores were discovered and a risk model was built. Our model demonstrated a significant prognostic capacity with hazard ratios (HR) ranging from 18.96 to 41.20, P values below 0.0001, and area under the receiver operating characteristic curve (AUC) values of 0.918 in the training set, suggesting high predictive accuracy which was further confirmed by external verification. Individuals with a low four-gene signature score had a favorable outcome and better immune responses. Finally, a nomogram for clinical practice was constructed by integrating the four-gene signature and INSS stage.

Conclusions

Our findings confirm the influence of CSC features in NB prognosis. The newly developed NB stemness-related four-gene signature prognostic signature could facilitate the prognostic prediction, and the identified hub genes may serve as promising targets for individualized treatments.

Application of data science and bioinformatics in RNA therapeutics

Nowadays, information technology (IT) has been holding a significant role in daily life worldwide. The trajectory of data science and bioinformatics promises pioneering personalized therapies, reshaping medical landscapes and patient care. For RNA therapy to reach more patients, a comprehensive understanding of the application of data science and bioinformatics to this therapy is essential. Thus, this chapter has summarized the application of data science and bioinformatics in RNA therapeutics. Data science applications in RNA therapy, such as data integration and analytics, machine learning, and drug development, have been discussed. In addition, aspects of bioinformatics such as RNA design and evaluation, drug delivery system simulation, and databases for personalized medicine have also been covered in this chapter. These insights have shed light on existing evidence and opened potential future directions. From there, scientists can elevate RNA-based therapeutics into an era of tailored treatments and revolutionary healthcare.