Bioinformatics profiling identifies seven immune-related risk signatures for hepatocellular carcinoma

Similarity of immune-associated markers in COVID-19 and Kawasaki disease: analyses from bioinformatics and machine learning

BACKGROUND

Infection by the SARS-CoV-2 virus can cause coronavirus disease 2019 (COVID-19) and can also exacerbate the symptoms of Kawasaki disease (KD), an acute vasculitis that mostly affects children. This study used bioinformatics and machine learning to examine similarities in the molecular pathogenesis of COVID-19 and KD.

METHODS

We first identified disease-associated modules in KD using weighted gene co-expression network analysis. Then, we determined shared differentially expressed genes (DEGs) in training datasets for KD (GSE100154) and COVID-19 (GSE225220), performed functional annotation of these shared DEGs, and used Cytoscape plug-ins (MCODE and Cytohubba) to characterize the protein-protein interaction (PPI) network and identify the hub genes. We performed Least Absolute Shrinkage and Selection Operator(LASSO) regression and receiver operating characteristic (ROC) curve analysis to identify the most robust markers, validated these results by analysis of two other datasets (GSE73461 and GSE18606), and then calculated the correlations of these key genes with immune cells.

RESULTS

This analysis identified 26 shared DEGs in COVID-19 and KD. The results from functional annotation showed that the shared DEGs primarily functioned in immune responses, the formation of neutrophil extracellular traps, and NOD-like receptor signaling pathways. There were three key genes (PGLYRP1, DEFA4, RETN), and they had positive correlations with monocytes, M0 macrophages, and dendritic cells, which function as immune infiltrating cells in KD.

CONCLUSION

The potential immune-associated biomarkers (PGLYRP1, DEFA4, RETN) along with their shared pathways, hold promise for advancing investigations into the underlying pathogenesis of KD and COVID-19.

Comparative transcriptomic analysis uncovers molecular heterogeneity in hepatobiliary cancers

Hepatobiliary cancers (HBCs) pose a major global health challenge, with a lack of effective targeted biomarkers. Due to their complex anatomical locations, shared risk factors, and the limitations of targeted therapies, generalized treatment strategies are often used for gallbladder cancer (GBC), hepatocellular carcinoma (HCC), and intrahepatic cholangiocarcinoma (ICC). This study aimed to identify specific transcriptomic signatures in GBC, HCC, and ICC. The transcriptomic data analysis revealed distinct expression profiles, highlighting complex molecular heterogeneity within these cancers, even within the same organ system. Functional annotation revealed distinct biological pathways associated with each type of HBCs. GBC was linked to cell cycle regulation, HCC was associated with immune system modulation, and ICC was involved in metabolic dysregulation, particularly lipid metabolism. Gene co-expression network (GCN) and protein-protein interaction (PPI) network analyses identified potential key genes, such as MAPK3 and ERBB2 in GBC, AC069287.1 and ACTN2 in HCC, and TRPC1 and BACE1 in ICC. The FOX family of transcription factors (TFs) was conserved across all three cancer types. To further explore the relationship between Epithelial-Mesenchymal Transition (EMT) and the identified hub genes and TFs, an EMT score analysis was conducted. This analysis revealed distinct phenotypic characteristics in each cancer type, with TFs identified in GBC and ICC showing a stronger correlation with EMT compared to those in HCC. External validation using The Cancer Genome Atlas (TCGA) databases confirmed the expression of candidate genes, underscoring their potential as therapeutic targets. These findings provide valuable insights into the molecular heterogeneity and complexity of HBCs, opening new avenues for personalized therapeutic interventions.

MRI T2WI-based radiomics combined with KRAS gene mutation constructed models for predicting liver metastasis in rectal cancer

BACKGROUND

The study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various prediction models to aid clinicians in early diagnosis and precise decision-making.

METHODS

A retrospective analysis was conducted on 193 patients diagnosed with rectal adenocarcinoma were randomly divided into training set (n = 136) and validation set (n = 57) at a ratio of 7:3. The predictive performance of three models was internally validated by 10-fold cross-validation in the training set. Delineation of the tumor region of interest (ROI) was performed, followed by the extraction of radiomics features from the ROI. The least absolute shrinkage and selection operator (LASSO) regression algorithm and multivariate Cox analysis were employed to reduce the dimensionality of radiomics features and identify significant features. Logistic regression was employed to construct three prediction models: clinical, radiomics, and combined models (radiomics + clinical). The predictive performance of each model was assessed and compared.

RESULTS

KRAS mutation emerged as an independent predictor of liver metastasis, yielding an odds ratio (OR) of 8.296 (95%CI: 3.471-19.830; p < 0.001). 5 radiomics features will be used to construct radiomics model. The combined model was built by integrating radiomics model with clinical model. In both the training set (AUC:0.842, 95%CI: 0.778-0.907) and the validation set (AUC: 0.805; 95%CI: 0.692-0.918), the AUCs for the combined model surpassed those of the radiomics and clinical models.

CONCLUSIONS

Our study reveals that KRAS mutation stands as an independent predictor of RCLM. The radiomics features based on MR play a crucial role in the evaluation of RCLM. The combined model exhibits superior performance in the prediction of liver metastasis.

CLINICAL TRIAL NUMBER

Not applicable.

A Prognostic Model for Prostate Cancer Patients Based on Two DNA Damage Response Mutation-Related Immune Genes

Background: DNA damage response (DDR) mutation-related genes and composition of immune cells are core factors affecting the effectiveness of immune checkpoint inhibitor therapy. The aim of this study is to combine DDR with immune-related genes to screen the prognostic signature for prostate cancer (PCa). Methods: Gene expression profile and somatic mutation were downloaded from The Cancer Genome Atlas (TCGA). DDR-related genes were obtained from published study. After identification of prognostic-related DDR genes, samples were divided into mutation and nonmutation groups. Differentially expressed genes between these two groups were screened, followed by selection of immune-related DDR genes. Univariate and multivariate Cox analyses were performed to screen genes for constructing prognostic model. Nomogram model was also developed. The expression level of signature was detected by quantitative real-time PCR (qPCR). Results: Two genes (MYBBP1A and PCDHA9) were screened to construct the prognostic model, and it showed good risk prediction of PCa prognosis. Survival analysis showed that patients in high-risk group had worse overall survival than those in low-risk group. Cox analyses indicated that risk score could be used as an independent prognostic factor for PCa. qPCR results indicated that MYBBP1A was upregulated, whereas PCDHA9 was downregulated in PCa cell lines. Conclusions: A prognostic model based on DDR mutation-related genes for PCa was established, which serves as an effective tool for prognostic differentiation in patients with PCa.

Single-cell profiling reveals the metastasis-associated immune signature of hepatocellular carcinoma

AIM

Metastasis is the leading cause of mortality in hepatocellular carcinoma (HCC). The metastasis-associated immune signature in HCC is worth exploring.

METHODS

Bioinformatic analysis was conducted based on the single-cell transcriptome data derived from HCC patients in different stages. Cellular composition, pseudotime state transition, and cell-cell interaction were further analyzed and verified.

RESULTS

Generally, HCC with metastasis exhibited suppressive immune microenvironment, while HCC without metastasis exhibited active immune microenvironment. Concretely, effector regulatory T cells (eTregs) were found to be enriched in HCC with metastasis. PHLDA1 was identified as one of exhaustion-specific genes and verified to be associated with worse prognosis in HCC patients. Moreover, A novel cluster of CCR7+ dendritic cells (DCs) was identified with high expression of maturation and migration marker genes. Pseudotime analysis showed that inhibition of differentiation occurred in CCR7+ DCs rather than cDC1 in HCC with metastasis. Furthermore, interaction analysis showed that the reduction of CCR7+ DCs lead to impaired CCR7/CCL19 interaction in HCC with metastasis.

CONCLUSIONS

HCC with metastasis exhibited upregulation of exhaustion-specific genes of eTregs and inhibition of CCL signal of a novel DC cluster, which added new dimensions to the immune landscape and provided new immune therapeutic targets in advanced HCC.

Application of adoptive cell therapy in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains a global health challenge. Novel treatment modalities are urgently needed to extend the overall survival of patients. The liver plays an immunomodulatory function due to its unique physiological structural characteristics. Therefore, following surgical resection and radiotherapy, immunotherapy regimens have shown great potential in the treatment of hepatocellular carcinoma. Adoptive cell immunotherapy is rapidly developing in the treatment of hepatocellular carcinoma. In this review, we summarize the latest research on adoptive immunotherapy for hepatocellular carcinoma. The focus is on chimeric antigen receptor (CAR)-T cells and T cell receptor (TCR) engineered T cells. Then tumour-infiltrating lymphocytes (TILs), natural killer (NK) cells, cytokine-induced killer (CIK) cells, and macrophages are briefly discussed. The main overview of the application and challenges of adoptive immunotherapy in hepatocellular carcinoma. It aims to provide the reader with a comprehensive understanding of the current status of HCC adoptive immunotherapy and offers some strategies. We hope to provide new ideas for the clinical treatment of hepatocellular carcinoma.

A novel immune-related model to predict prognosis and responsiveness to checkpoint and angiogenesis blockade therapy in advanced renal cancer

Background

Immune checkpoint blockade (ICB) and anti-angiogenic drug combination has prolonged the survival of patients with advanced renal cell carcinoma (RCC). However, not all patients receive clinical benefits from this intervention. In this study, we aimed to establish a promising immune-related prognostic model to stratify the patients responding to ICB and anti-angiogenic drug combination and facilitate the development of personalized therapies for patients with RCC.

Materials and methods

Based on clinical annotations and RNA-sequencing (RNA-seq) data of 407 patients with advanced RCC from the IMmotion151 cohort, nine immune-associated differentially expressed genes (DEGs) between responders and non-responders to atezolizumab (anti-programmed death-ligand 1 antibody) plus bevacizumab (anti-vascular endothelial growth factor antibody) treatment were identified via weighted gene co-expression network analysis. We also conducted single-sample gene set enrichment analysis to develop a novel immune-related risk score (IRS) model and further estimate the prognosis of patients with RCC by predicting their sensitivity to chemotherapy and responsiveness to immunotherapy. IRS model was further validated using the JAVELIN Renal 101 cohort, the E-MTAB-3218 cohort, the IMvigor210 and GSE78220 cohort. Predictive significance of the IRS model for advanced RCC was assessed using receiver operating characteristic curves.

Results

The IRS model was constructed using nine immune-associated DEGs: SPINK5, SEMA3E, ROBO2, BMP5, ORM1, CRP, CTSE, PMCH and CCL3L1. Advanced RCC patients with high IRS had a high risk of undesirable clinical outcomes (hazard ratio = 1.91; 95% confidence interval = 1.43–2.55; P &lt; 0.0001). Transcriptome analysis revealed that the IRS-low group exhibited significantly high expression levels of CD8+ T effectors, antigen-processing machinery, and immune checkpoints, whereas the epithelial–mesenchymal transition pathway was enriched in the IRS-high group. IRS model effectively differentiated the responders from non-responders to ICB combined with angiogenesis blockade therapy or immunotherapy alone, with area under the curve values of 0.822 in the IMmotion151 cohort, 0.751 in the JAVELIN Renal 101 cohort, and 0.776 in the E-MTAB-3218 cohort.

Conclusion

IRS model is a reliable and robust immune signature that can be used for patient selection to optimize the efficacy of ICB plus anti-angiogenic drug therapies in patients with advanced RCC.

Selective profiling of liver-related specific proteins based on sofosbuvir-modified magnetic separation material

It has great significance in profiling specific proteins throughout for better understanding of complex pathological processes and in-depth pharmacological studies. In this work, an efficient protein profiling strategy was developed based on the specific protein-drug interaction. Sofosbuvir (SOF), as a first-line drug for the treatment of hepatitis C, was modified onto the surface of nanoparticles through stable chemical bonds to fabricate a novel magnetic separation material denoted as Fe₃O₄@SiO₂@PAA@SOF. With sequence coverage as the screening parameter, nine proteins were profiled from fetal bovine serum (FBS) of which eight were liver related. Similarly, the strategy was applied to hepatocellular carcinoma (HCC) patient serum. Eight proteins were profiled and all of them were liver related, demonstrating the superb specificity and selectivity of this strategy for profiling liver-related proteins by virtue of protein-SOF interaction. When serum proteins from HCC patients were compared to those from healthy people, one unique differential protein (D3DQX7) was profiled, which was liver related and was a potential target for ameliorating liver diseases. For further research, this material design concept and protein profiling strategy can be extended to employ other drugs for corresponding studies. Sofosbuvir, as a therapeutic drug for liver diseases, was modified onto the surface of magnetic nanoparticles to fabricate the specific selective separation material (Fe₃O₄@SiO₂@PAA@SOF). Based on protein-SOF interaction, the material was applied to adsorb specific proteins from different serum samples. After MS analysis, specific proteins, most of which were liver related, were successfully profiled from FBS and HCC patient serum, fully demonstrating the superb specificity and selectivity of this protein profiling strategy.

Identification of Key Prognostic Genes of Triple Negative Breast Cancer by LASSO-Based Machine Learning and Bioinformatics Analysis

Improved insight into the molecular mechanisms of triple negative breast cancer (TNBC) is required to predict prognosis and develop a new therapeutic strategy for targeted genes. The aim of this study is to identify key genes which may affect the prognosis of TNBC patients by bioinformatic analysis. In our study, the RNA sequencing (RNA-seq) expression data of 116 breast cancer lacking ER, PR, and HER2 expression and 113 normal tissues were downloaded from The Cancer Genome Atlas (TCGA). We screened out 147 differentially co-expressed genes in TNBC compared to non-cancerous tissue samples by using weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were constructed, revealing that 147 genes were mainly enriched in nuclear division, chromosomal region, ATPase activity, and cell cycle signaling. After using Cytoscape software for protein-protein interaction (PPI) network analysis and LASSO feature selection, a total of fifteen key genes were identified. Among them, BUB1 and CENPF were significantly correlated with the overall survival rate (OS) difference of TNBC patients (p value < 0.05). In addition, BUB1, CCNA2, and PACC1 showed significant poor disease-free survival (DFS) in TNBC patients (p value < 0.05), and may serve as candidate biomarkers in TNBC diagnosis. Thus, our results collectively suggest that BUB1, CCNA2, and PACC1 genes could play important roles in the progression of TNBC and provide attractive therapeutic targets.

Bioinformatics analysis and identification of hub genes and immune-related molecular mechanisms in chronic myeloid leukemia

BACKGROUND

Chronic myeloid leukemia (CML) is a malignant hyperplastic tumor of the bone marrow originating from pluripotent hematopoietic stem cells. The advent of tyrosine kinase inhibitors (TKIs) has greatly improved the survival rate of patients with CML. However, TKI-resistance leads to the disease recurrence and progression. This study aimed to identify immune-related genes (IRGs) associated with CML progression.

METHODS

We extracted the gene's expression profiles from the Gene Expression Omnibus (GEO). Bioinformatics analysis was used to determine the differentially expressed IRGs of CML and normal peripheral blood mononuclear cells (PBMCs). Functional enrichment and gene set enrichment analysis (GSEA) were used to explore its potential mechanism. Hub genes were identified using Molecular Complex Detection (MCODE) and the CytoHubba plugin. The hub genes' diagnostic value was evaluated using the receiver operating characteristic (ROC). The relative proportions of infiltrating immune cells in each CML sample were evaluated using CIBERSORT. Quantitative real-time PCR (RT-qPCR) was used to validate the hub gene expression in clinical samples.

RESULTS

A total of 31 differentially expressed IRGs were identified. GO analyses revealed that the modules were typically enriched in the receptor ligand activity, cytokine activity, and endopeptidase activity. KEGG enrichment analysis of IRGs revealed that CML involved Th17 cell differentiation, the NF-kappa B signaling pathway, and cytokine-cytokine receptor interaction. A total of 10 hub genes were selected using the PPI network. GSEA showed that these hub genes were related to the gamma-interferon immune response, inflammatory response, and allograft rejection. ROC curve analysis suggested that six hub genes may be potential biomarkers for CML diagnosis. Further analysis indicated that immune cells were associated with the pathogenesis of CML. The RT-qPCR results showed that proteinase 3 (PRTN3), cathepsin G (CTSG), matrix metalloproteinase 9 (MMP9), resistin (RETN), eosinophil derived neurotoxin (RNase2), eosinophil cationic protein (ECP, RNase3) were significantly elevated in CML patients' PBMCs compared with healthy controls.

CONCLUSION

These results improved our understanding of the functional characteristics and immune-related molecular mechanisms involved in CML progression and provided potential diagnostic biomarkers and therapeutic targets.

Identification of prognostic alternative splicing events related to the immune microenvironment of hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world, and its 5-year survival rate is less than 20%, despite various treatments being available. Increasing evidence indicates that alternative splicing (AS) plays a nonnegligible role in the formation and development of the tumor microenvironment (TME). However, the comprehensive analysis of the impact on prognostic AS events on immune-related perspectives in HCC is lacking but urgently needed.

Methods

The transcriptional data and clinical information of HCC patients were downloaded from TCGA (The Cancer Genome Atlas) database for calculating immune and stromal scores by ESTIMATE algorithm. We then divided patients into high/low score groups and explored their prognostic significance using Kaplan–Meier curves. Based on stromal and immune scores, differentially expressed AS events (DEASs) were screened and evaluated with functional enrichment analysis. Additionally, a risk score model was established by applying univariate and multivariate Cox regression analyses. Finally, gene set variation analysis (GSVA) was adopted to explore differences in biological behaviors between the high- and low-risk subgroups.

Results

A total of 370 HCC patients with complete and qualified corresponding data were included in the subsequent analysis. According to the results of ESTIMATE analysis, we observed that the high immune/stromal score group had a longer survival probability, which was significantly correlated with prognosis in HCC patients. In addition, 467 stromal/immune score-related DEASs were identified, and enrichment analysis revealed that DEASs were significantly enriched in pathways related to HCC tumorigenesis and the immune microenvironment. More importantly, the final prognostic signature containing 16 DEASs showed powerful predictive ability. Finally, GSVA demonstrated that activation of carcinogenic pathways and immune-related pathways in the high-risk group may lead to poor prognosis.

Conclusions

Collectively, these outcomes revealed prognostic AS events related to carcinogenesis and the immune microenvironment, which may yield new directions for HCC immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10020-021-00294-3.

"Complimenting the Complement": Mechanistic Insights and Opportunities for Therapeutics in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver and a leading cause of death in the US and worldwide. HCC remains a global health problem and is highly aggressive with unfavorable prognosis. Even with surgical interventions and newer medical treatment regimens, patients with HCC have poor survival rates. These limited therapeutic strategies and mechanistic understandings of HCC immunopathogenesis urgently warrant non-palliative treatment measures. Irrespective of the multitude etiologies, the liver microenvironment in HCC is intricately associated with chronic necroinflammation, progressive fibrosis, and cirrhosis as precedent events along with dysregulated innate and adaptive immune responses. Central to these immunological networks is the complement cascade (CC), a fundamental defense system inherent to the liver which tightly regulates humoral and cellular responses to noxious stimuli. Importantly, the liver is the primary source for biosynthesis of >80% of complement components and expresses a variety of complement receptors. Recent studies implicate the complement system in liver inflammation, abnormal regenerative responses, fibrosis, carcinogenesis, and development of HCC. Although complement activation differentially promotes immunosuppressive, stimulant, and angiogenic microenvironments conducive to HCC development, it remains under-investigated. Here, we review derangement of specific complement proteins in HCC in the context of altered complement regulatory factors, immune-activating components, and their implications in disease pathogenesis. We also summarize how complement molecules regulate cancer stem cells (CSCs), interact with complement-coagulation cascades, and provide therapeutic opportunities for targeted intervention in HCC.

Construction and Validation of a Prognostic Gene-Based Model for Overall Survival Prediction in Hepatocellular Carcinoma Using an Integrated Statistical and Bioinformatic Approach

Hepatocellular carcinoma (HCC) is one of the most common lethal cancers worldwide and is often related to late diagnosis and poor survival outcome. More evidence is demonstrating that gene-based prognostic models can be used to predict high-risk HCC patients. Therefore, our study aimed to construct a novel prognostic model for predicting the prognosis of HCC patients. We used multivariate Cox regression model with three hybrid penalties approach including least absolute shrinkage and selection operator (Lasso), adaptive lasso and elastic net algorithms for informative prognostic-related genes selection. Then, the best subset regression was used to identify the best prognostic gene signature. The prognostic gene-based risk score was constructed using the Cox coefficient of the prognostic gene signature. The model was evaluated by Kaplan-Meier (KM) and receiver operating characteristic curve (ROC) analyses. A novel four-gene signature associated with prognosis was identified and the risk score was constructed based on the four-gene signature. The risk score efficiently distinguished the patients into a high-risk group with poor prognosis. The time-dependent ROC analysis revealed that the risk model had a good performance with an area under the curve (AUC) of 0.780, 0.732, 0.733 in 1-, 2- and 3-year prognosis prediction in The Cancer Genome Atlas (TCGA) dataset. Moreover, the risk score revealed a high diagnostic performance to classify HCC from normal samples. The prognosis and diagnosis prediction performances of risk scores were verified in external validation datasets. Functional enrichment analysis of the four-gene signature and its co-expressed genes involved in the metabolic and cell cycle pathways was constructed. Overall, we developed a novel-gene-based prognostic model to predict high-risk HCC patients and we hope that our findings can provide promising insight to explore the role of the four-gene signature in HCC patients and aid risk classification.