Identification of immune-infiltrating cell-related biomarkers in hepatocellular carcinoma based on gene co-expression network analysis

Natural killer (NK) cells-related gene signature reveals the immune environment heterogeneity in hepatocellular carcinoma based on single cell analysis

The early diagnosis of liver cancer is crucial for the treatment and depends on the coordinated use of several test procedures. Early diagnosis is crucial for precision therapy in the treatment of the hepatocellular carcinoma (HCC). Therefore, in this study, the NK cell-related gene prediction model was used to provide the basis for precision therapy at the gene level and a novel basis for the treatment of patients with liver cancer. Natural killer (NK) cells have innate abilities to recognize and destroy tumor cells and thus play a crucial function as the "innate counterpart" of cytotoxic T cells. The natural killer (NK) cells is well recognized as a prospective approach for tumor immunotherapy in treating patients with HCC. In this research, we used publicly available databases to collect bioinformatics data of scRNA-seq and RNA-seq from HCC patients. To determine the NK cell-related genes (NKRGs)-based risk profile for HCC, we isolated T and natural killer (NK) cells and subjected them to analysis. Uniform Manifold Approximation and Projection plots were created to show the degree of expression of each marker gene and the distribution of distinct clusters. The connection between the immunotherapy response and the NKRGs-based signature was further analyzed, and the NKRGs-based signature was established. Eventually, a nomogram was developed using the model and clinical features to precisely predict the likelihood of survival. The prognosis of HCC can be accurately predicted using the NKRGs-based prognostic signature, and thorough characterization of the NKRGs signature of HCC may help to interpret the response of HCC to immunotherapy and propose a novel tumor treatment perspective.

Three-dimensional morphology scoring of hepatocellular carcinoma stratifies prognosis and immune infiltration

BACKGROUND

The morphological attributes could serve as pivotal indicators precipitating early recurrence and dismal overall survival in hepatocellular carcinoma (HCC), and quantifying morphological features may better stratify the prognosis of HCC.

OBJECTIVE

To develop a radiomics approach based on 3D tumor morphology features for predicting the prognosis of HCC and identifying differentially expressed genes related to morphology to guide HCC treatment.

MATERIALS AND METHODS

Retrospective study of 357 HCC patients. Radiomic features were extracted from MRI tumor regions; 14 morphology-related features predicted early HCC recurrence and patient stratification via LASSO-Cox modeling. Overall survival (OS) and recurrence-free survival (RFS) were analyzed. RNA sequencing from the Cancer Imaging Archive (TCIA) examined drug sensitivity and stratified HCC using morphological immunity genes, validating recurrence and prognosis.

RESULTS

Patients were split into training (n = 225), test (n = 132), and 50 TCIA dataset cohorts. Two features (Maximum2DdiameterColumn, Sphericity) in Cox regression stratified patients into high/low-risk Morphological Radiological Score (Morph-RS) groups. Significant OS and RFS were seen across all sets. Differentially expressed genes focused on T cell receptor signaling; low-risk group had higher T cells (P = 0.039), B cells (P = 0.041), NK cells (P = 0.018). SN-38, GSK2126458 might treat high-risk morphology. Morphology-immune genes stratified HCC, showing significant RFS/OS differences.

CONCLUSION

Tumor Morph-RS effectively stratifies HCC patients' recurrence and prognosis. Limited immune infiltration seen in Morph-RS high-risk groups signifies the potential of employing tumor morphology as a potent visual biomarker for diagnosing and managing HCC.

Predictive Value of Gene Databases in Discovering New Biomarkers and New Therapeutic Targets in Lung Cancer

Background and Objectives: The molecular mechanisms of lung cancer are still unclear. Investigation of immune cell infiltration (ICI) and the hub gene will facilitate the identification of specific biomarkers. Materials and Methods: Key modules of ICI and immune cell-associated differential genes, as well as ICI profiles, were identified using lung cancer microarray data from the single sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) in the gene expression omnibus (GEO) database. Protein–protein interaction networks were used to identify hub genes. The receiver operating characteristic (ROC) curve was used to assess the diagnostic significance of the hub genes, and survival analysis was performed using gene expression profiling interactive analysis (GEPIA). Results: Significant changes in ICI were found in lung cancer tissues versus adjacent normal tissues. WGCNA results showed the highest correlation of yellow and blue modules with ICI. Protein–protein interaction networks identified four hub genes, namely CENPF, AURKA, PBK, and CCNB1. The lung adenocarcinoma patients in the low hub gene expression group showed higher overall survival and longer median survival than the high expression group. They were associated with a decreased risk of lung cancer in patients, indicating their potential role as cancer suppressor genes and potential targets for future therapeutic development. Conclusions: CENPF, AURKA, PBK, and CCNB1 show great potential as biomarkers and immunotherapeutic targets specific to lung cancer. Lung cancer patients’ prognoses are often foreseen using matched prognostic models, and genes CENPF, AURKA, PBK, and CCNB1 in lung cancer may serve as therapeutic targets, which require further investigations.

Comparative RNA-Sequencing Analysis Reveals High Complexity and Heterogeneity of Transcriptomic and Immune Profiles in Hepatocellular Carcinoma Tumors of Viral (HBV, HCV) and Non-Viral Etiology

Background and Objectives: Hepatocellular carcinoma (HCC), the most common type of primary liver cancer, is the leading cause of cancer-related mortality. It arises and progresses against fibrotic or cirrhotic backgrounds mainly due to infection with hepatitis viruses B (HBV) or C (HCV) or non-viral causes that lead to chronic inflammation and genomic changes. A better understanding of molecular and immune mechanisms in HCC subtypes is needed. Materials and Methods: To identify transcriptional changes in primary HCC tumors with or without hepatitis viral etiology, we analyzed the transcriptomes of 24 patients by next-generation sequencing. Results: We identified common and unique differentially expressed genes for each etiological tumor group and analyzed the expression of SLC, ATP binding cassette, cytochrome 450, cancer testis, and heat shock protein genes. Metascape functional enrichment analysis showed mainly upregulated cell-cycle pathways in HBV and HCV and upregulated cell response to stress in non-viral infection. GeneWalk analysis identified regulator, hub, and moonlighting genes and highlighted CCNB1, ACTN2, BRCA1, IGF1, CDK1, AURKA, AURKB, and TOP2A in the HCV group and HSF1, HSPA1A, HSP90AA1, HSPB1, HSPA5, PTK2, and AURKB in the group without viral infection as hub genes. Immune infiltrate analysis showed that T cell, cytotoxic, and natural killer cell markers were significantly more highly expressed in HCV than in non-viral tumors. Genes associated with monocyte activation had the highest expression levels in HBV, while high expression of genes involved in primary adaptive immune response and complement receptor activity characterized tumors without viral infection. Conclusions: Our comprehensive study underlines the high degree of complexity of immune profiles in the analyzed groups, which adds to the heterogeneous HCC genomic landscape. The biomarkers identified in each HCC group might serve as therapeutic targets.

Betulinic acid prevents liver fibrosis by binding Lck and suppressing Lck in HSC activation and proliferation

ETHNOPHARMACOLOGICAL RELEVANCE

Hypericum japonicum Thunb. ex Murray (Hypericaceae), named 'Tianjihuang' is a traditional Chinese medicine with hepatoprotective, antibacterial, and antitumour effects. Betulinic acid (BA) is its active constituent and has been found to have a number of biological effects, including antiviral, anti-inflammatory, and anti-malarial therapeutic properties. Non-alcoholic fatty liver disease and acute alcoholic liver injury have both been proven to benefit from BA. BA's effects and mechanism on liver fibrosis are still unknown.

AIM OF THE STUDY

The purpose of this study was to explore the influence of BA on lymphocyte-specific protein tyrosine kinase (Lck), a non-receptor Src family kinase, that reduces liver fibrosis by inhibiting the phosphorylation of the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) pathways through the interaction of Lck and SOCS1.

MATERIALS AND METHODS

A liver fibrosis model was established in vivo with CCl₄ using haematoxylin and eosin (HE) staining, Masson staining, immunohistochemical staining, and immunofluorescence staining. Hepatic stellate cells were induced with transforming growth factor (TGF)-β1 in vitro, using Western blotting, immunofluorescence staining, and a cell scratch assay.

RESULTS

In a CCl₄-induced mouse hepatic fibrosis model and in TGF-β1-activated HSC-T6 cells, BA markedly reduced fibrosis, as demonstrated by the dramatic downregulation of α-smooth muscle actin (α-SMA) and type I collagen alpha-1 (Col1α1) protein levels in vivo and in vitro. BA significantly suppressed the activity and expression of Lck in vitro. Overexpression of Lck may diminish the effect of BA on liver fibrosis. In vitro, BA also greatly increased the expression of suppressor of cytokine signalling 1 (SOCS1) while it considerably inhibited the expression of p-JAK and p-STAT1.

CONCLUSIONS

These findings suggest that BA promotes the expression of SOCS1 by the inhibiting the interaction between Lck and SOCS1, followed by the inhibition of JAK/STAT phosphorylation to prevent the progression of liver fibrosis. Therefore, BA could be used as a promising natural supplement for the treatment of liver fibrosis.

Low transcriptomic of PTPRCv1 and CD3E is an independent predictor of mortality in HIV and tuberculosis co-infected patient

A comprehensive assessment of immunological profiles during HIV-TB co-infection is essential to predict mortality, and facilitate the development of effective diagnostic assays, therapeutic agents, and vaccines. Expression levels of 105 immune-related genes were measured at enrolment and 6th month follow-up from 9 deceased HIV and TB coinfected patients who died between 3 and 7th months follow-up and at enrolment, 6th and 18th month from 18 survived matched controls groups for 2 years. Focused gene expression profiling was assessed from peripheral whole blood using a dual-color Reverse-Transcription Multiplex Ligation-dependent Probe Amplification assay. Eleven of the 105 selected genes were differentially expressed between deceased individuals and survivor-matched controls at baseline. At baseline, IL4δ2 was significantly more highly expressed in the deceased group than survivor matched controls, whereas CD3E, IL7R, PTPRCv1, CCL4, GNLY, BCL2, CCL5, NOD1, TLR3, and NLRP13 had significantly lower expression levels in the deceased group compared to survivor matched controls. At baseline, a non-parametric receiver operator characteristic curve was conducted to determine the prediction of mortality of single genes identified CCL5, PTPRCv1, CD3E, and IL7R with Area under the Curve of 0.86, 0.86, 0.86, and 0.85 respectively. The expression of these genes in the survived control was increased at the end of TB treatment from that at baseline, while decreased in the deceased group. The expression of PTPRCv1, CD3E, CCL5, and IL7R host genes in peripheral blood of patients with TB-HIV coinfected can potentially be used as a predictor of mortality in the Ethiopian setting. Anti-TB treatment might be less likely to restore gene expression in the level expression of the deceased group. Therefore, other new therapeutics that can restore these genes (PTPRCv1, CD3E, IL7R, and CCL5) in the deceased groups at baseline might be needed to save lives.

Identification of Transcriptional Pattern Related to Immune Cell Infiltration With Gene Co-Expression Network in Papillary Thyroid Cancer

BACKGROUND

A growing body of evidence suggests that immune cell infiltration in cancer is closely related to clinical outcomes. However, there is still a lack of research on papillary thyroid cancer (PTC).

METHODS

Based on single-sample gene set enrichment analysis (SSGSEA) algorithm and weighted gene co-expression network analysis (WGCNA) tool, the infiltration level of immune cell and key modules and genes associated with the level of immune cell infiltration were identified using PTC gene expression data from The Cancer Genome Atlas (TCGA) database. In addition, the co-expression network and protein-protein interactions network analysis were used to identify the hub genes. Moreover, the immunological and clinical characteristics of these hub genes were verified in TCGA and GSE35570 datasets and quantitative real-time polymerase chain reaction (PCR). Finally, receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic value of hub genes.

RESULTS

Activated B cell, activated dendritic cell, CD56bright natural killer cell, CD56dim natural killer cell, Eosinophil, Gamma delta T cell, Immature dendritic cell, Macrophage, Mast cell, Monocyte, Natural killer cell, Neutrophil and Type 17 T helper cell were significantly changed between PTC and adjacent normal groups. WGCNA results showed that the black model had the highest correlation with the infiltration level of activated dendritic cells. We found 14 hub genes whose expression correlated to the infiltration level of activated dendritic cells in both TCGA and GSE35570 datasets. After validation in the TCGA dataset, the expression level of only 5 genes (C1QA, HCK, HLA-DRA, ITGB2 and TYROBP) in 14 hub genes were differentially expressed between PTC and adjacent normal groups. Meanwhile, the expression levels of these 5 hub genes were successfully validated in GSE35570 dataset. Quantitative real-time PCR results showed the expression of these 4 hub genes (except C1QA) was consistent with the results in TCGA and GSE35570 dataset. Finally, these 4 hub genes had diagnostic value to distinguish PTC and adjacent normal controls.

CONCLUSIONS

HCK, HLA-DRA, ITGB2 and TYROBP may be key diagnostic biomarkers and immunotherapy targets in PTC.