Diagnostic and Prognostic Values of MANF Expression in Hepatocellular Carcinoma

Endoplasmic reticulum stress promotes hepatocellular carcinoma by modulating immunity: a study based on artificial neural networks and single-cell sequencing

INTRODUCTION

Hepatocellular carcinoma (HCC) is characterized by the complex pathogenesis, limited therapeutic methods, and poor prognosis. Endoplasmic reticulum stress (ERS) plays an important role in the development of HCC, therefore, we still need further study of molecular mechanism of HCC and ERS for early diagnosis and promising treatment targets.

METHOD

The GEO datasets (GSE25097, GSE62232, and GSE65372) were integrated to identify differentially expressed genes related to HCC (ERSRGs). Random Forest (RF) and Support Vector Machine (SVM) machine learning techniques were applied to screen ERSRGs associated with endoplasmic reticulum stress, and an artificial neural network (ANN) diagnostic prediction model was constructed. The ESTIMATE algorithm was utilized to analyze the correlation between ERSRGs and the immune microenvironment. The potential therapeutic agents for ERSRGs were explored using the Drug Signature Database (DSigDB). The immunological landscape of the ERSRGs central gene PPP1R16A was assessed through single-cell sequencing and cell communication, and its biological function was validated using cytological experiments.

RESULTS

An ANN related to the ERS model was constructed based on SRPX, THBS4, CTH, PPP1R16A, CLGN, and THBS1. The area under the curve (AUC) of the model in the training set was 0.979, and the AUC values in three validation sets were 0.958, 0.936, and 0.970, respectively, indicating high reliability and effectiveness. Spearman correlation analysis suggests that the expression levels of ERSRGs are significantly correlated with immune cell infiltration and immune-related pathways, indicating their potential as important targets for immunotherapy. Mometasone was predicted to be the most promising treatment drug based on its highest binding score. Among the six ERSRGs, PPP1R16A had the highest mutation rate, predominantly copy number mutations, which may be the core gene of the ERSRGs model. Single-cell analysis and cell communication indicated that PPP1R16A is predominantly distributed in liver malignant parenchymal cells and may reshape the tumor microenvironment by enhancing macrophage migration inhibitory factor (MIF)/CD74 + CXCR4 signaling pathways. Functional experiments revealed that after siRNA knockdown, the expression of PPP1R16A was downregulated, which inhibited the proliferation, migration, and invasion capabilities of HCCLM3 and Hep3B cells in vitro.

CONCLUSION

The consensus of various machine learning algorithms and artificial intelligence neural networks has established a novel predictive model for the diagnosis of liver cancer associated with ERS. This study offers a new direction for the diagnosis and treatment of HCC.

Identifying endoplasmic reticulum stress-related molecular subtypes and prognostic model for predicting the immune landscape and therapy response in pancreatic cancer

Endoplasmic reticulum stress (ERS) is caused by the accumulation of intracellular misfolded or unfolded proteins and is associated with cancer development. In this study, pan-cancer analysis revealed complex genetic variations, including copy number variation, methylation, and somatic mutations for ERS-related genes (ERGs) in 33 kinds of cancer. Consensus clustering divided pancreatic cancer (PC) patients from TCGA and GEO databases into two ERS-related subtypes: ERGcluster A and B. Compared with ERGcluster A, ERGcluster B had a more active ERS state and worse prognosis. Subsequently, the ERS-related prognostic model was established to quantify the ERS score for a single sample. The patient with a low ERS score had remarkably longer survival times. ssGSEA and CIBERSORT algorithms revealed that activated B cells and CD8+ T cells had higher infiltration in the low ERS score group, but higher infiltration of activated CD4+ T cells, activated dendritic cells, macrophages, and neutrophils in the high ERS score group. Drug sensitivity analysis indicated the low ERS score group had a better response to gemcitabine, paclitaxel, 5-fluorouracil, oxaliplatin, and irinotecan. RT-qPCR validated that MET, MUC16, and KRT7 in the model had higher expression levels in pancreatic tumour tissues. Single-cell analysis further revealed that MET, MUC16, and KRT7 were mainly expressed in cancer cells in PC tumour microenvironment. In all, we first constructed the ERS-related molecular subtypes and prognostic model in PC. Our research highlighted the vital role of ERS in PC and contributed to further research on molecular mechanisms and novel therapeutic strategies for PC in the future.

Mesencephalic astrocyte-derived neurotrophic factor (MANF): Structure, functions and therapeutic potential

Mesencephalic astrocyte-derived neurotrophic factor (MANF) is a novel evolutionarily conserved protein present in both vertebrate and invertebrate species. MANF shows distinct structural and functional properties than the traditional neurotrophic factors (NTF). MANF is composed of an N-terminal saposin-like lipid-binding domain and a C-terminal SAF-A/B, Acinus and PIAS (SAP) domain connected by a short linker. The two well-described activities of MANF include (1) role as a neurotrophic factor that plays direct neuroprotective effects in the nervous system and (2) cell protective effects in the animal models of non-neuronal diseases, including retinal damage, diabetes mellitus, liver injury, myocardial infarction, nephrotic syndrome, etc. The main objective of the current review is to provide up-to-date insights regarding the structure of MANF, mechanisms regulating its expression and secretion, physiological functions in various tissues and organs, protective effects during aging, and potential clinical applications. Together, this review highlights the importance of MANF in reversing age-related dysfunction and geroprotection.

A Pan-Cancer Analysis of Heat-Shock Protein 90 Beta1(HSP90B1) in Human Tumours

Background: HSP90B1, a member of the heat-shock protein 90 family, plays a vital role as a molecular chaperone for oncogenes and stimulates tumour growth. However, its role in various cancers remains unexplored. Methods: Using the cancer genome atlas, gene expression omnibus the Human Protein Atlas databases and various other bioinformatic tools, this study investigated the involvement of HSP90B1 in 33 different tumour types. Results: The over-expression of HSP90B1 generally predicted poor overall survival and disease-free survival for patients with tumours, such as adrenocortical carcinoma, bladder urothelial carcinoma, kidney renal papillary cell carcinoma, and lung adenocarcinoma. In this study, HSP90B1 was highly expressed in the majority of tumours. A comparison was made between the phosphorylation of HSP90B1 in normal and primary tumour tissues, and putative functional mechanisms in HSP90B1-mediated oncogenesis were investigated. Additionally, the mutation burden of HSP90B1 in cancer was evaluated along with the survival rate of patients with cancer patients. Conclusion: This first pan-cancer investigation reveals the oncogenic functions of HSP90B1 in various cancers.

Identification of an Endoplasmic Reticulum Stress-Related Gene Signature to Evaluate the Immune Status and Predict the Prognosis of Hepatocellular Carcinoma

Liver cancer is the sixth most frequently diagnosed primary malignancy and ranks as the third leading cause of cancer-related death worldwide in 2020. ER stress also plays a vital role in the pathogenesis of malignancies. In the current study, we aimed to construct an endoplasmic reticulum stress-related genes (ERGs) signature to predict the overall survival (OS) of patients with HCC. Differentially expressed ERGs (DE-ERGs) were analyzed using The Cancer Genome Atlas (TCGA-LIHC cohort) and International Cancer Genome Consortium (ICGC-LIRI-JP cohort) databases. The prognostic gene signature was identified by the univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox proportional hazards regression analysis. The predictive ability of the model was evaluated by utilizing Kaplan-Meier curves and time-dependent receiver operating characteristic (ROC) curves. Gene set variant analysis (GSVA) was performed to explore the underlying biological processes and signaling pathways. CIBERPORT and single-sample Gene Set Enrichment Analysis (ssGSEA) were implemented to estimate the immune status between the different risk groups. A total of 113 DE-ERGs were identified between 50 normal samples and 365 HCC samples in the TCGA-LIHC cohort, and 48 DE-ERGs were associated with OS through the univariate Cox regression. A six DE-ERGs (PPARGC1A, SQSTM1, SGK1, PON1, CDK1, and G6PD) signature was constructed and classified patients into high-risk and low-risk groups. The risk score was an independent prognostic indicator for OS (HR > 1, p < 0.001). The function enrichment analysis indicated that cell cycle, RNA degradation, protein localization, and cell division were the main biological processes. The high-risk group had higher immune cell infiltration levels than those of the low-risk group. We predicted the response to targeted therapy in high- and low-risk patients with HCC and found that the high-risk patients were more sensitive to pazopanib. At last, we verified the expression of the six gene patterns in HCC tissues by qRT-PCR and immunohistochemistry. This signature may be a potential tool to provide a choice for prognosis prediction and personal management of patients with HCC.

Identification and Validation of Novel Biomarkers for Hepatocellular Carcinoma, Liver Fibrosis/Cirrhosis and Chronic Hepatitis B via Transcriptome Sequencing Technology

Purpose

The aim of this study was to identify and validate novel biomarkers for distinguishing among hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC), liver fibrosis/liver cirrhosis (LF/LC) and chronic hepatitis B (CHB).

Patients and Methods

Transcriptomic sequencing was conducted on the liver tissues of 5 patients with HCC, 5 patients with LF/LC, 5 patients with CHB, and 4 healthy controls. The expression levels of selected mRNAs and proteins were assessed by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemical (IHC) staining, and were verified in validation set (n=200) and testing set (n=400) via enzyme-linked immunosorbent assay (ELISA).

Results

A total of 9 hub mRNAs were identified by short time-series expression miner and weighted gene co-expression network analysis. Of note, the results of qRT-PCR and IHC staining demonstrated that SHC adaptor protein 1 (SHC1), SLAM family member 8 (SLAMF8), and interleukin-32 (IL-32) exhibited gradually increasing trends in the four groups. Subsequent ELISA tests on the validation cohort indicated that the plasma levels of SHC1, SLAMF8 and IL-32 also gradually increased. Furthermore, a diagnostic model APFSSI (age, PLT, ferritin, SHC1, SLAMF8 and IL-32) was established to distinguish among CHB, LF/LC and HCC. The performance of APFSSI model for discriminating CHB from healthy subjects (AUC=0.966) was much greater compared to SHC1 (AUC=0.900), SLAMF8 (AUC=0.744) and IL-32 (AUC=0.821). When distinguishing LF/LC from CHB, APFSSI was the most outstanding diagnostic parameter (AUC=0.924), which was superior to SHC1, SLAMF8 and IL-32 (AUC=0.812, 0.684 and 0.741, respectively). Likewise, APFSSI model with the greatest AUC value displayed an excellent performance for differentiating between HCC and LF/LC than other variables (SHC1, SLAMF8 and IL-32) via ROC analysis. Finally, the results in the test set were consistent with those in the validation set.

Conclusion

SHC1, SLAMF8 and IL-32 can differentiate among patients with HCC, LF/LC, CHB and healthy controls. More importantly, the APFSSI model greatly improves the diagnostic accuracy of HBV-associated liver diseases.

MANF: A Novel Endoplasmic Reticulum Stress Response Protein-The Role in Neurological and Metabolic Disorders

The mesencephalic astrocyte-derived neurotrophic factor (MANF), also named as arginine-rich protein (ARP) or arginine-rich mutated in early-stage tumors (ARMET), is a novel evolutionary conserved protein related to unfolded protein response. Growing evidence suggests that MANF critically involves in many ER stress-related diseases with a protective effect. Here, we review the function of MANF based on its structure in neurological and metabolic disorders and summarize its potential applications in disease diagnosis and therapies.

Expression of cyclin-dependent kinases and their clinical significance with immune infiltrates could predict prognosis in colorectal cancer

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The expression and prognostic values of AURKA and RB1 may also be significant to CRC diagnosis than previously studies.

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The association of CDKs with immune infiltrates may serve as target molecules for immunotherapy in CRC.

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The expression of CDK is significant among CRC subtypes and therefore, it can be inferred as a potential biomarker in the cancer subtype.

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An increase in tumor purity was positively correlated with the expression of CDK-1 in COAD due to CD4+ cells and CDK-4 in COAD and READ resulting from a fraction of immune cells.

Introduction

Colorectal cancer (CRC) is one of the most cancer-related mortalities worldwide and remains a major public health issue. Despite several attempts to develop promising therapies for CRC, its survival rate decreases with metastasis. Cyclin-dependent kinases (CDKs) are a family of protein kinases with various regulatory activities including cell cycle, mRNA expression, transcription, and differentiation. Aside from their role in cell proliferation when mutated, abnormal expression of these genes has been reported in some human cancer subtypes. This study explored the roles and therapeutic potentials of CDK 1 and 4 as prognostic biomarkers in CRC.

Methods

Bioinformatics analyses were carried out to demonstrate the expression and prognostic values of CDK-1 and CDK-4 with immune infiltrate in CRC.

Discussion

CDK levels in CRC were remarkably higher than those in normal tissues (p < 0.05), and overexpression in CRC tissues was significantly related to nodal metastatic status (p < 0.05) and histological subtypes. Kaplan-Meier analyses showed that patients with CRC who exhibited CDK-1 overexpression had worse overall survival (OS) as against patients with CDK-4 overexpression. The alteration observed was a mutation while the mutation hotspots include E163* and R24A/C/H/L respectively for CDK-1 and CDK-4 on the Pkinase domain. Of the associated genes, AURKA and RB1 were predominantly altered. Furthermore, CDK-4 is positively correlated with tumor purity in both COAD and READ while CDK-1is only positively correlated in COAD. CDK-1 overexpression was significantly associated with poor prognosis as opposed to CDK-4.

Conclusion

The expression and prognostic values of AURKA and RB1 may also be significant to CRC diagnosis. CDKs together with the co-expressed genes and their association with immune infiltrates may serve as target molecules for immunotherapy in CRC.