Two predictive precision medicine tools for hepatocellular carcinoma

Pregnancy zone protein, a potential research target in multiple diseases

Pregnancy zone protein (PZP) is an antiprotease-resistant immunosuppressant belonging to the α-macroglobulin (αM) protein family. PZP is secreted by the liver and was found to be upregulated in plasma during pregnancy. α-2-macroglobulin (Α2M) shares 71 % serial homology with PZP, but low PZP levels do not lead to increased A2M levels in pregnancy. PZP can interact with several factors such as low-density lipoprotein receptor-associated protein (LRP), transforming growth factor-β (TGF-β), 78 kDa glucose-regulated protein (GRP78), and glycoside A (GdA). PZP is involved in the development of glycolipid metabolism disorders, bronchiectasis, Alzheimer's disease (AD), rheumatoid arthritis (RA), myocardial infarction (MI) and inflammatory bowel disease (IBD). PZP is also associated with the progression of tumorigenesis such as breast cancer (BC), homologyepatocellular carcinoma (HCC), lung adenocarcinoma (LAC), and colorectal cancer (CRC). Therefore, this review analyzes the role of PZP in pathophysiology of various diseases.

Pan-cancer analysis of COL15A1: an immunological and prognostic biomarker

Collagen, type XV, alpha 1 (COL15A1) belongs to the collagen superfamily, which can influence disease progression by modulating immune pathways. Although the growing number of investigations demonstrating the indispensable role of COL15A1 in the progression of certain tumors, no pan-cancer assessment of COL15A1 is accessible to date. Therefore, the available data was used to explore the role of COL15A1 in 33 types of tumors and to investigate their potential immune function. Numerous bioinformatics approaches were used to research the potential oncogenic role of COL15A1, including analysis of tumor prognosis, microsatellite instability (MSI), tumor mutational burden (TMB), single nucleotide polymorphism (SNP), drug sensitivity, immune cell infiltration, and the correlation between cancer stem cells (CSCs) and COL15A1 expression. The outcome implies that most tumors had a high expression of COL15A1, and COL15A1 manifested different relationships with prognosis in different tumors, including both positive and negative correlations. COL15A1 was also found to have a significant correlation with MSI, TMB, and immune infiltrating cells. Our study suggests that COL15A1 may serve as a prognostic marker for malignancy because of its differential expression in tissues and their function in tumor immunity.

Construction of a Prognostic Model for Hepatocellular Carcinoma Based on Macrophage Polarization-Related Genes

Background

The progression of hepatocellular carcinoma (HCC) is related to macrophage polarization (MP). Our aim was to identify genes associated with MP in HCC patients and develop a prognostic model based on these genes.

Results

We successfully developed a prognostic model consisting of six MP-related genes (SCN4A, EBF3, ADGRB2, HOXD9, CLEC1B, and MSC) to calculate the risk score for each patient. Patients were then classified into high- and low-risk groups based on their median risk score. The performance of the MP-related prognostic model was evaluated using Kaplan-Meier and ROC curves, which yielded favorable results. Additionally, the nomogram demonstrated good clinical effectiveness and displayed consistent survival predictions with actual observations. Gene Set Enrichment Analysis (GSEA) revealed enrichment of pathways related to KRAS signaling downregulation, the G2M checkpoint, and E2F targets in the high-risk group. Conversely, pathways associated with fatty acid metabolism, xenobiotic metabolism, bile acid metabolism, and adipogenesis were enriched in the low-risk group. The risk score positively correlated with the number of invasion-related genes. Immune checkpoint expression differed significantly between the two groups. Patients in the high-risk group exhibited increased sensitivity to mitomycin C, cisplatin, gemcitabine, rapamycin, and paclitaxel, while those in the low-risk group showed heightened sensitivity to doxorubicin. These findings suggest that the high-risk group may have more invasive HCC with greater susceptibility to specific drugs. IHC staining revealed higher expression levels of SCN4A in HCC tissues. Furthermore, experiments conducted on HepG2 cells demonstrated that supernatants from cells with reduced SCN4A expression promoted M2 macrophage polarization marker, CD163 in THP-1 cells. Reduced SCN4A expression induced HCC-related genes, while increased SCN4A expression reduced their expression in HepG2 cells.

Conclusion

The MP-related prognostic model comprising six MPRGs can effectively predict HCC prognosis, infer invasiveness, and guide drug therapy. SCN4A is identified as a suppressor gene in HCC.

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.

Epigenetic reactivation of tumor suppressor genes with CRISPRa technologies as precision therapy for hepatocellular carcinoma

BACKGROUND

Epigenetic silencing of tumor suppressor genes (TSGs) is a key feature of oncogenesis in hepatocellular carcinoma (HCC). Liver-targeted delivery of CRISPR-activation (CRISPRa) systems makes it possible to exploit chromatin plasticity, by reprogramming transcriptional dysregulation.

RESULTS

Using The Cancer Genome Atlas HCC data, we identify 12 putative TSGs with negative associations between promoter DNA methylation and transcript abundance, with limited genetic alterations. All HCC samples harbor at least one silenced TSG, suggesting that combining a specific panel of genomic targets could maximize efficacy, and potentially improve outcomes as a personalized treatment strategy for HCC patients. Unlike epigenetic modifying drugs lacking locus selectivity, CRISPRa systems enable potent and precise reactivation of at least 4 TSGs tailored to representative HCC lines. Concerted reactivation of HHIP, MT1M, PZP, and TTC36 in Hep3B cells inhibits multiple facets of HCC pathogenesis, such as cell viability, proliferation, and migration.

CONCLUSIONS

By combining multiple effector domains, we demonstrate the utility of a CRISPRa toolbox of epigenetic effectors and gRNAs for patient-specific treatment of aggressive HCC.

Overexpression of Pregnancy Zone Protein in Fat Antagonizes Diet-Induced Obesity Under an Intermittent Fasting Regime

The intermittent fasting regimen (IFR) has been certified as an effective strategy for improving metabolism. But the underlying mechanism is still obscure. Beige induction in white adipose tissue (WAT) by IFR may account for this. It has been demonstrated that the erupting of pregnancy zone protein (PZP) from the liver coincides with membrane translocation of grp78 in brown adipocytes during IFR to activate brown adipose tissue (BAT), which may partly explain the metabolic benefits of IFR. Liver-derived PZP appears to be responsible for all metabolic regulatory functions; the effect of boosting energy expenditure disappeared in liver-deficient mice. To verify whether any liver-specific modification was essential for functional PZP, we used the PZP adipose tissue-specific overexpression mice model (PZP TG). We found that the metabolic disorders induced by high-fat diet were improved in PZP TG mice under IFR. Additionally, in addition to the activation of BAT, UCP1 protein and angiogenesis were increased in WAT, as well as the expression of genes associated with glucose utilization. These results demonstrate that PZP fat-specific TG increased the energy conversion of WAT, indicating that WAT may be another direct target for PZP during IFR.

N4BP3 promotes angiogenesis in Hepatocellular Carcinoma by binding with KAT2B

Although angiogenesis is a critical event in hepatocellular carcinoma (HCC), and this process provides the tumor with sufficient oxygen and nutrients, the precise molecular mechanism by which it occurs is not fully understood. NEDD4 binding protein 3 (N4BP3) was identified in this study as a novel pro‐angiogenic factor in HCC cell lines and tissues. We discovered that N4BP3 was significantly expressed in HCC and that its level of expression was positively correlated with the density of tumor microvessels in HCC tissues. Cell biology experiments have shown that N4BP3 knockdown in HCC cells significantly inhibits the formation of complete tubular structures by HUVECs in vitro and HCC angiogenesis in vivo. In HCC cells, overexpression of N4BP3 has the opposite effects. Further cell and molecular biology experiments have revealed that N4BP3 interacts with KAT2B (lysine acetyltransferase 2B), increasing signal transducer and activator of transcription 3 (STAT3) expression by regulating the distribution of acetyl‐histone H3 (Lys27) (H3K27ac) in its promoter region. This, in addition, regulates the activity of the STAT3 signaling pathway, which promotes the proliferation of microvessels in HCC and accelerates the malignant process of the tumor. In vivo experiments in nude mice have confirmed our findings, and also suggested that N4BP3 could be a potential target for the treatment of HCC in combination with sorafenib.

Improve individual treatment by comparing treatment benefits: cancer artificial intelligence survival analysis system for cervical carcinoma

Purpose

The current study aimed to construct a novel cancer artificial intelligence survival analysis system for predicting the individual mortality risk curves for cervical carcinoma patients receiving different treatments.

Methods

Study dataset (n = 14,946) was downloaded from Surveillance Epidemiology and End Results database. Accelerated failure time algorithm, multi-task logistic regression algorithm, and Cox proportional hazard regression algorithm were used to develop prognostic models for cancer specific survival of cervical carcinoma patients.

Results

Multivariate Cox regression identified stage, PM, chemotherapy, Age, PT, and radiation_surgery as independent influence factors for cervical carcinoma patients. The concordance indexes of Cox model were 0.860, 0.849, and 0.848 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.881, 0.845, and 0.841 in validation dataset. The concordance indexes of accelerated failure time model were 0.861, 0.852, and 0.851 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.882, 0.847, and 0.846 in validation dataset. The concordance indexes of multi-task logistic regression model were 0.860, 0.863, and 0.861 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.880, 0.860, and 0.861 in validation dataset. Brier score indicated that these three prognostic models have good diagnostic accuracy for cervical carcinoma patients. The current research lacked independent external validation study.

Conclusion

The current study developed a novel cancer artificial intelligence survival analysis system to provide individual mortality risk predictive curves for cervical carcinoma patients based on three different artificial intelligence algorithms. Cancer artificial intelligence survival analysis system could provide mortality percentage at specific time points and explore the actual treatment benefits under different treatments in four stages, which could help patient determine the best individualized treatment. Cancer artificial intelligence survival analysis system was available at: https://zhangzhiqiao15.shinyapps.io/Tumor_Artificial_Intelligence_Survival_Analysis_System/.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03491-8.

Artificial intelligence predictive system of individual survival rate for lung adenocarcinoma

Background

The current research aimed to develop an artificial intelligence predictive system for individual survival rate of lung adenocarcinoma (LUAD).

Methods

Independent risk variables were identified by multivariate Cox regression. Artificial intelligence predictive system was constructed using three different data mining algorithms.

Results

Stage, PM, chemotherapy, PN, age, PT, sex, and radiation_surgery were determined as risk factors for LUAD patients. For 12-month survival rate in model cohort, concordance indexes of RFS, MTLR, and Cox models were 0.852, 0.821, and 0.835, respectively. For 36-month survival rate in model cohort, concordance indexes of RFS, MTLR, and Cox models were 0.901, 0.864, and 0.862, respectively. For 60-month survival rate in model cohort, concordance indexes of RFS, MTLR, and Cox models were 0.899, 0.874, and 0.866, respectively. The concordance indexes in validation dataset were similar to those in model dataset.

Conclusions

The current study designed an individualized survival predictive system, which could provide individual survival curves using three different artificial intelligence algorithms. This artificial intelligence predictive system could directly convey treatment benefits by comparing individual mortality risk curves under different treatments. This artificial intelligence predictive tool is available at https://zhangzhiqiao11.shinyapps.io/Artificial_Intelligence_Survival_Prediction_System_AI_E1001/.

Methionine restriction enhances the chemotherapeutic sensitivity of colorectal cancer stem cells by miR-320d/c-Myc axis

Chemotherapy resistance of colorectal cancer stem cells (CRC-SCs) has become a major challenge in clinical treatment of cancer. Methionine restriction (MR) enhances the therapeutic effect of chemotherapeutic agents. The aim of this study was to explore the molecular pathways that MR affects the chemotherapeutic sensitivity of CRC-SCs. CD133⁺ and CD133^- SW480 or SW620 cells were isolated by magnetic-activated cell sorting (MACS). Mouse xenograft tumor model was established by subcutaneous inoculation of CD133⁺ SW480. MTT assay was used to detect cell viability. Phase distribution of cell cycle was detected by flow cytometry. Western blotting was used to detect drug-resistant related protein expression. miR-320d and transcription factor c-Myc expressions were detected by qRT-PCR. The interaction between miR-320d and c-Myc was verified by luciferase assay. CD133⁺ SW480 and SW620 cells were more resistant to 5-fluorouracil (5-FU) than CD133^- cells. In vitro and in vivo experiments showed that 5-FU and MR combined therapy further inhibited CD133⁺ cell activity and ATP binding cassette subfamily G member 2 (ABCG2) expression, and reduced tumor volume compared with drug administration alone. Interference with miR-320d or overexpression of c-Myc reversed the increased chemotherapeutic sensitivity of CRC-SCs induced by synergistic therapy with 5-FU and MR. miR-320d can target and regulate c-Myc. Interference with c-Myc could reverse the increase in cell viability and ABCG2 expression caused by down-regulation of miR-320d. In conclusion, the combined chemotherapy with MR can enhance the chemotherapeutic sensitivity of CRC-SCs by up-regulation of miR-320d to inhibit c-Myc expression, which lays a molecular basis for MR regulation of chemotherapeutic sensitivity of CRC-SCs.

Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system

Objectives

Immune microenvironment was closely related to the occurrence and progression of colorectal cancer (CRC). The objective of the current research was to develop and verify a Machine learning survival predictive system for CRC based on immune gene expression data and machine learning algorithms.

Methods

The current study performed differentially expressed analyses between normal tissues and tumor tissues. Univariate Cox regression was used to screen prognostic markers for CRC. Prognostic immune genes and transcription factors were used to construct an immune-related regulatory network. Three machine learning algorithms were used to create an Machine learning survival predictive system for CRC. Concordance indexes, calibration curves, and Brier scores were used to evaluate the performance of prognostic model.

Results

Twenty immune genes (BCL2L12, FKBP10, XKRX, WFS1, TESC, CCR7, SPACA3, LY6G6C, L1CAM, OSM, EXTL1, LY6D, FCRL5, MYEOV, FOXD1, REG3G, HAPLN1, MAOB, TNFSF11, and AMIGO3) were recognized as independent risk factors for CRC. A prognostic nomogram was developed based on the previous immune genes. Concordance indexes were 0.852, 0.778, and 0.818 for 1-, 3- and 5-year survival. This prognostic model could discriminate high risk patients with poor prognosis from low risk patients with favorable prognosis.

Conclusions

The current study identified twenty prognostic immune genes for CRC patients and constructed an immune-related regulatory network. Based on three machine learning algorithms, the current research provided three individual mortality predictive curves. The Machine learning survival predictive system was available at: https://zhangzhiqiao8.shinyapps.io/Artificial_Intelligence_Survival_Prediction_for_CRC_B1005_1/, which was valuable for individualized treatment decision before surgery.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04657-3.

Hepatokine Pregnancy Zone Protein Governs the Diet-Induced Thermogenesis Through Activating Brown Adipose Tissue

Intermittent fasting (IF), as a dietary intervention for weight loss, takes effects primarily through increasing energy expenditure. However, whether inter-organ systems play a key role in IF remains unclear. Here, a novel hepatokine, pregnancy zone protein (PZP) is identified, which has significant induction during the refeeding stage of IF. Further, loss of function studies and protein therapeutic experiment in mice revealed that PZP promotes diet-induced thermogenesis through activating brown adipose tissue (BAT). Mechanistically, circulating PZP can bind to cell surface glucose-regulated protein of 78 kDa (GRP78) to promote uncoupling protein 1 (UCP1) expression via a p38 MAPK-ATF2 signaling pathway in BAT. These studies illuminate a systemic regulation in which the IF promotes BAT thermogenesis through the endocrinal system and provide a novel potential target for treating obesity and related disorders.

Identification of Aging-Related Genes Associated With Clinical and Prognostic Features of Hepatocellular Carcinoma

Background: Aging is a well-studied concept, but no studies have comprehensively analyzed the association between aging-related genes (AGs) and hepatocellular carcinoma (HCC) prognosis. Methods: Gene candidates were selected from differentially expressed genes and prognostic genes in The Cancer Genome Atlas (TCGA) database. A gene risk score for overall survival prediction was established using the least absolute shrinkage and selection operator (LASSO) regression analysis, and this was validated using data from the International Cancer Genome Consortium (ICGC) database. Functional analysis was conducted using gene ontology enrichment, Kyoto Encyclopedia of Genes and Genomes analysis, gene set enrichment analysis, and immune microenvironment and tumor stemness analyses. Results: Initially, 72 AGs from the TCGA database were screened as differentially expressed between normal and tumor tissues and as genes associated with HCC prognosis. Then, seven AGs (POLA1, CDK1, SOCS2, HDAC1, MAPT, RAE1, and EEF1E1) were identified using the LASSO regression analysis. The seven AGs were used to develop a risk score in the training set, and the risk was validated to have a significant prognostic value in the ICGC set (p < 0.05). Patients with high risk scores had lower tumor differentiation, higher stage, and worse prognosis (all p < 0.05). Multivariate Cox regression analyses also confirmed that the risk score was an independent prognostic factor for HCC in both the TCGA and ICGC sets (all p < 0.05). Further analysis showed that a high risk score was correlated with the downregulation of metabolism and tumor immunity. Conclusion: The risk score predicts HCC prognosis and could thus be used as a biomarker not only for predicting HCC prognosis but also for deciding on treatment.

Immune gene prognostic signature for disease free survival of gastric cancer: Translational research of an artificial intelligence survival predictive system

The progress of artificial intelligence algorithms and massive data provide new ideas and choices for individual mortality risk prediction for cancer patients. The current research focused on depict immune gene related regulatory network and develop an artificial intelligence survival predictive system for disease free survival of gastric cancer.

Multi-task logistic regression algorithm, Cox survival regression algorithm, and Random survival forest algorithm were used to develop the artificial intelligence survival predictive system.

Nineteen transcription factors and seventy immune genes were identified to construct a transcription factor regulatory network of immune genes. Multivariate Cox regression identified fourteen immune genes as prognostic markers. These immune genes were used to construct a prognostic signature for gastric cancer. Concordance indexes were 0.800, 0.809, and 0.856 for 1-, 3- and 5- year survival. An interesting artificial intelligence survival predictive system was developed based on three artificial intelligence algorithms for gastric cancer. Gastric cancer patients with high risk score have poor survival than patients with low risk score.

The current study constructed a transcription factor regulatory network and developed two artificial intelligence survival prediction tools for disease free survival of gastric cancer patients. These artificial intelligence survival prediction tools are helpful for individualized treatment decision.

Genetic ablation of pregnancy zone protein promotes breast cancer progression by activating TGF-β/SMAD signaling

PURPOSE

Pregnancy zone protein (PZP) is best known as protease inhibitor and its concentration in human blood plasma increases dramatically during pregnancy. Recent investigation revealed a role of PZP inactivating germ-line mutation in breast cancer predisposition, and therefore we designed a study to evaluate functional involvement of this protein in tumor pathogenesis.

METHODS

PZP knockout cells were generated utilizing the CRISPR-Cas9 approach in MCF7 and T47D (breast cancer) cell lines, and colony formation, cell proliferation, and migration assays carried out. TGF-β and SMAD expression studies were performed using qRT-PCR and Western blot. PZP expression in tumor vs normal tissue was compared using meta-analyses of data records of breast cancer patients (n = 1211) included in the TCGA consortium registry as well as in independent cohorts of hormone receptor-positive (n = 118) and triple-negative breast cancer (TNBC) patients (n = 116).

RESULTS

We demonstrated that genetic ablation of PZP efficiently inhibits tamoxifen-induced apoptosis and enhances cell proliferation, migration, and colony-forming capacity. We found a significant increase in survival fraction of CRISPR/Cas9-mediated PZP knockout clones compared to wild-type counterpart after tamoxifen treatment (p < 0.05). The PZP knockout significantly promoted breast cancer cell migration (p < 0.01) in vitro. We observed high expression of TGF-β2 ligand, TGF-β- receptor 2, and upregulation of phosphorylated regulatory-SMADs (pSMAD2 and pSMAD3) activating the pro-survival function of TGF-β/SMAD signaling in PZP knockout clones. Meta-analyses of data records of breast cancer patients indicated that low PZP expression is associated with poor overall survival at 6 years (51.7% vs 62.9% in low vs high expressers, respectively; p = 0.026). We also observed a significantly lower PZP mRNA expression in TNBC as compared with hormone receptor-positive tumors (p = 0.019).

CONCLUSION

Taken together, our results suggest that genetic ablation of PZP results in tumor progression and low expression of PZP is associated with poor survival of breast cancer patients.