Identifying Apoptosis-Related Transcriptomic Aberrations and Revealing Clinical Relevance as Diagnostic and Prognostic Biomarker in Hepatocellular Carcinoma

Primary ovarian insufficiency associated with lenvatinib therapy in a patient with hepatocellular carcinoma: A case report

The therapeutic effects of molecular targeted drugs are, in some cases, more pronounced than those of conventional chemotherapy, and their introduction as a standard treatment is increasing. The present report describes a case of ovarian insufficiency in a young woman caused by tyrosine kinase inhibitor lenvatinib. The 25-year-old woman received lenvatinib (8 mg/day) for 98 days as preoperative chemotherapy for hepatocellular carcinoma. Blood testing the day before starting lenvatinib administration indicated 4.40 mIU/ml luteinizing hormone (LH), 5.2 mIU/ml follicle-stimulating hormone (FSH) and age-equivalent hormone values. Amenorrhea occurred after the start of administration, and 48 days later, the LH level was 41.8 mIU/ml and the FSH level was 44 mIU/ml, indicating a decrease in ovarian function. The patient underwent hepatectomy, and 49 days after the end of lenvatinib administration, the LH level had improved to 4.5 mIU/ml and the FSH level had improved to 2.5 mIU/ml. After the hepatectomy, the patient began to have regular menstrual cycles once again. Ovarian toxicity has not been recognized as a side effect of lenvatinib. However, the present report describes primary ovarian insufficiency considered to be caused by this drug. Potential damage to ovarian function may need to be considered when molecular targeted drugs with the same mechanism of action as lenvatinib are used in young women.

A programmed cell death-related model based on machine learning for predicting prognosis and immunotherapy responses in patients with lung adenocarcinoma

Background

lung adenocarcinoma (LUAD) remains one of the most common and lethal malignancies with poor prognosis. Programmed cell death (PCD) is an evolutionarily conserved cell suicide process that regulates tumorigenesis, progression, and metastasis of cancer cells. However, a comprehensive analysis of the role of PCD in LUAD is still unavailable.

Methods

We analyzed multi-omic variations in PCD-related genes (PCDRGs) for LUAD. We used cross-validation of 10 machine learning algorithms (101 combinations) to synthetically develop and validate an optimal prognostic cell death score (CDS) model based on the PCDRGs expression profile. Patients were classified based on their median CDS values into the high and low-CDS groups. Next, we compared the differences in the genomics, biological functions, and tumor microenvironment of patients between both groups. In addition, we assessed the ability of CDS for predicting the response of patients from the immunotherapy cohort to immunotherapy. Finally, functional validation of key genes in CDS was performed.

Results

We constructed CDS based on four PCDRGs, which could effectively and consistently stratify patients with LUAD (patients with high CDS had poor prognoses). The performance of our CDS was superior compared to 77 LUAD signatures that have been previously published. The results revealed significant genetic alterations like mutation count, TMB, and CNV were observed in patients with high CDS. Furthermore, we observed an association of CDS with immune cell infiltration, microsatellite instability, SNV neoantigens. The immune status of patients with low CDS was more active. In addition, CDS could be reliable to predict therapeutic response in multiple immunotherapy cohorts. In vitro experiments revealed that high DNA damage inducible transcript 4 (DDIT4) expression in LUAD cells mediated protumor effects.

Conclusion

CDS was constructed based on PCDRGs using machine learning. This model could accurately predict patients' prognoses and their responses to therapy. These results provide new promising tools for clinical management and aid in designing personalized treatment strategies for patients with LUAD.

Identification of PANoptosis-relevant subgroups to evaluate the prognosis and immune landscape of patients with liver hepatocellular carcinoma

Liver hepatocellular carcinoma (LIHC) is one of the most common malignant tumors, which is difficult to be diagnosed at an early stage due to its poor prognosis. Despite the fact that PANoptosis is important in the occurrence and development of tumors, no bioinformatic explanation related to PANoptosis in LIHC can be found. A bioinformatics analysis on the data of LIHC patients in TCGA database was carried out on the basis of previously identified PANoptosis-related genes (PRGs). LIHC patients were divided into two PRG clusters whose gene characteristics of differentially expressed genes (DEGs) were discussed. According to DEGs, the patients were further divided into two DEG clusters, and prognostic-related DEGs (PRDEGs) were applied to risk score calculation, the latter of which turned out to be practical in identifying the relationship among risk score, patient prognosis, and immune landscape. The results suggested that PRGs and relevant clusters were bound up with the survival and immunity of patients. Moreover, the prognostic value based on two PRDEGs was evaluated, the risk scoring model was constructed, and the nomogram model for predicting the survival rate of patients was further developed. Therefore, it was found that the prognosis of the high-risk subgroup was poor. Additionally, three factors, namely, the abundance of immune cells, the expression of immune checkpoints, and immunotherapy and chemotherapy were considered to be associated with the risk score. RT-qPCR results indicated higher positive expression of CD8A and CXCL6 in both LIHC tissues and most human liver cancer cell lines. In summary, the results suggested that PANoptosis was bound up with LIHC-related survival and immunity. Two PRDEGs were identified as potential markers. Thus, the understanding of PANoptosis in LIHC was enriched, with some strategies provided for the clinical therapy of LIHC.

An integrative analysis revealing cuproptosis-related lncRNAs signature as a novel prognostic biomarker in hepatocellular carcinoma

Background: Cuproptosis is a newly defined form of cell death, whether cuproptosis involved in hepatocellular carcinoma (HCC) remains elusive. Method: We obtained patients' RNA expression data and follow-up information from University of California Santa Cruz (UCSC) and The Cancer Genome Atlas (TCGA). We analyzed the mRNA level of Cuproptosis-related genes (CRGs) and performed univariate Cox analysis. Liver hepatocellular carcinoma (LIHC) was chosen for further investigation. Real-Time quantitative PCR (RT-qPCR), Western blotting (WB), Immunohistochemical (IHC), and Transwell assays were used to determine expression patterns and functions of CRGs in LIHC. Next, we identified CRGs-related lncRNAs (CRLs) and differentially expressed CRLs between HCC and normal cases. Univariate Cox analysis, least absolute shrinkage selection operator (LASSO) analysis and Cox regression analysis were used to construct the prognostic model. Univariate and multivariate Cox analysis was used to assess whether the risk model can act as an independent risk factor of overall survival duration. Different risk groups performed immune correlation analysis, tumor mutation burden (TMB), and Gene Set Enrichment Analysis (GSEA) analysis were performed in different risk groups. Finally, we assessed the performance of the predictive model in drug sensitivity. Results: CRGs expression levels have significant differences between tumor and normal tissues. High expression of Dihydrolipoamide S-Acetyltransferase (DLAT) correlated to metastasis of HCC cells and indicated poor prognosis for HCC patients. Our prognostic model consisted of four cuproptosis-related lncRNA (AC011476.3, AC026412.3, NRAV, MKLN1-AS). The prognostic model performed well in predicting survival rates. The results from Cox regression analysis suggested that risk score can serve as an independent prognostic element for survival durations. Survival analysis revealed that low risk patients have extended survival periods compared with those with high risk. The results of the immune analysis indicated that risk score has a positive correlation with B cell and CD4⁺ T cell Th2, while has a negative relationship with endothelial cell and hematopoietic cells. Besides, immune checkpoint genes have higher expression folds in the high-risk set than in the low-risk set. The high-risk group had higher rates of genetic mutation than the low-risk set while having a shorter survival time. GSEA revealed the signaling pathways enriched in the high-risk group were mostly immune-related, while metabolic-related pathways were enriched in the low-risk group. Drugs sensitivity analysis indicated that our model has the ability to predict the efficacy of clinical treatment. Conclusion: The Cuproptosis-related lncRNAs prognostic formula is a novel predictor of HCC patients' prognosis and drug sensitivity.

Genes Modulating Butyrate Metabolism for Assessing Clinical Prognosis and Responses to Systematic Therapies in Hepatocellular Carcinoma

Butyrate, one of the major products of the gut microbiota, has played notable roles in diverse therapies for multiple tumors. Our study aimed to determine the roles of genes that modulate butyrate metabolism (BM) in predicting the clinical prognosis and responses to systemic therapies in hepatocellular carcinoma (HCC). The genes modulating BM were available from the GeneCard database, and gene expression and clinical information were obtained from TCGA-LIHC, GEO, ICGC-JP, and CCLE databases. Candidate genes from these genes that regulate BM were then identified by univariate Cox analysis. According to candidate genes, the patients in TCGA were grouped into distinct subtypes. Moreover, BM- related gene signature (BMGs) was created via the LASSO Cox algorithm. The roles of BMGs in identifying high-risk patients of HCC, assessing the prognoses, and predicting systematic therapies were determined in various datasets. The statistical analyses were fulfilled with R 4.1.3, GraphPad Prism 8.0 and Perl 5.30.0.1 software. In the TCGA cohort, most butyrate-related genes were over-expressed in the B cluster, and patients in the B cluster showed worse prognoses. BMGs constructed by LASSO were composed of eight genes. BMGs exhibited a strong performance in evaluating the prognoses of HCC patients in various datasets, which may be superior to 33 published biomarkers. Furthermore, BMGs may contribute to the early surveillance of HCC, and BMGs could play active roles in assessing the effectiveness of immunotherapy, TACE, ablation therapy, and chemotherapeutic drugs for HCC. BMGs may be served as novel promising biomarkers for early identifying high-risk groups of HCC, as well as assessing prognoses, drug sensitivity, and the responses to immunotherapy, TACE, and ablation therapy in patients with HCC.

Construction of a prognostic risk model based on apoptosis-related genes to assess tumor immune microenvironment and predict prognosis in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a serious malignant disease with high incidence, high mortality and poor prognosis. This study aimed to establish a novel signature based on apoptosis-related genes (ARGs) to predict the prognosis of HCC.

METHODS

Expression data of HCC from TCGA database and the list of 160 ARGs from MSigDB were downloaded. The genes included in apoptosis-related signature were selected by univariate Cox regression analysis and lasso Cox regression analysis. Subsequently, a prognostic risk model for scoring patients was developed, and then separates patients into two groups. Kaplan-Meier and receiver operating characteristic analysis were performed to evaluate the prognostic value of the model in TCGA, GEO and ICGC databases. The characteristics of immune cell infiltration between two groups of HCC were investigated. Finally, a nomogram was plotted to visualize the prognosis prediction.

RESULTS

Nine genes (CDC25B, DAP3, ETF1, GSR, LGALS3, MGMT, PPP2R5B, SQSTM1 and VDAC2) were included in the prognostic risk model. Survival was lower in the high-risk group. Surprisingly, the high-risk group was significantly more in immune cell infiltration and with higher immunoscore and stromalscore than in the low-risk group. In addition, the risk score was an independent prognostic factor for HCC.

CONCLUSIONS

Prognostic signature comprising nine ARGs could be used as a potential prognostic factor for HCC. It also provides an important idea for further understanding the immunotherapy of HCC.

Advances in Cancer Diagnosis: Bio-Electrochemical and Biophysical Characterizations of Cancer Cells

Cancer is a worldwide leading cause of death, and it is projected that newly diagnosed cases globally will reach 27.5 million each year by 2040. Cancers (malignant tumors), unlike benign tumors are characterized by structural and functional dedifferentiation (anaplasia), breaching of the basement membrane, spreading to adjacent tissues (invasiveness), and the capability to spread to distant sites (metastasis). In the cancer biology research field, understanding and characterizing cancer metastasis as well as features of cell death (apoptosis) is considered a technically challenging subject of study and clinically is very critical and necessary. Therefore, in addition to the cytochemical methods traditionally used, novel biophysical and bioelectrochemical techniques (e.g., cyclic voltammetry and electrochemical impedance spectroscopy), atomic force microscopy, and electron microscopic methods are increasingly being deployed to better understand these processes. Implementing those methods at the preclinical level enables the rapid screening of new anticancer drugs with understanding of their central mechanism for cancer therapy. In this review, principles and basic concepts of new techniques suggested for metastasis, and apoptosis examinations for research purposes are introduced, along with examples of each technique. From our recommendations, the privilege of combining the bio-electrochemical and biosensing techniques with the conventional cytochemical methods either for research or for biomedical diagnosis should be emphasized.

Non-Apoptotic Programmed Cell Death-Related Gene Signature Correlates With Stemness and Immune Status and Predicts the Responsiveness of Transarterial Chemoembolization in Hepatocellular Carcinoma

Background: Non-apoptotic programmed cell death, including autophagy, ferroptosis, and pyroptosis, newly discovered in recent years, plays an important role in hepatocellular carcinoma (HCC). So, this study attempted to explore the relationship between non-apoptotic programmed cell death-related genes and the molecular characteristics, tumor microenvironment, and prognosis in HCC patients.

Methods: The transcriptomic and clinical data of HCC samples were downloaded from various public datasets, followed by acquiring non-apoptotic programmed cell death-related genes from the database. A gene signature model was then constructed using univariate and multivariate Cox regression analyses and validated in other cohorts as well as our institution sequencing data. Kaplan–Meier survival curves and time-dependent receiver operating characteristic curves were generated to evaluate the model’s predictive capability. Furthermore, the relationships among the gene signature, TP53 mutation, stemness, immune status, and responsiveness of transarterial chemoembolization (TACE) were analyzed.

Results: The gene signature model was constructed based on five autophagy-, three ferroptosis-, and two pyroptosis-related differentially expressed genes. The model accurately predicted that patients classified as low risk would have better overall survival than high-risk patients, which was robustly consistent with data from other cohorts as well as our institution sequencing data. The comprehensive results indicated that a high-risk index was correlated with a high TP53 mutation rate, high cancer cell stemness, high infiltration of immunosuppressive cells and low immunophenoscore, and low TACE responsiveness of HCC patients.

Conclusion: Collectively, the established non-apoptotic programmed cell death-related gene signature was shown to accurately predict prognosis, associated with the TP53 mutation and liver cancer cell stemness, reflect the tumor immune microenvironment, and predict TACE responsiveness in HCC patients.

Adenosine triphosphate-binding cassette subfamily C members in liver hepatocellular carcinoma: Bioinformatics-driven prognostic value

Aberrant expression of adenosine triphosphate-binding cassette subfamily C (ABCC), one of the largest superfamilies and transporter gene families of membrane proteins, is associated with various tumors. However, its relationship with liver hepatocellular carcinoma (LIHC) remains unclear.We used the Oncomine, UALCAN, Human Protein Atlas, GeneMANIA, GO, Kyoto Encyclopedia of Genes and Genomes (KEGG), TIMER, and Kaplan-Meier Plotter databases. On May 20, 2021, we searched these databases for the terms ABCC1, ABCC2, ABCC3, ABCC4, ABCC5, ABCC6, ABCC7, ABCC8, ABCC9, ABCC10, ABCC11, ABCC12, ABCC13, and "liver cancer." The exposure group comprised LIHC patients, and the control group comprised normal patients (those with noncancerous liver tissue). All patients shown in the retrieval language search were included. We compared the mRNA expression of these proteins in LIHC and control patients to examine the potential role of ABCC1-13 in LIHC.Relative to the normal liver tissue, mRNA expression of ABCC1/2/3/4/5/6/10 was significantly upregulated (P < .001), and that of ABCC9/11 significantly downregulated (both P < .001), in LIHC. ABCC mRNA expression varied with gender (P < .05), except for ABCC11-13; with tumor grade (P < 0.05), except for ABCC7/12/13; with tumor stage (P < .05), except for ABCC11-13; and with lymph node metastasis status (P < .05), except for ABCC7/8/11/12/13. Based on KEGG enrichment analysis, these genes were associated with the following pathways: ABC transporters, Bile secretion, Antifolate resistance, and Peroxisome (P < .05). Except for ABCC12/13, the ABCCs were significantly associated with B cell, CD8+ T cell, CD4+ T cell, macrophage, neutrophil, and dendritic cell infiltration (P < .05). High mRNA expression of ABCC1/4/5/8 (P < .05) and low expression of ABCC6/7/9/12/13 (P < .05) indicated poor prognosis. Prognostic significance was indicated for ABCC2/13 for both men and women (P < .05); for ABCC1/6/12/13 for tumor grades 1-3 (P < .05); for ABCC5/11/12/13 for all tumor stages (P < .05); for ABCC1/11/12/13 for American Joint Committee on Cancer T stages 1-3 (P < .05); and for ABCC1/5/6/13 for vascular invasion. None showed prognostic significance for microvascular invasion (P < .05).We identified ABCC1/2/3/4/5/6/9/10/11 as potential diagnostic markers, and ABCC1/4/5/6/7/8/9/12/13 as prognostic markers, of LIHC. Our future work will promote the use of ABCCs in the diagnosis and treatment of LIHC.

Novel Risk Classification Based on Pyroptosis-Related Genes Defines Immune Microenvironment and Pharmaceutical Landscape for Hepatocellular Carcinoma

Growing evidence has indicated that pyroptosis functions in the development of cancer. Nonetheless, specific roles of pyroptosis-related genes in tumor progression, immune response, prognosis, and immunotherapy have not been thoroughly elucidated. After a comprehensive evaluation of pyroptosis genes, unsupervised clustering was performed to generate three distinct clusters from hepatocellular carcinoma (HCC) samples. Three distinct pyroptosis-related molecular subtypes comprising three gene clusters that had differential prognostic effects on patient survival were then identified. Immune characteristics analyses revealed diversified immune cell infiltration among the subtypes. Two clusters served as immune-hot phenotypes associated with significantly poorer survival compared to a remaining third immune-cold cluster. Among these, the immune-hot clusters were characterized by abundant adaptive immune cell infiltration, active CD4+ and CD8+ T cells, high total leukocyte counts and tumor growth status, and lower Th17 cell and M2 macrophage densities. Then, risk scores indicated that low-risk patients were more sensitive to anti-tumor therapy. Subsequently, we found a significant correlation between pyroptosis and prognosis in HCC and that pyroptosis genes drive the heterogeneity of the tumor microenvironment. The risk scoring system, based on pyroptosis-related differentially expressed genes, was established to evaluate the individual outcomes and contribute to new insights into the molecular characterization of pyroptosis-related subtypes.

A Pyroptosis-Based Prognostic Model for Immune Microenvironment Estimation of Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC), an aggressive malignant tumor, has a high incidence and unfavorable prognosis. Recently, the synergistic effect of pyroptosis in antitumor therapy and regulation of tumor immune microenvironment has made it possible to become a novel therapeutic method, but its potential mechanism still needs further exploration.

Methods

Differentially expressed genes with prognostic value in Liver Hepatocellular Carcinoma Project of The Cancer Genome Atlas (TCGA-LIHC) cohort were screened and incorporated into the risk signature by Cox proportional hazards regression model and least absolute shrinkage and selection operator. Kaplan-Meier (KM) curves and receiver operating characteristic (ROC) curves were applied to conduct survival comparisons and estimate prediction ability. The dataset of Liver Cancer-RIKEN, Japan Project from International Cancer Genome Consortium (ICGC-LIRI-JP) cohort was used to verify the reliability of the signature. Correlation analysis between clinicopathological characteristics, immune infiltration, drug sensitivities, and risk scores was conducted. Functional annotation analyses were performed for the genes differentially expressed between high-risk and low-risk groups.

Results

A risk signature consisting of 6 pyroptosis-related genes in HCC was developed and validated. KM curves and ROC curves revealed its considerable predictive accuracy. Higher risk scores meant more advanced grade, higher alpha-fetoprotein level, and stronger invasive ability. Overexpressed genes in high-risk population were more enriched in the immune-associated pathways, and these patients might be more sensitive to immune checkpoint inhibitors instead of Sorafenib. Intriguingly, 6 identified genes were promising to be prognostic biomarkers and therapeutic targets of HCC.

Conclusions

The signature may have crucial clinical significance in predicting survival prognosis, immune infiltration, and drug efficacy based on pyroptosis-related genes.

Bi-specific and Tri-specific NK Cell Engagers: The New Avenue of Targeted NK Cell Immunotherapy

Natural killer (NK) cell-mediated cancer immunotherapy has grown significantly over the past two decades. More recently, multi-specific engagers have been developed as cancer therapeutics to effectively arm endogenous NK cells to more potently induce specific cytolytic responses against tumor targets. This review explores the bi- and tri-specific NK/tumor engagers that are emerging as a new generation of immunotherapeutics. These molecules vary in configuration, but they typically have small molecular weights and domains that engage specific tumor antigens and NK cell-activating receptors such as CD16, NKp30, NKp46, and NKG2D. They have demonstrated compelling potential in boosting NK cell cytotoxicity against specific tumor targets. This highly adaptable off-the-shelf platform, which in some formats also integrates cytokines, is poised to revolutionize targeted NK cell immunotherapy, either as a monotherapy or in combination with other effective anti-cancer therapies.

A Newly Defined Pyroptosis-Related Gene Signature for the Prognosis of Bladder Cancer

BACKGROUND

Bladder cancer (BC), as the most common urinary system tumor type and the main cause of tumor-related death, has an unsatisfactory prognosis. In recent years, related literature has proposed that cell pyroptosis is an inflammatory form of programmed cell death. However, in BC, the relationship between the expression of pyroptosis-related genes and the prognosis has not been elucidated.

METHODS

We got the RNA sequencing data from TCGA and GEO datasets. Fifty-two pyroptosis-related genes were extracted for further explore. Then, we compared the gene expression levels between the normal bladder and BC tissues. After that, we develop and validate a pyroptosis-related gene prognostic model and made following functional enrichment analysis and single-sample gene set enrichment analysis of the differentially expressed genes between the high- and low-risk groups.

RESULTS

Twenty-nine differentially expressed genes (DEGs) were found between normal and tumor tissues. Based on the median score calculated by the risk score formula from 8 pyroptosis-related genes, 414 patients were equally divided into low- and high-risk subgroups. The survival probability of BC patients in the high-risk group was significantly lower than that in the low-risk group (P < 0.001). Through multivariate analysis, our risk score is an independent factor predicting OS in BC patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis show that high-risk populations are rich in immune-related genes and have a decreased immune status. All the above results have been externally verified from GEO cohort.

CONCLUSION

Pyroptosis-related genes are closely related to tumor immunity and are a potential prognostic tool for predicting BCs.