Identifying a Novel Endoplasmic Reticulum-Related Prognostic Model for Hepatocellular Carcinomas

KIF2C/MCAK a prognostic biomarker and its oncogenic potential in malignant progression, and prognosis of cancer patients: a systematic review and meta-analysis as biomarker

KIF2C/MCAK (KIF2C) is the most well-characterized member of the kinesin-13 family, which is critical in the regulation of microtubule (MT) dynamics during mitosis, as well as interphase. This systematic review briefly describes the important structural elements of KIF2C, its regulation by multiple molecular mechanisms, and its broad cellular functions. Furthermore, it systematically summarizes its oncogenic potential in malignant progression and performs a meta-analysis of its prognostic value in cancer patients. KIF2C was shown to be involved in multiple crucial cellular processes including cell migration and invasion, DNA repair, senescence induction and immune modulation, which are all known to be critical during the development of malignant tumors. Indeed, an increasing number of publications indicate that KIF2C is aberrantly expressed in multiple cancer entities. Consequently, we have highlighted its involvement in at least five hallmarks of cancer, namely: genome instability, resisting cell death, activating invasion and metastasis, avoiding immune destruction and cellular senescence. This was followed by a systematic search of KIF2C/MCAK's expression in various malignant tumor entities and its correlation with clinicopathologic features. Available data were pooled into multiple weighted meta-analyses for the correlation between KIF2Chigh protein or gene expression and the overall survival in breast cancer, non-small cell lung cancer and hepatocellular carcinoma patients. Furthermore, high expression of KIF2C was correlated to disease-free survival of hepatocellular carcinoma. All meta-analyses showed poor prognosis for cancer patients with KIF2Chigh expression, associated with a decreased overall survival and reduced disease-free survival, indicating KIF2C's oncogenic potential in malignant progression and as a prognostic marker. This work delineated the promising research perspective of KIF2C with modern in vivo and in vitro technologies to further decipher the function of KIF2C in malignant tumor development and progression. This might help to establish KIF2C as a biomarker for the diagnosis or evaluation of at least three cancer entities.

Construction and validation of a prognostic signature based on seven endoplasmic reticulum stress-related lncRNAs for patients with head and neck squamous cell carcinoma

Endoplasmic reticulum stress (ERS) occurs when misfolded or unfolded proteins accumulate in the endoplasmic reticulum (ER), and it is often observed in tumors, including head and neck squamous cell carcinoma (HNSCC). Relevant studies have demonstrated the prognostic significance of ERS-related long non-coding RNAs (lncRNAs) in various cancers. However, the relationship between ERS and lncRNAs in HNSCC has received limited attention in previous studies. In this study, we aimed to develop an ERS-related lncRNAs prognostic model using correlation analysis, Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression analysis based on data from The Cancer Genome Atlas (TCGA) database. The survival and predictive ability of this model were evaluated using Kaplan-Meier analysis and time-dependent receiver operating characteristics (ROC), while nomograms and calibration curves were constructed. Then, functional enrichment analyses, tumor mutation burden (TMB), tumor infiltration of immune cells, single sample Gene Set Enrichment Analysis (ssGSEA), and drug sensitivity analysis were performed. Additionally, we conducted a consensus cluster analysis to compare differences between subtypes of tumors. Finally, we validated the expression of the ERS-related lncRNAs that constructed prognostic risk score model in HNSCC tissues through quantitative real-time PCR (qRT-PCR). We developed a prognostic signature based on seven ERS-related lncRNAs, which showed better predictive performance than other clinicopathological features. The high-risk poor prognosis group had a poorer prognosis in comparison to the low-risk good prognosis. The area under the ROC curve (AUC) predicted by this model for 3-year survival rates of HNSCC patients was 0.805. Enrichment analysis revealed that the differentially expressed genes were primarily enriched in pathways related to immune responses and signal transduction. Low-risk patients had lower TMB, more immune cell infiltrations, and enhanced anti-tumor immunity. Cluster analysis indicated that cluster 3 may have a better prognosis and immunotherapy effect. In addition, the result of qRT-PCR was consistent with our analysis. This prognostic model based on seven ERS-related lncRNAs is a promising tool for risk stratification, survival prediction, and immune cell infiltration status assessment.

A comprehensive analysis of the potential role of necroptosis in hepatocellular carcinoma using single-cell RNA Seq and bulk RNA Seq

PURPOSE

Necroptosis plays an essential role in oncogenesis and tumor progression in hepatocellular carcinoma (HCC). This study aimed to investigate the role of necroptosis in the development and progression of HCC. Specifically, we constructed a prognostic prediction model using necroptosis-associated genes (NAGs) to predict patient outcomes.

METHODS

Using data from The Cancer Genome Atlas (TCGA) database, we analyzed gene expression and clinical data. We identified a 5-gene model associated with NAGs and explored genetic features and immune cell infiltration using the CIBERSORT algorithm. In addition, we conducted single-cell RNA sequencing to investigate the potential role of necroptosis in HCC.

RESULTS

We constructed a 5-gene prognostic model based on NAGs that demonstrated excellent predictive accuracy in both training and validation sets. Using multifactorial cox regression analysis, we confirmed the risk score derived from the model as an independent predictor of prognosis, surpassing other clinical characteristics. Patients with high risk scores had significantly worse prognosis than those with low risk scores. To enhance the clinical utility of the necroptosis score, we constructed an accurate nomogram. Additionally, we compared metabolic pathway and immune microenvironment differences between HCC tumors with high and low risk scores. Our single-cell RNA sequencing analyses revealed that necroptosis in HCC was primarily associated with a specific subset of macrophages.

CONCLUSIONS

Our study revealed the presence of two distinct necroptosis subtypes in HCC and developed a robust prognostic model with exceptional predictive accuracy. We observed significantly higher infiltration of M0 macrophages in the high-risk group. We propose that rescuing cytochrome c metabolism in HCC could serve as a potential therapeutic strategy. Furthermore, at a single-cell resolution, our analysis identified myeloid cells as the primary cells exhibiting necroptosis. Specifically, macrophages expressing CD5L, CETP, and MARCO, which may belong to a subset of tissue-resident macrophages, were found to be highly susceptible to necroptosis. These findings suggest the involvement of this specific macrophage subset in potential antitumor therapies. Our study provides novel insights into predicting patient prognosis and developing personalized therapeutic approaches for HCC.

A novel investigation into an E2F transcription factor-related prognostic model with seven signatures for colon cancer patients

The pathogenesis of colon cancer, a common gastrointestinal tumour, involves complicated factors, especially a series of cell cycle-related genes. E2F transcription factors during the cell cycle play an essential role in the occurrence of colon cancer. It is meaningful to establish an efficient prognostic model of colon cancer targeting cellular E2F-associated genes. This has not been reported previously. The authors first aimed to explore the links of E2F genes with the clinical outcomes of colon cancer patients by integrating data from the TCGA-COAD (n = 521), GSE17536 (n = 177) and GSE39582 (n = 585) cohorts. The Cox regression and Lasso modelling approach to identify a novel colon cancer prognostic model involving several hub genes (CDKN2A, GSPT1, PNN, POLD3, PPP1R8, PTTG1 and RFC1) were utilised. Moreover, an E2F-related nomogram that efficiently predicted the survival rates of colon cancer patients was created. Additionally, the authors first identified two E2F tumour clusters, which showed distinct prognostic features. Interestingly, the potential links of E2F-based classification and 'protein secretion' issues of multiorgans and tumour infiltration of 'T-cell regulatory (Tregs)' and 'CD56dim natural killer cell' were detected. The authors' findings are of potential clinical significance for the prognosis assessment and mechanistic exploration of colon cancer.

Novel roles of karyopherin subunit alpha 2 in hepatocellular carcinoma

Hepatocellular carcinoma is the most common type of liver cancer and associated with a high fatality rate. This disease poses a major threat to human health worldwide. A considerable number of genetic and epigenetic factors are involved in the development of hepatocellular carcinoma. However, the molecular mechanism underlying the progression of hepatocellular carcinoma remains unclear. Karyopherin subunit alpha 2 (KPNA2), also termed importin α1, is a member of the nuclear transporter family. In recent years, KPNA2 has been gradually linked to the nuclear transport pathway for a variety of tumor-associated proteins. Furthermore, it promotes tumor development by participating in various pathophysiological processes such as cell proliferation, apoptosis, immune response, and viral infection. In hepatocellular carcinoma, it has been found that KPNA2 expression is significantly higher in liver cancer tissues versus paracancerous tissues. Moreover, it has been identified as a marker of poor prognosis and early recurrence in patients with hepatocellular carcinoma. Nevertheless, the role of KPNA2 in the development of hepatocellular carcinoma remains to be determined. This review summarizes the current knowledge on the pathogenesis and role of KPNA2 in hepatocellular carcinoma, and provides new directions and strategies for the diagnosis, treatment, and prediction of prognosis of this disease.

Chromosome instability-associated prognostic signature and cluster investigation for cutaneous melanoma cases

Chromosomal instability (CIN) is closely associated to the early detection of several clinical tumours. In this study, the authors first established a novel prognostic model of melanoma using the hub genes of CIN, based on the datasets of The cancer genome atlas-skin cutaneous melanoma (TCGA-SKCM) and GSE65904 cohorts. Based on the risk scores of our model, the disease-specific survival (DSS) prognosis was worse in the high-risk group. Combining risk score, stage, age, ulceration, and clark factors, a Nomogram was generated to predict 1, 3, 5-year survival rates, which indicated a good clinical validity. Our finding also showed a correlation between high/low risk and tumour infiltration levels of 'activated CD8 T cells' and 'effector memory CD8 T cells'. Moreover, the authors first performed a CIN-based tumour clustering analysis using TCGA-SKCM cases, and identified two melanoma clusters, which exhibit the distinct DSS prognosis and the tumour-infiltrating levels of CD8 T cells. Taken together, a promising CIN-related prognostic signature and clustering for melanoma cases were first established in our study.

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.

Integration of single-cell RNA sequencing and bulk RNA sequencing to reveal an immunogenic cell death-related 5-gene panel as a prognostic model for osteosarcoma

Background

Despite the comparatively low prevalence of osteosarcoma (OS) compared to other cancer types, metastatic OS has a poor overall survival rate of fewer than 30%. Accumulating data has shown the crucial functions of immunogenic cell death (ICD) in various cancers; nevertheless, the relationship between ICD and OS was not previously well understood. This research aims to determine the function of ICD in OS and construct an ICD-based prognostic panel.

Methods

Single cell RNA sequencing data from GSE162454 dataset distinguished malignant cells from normal cells in OS. The discrepancy in ICD scores and corresponding gene expression was intensively explored between malignant cells and normal cells. Using the RNA sequencing data of the TARGET-OS, GSE16091, GSE21257, and GSE39058 datasets, the molecular subtype of OS was determined by clustering seventeen ICD-related genes obtained from the literature. Differentially expressed genes (DEGs) between different molecular subtypes were identified to develop a novel ICD-associated prognostic panel.

Results

The malignant cells had a remarkable decrease in the ICD scores and corresponding gene expression compared with normal cells. A total of 212 OS patients were successfully stratified into two subtypes: C1 and C2. C1-like OS patients were characterized by better prognostic outcomes, overexpression of ICD genes, activation of the ICD pathway, high inflitration abundance of immunocytes, and low expression levels of immune checkpoint genes (ICGs); however, the reverse is true in C2-like OS patients. Utilizing the limma programme in R, the DEGs between two subtypes were determined, and a 5-gene risk panel consisting of BAMBI, TMCC2, NOX4, DKK1, and CBS was developed through LASSO-Cox regression analysis. The internal- and external-verification cohorts were employed to verify the efficacy and precision of the risk panel. The AUC values of ROC curves indicated excellent prognostic prediction values of our risk panel.

Conclusions

Overall, ICD represented a protective factor against OS, and our 5-gene risk panel serving as a biomarker could effectively evaluate the prognostic risk in patients with OS.

Comprehensive characterisation of immunogenic cell death in melanoma revealing the association with prognosis and tumor immune microenvironment

Increasing evidence has highlighted the critical functions of immunogenic cell death (ICD) within many tumors. However, the therapeutic possibilities and mechanism of utilizing ICD in melanoma are still not well investigated. Melanoma samples involved in our study were acquired from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. First, pan-cancer analysis of ICD systematically revealed its expression characteristics, prognostic values, mutation information, methylation level, pathway regulation relationship in multiple human cancers. The non-negative matrix factorization clustering was utilized to separate the TCGA-melanoma samples into two subtypes (i.e. C1 and C2) with different prognosis and immune microenvironment based on the expression traits of ICD. Then, LASSO-Cox regression analysis was utilized to determine an ICD-dependent risk signature (ICDRS) based on the differentially expressed genes (DEGs) between the two subtypes. Principal component analysis and t-distributed stochastic neighbor embedding analysis of ICDRS showed that high- and low-risk subpopulations could be clearly distinguished. Survival analysis and ROC curves in the training, internal validation, and external validation cohorts highlighted the accurate prognosis evaluation of ICDRS. The obvious discrepancies of immune microenvironment between the different risk populations might be responsible for the different prognoses of patients with melanoma. These findings revealed the close association of ICD with prognosis and tumor immune microenvironment. More importantly, ICDRS-based immunotherapy response and targeted drug prediction might be beneficial to different risk subpopulations of patients with melanoma. The innotative ICDRS could function as a marker to determine the prognosis and tumor immune microenvironment in melanoma. This will aid in patient classification for individualized melanoma treatment.