Identification and Validation of a Novel Six-Gene Expression Signature for Predicting Hepatocellular Carcinoma Prognosis

Crosstalk of non-apoptotic RCD panel in hepatocellular carcinoma reveals the prognostic and therapeutic optimization

Non-apoptotic regulated cell death (RCD) of tumor cells profoundly affects tumor progression and plays critical roles in determining response to immune checkpoint inhibitors (ICIs). Prognosis-distinctive HCC subtypes were identified by consensus cluster analysis based on the expressions of 507 non-apoptotic RCD genes obtained from databases and literature. Meanwhile, a set of bioinformatic tools was integrated to analyze the differences of the tumor immune microenvironment infiltration, genetic mutation, copy number variation, and epigenetics alternations within two subtypes. Finally, a non-apoptotic RCDRS signature was constructed and its reliability was evaluated in HCC patients' tissues. The high-RCDRS HCC subgroup showed a significantly lower overall survival and less sensitivity to ICIs compared to low-RCDRS subgroup, but higher sensitivity to cisplatin, paclitaxel, and sorafenib. Overall, we established an RCDRS panel consisting of four non-apoptotic RCD genes, which might be a promising predictor for evaluating HCC prognosis, guiding therapeutic decision-making, and ultimately improving patient outcomes.

Identification of BMAL1-Regulated circadian genes in mouse liver and their potential association with hepatocellular carcinoma: Gys2 and Upp2 as promising candidates

Identification and functional analysis of key genes regulated by the circadian clock system will provide a comprehensive understanding of the underlying mechanisms through which circadian clock disruption impairs the health of living organisms. The initial phase involved bioinformatics analysis, drawing insights from three RNA-seq datasets (GSE184303, GSE114400, and GSE199061) derived from wild-type mouse liver tissues, which encompassed six distinct time points across a day. As expected, 536 overlapping genes exhibiting rhythmic expression patterns were identified. By intersecting these genes with differentially expressed genes (DEGs) originating from liver RNA-seq data at two representative time points (circadian time, CT: CT2 and CT14) in global Bmal1 knockout mice (Bmal1-/-), hepatocyte-specific Bmal1 knockout mice (L-Bmal1-/-), and their corresponding control groups, 80 genes potentially regulated by BMAL1 (referred to as BMAL1-regulated genes, BRGs) were identified. These genes were significantly enriched in glycolipid metabolism, immune response, and tumorigenesis pathways. Eight BRGs (Nr1d1, Cry1, Gys2, Homer2, Serpina6, Slc2a2, Nmrk1, and Upp2) were selected to validate their expression patterns in both control and L-Bmal1-/- mice livers over 24 h. Real-time quantitative polymerase chain reaction results demonstrated a comprehensive loss of rhythmic expression patterns in the eight selected BRGs in L-Bmal1-/- mice, in contrast to the discernible rhythmic patterns observed in the livers of control mice. Additionally, significant reductions in the expression levels of these selected BRGs, excluding Cry1, were also observed in L-Bmal1-/- mice livers. Chromatin immunoprecipitation (ChIP)-seq (GSE13505 and GSE39860) and JASPAR analyses validated the rhythmic binding of BMAL1 to the promoter and intron regions of these genes. Moreover, the progression of conditions, from basic steatosis to non-alcoholic fatty liver disease, and eventual malignancy, demonstrated a continuous gradual decline in Bmal1 transcripts in the human liver. Combining the aforementioned BRGs with DEGs derived from human liver cancer datasets identified Gys2 and Upp2 as potential node genes bridging the circadian clock system and hepatocellular carcinoma (HCC). In addition, CCK8 and wound healing assays demonstrated that the overexpression of human GYS2 and UPP2 proteins inhibited the proliferation and migration of HepG2 cells, accompanied by elevated expression of p53, a tumor suppressor protein. In summary, this study systematically identified rhythmic genes in the mouse liver, and a subset of circadian genes potentially regulated by BMAL1. Two circadian genes, Gys2 and Upp2, have been proposed and validated as potential candidates for advancing the prevention and treatment of HCC.

Identification and Validation of a Novel Anoikis-Related Gene Signature for Predicting Survival in Patients With Serous Ovarian Cancer

Background

Ovarian cancer is an extremely deadly gynecological malignancy, with a 5-year survival rate below 30%. Among the different histological subtypes, serous ovarian cancer (SOC) is the most common. Anoikis significantly contributes to the progression of ovarian cancer. Therefore, identifying an anoikis-related signature that can serve as potential prognostic predictors for SOC is of great significance.

Methods

We intersected 308 anoikis-related genes (ARGs) and identified those significantly associated with SOC prognosis using univariate Cox regression. A LASSO Cox regression model was constructed and evaluated using Kaplan-Meier and receiver operating characteristic (ROC) analyses in TCGA (The Cancer Genome Atlas) and GSE26193 cohorts. We conducted quantitative real-time polymerase chain reaction (qPCR) to assess mRNA levels and applied bioinformatics to investigate the correlation between risk groups and gene expression, mutations, pathways, tumor immune microenvironment (TIME), and drug sensitivity in SOC.

Results

Among 308 ARGs, 28 were significantly associated with SOC prognosis. A 13-gene prognostic model was established through LASSO Cox regression in TCGA cohort. High-risk group had poorer prognosis than low-risk group (median overall survival (mOS): 34.2 vs. 57.1 months, hazard ratio (HR): 2.590, 95% confidence interval (CI): 0.159 - 6.00, P < 0.001). The area under the curve (AUC) values of 0.63, 0.65, and 0.74 reflected the predictive performance for 3-, 5-, and 8-year overall survival (OS) in GSE26193 validation cohort. Functional enrichment, pathway analysis, and TIME analysis identified distinct characteristics between risk groups. Drug sensitivity analysis revealed potential drug advantages for each group. Furthermore, qPCR validation once again confirmed the effectiveness of the risk model in SOC patients.

Conclusions

We developed and validated a robust ARG model, which could be used to predict OS in SOC patients. By systematically analyzing the correlation between the risk score of the ARGs signature model and various patterns, including the TIME and drug sensitivity, our findings suggest that this prognostic model contributes to the advancement of personalized and precise therapeutic strategies. Nevertheless, further validation studies and investigations into the underlying mechanisms are warranted.

Development and validation of robust metabolism-related gene signature in the prognostic prediction of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumours worldwide. Given metabolic reprogramming in tumours was a crucial hallmark, several studies have demonstrated its value in the diagnostics and surveillance of malignant tumours. The present study aimed to identify a cluster of metabolism-related genes to construct a prediction model for the prognosis of HCC. Multiple cohorts of HCC cases (466 cases) from public datasets were included in the present analysis. (GEO cohort) After identifying a list of metabolism-related genes associated with prognosis, a risk score based on metabolism-related genes was formulated via the LASSO-Cox and LASSO-pcvl algorithms. According to the risk score, patients were stratified into low- and high-risk groups, and further analysis and validation were accordingly conducted. The results revealed that high-risk patients had a significantly worse 5-year overall survival (OS) than low-risk patients in the GEO cohort. (30.0% vs. 57.8%; hazard ratio [HR], 0.411; 95% confidence interval [95% CI], 0.302-0.651; p < 0.001) This observation was confirmed in the external TCGA-LIHC cohort. (34.5% vs. 54.4%; HR 0.452; 95% CI, 0.299-0.681; p < 0.001) To promote the predictive ability of the model, risk score, age, gender and tumour stage were integrated into a nomogram. According to the results of receiver operating characteristic curves and decision curves analysis, the nomogram score possessed a superior predictive ability than conventional factors, which indicate that the risk score combined with clinicopathological features was able to achieve a robust prediction for OS and improve the individualized clinical decision making of HCC patients. In conclusion, the metabolic genes related to OS were identified and developed a metabolism-based predictive model for HCC. Through a series of bioinformatics and statistical analyses, the predictive ability of the model was approved.

Identification of oncogenes and tumor-suppressor genes with hepatocellular carcinoma: A comprehensive analysis based on TCGA and GEO datasets

Aim: Existing targeted therapies for hepatocellular carcinoma (HCC) are resistant and have limitations. It is crucial to find new HCC-related target genes. Methods: RNA-sequencing data of HCC were gathered from The Cancer Genome Atlas and Gene Expression Omnibus datasets. Initially, differentially expressed genes between normal and tumor tissues were identified from four Gene Expression Omnibus datasets, GSE36376, GSE102079, GSE54236, and GSE45267. GO terms and KEGG pathway enrichment analyses were performed to explore the potential biological functions of differentially expressed genes. A PPI network was constructed by using the STRING database, and up-regulated and down-regulated hub genes were defined through 12 topological approaches. Subsequently, the correlation bounded by up-regulated genes and down-regulated genes in the diagnosis, prognosis, and clinicopathological features of HCC was analyzed. Beyond a shadow of doubt, the key oncogene PBK and tumor suppressor gene F9 were screened out, and the specific mechanism was investigated through GSEA enrichment analysis and immune correlation analysis. The role of PBK in HCC was further verified by western blot, CCK8, transwell, and tube formation experiments. Results: CDCA5, CDC20, PBK, PRC1, TOP2A, and NCAPG are good indicators of HCC diagnosis and prognosis. The low expressions of F9, AFM, and C8B indicate malignant progression and poor prognosis of HCC. PBK was found to be closely related to VEGF, VEGFR, and PDGFR pathways. Experiments showed that PBK promotes HCC cell proliferation, migration, invasion, and tube formation in HUVEC cells. F9 was negatively correlated with the degree of immune infiltration, and low expression of F9 suggested a poor response to immunotherapy. Conclusion: The role of HCC-related oncogenes and tumor-suppressor genes in diagnosis and prognosis was identified. In addition, we have found that PBK may promote tumor proliferation through angiogenesis and F9 may be a predictor of tumor immunotherapy response.

A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for acute myeloid leukemia

Background

Cuproptosis is a type of programmed cell death that is involved in multiple physiological and pathological processes, including cancer. We constructed a prognostic cuproptosis-related long non-coding RNA (lncRNA) signature for acute myeloid leukemia (AML).

Methods

RNA-seq and clinical data for AML patients were acquired from The Cancer Genome Atlas (TCGA) database. The cuproptosis-related prognostic lncRNAs were identified by co-expression and univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) was performed to construct a cuproptosis-related lncRNA signature, after which the AML patients were classified into two risk groups based on the risk model. Kaplan-Meier, ROC, univariate and multivariate Cox regression, nomogram, and calibration curves analyses were used to evaluate the prognostic value of the model. Then, expression levels of the lncRNAs in the signature were investigated in AML samples by quantitative polymerase chain reaction (qPCR). KEGG functional analysis, single-sample GSEA (ssGSEA), and the ESTIMATE algorithm were used to analyze the mechanisms and immune status between the different risk groups. The sensitivities for potential therapeutic drugs for AML were also investigated.

Results

Five hundred and three lncRNAs related to 19 CRGs in AML samples from the TCGA database were obtained, and 21 differentially expressed lncRNAs were identified based on the 2-year overall survival (OS) outcomes of AML patients. A 4-cuproptosis-related lncRNA signature for survival was constructed by LASSO Cox regression. High-risk AML patients exhibited worse outcomes. Univariate and multivariate Cox regression analyses demonstrated the independent prognostic value of the model. ROC, nomogram, and calibration curves analyses revealed the predictive power of the signature. KEGG pathway and ssGSEA analyses showed that the high-risk group had higher immune activities. Lastly, AML patients from different risk groups showed differential responses to various agents.

Conclusion

A cuproptosis-related lncRNA signature was established to predict the prognosis and inform on potential therapeutic strategies for AML patients.

A Novel Ferroptosis-Related LncRNA Pair Prognostic Signature Predicts Immune Landscapes and Treatment Responses for Gastric Cancer Patients

Background: The construction of ferroptosis-related lncRNA prognostic models in malignancies has been an intense area of research recently. However, most of the studies focused on the exact expression of lncRNAs and had limited application values. Herein, we aim to establish a novel prognostic model for gastric cancer (GC) patients and discuss its correlation with immune landscapes and treatment responses. Methods: The present study retrieved transcriptional data of GC patients from the Cancer Genome Atlas (TCGA) database. We identified differentially expressed ferroptosis-related lncRNAs between tumor and normal controls of GC samples. Based on a new method of cyclically single pairing, we constructed a 0 or 1 matrix of ferroptosis-related lncRNA pairs (FRLPs). A risk score signature consisting of 10 FRLPs was established using multi-step Cox regression analysis. Next, we performed a series of systematic analyses to investigate the association of the FRLP model and tumor microenvironment, biological function, and treatment responses. An alternative model to the FRLP risk score signature, the gene set score (GS) model was also constructed, which could represent the former when lncRNA expression was not available. Results: We established a novel prognostic signature of 10 ferroptosis-related lncRNA pairs. High-risk patients in our risk score model were characterized by high infiltration of immune cells, upregulated carcinogenic and stromal activities, and heightened sensitivity to a wide range of anti-tumor drugs, whereas low-risk patients were associated with better responses to methotrexate treatment and elevated immunotherapeutic sensitivity. The practicability of the FRLP risk score model was also validated in two independent microarray datasets downloaded from Gene Expression Omnibus (GEO) using the GS model. Finally, two online dynamic nomograms were built to enhance the clinical utility of the study. Conclusion: In this study, we developed a ferroptosis-related lncRNA pair-based risk score model that did not rely on the exact lncRNA expression level. This novel model might provide insights for the accurate prediction and comprehensive management for GC patients.

Identification of Hypoxia-Related Subtypes, Establishment of Prognostic Models, and Characteristics of Tumor Microenvironment Infiltration in Colon Cancer

Background: Immunotherapy is a treatment that can significantly improve the prognosis of patients with colon cancer, but the response to immunotherapy is different in patients with colon cancer because of the heterogeneity of colon carcinoma and the complex nature of the tumor microenvironment (TME). In the precision therapy mode, finding predictive biomarkers that can accurately identify immunotherapy-sensitive types of colon cancer is essential. Hypoxia plays an important role in tumor proliferation, apoptosis, angiogenesis, invasion and metastasis, energy metabolism, and chemotherapy and immunotherapy resistance. Thus, understanding the mechanism of hypoxia-related genes (HRGs) in colon cancer progression and constructing hypoxia-related signatures will help enrich our treatment strategies and improve patient prognosis.

Methods: We obtained the gene expression data and corresponding clinical information of 1,025 colon carcinoma patients from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, respectively. We identified two distinct hypoxia subtypes (subtype A and subtype B) according to unsupervised clustering analysis and assessed the clinical parameters, prognosis, and TME cell-infiltrating characteristics of patients in the two subtypes. We identified 1,132 differentially expressed genes (DEGs) between the two hypoxia subtypes, and all patients were randomly divided into the training group (n = 513) and testing groups (n = 512). Following univariate Cox regression with DEGs, we construct the prognostic model (HRG-score) including six genes (S1PR3, ETV5, CD36, FOXC1, CXCL10, and MMP12) through the LASSO–multivariate cox method in the training group. We comprehensively evaluated the sensitivity and applicability of the HRG-score model from the training group and the testing group, respectively. We explored the correlation between HRG-score and clinical parameters, tumor microenvironment, cancer stem cells (CSCs), and MMR status. In order to evaluate the value of the risk model in clinical application, we further analyzed the sensitivity of chemotherapeutics and immunotherapy between the low-risk group and high-risk group and constructed a nomogram for improving the clinical application of the HRG-score.

Result: Subtype A was significantly enriched in metabolism-related pathways, and subtype B was significantly enriched in immune activation and several tumor-associated pathways. The level of immune cell infiltration and immune checkpoint-related genes, stromal score, estimate score, and immune dysfunction and exclusion (TIDE) prediction score was significantly different in subtype A and subtype B. The level of immune checkpoint-related genes and TIDE score was significantly lower in subtype A than that in subtype B, indicating that subtype A might benefit from immune checkpoint inhibitors. Finally, an HRG-score signature for predicting prognosis was constructed through the training group, and the predictive capability was validated through the testing group. The survival analysis and correlation analysis of clinical parameters revealed that the prognosis of patients in the high-risk group was significantly worse than that in the low-risk group. There were also significant differences in immune status, mismatch repair status (MMR), and cancer stem cell index (CSC), between the two risk groups. The correlation analysis of risk scores with IC₅₀ and IPS showed that patients in the low-risk group had a higher benefit from chemotherapy and immunotherapy than those in the high-risk group, and the external validation IMvigor210 demonstrated that patients with low risk were more sensitive to immunotherapy.

Conclusion: We identified two novel molecular subgroups based on HRGs and constructed an HRG-score model consisting of six genes, which can help us to better understand the mechanisms of hypoxia-related genes in the progression of colon cancer and identify patients susceptible to chemotherapy or immunotherapy, so as to achieve precision therapy for colon cancer.