A novel prognostic index of stomach adenocarcinoma based on immunogenomic landscape analysis and immunotherapy options

Comprehensive Analysis for Predicting Prognoses and Immune Responses of m6A-Related lncRNAs in Women with Lung Adenocarcinoma

During the past decade, the average 5-year survival rate of patients with Lung adenocarcinoma (LUAD) has remained < 20%, although the targeted therapies and novel immunotherapy approaches have held promise. Epigenetic modifications could provide prognostic value as molecular biomarkers, and we aimed to identify the independent risk of m6A-related lncRNAs to establish a risk model for the clinical prediction of prognoses in women with LUAD. In this study, we first assessed 31 N6-methyladenosine (m6A)-related lncRNAs associated with overall survival. Moreover, we evaluated the expression of the oncogenic driver and the tumor immune microenvironment (TIME) in two female LUAD subtypes (clusters 1 and 2) using consensus clustering. We also found 16 m6A-related lncRNAs as the independent prognostic indicator of women with LUAD and established a risk model developed from these lncRNAs. We comprehensively investigated the correlation between the TIME and m6A-related lncRNA and found that m6A-related lncRNA may significantly affect the immune cell infiltration level in LUAD. In conclusion, our study provides evidence on the prognostic prediction in women with LUAD and may help elucidate the processes and mechanisms of m6A-regulated lncRNAs.

Polyene phosphatidylcholine enhances the therapeutic response of oxaliplatin in gastric cancer through Nrf2/HMOX1 mediated ferroptosis

Oxaliplatin (OXA)-based chemotherapy is one of the first-line treatments for advanced gastric cancer. However, the potential risk for chemotherapy-induced hepatic injury can hinder its effectiveness. Polyene phosphatidylcholine (PPC) is often used as a hepatoprotective agent to counter OXA-induced hepatic injury; however, its impact on the antitumour effectiveness of OXA remains uncertain. Our retrospective study examined 98 patients with stage IV gastric cancer to assess the impact of PPC on progression-free survival (PFS) and disease control rate (DCR). Furthermore, in vitro and in vivo assays were conducted to elucidate the combined biological effects of OXA and PPC (OXA+PPC) on gastric cancer. RNA sequencing, luciferase reporter assays, live/dead cell assays, immunofluorescence, and western blotting were used to identify the activated signalling pathways and downstream factors post OXA+PPC treatment. The findings indicated that PPC served as an independent prognostic factor, correlating with prolonged PFS and improved DCR in patients with gastric cancer. The combination of OXA and PPC significantly inhibited tumour cell growth both in vitro and in vivo. RNA sequencing revealed that OXA+PPC treatment amplified reactive oxygen species and ferroptosis signalling pathways. Mechanistically, OXA+PPC upregulated the expression of haem oxygenase-1 by promoting the nuclear migration of nuclear factor erythroid 2-related factor (Nrf2), thereby enhancing its transcriptional activity. Drug-molecule docking analysis demonstrated that PPC competitively bound to the peptide structural domains of both Nrf2 and Kelch-like ECH-associated protein 1 (KEAP1), accounting for the increased translocation of Nrf2. In conclusion, our study reveals the synergistic antitumour potential of PPC and OXA while protecting patients against hepatic injury. This suggests a promising combined treatment approach for patients with advanced gastric cancer.

Prognostic analysis of hepatocellular carcinoma based on cuproptosis -associated lncRNAs

OBJECTIVES

Cuproptosis represents an innovative type of cell death, distinct from apoptosis, driven by copper dependency, yet the involvement of copper apoptosis-associated long non-coding RNAs (CRLncRNAs) in hepatocellular carcinoma (HCC) remains unclear. This study is dedicated to unveiling the role and significance of these copper apoptosis-related lncRNAs within the context of HCC, focusing on their impact on both the development of the disease and its prognosis.

METHODS

We conducted an analysis of gene transcriptomic and clinical data for HCC cases by sourcing information from The Cancer Genome Atlas database. By incorporating cuproptosis-related genes, we established prognostic features associated with cuproptosis-related lncRNAs. Furthermore, we elucidated the mechanism of cuproptosis-related lncRNAs in the prognosis and treatment of HCC through comprehensive approaches, including Lasso and Cox regression analyses, survival analyses of samples, as well as examinations of tumor mutation burden and immune function.

RESULTS

We developed a prognostic model featuring six cuproptosis-related lncRNAs: AC026412.3, AC125437.1, AL353572.4, MKLN1-AS, TMCC1-AS1, and SLC6A1-AS1. This model demonstrated exceptional prognostic accuracy in both training and validation cohorts for patients with tumors, showing significantly longer survival times for those categorized in the low-risk group compared to the high-risk group. Additionally, our analyses, including tumor mutation burden, immune function, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway enrichment, and drug sensitivity, further elucidated the potential mechanisms through which cuproptosis-associated lncRNAs may influence disease outcome.

CONCLUSIONS

The model developed using cuproptosis-related long non-coding RNAs (lncRNAs) demonstrates promising predictive capabilities for both the prognosis and immunotherapy outcomes of tumor patients. This could play a crucial role in patient management and the optimization of immunotherapeutic strategies, offering valuable insights for future research.

Development of a novel lncRNA-derived immune gene score using machine learning-based ensembles for predicting the survival of HCC

BACKGROUND

Long noncoding RNAs (lncRNAs) are implicated in the tumor immunology of hepatocellular carcinoma (HCC).

METHODS

HCC mRNA and lncRNA expression profiles were used to extract immune-related genes with the ImmPort database, and immune-related lncRNAs with the ImmLnc algorithm. The MOVICS package was used to cluster immune-related mRNA, immune-related lncRNA, gene mutation and methylation data on HCC from the TCGA. GEO and ICGC datasets were used to validate the model. Data from single-cell sequencing was used to determine the expression of genes from the model in various immune cell types.

RESULTS

With this model, the area under the curve (AUC) for 1-, 3- and 5-year survival of HCC patients was 0.862, 0.869 and 0.912, respectively. Single-cell sequencing showed EREG was significantly expressed in a variety of immune cell types. Knockdown of the EREG target gene resulted in significant anti-apoptosis, pro-proliferation and pro-migration effects in HepG2 and HUH7 cells. Moreover, serum and liver tissue EREG levels in HCC patients were significantly higher than those of healthy control patients.

CONCLUSION

We built a prognostic model with good accuracy for predicting HCC patient survival. EREG is a potential immunotherapeutic target and a promising prognostic biomarker.

Exploration and validation of a combined Hypoxia and m6A/m5C/m1A regulated gene signature for prognosis prediction of liver cancer

BACKGROUND

It is widely acknowledged that hypoxia and m6A/m5C/m1A RNA modifications promote the occurrence and development of tumors by regulating the tumor microenvironment. This study aimed to establish a novel liver cancer risk signature based on hypoxia and m6A/m5C/m1A modifications.

METHODS

We collected data from The Cancer Genome Atlas (TCGA-LIHC), the National Omics Data Encyclopedia (NODE-HCC), the International Cancer Genome Consortium (ICGC), and the Gene Expression Omnibus (GEO) databases for our study (GSE59729, GSE41666). Using Cox regression and least absolute shrinkage and selection operator (LASSO) method, we developed a risk signature for liver cancer based on differentially expressed genes related to hypoxia and genes regulated by m6A/m5C/m1A modifications. We stratified patients into high- and low-risk groups and assessed differences between these groups in terms of gene mutations, copy number variations, pathway enrichment, stemness scores, immune infiltration, and predictive capabilities of the model for immunotherapy and chemotherapy efficacy.

RESULTS

Our analysis revealed a significantly correlated between hypoxia and methylation as well as m6A/m5C/m1A RNA methylation. The three-gene prognosis signature (CEP55, DPH2, SMS) combining hypoxia and m6A/m5C/m1A regulated genes exhibited strong predictive performance in TCGA-LIHC, NODE-HCC, and ICGC-LIHC-JP cohorts. The low-risk group demonstrated a significantly better overall survival compared to the high-risk group (p < 0.0001 in TCGA, p = 0.0043 in NODE, p = 0.0015 in ICGC). The area under the curve (AUC) values for survival at 1, 2, and 3 years are all greater than 0.65 in the three cohorts. Univariate and Multivariate Cox regression analyses of the three datasets indicated that the signature could serve as an independent prognostic predictor (p < 0.001 in the three cohorts). The high-risk group exhibited more genome changes and higher homologous recombination deficiency scores and stemness scores. Analysis of immune infiltration and immune activation confirmed that the signature was associated with various immune microenvironment characteristics. Finally, patients in the high-risk group experienced a more favorable response to immunotherapy, and various common chemotherapy drugs.

CONCLUSION

Our prognostic signature which integrates hypoxia and m6A/m5C/m1A-regulated genes, provides valuable insights for clinical prediction and treatment guidance for liver cancer patients.

Disulfidptosis-related signatures for prognostic and immunotherapy reactivity evaluation in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common cancers in the world and a nonnegligible health concern on a worldwide scale. Disulfidptosis is a novel mode of cell death, which is mainly caused by the collapse of the actin skeleton. Although many studies have demonstrated that various types of cell death are associated with cancer treatment, the relationship between disulfidptosis and HCC has not been elucidated.

METHODS

Here, we mainly applied bioinformatics methods to construct a disulfidptosis related risk model in HCC patients. Specifically, transcriptome data and clinical information were downloaded from the Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) database. A total of 45 co-expressed genes were extracted between the disulfidptosis-related genes (DRGs) and the differential expression genes (DEGs) of liver hepatocellular carcinoma (LIHC) in the TCGA database. The LIHC cohort was divided into two subgroups with different prognosis by k-mean consensus clustering and functional enrichment analysis was performed. Subsequently, three hub genes (CDCA8, SPP2 and RDH16) were screened by Cox regression and LASSO regression analysis. In addition, a risk signature was constructed and the HCC cohort was divided into high risk score and low risk score subgroups to compare the prognosis, clinical features and immune landscape between the two subgroups. Finally, the prognostic model of independent risk factors was constructed and verified.

CONCLUSIONS

High DRGs-related risk score in HCC individuals predict poor prognosis and are associated with poor immunotherapy response, which indicates that risk score assessment model can be utilized to guide clinical treatment strategy.

Aberrant expression of cuproptosis‑related gene LIPT1 is associated with metabolic dysregulation of fatty acid and prognosis in hepatocellular carcinoma

PURPOSE

Lipoyltransferase 1 (LIPT1) has been recently identified as a cuproptosis‑related gene. As a key enzyme of lipoic acid metabolism, LIPT1 has been revealed to play important roles in hereditary diseases involved with lipoic acid biosynthesis defects, while its roles in hepatocellular carcinoma (HCC) remain to be elucidated. Hence, we aimed to explore the roles and mechanisms of LIPT1 in HCC progression.

METHODS

The expression of LIPT1 in HCC tissues and its clinical significance for HCC were evaluated by bioinformatic analysis and in our patient cohort. The influences of LIPT1 on the growth, migration, and lipid metabolism of HCC cells were assessed in vitro. The underlying mechanisms were explored using gene set enrichment analysis (GSEA) and molecular experiments.

RESULTS

LIPT1 expression was significantly elevated in HCC tissues compared to the normal tissues, and such upregulation was associated with more malignant pathological features and poor prognosis of patients with HCC. LIPT1 silencing significantly inhibited cell proliferation, migration, and lipid content. GSEA revealed that LIPT1 upregulation was significantly associated with various cancer-associated signaling pathways, including the PI3K-AKT signaling pathway and the Wnt/β-catenin pathway. Further molecular experiments indicated that LIPT1 silencing repressed the expression of peroxisome proliferator-activated receptor gamma (PPARγ) and inactivated the AKT/GSK-3β/β-catenin signaling axis.

CONCLUSIONS

Upregulation of LIPT1 is involved in metabolic dysregulation of fatty acid and poor prognosis of HCC patients, which suggests that LIPT1 plays an important role in reprogramming lipid metabolism and could act as a potential prognostic marker and therapeutic target for HCC.

Integrative analysis reveals that SLC38A1 promotes hepatocellular carcinoma development via PI3K/AKT/mTOR signaling via glutamine mediated energy metabolism

Although hepatocellular carcinoma (HCC) is rather frequent, little is known about the molecular pathways underlying its development, progression, and prognosis. In the current study, we comprehensively analyzed the deferentially expressed metabolism-related genes (MRGs) in HCC based on TCGA datasets attempting to discover the potentially prognostic genes in HCC. The up-regulated MRGs were further subjected to analyze their prognostic values and protein expressions. Twenty-seven genes were identified because their high expressions were significant in OS, PFS, DFS, DSS, and HCC tumor samples. They were then used for GO, KEGG, methylation, genetics changes, immune infiltration analyses. Moreover, we established a prognostic model in HCC using univariate assays and LASSO regression based on these MRGs. Additionally, we also found that SLC38A1, an amino acid metabolism closely related transporter, was a potential prognostic gene in HCC, and its function in HCC was further studied using experiments. We found that the knockdown of SLC38A1 notably suppressed the growth and migration of HCC cells. Further studies revealed that SLC38A1 modulated the development of HCC cells by regulating PI3K/AKT/mTOR signaling via glutamine mediated energy metabolism. In conclusion, this study identified the potentially prognostic MRGs in HCC and uncovered that SLC38A1 regulated HCC development and progression by regulating PI3K/AKT/mTOR signaling via glutamine mediated energy metabolism, which might provide a novel marker and potential therapeutic target in HCC.

The role of KPNA2 as a monotonically changing differentially expressed gene in the diagnosis, risk stratification, and chemotherapy sensitivity of chronic hepatitis B-liver cirrhosis-hepatocellular carcinoma

PURPOSE

Chronic hepatitis B-liver cirrhosis-hepatocellular carcinoma (CLH), commonly called the "liver cancer trilogy", is a crucial evolutionary phase in the emergence of hepatocellular carcinoma (HCC) in China. Previous studies on early diagnostic biomarkers of HCC were limited to the end-stage of HCC and did not focus on the evolutionary process of CLH.

METHODS

11 monotonically changing differentially expressed genes (MCDEGs) highly correlated with CLH were screened through bioinformatic analysis and KPNA2 was identified for further research. The serum KPNA2 expression in different CLH states was detected by Enzyme linked immunosorbent assay (ELISA). A nomogram model was constructed using univariate and multivariate Cox regression methods.

RESULTS

The single-cell RNA-seq and bulk RNA-seq revealed that KPNA2 related to immune infiltration in HCC and may participate in cell cycle pathways in HCC. The serum KPNA2 expression was monotonically upregulated in CLH and was valuable for diagnosing different CLH states. Besides, chronic hepatitis B(CHB) patients, liver cirrhosis (LC) patients, and HCC patients were classified into subgroups with distinct serum KPNA2 expressions. Accordingly, patients with different serum KPNA2 expressions displayed various clinicopathological features. The AUC value of the nomogram model was 0.959 in predicting the likelihood of developing HCC in CHB patients or LC patients. Finally, we found that KPNA2 expression was negatively correlated with the IC50 of four chemotherapeutic drugs in HCC.

CONCLUSION

KPNA2 was a novel serum biomarker for diagnosing different CLH states, monitoring the dynamic evolution of CLH, and a new therapeutic target for intervening in the progression of CLH.

Seven chromatin regulators as immune cell infiltration characteristics, potential diagnostic biomarkers and drugs prediction in hepatocellular carcinoma

Treatment is challenging due to the heterogeneity of hepatocellular carcinoma (HCC). Chromatin regulators (CRs) are important in epigenetics and are closely associated with HCC. We obtained HCC-related expression data and relevant clinical data from The Cancer Genome Atlas (TCGA) databases. Then, we crossed the differentially expressed genes (DEGs), immune-related genes and CRs to obtain immune-related chromatin regulators differentially expressed genes (IRCR DEGs). Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to select the prognostic gene and construct a risk model for predicting prognosis in HCC, followed by a correlation analysis of risk scores with clinical characteristics. Finally, we also carried out immune microenvironment analysis and drug sensitivity analysis, the correlation between risk score and clinical characteristics was analyzed. In addition, we carried out immune microenvironment analysis and drug sensitivity analysis. Functional analysis suggested that IRCR DEGs was mainly enriched in chromatin-related biological processes. We identified and validated PPARGC1A, DUSP1, APOBEC3A, AIRE, HDAC11, HMGB2 and APOBEC3B as prognostic biomarkers for the risk model construction. The model was also related to immune cell infiltration, and the expression of CD48, CTLA4, HHLA2, TNFSF9 and TNFSF15 was higher in high-risk group. HCC patients in the high-risk group were more sensitive to Axitinib, Docetaxel, Erlotinib, and Metformin. In this study, we construct a prognostic model of immune-associated chromatin regulators, which provides new ideas and research directions for the accurate treatment of HCC.

Cellular senescence-related genes: predicting prognosis in hepatocellular carcinoma

Recent studies have shown that the high incidence and low cure rate of hepatocellular carcinoma (HCC) have not improved significantly. Surgery and liver transplantation are the mainstays of prolonging the survival of HCC patients. However, the surgical resection rate of HCC patients is very low, and even after radical surgical resection, the recurrence rate at 5 years postoperatively remains high and the prognosis is very poor, so more treatment options are urgently needed. Increasing evidence suggests that cellular senescence is not only related to cancer development but may also be one of its primary driving factors. We aimed to establish a prognostic signature of senescence-associated genes to predict the prognosis and therapeutic response of HCC patients. The aim of this study was to develop a risk model associated with cellular senescence and to search for potential strategies to treat HCC. We divided HCC patients into two clusters and identified differentially expressed genes (DEGs) between clusters. In this study, low-risk patients had a better prognosis, higher levels of immune cell infiltration, and better efficacy to fluorouracil, Paclitaxel and Cytarabine chemotherapy compared to high-risk patients. To further identify potential biomarkers for HCC, we further validated the expression levels of the four signature genes in HCC and neighbouring normal tissues by in vitro experiments. In conclusion, we identified and constructed a relevant prognostic signature, which performed well in predicting the survival and treatment response of HCC patients. This helps to differentiate between low-score and high-risk HCC, and the results may contribute to precise treatment protocols in clinical practice.

A novel coagulation-related lncRNA predicts the prognosis and immune of clear cell renal cell carcinoma

Renal cell cancer is associated with the coagulation system. Long non-coding RNA (lncRNA) expression is closely associated with the development of clear cell renal cell carcinoma (ccRCC). The aim of this study was to build a novel lncRNA model to predict the prognosis and immunological state of ccRCC. The transcriptomic data and clinical data of ccRCC were retrieved from TCGA database, subsequently, the lasso regression and lambda spectra were used to filter prognostic lncRNAs. ROC curves and the C-index were used to confirm the predictive effectiveness of this model. We also explored the difference in immune infiltration, immune checkpoints, tumor mutation burden (TMB) and drug sensitivity between the high- and low-risk groups. We created an 8 lncRNA model for predicting the outcome of ccRCC. Multivariate Cox regression analysis showed that age, tumor grade, and risk score are independent prognostic factors for ccRCC patients. ROC curve and C-index revealed the model had a good performance in predicting prognosis of ccRCC. GO and KEGG analysis showed that coagulation related genes were related to immune response. In addition, high risk group had greater TMB level and higher immune checkpoints expression. Sorafenib, Imatinib, Pazopanib, and etoposide had higher half maximal inhibitory concentration (IC50) in the high risk group whereas Sunitinib and Bosutinib had lower IC50. This novel coagulation-related long noncoding RNAs model could predict the prognosis of patients with ccRCC, and coagulation-related lncRNA may be connected to the tumor microenvironment and gene mutation of ccRCC.