Ferroptosis-related lncRNA signature predicts the prognosis and immune microenvironment of hepatocellular carcinoma

Targeting cell death mechanisms: the potential of autophagy and ferroptosis in hepatocellular carcinoma therapy

Ferroptosis is a type of cell death that plays a remarkable role in the growth and advancement of malignancies including hepatocellular carcinoma (HCC). Non-coding RNAs (ncRNAs) have a considerable impact on HCC by functioning as either oncogenes or suppressors. Recent research has demonstrated that non-coding RNAs (ncRNAs) have the ability to control ferroptosis in HCC cells, hence impacting the advancement of tumors and the resistance of these cells to drugs. Autophagy is a mechanism that is conserved throughout evolution and plays a role in maintaining balance in the body under normal settings. Nevertheless, the occurrence of dysregulation of autophagy is evident in the progression of various human disorders, specifically cancer. Autophagy plays dual roles in cancer, potentially influencing both cell survival and cell death. HCC is a prevalent kind of liver cancer, and genetic mutations and changes in molecular pathways might worsen its advancement. The role of autophagy in HCC is a subject of debate, as it has the capacity to both repress and promote tumor growth. Autophagy activation can impact apoptosis, control proliferation and glucose metabolism, and facilitate tumor spread through EMT. Inhibiting autophagy can hinder the growth and spread of HCC and enhance the ability of tumor cells to respond to treatment. Autophagy in HCC is regulated by several signaling pathways, such as STAT3, Wnt, miRNAs, lncRNAs, and circRNAs. Utilizing anticancer drugs to target autophagy may have advantageous implications for the efficacy of cancer treatment.

Comprehensive analysis of POLH-AS1 as a prognostic biomarker in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC), a prevalent primary malignant tumor, is notorious for its high mortality rate. Despite advancements in HCC treatment, patient outcomes remain suboptimal. This study endeavors to assess the potential prognostic significance of POLH-AS1 in HCC.

METHODS

In this research, we gathered RNA-Seq information from individuals with HCC in The Cancer Genome Atlas (TCGA). We analyzed the levels of POLH-AS1 expression in both HCC cells and tissues using statistical tests. Additionally, we examined various prognostic factors in HCC using advanced methodologies. Furthermore, we employed Spearman's rank correlation analysis to examine the association between POLH-AS1 expression and the tumor's immune microenvironment. Finally, the functional roles of POLH-AS1 in HCC were validated in two HCC cell lines (HEP3B and HEPG2).

RESULTS

Our analysis revealed elevated POLH-AS1 expression across various cancers, including HCC, with heightened expression correlating with HCC progression. Notably, POLH-AS1 expression emerged as a potential biomarker for HCC patient survival and prognosis. Mechanistically, we identified the involvement of POLH-AS1 in tumorigenesis pathways such as herpes simplex virus 1 infection, interactions with neuroactive receptors, and the cAMP signaling pathway. Lastly, inhibition of POLH-AS1 was discovered to hinder the proliferation, invasion and migration of HEP3B and HEPG2 HCC cells.

CONCLUSIONS

POLH-AS1 emerges as a promising prognostic biomarker and therapeutic target for HCC, offering potential avenues for enhanced patient management and treatment strategies.

Natural killer (NK) cells-related gene signature reveals the immune environment heterogeneity in hepatocellular carcinoma based on single cell analysis

The early diagnosis of liver cancer is crucial for the treatment and depends on the coordinated use of several test procedures. Early diagnosis is crucial for precision therapy in the treatment of the hepatocellular carcinoma (HCC). Therefore, in this study, the NK cell-related gene prediction model was used to provide the basis for precision therapy at the gene level and a novel basis for the treatment of patients with liver cancer. Natural killer (NK) cells have innate abilities to recognize and destroy tumor cells and thus play a crucial function as the "innate counterpart" of cytotoxic T cells. The natural killer (NK) cells is well recognized as a prospective approach for tumor immunotherapy in treating patients with HCC. In this research, we used publicly available databases to collect bioinformatics data of scRNA-seq and RNA-seq from HCC patients. To determine the NK cell-related genes (NKRGs)-based risk profile for HCC, we isolated T and natural killer (NK) cells and subjected them to analysis. Uniform Manifold Approximation and Projection plots were created to show the degree of expression of each marker gene and the distribution of distinct clusters. The connection between the immunotherapy response and the NKRGs-based signature was further analyzed, and the NKRGs-based signature was established. Eventually, a nomogram was developed using the model and clinical features to precisely predict the likelihood of survival. The prognosis of HCC can be accurately predicted using the NKRGs-based prognostic signature, and thorough characterization of the NKRGs signature of HCC may help to interpret the response of HCC to immunotherapy and propose a novel tumor treatment perspective.

Identifying epithelial-mesenchymal transition-related genes as prognostic biomarkers and therapeutic targets of hepatocellular carcinoma by integrated analysis of single-cell and bulk-RNA sequencing data

Background

Hepatocellular carcinoma (HCC) remains one of the most lethal cancers globally. Patients with advanced HCC tend to have poor prognoses and shortened survival. Recently, data from bulk RNA sequencing have been employed to discover prognostic markers for various cancers. However, they fall short in precisely identifying core molecular and cellular activities within tumor cells. In our present study, we combined bulk-RNA sequencing (bulk RNA-seq) data with single-cell RNA sequencing (scRNA-seq) to develop a prognostic model for HCC. The goal of our research is to uncover new biomarkers and enhance the accuracy of HCC prognosis prediction.

Methods

Integrating single-cell sequencing data with transcriptomics were used to identify epithelial-mesenchymal transition (EMT)-related genes (ERGs) implicated in HCC progression and their clinical significance was elucidated. Utilizing marker genes derived from core cells and ERGs, we constructed a prognostic model using univariate Cox analysis, exploring a multitude of algorithmic combinations, and further refining it through multivariate Cox analysis. Additionally, we conducted an in-depth investigation into the disparities in clinicopathological features, immune microenvironment composition, immune checkpoint expression, and chemotherapeutic drug sensitivity profiles between high- and low-risk patient cohorts.

Results

We developed a prognostic model predicated on the expression profiles of eight signature genes, namely HSP90AA1, CIRBP, CCR7, S100A9, ADAM17, ENG, PGF, and INPP4B, aiming at predicting overall survival (OS) outcomes. Notably, patients classified with high-risk scores exhibited a propensity towards diminished OS rates, heightened frequencies of stage III-IV disease, increased tumor mutational burden (TMB), augmented immune cell infiltration, and diminished responsiveness to immunotherapeutic interventions.

Conclusions

This study presented a novel prognostic model for predicting the survival of HCC patients by integrating scRNA-seq and bulk RNA-seq data. The risk score emerges as a promising independent prognostic factor, showing a correlation with the immune microenvironment and clinicopathological features. It provided new clinical tools for predicting prognosis and aided future research into the pathogenesis of HCC.

Targeting the regulation of iron homeostasis as a potential therapeutic strategy for nonalcoholic fatty liver disease

With aging and the increasing incidence of obesity, nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease worldwide. NAFLD mainly includes simple hepatic steatosis, nonalcoholic steatohepatitis (NASH), liver fibrosis and hepatocellular carcinoma (HCC). An imbalance in hepatic iron homeostasis is usually associated with the progression of NAFLD and induces iron overload, reactive oxygen species (ROS) production, and lipid peroxide accumulation, which leads to ferroptosis. Ferroptosis is a unique type of programmed cell death (PCD) that is characterized by iron dependence, ROS production and lipid peroxidation. The ferroptosis inhibition systems involved in NAFLD include the solute carrier family 7 member 11 (SLC7A11)/glutathione (GSH)/glutathione peroxidase 4 (GPX4) and ferroptosis suppressor protein 1 (FSP1)/coenzyme Q10 (CoQ10)/nicotinamide adenine dinucleotide phosphate (NADPH) regulatory axes. The main promotion system involved is the acyl-CoA synthetase long-chain family (ACSL4)/arachidonic lipoxygenase 15 (ALOX15) axis. In recent years, an increasing number of studies have focused on the multiple roles of iron homeostasis imbalance and ferroptosis in the progression of NAFLD. This review highlights the latest studies about iron homeostasis imbalance- and ferroptosis-associated NAFLD, mainly including the physiology and pathophysiology of hepatic iron metabolism, hepatic iron homeostasis imbalance during the development of NAFLD, and key regulatory molecules and roles of hepatic ferroptosis in NAFLD. This review aims to provide innovative therapeutic strategies for NAFLD.

MKLN1-AS promotes pancreatic cancer progression as a crucial downstream mediator of HIF-1α through miR-185-5p/TEAD1 pathway

In pancreatic ductal adenocarcinomas (PDAC), profound hypoxia plays key roles in regulating cancer cell behavior, including proliferation, migration, and resistance to therapies. The initial part of this research highlights the important role played by long noncoding RNA (lncRNA) MKLN1-AS, which is controlled by hypoxia-inducible factor-1 alpha (HIF-1α), in the progression of PDAC. Human samples of PDAC showed a notable increase in MKLN1-AS expression, which was linked to a worse outcome. Forced expression of MKLN1-AS greatly reduced the inhibitory impact on the growth and spread of PDAC cells caused by HIF-1α depletion. Experiments on mechanisms showed that HIF-1α influences the expression of MKLN1-AS by directly attaching to a hypoxia response element in the promoter region of MKLN1-AS.MKLN1-AS acts as a competitive endogenous RNA (ceRNA) by binding to miR-185-5p, resulting in the regulation of TEAD1 expression and promoting cell proliferation, migration, and tumor growth. TEAD1 subsequently enhances the development of PDAC. Our study results suggest that MKLN1-AS could serve as a promising target for treatment and a valuable indicator for predicting outcomes in PDAC. PDAC is associated with low oxygen levels, and the long non-coding RNA MKLN1-AS interacts with TEAD1 in this context.

Ferroptosis heterogeneity within the tumor microenvironment revealed a genetic blueprint of breast cancer

The tumor microenvironment (TME) significantly influences disease progression through immune infiltration, while ferroptosis, a recently discovered cell death mechanism, plays a crucial role in tumor suppression. However, its role in breast cancer is not clear. In this study, we analyzed bulk RNA and single-cell RNA sequencing data from 1217 samples, including 1104 breast cancer patients and 113 controls, to identify ferroptosis-related genes (FRGs) and construct a prognostic model. Using univariate cox regression, LASSO regression, and multivariate cox regression analysis, we discovered 21 FRGs and 3 TME-related immune cell types with prognostic value. Dimensionality reduction clustering and visualization were performed using the UMAP method, while the immune infiltration process was calculated with the TIP online tool. We employed GSEA enrichment analysis, WGCNA clustering analysis, and correlation analysis to examine functional differences, and the mutation analysis of the best and worst prognosis groups was conducted using the maftools package. Our findings revealed that knocking down the expression of the hub gene SLC39A7 significantly impacted cancer cell apoptosis and combining ferroptosis and TME scores yielded high prognostic power. Epithelial cells and B cells exhibited higher ferroptosis scores, which were independently associated with immune checkpoint blockade (ICB) response and ICB gene expression. This study provides a foundation for further exploration of the relationship between ferroptosis and ICB response in breast cancer. In conclusion, we developed a prognostic model based on ferroptosis and infiltrated immune cells that effectively stratified breast cancer patients and demonstrated the role of SLC39A7 in breast cancer pathogenesis through the regulation of apoptosis.

Tracing vitamins on the long non-coding lane of the transcriptome: vitamin regulation of LncRNAs

A major revelation of genome-scale biological studies in the post-genomic era has been that two-thirds of human genes do not encode proteins. The majority of non-coding RNA transcripts in humans are long non-coding RNA (lncRNA) molecules, non-protein-coding regulatory transcripts with sizes greater than 500 nucleotides. LncRNAs are involved in nearly every aspect of cellular physiology, playing fundamental regulatory roles both in normal cells and in disease. As result, they are functionally linked to multiple human diseases, from cancer to autoimmune, inflammatory, and neurological disorders. Numerous human conditions and diseases stem from gene-environment interactions; in this regard, a wealth of reports demonstrate that the intake of specific and essential nutrients, including vitamins, shapes our transcriptome, with corresponding impacts on health. Vitamins command a vast array of biological activities, acting as coenzymes, antioxidants, hormones, and regulating cellular proliferation and coagulation. Emerging evidence suggests that vitamins and lncRNAs are interconnected through several regulatory axes. This type of interaction is expected, since lncRNA has been implicated in sensing the environment in eukaryotes, conceptually similar to riboswitches and other RNAs that act as molecular sensors in prokaryotes. In this review, we summarize the peer-reviewed literature to date that has reported specific functional linkages between vitamins and lncRNAs, with an emphasis on mammalian models and humans, while providing a brief overview of the source, metabolism, and function of the vitamins most frequently investigated within the context of lncRNA molecular mechanisms, and discussing the published research findings that document specific connections between vitamins and lncRNAs.

Integrating bioinformatics and experimental validation to unveil disulfidptosis-related lncRNAs as prognostic biomarker and therapeutic target in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) stands as a prevalent malignancy globally, characterized by significant morbidity and mortality. Despite continuous advancements in the treatment of HCC, the prognosis of patients with this cancer remains unsatisfactory. This study aims at constructing a disulfidoptosis‑related long noncoding RNA (lncRNA) signature to probe the prognosis and personalized treatment of patients with HCC.

METHODS

The data of patients with HCC were extracted from The Cancer Genome Atlas (TCGA) databases. Univariate, multivariate, and least absolute selection operator Cox regression analyses were performed to build a disulfidptosis-related lncRNAs (DRLs) signature. Kaplan-Meier plots were used to evaluate the prognosis of the patients with HCC. Functional enrichment analysis was used to identify key DRLs-associated signaling pathways. Spearman's rank correlation was used to elucidate the association between the DRLs signature and immune microenvironment. The function of TMCC1-AS1 in HCC was validated in two HCC cell lines (HEP3B and HEPG2).

RESULTS

We identified 11 prognostic DRLs from the TCGA dataset, three of which were selected to construct the prognostic signature of DRLs. We found that the survival time of low-risk patients was considerably longer than that of high-risk patients. We further observed that the composition and the function of immune cell subpopulations were significantly different between high- and low-risk groups. Additionally, we identified that sorafenib, 5-Fluorouracil, and doxorubicin displayed better responses in the low-score group than those in the high-score group, based on IC50 values. Finally, we confirmed that inhibition of TMCC1-AS1 impeded the proliferation, migration, and invasion of hepatocellular carcinoma cells.

CONCLUSIONS

The DRL signatures have been shown to be a reliable prognostic and treatment response indicator in HCC patients. TMCC1-AS1 showed potential as a novel prognostic biomarker and therapeutic target for HCC.

Integrating bulk and single-cell RNA sequencing data to establish necroptosis-related lncRNA risk model and analyze the immune microenvironment in hepatocellular carcinoma

Background

The increasing evidence suggests that necroptosis mediates many behaviors of tumors, as well as the regulation of the tumor microenvironment. Long non-coding RNAs (lncRNAs) are involved in a variety of regulatory processes during tumor development and are significantly associated with patient prognosis. It suggests that necroptosis-related lncRNAs (NRlncRNAs) may serve as biomarkers for the prognosis of hepatocellular carcinoma (HCC).

Methods

lncRNA expression profiles of HCC were obtained from TCGA database. LncRNAs associated with necroptosis were extracted using correlation analysis. Prognostic models were constructed based on least absolute shrinkage and selection operator algorithm (LASSO) and multivariate Cox regression analysis. The differences of tumor microenvironment between high-risk and low-risk groups were further analyzed. Single-cell RNA sequencing data of HCC was performed to assess the enrichment of necroptosis-related genes in immune cell subsets. Finally, real-time RT-PCR was used to detect the prognosis-related lncRNAs expression in different HCC cell lines.

Results

We constructed a prognostic signature based on 8 NRlncRNAs, which also showed good predictive accuracy. The model showed that the prognosis of patients with high-risk score was significantly worse than that of patients with low-risk score (P < 0.05). Combined with the clinical characteristics and risk score of HCC, Nomogram was drawn for reference in clinical practice. In addition, immune cell infiltration analysis and single cell RNA sequencing analysis showed that a low level of immune infiltration was observed in patients at high risk and that there was a significant correlation between NRlncRNAs and macrophages. The results of RT-qPCR also showed that 8 necroptosis-related lncRNAs were highly expressed in HCC cell lines and human liver cancer tissues.

Conclusion

This prognostic signature based on the necroptosis-related lncRNAs may provide meaningful clinical insights for the prognosis and immunotherapy responses in patients with HCC.

Cuproptosis-related lncRNAs predict the prognosis and immune response in hepatocellular carcinoma

Cuproptosis has been recently used to indicate unique biological processes triggered by Cu action as a new term. This study aimed to explore the relationship between cuproptosis-related lncRNA and hepatocellular carcinoma (HCC) with regard to immunity and prognosis. RNA sequencing and the clinical data were downloaded from the TCGA database. The cuproptosis-related genes were sorted out through literature study. The cuproptosis-related IncRNA signature was identified by Cox regression analysis and the least absolute shrinkage and selection operator analysis. The K-M survival analysis, receiver operating characteristic analysis, and C-index analysis were adopted to evaluate the prognostic prediction performance of the signature. The functional enrichment, immune infiltration and tumor mutation analysis were further analyzed. Subsequently, we predicted the differences in chemosensitivity from tumor gene expression levels for some chemotherapy drugs. The prognostic signature consisting of 5 overall survival-related CUPlncRNAs. It showed an extraordinary ability to predict the prognoses of patients with HCC. The signature can predict the abundance of immune cell infiltration, immune functions, expression of immune checkpoint inhibitors, m6A genes, which was supported by the GO biological process and KEGG analysis. And it may also have a guiding effect in the sensitivity of different chemotherapeutic drugs and tumor mutation burden. We constructed a new cuproptosis-related lncRNA signature for HCC patients. The model can be used for prognostic prediction and immune evaluation, providing a reference for immunotherapies and targeted therapies.

Basement Membrane-Associated lncRNA Risk Model Predicts Prognosis and Guides Clinical Treatment in Clear Cell Renal Cell Carcinoma

The basement membrane (BM) affects the invasion and growth of malignant tumors. The role and mechanism of BM-associated lncRNAs in clear cell renal cell carcinoma (ccRCC) are unknown. In this study, we identified biomarkers of ccRCC and developed a risk model to assess patient prognosis. We downloaded transcripts and clinical data from the Cancer Genome Atlas (TCGA). Differential analysis, co-expression analysis, Cox regression analysis, and lasso regression were used to identify BM-associated prognostic lncRNAs and create a risk prediction model. We evaluated and validated the accuracy of the model using multiple methods and constructed a nomogram to predict the prognosis of ccRCC. GO, KEGG, and immunity analyses were used to explore differences in biological function. We constructed a risk model containing six BM-associated lncRNAs (LINC02154, IGFL2-AS1, NFE4, AC112715.1, AC092535.5, and AC105105.3). The risk model has higher diagnostic efficiency compared to clinical characteristics and can be used to forecast patient prognoses. We used renal cancer cells and tissue microarrays to verify the expression of lncRNAs in the risk model. We found that knocking down LINC02154 and AC112715.1 could inhibit the invasion ability of renal cancer cells. The risk model based on BM-associated lncRNAs can well predict ccRCC and guide clinical treatment.

Prognostic and immunological role of alpha-L-fucosidase 2 (FUCA2) in hepatocellular carcinoma

Background

This study investigated the prognostic and immunological significance of alpha-L-fucosidase 2 (FUCA2) in hepatocellular cancer (HCC).

Methods

The expression of FUCA2 and its clinical and prognostic values were explored across several databases, namely the University of Alabama Cancer Database, The Cancer Genome Atlas, Gene Expression Profiling Interactive Analysis, and the Human Protein Atlas. The prognostic relevance of FUCA2 was investigated using Kaplan-Meier curves, nomograms, and Cox analysis. The "limma" package in R was used to identify differentially expressed genes between high and low FUCA2 expression. A protein interaction network was established using the Search Tool for the Retrieval of Interacting Genes (STRING), whereas hub genes and clustering modules were identified using Cytoscape. "clusterProfiler", an R package, was used to examine the potential function of FUCA2. Using gene set enrichment analysis, signaling pathways associated with FUCA2 expression were identified. Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), Tumor Immune Estimation Resource (TIMER) 2.0, and Tumor and Immune System Interaction Database (TISIDB) were used to examine immune infiltration and FUCA2 in HCC.

Results

Many datasets indicated that FUCA2 expression is higher in HCC, and that this is related to age and overall survival (OS). With the cutoff value of 50% as the dividing threshold, the patients were divided into a high-FUCA2 expression group (n=167) and a low-FUCA2 expression group (n=168). High levels of FUCA2 expression coincided with decreased OS. FUCA2 expression in HCC was associated with immune infiltrates. The functional mechanisms of FUCA2 depend on signal release, extracellular matrix collagen, and neuroactive ligands and receptors.

Conclusions

In HCC, increased FUCA2 expression is associated with a poor prognosis and immune infiltration. FUCA2 may serve as an immunological and predictive biomarker for HCC.

Role of ferroptosis and its non-coding RNA regulation in hepatocellular carcinoma

Ferroptosis is a newly discovered form of programmed cell death that involves the accumulation of iron-dependent lipid peroxides and plays a vital role in the tumorigenesis, development, and drug resistance of various tumors such as hepatocellular carcinoma (HCC). As a hotspot in molecular biology, non-coding RNAs (ncRNAs) participate in the initiation and progression of HCC, either act as oncogenes or tumor suppressors. Recent studies have shown that ncRNAs can regulate ferroptosis in HCC cells, which would affect the tumor progression and drug resistance. Therefore, clarifying the underlying role of ferroptosis and the regulatory role of ncRNA on ferroptosis in HCC could develop new treatment interventions for this disease. This review briefly summarizes the role of ferroptosis and ferroptosis-related ncRNAs in HCC tumorigenesis, progression, treatment, drug resistance and prognosis, for the development of potential therapeutic strategies and prognostic markers in HCC patients.

Identification and Validation of Ferroptosis-Related LncRNAs Signature as a Novel Prognostic Model for Chronic Lymphocytic Leukemia

Background

Chronic lymphocytic leukemia (CLL) is a subtype of B-cell malignancy with high heterogeneity. Ferroptosis is a novel cell death induced by iron and lipid peroxidation and exhibits prognostic value in many cancers. Emerging studies on long non-coding RNAs (lncRNAs) and ferroptosis reveal the unique value in tumorigenesis. However, the prognostic value of ferroptosis-related lncRNAs (FRLs) remains unclear in CLL.

Aim

We aimed to construct a FRLs risk model to predict prognosis and improve prognostic stratification for clinical practice.

Methods

RNA-sequencing data and clinical characteristics of CLL patients were downloaded from the GEO database. Based on ferroptosis-related genes from FerrDb database, differentially expressed FRLs with prognostic significance were identified and used to generate the risk model. The capability of the risk model was assessed and evaluated. GO and KEGG analyses were performed to confirm biological roles and potential pathways.

Results

A novel ferroptosis-related lncRNAs prognostic score (FPS) model containing six FRLs (PRKCQ, TRG.AS1, LNC00467, LNC01096, PCAT6 and SBF2.AS1) was identified. Patients in the training and validation cohort were evenly divided into high- and low-risk groups. Our results indicated that patients in the high-risk group had worse survival than those in the low-risk group. Functional enrichment analyses showed that the differently expressed genes (DEGs) between the two groups were enriched in the chemokine signaling pathway, hematopoietic cell lineage, T cell differentiation, TCR pathway and NF-κB pathway. Moreover, significant differences in immune cell infiltration were also observed. Surprisingly, FPS was proved to be an independent prognostic indicator for OS.

Conclusion

We established and evaluated a novel prognostic risk model with 6 FRLs that could predict prognosis accurately and describe the distinct immune infiltration in CLL.

Construction of prognostic signature of breast cancer based on N7-Methylguanosine-Related LncRNAs and prediction of immune response

Background: Long non-coding RNA (LncRNA) is a prognostic factor for malignancies, and N7-Methylguanosine (m7G) is crucial in the occurrence and progression of tumors. However, it has not been documented how well m7G-related LncRNAs predict the development of breast cancer (BC). This study aims to develop a predictive signature based on long non-coding RNAs (LncRNAs) associated with m7G to predict the prognosis of breast cancer patients.

Methods: The Cancer Genome Atlas (TCGA) database provided us with the RNA-seq data and matching clinical information of individuals with breast cancer. To identify the signature of N7-Methylguanosine-Related LncRNAs and create a prognostic model, we employed co-expression network analysis, least absolute shrinkage selection operator (LASSO) regression analysis, univariate Cox regression analysis, and multivariate Cox regression analysis. The signature was assessed using the Kaplan-Meier analysis and Receiver Operating Characteristic (ROC) curve. A nomogram and principal component analysis (PCA) were employed to confirm the predictive signature’s usefulness. Then, we examined the drug sensitivity between the two risk groups and utilized single-sample gene set enrichment analysis (ssGSEA) to investigate the association between predictive factors and the tumor immune microenvironment in high-risk and low-risk groups.

Results: Nine m7G-related LncRNAs (LINC01871, AP003469.4, Z68871.1, AC245297.3, EGOT, TFAP2A-AS1, AL136531.1, SEMA3B-AS1, AL606834.2) that are independently associated with the overall survival time (OS) of BC patients make up the signature we developed. For predicting 1-, 3-, and 5-year survival rates, the areas under the ROC curve (AUC) were 0.715, 0.724, and 0.726, respectively. The Kaplan-Meier analysis revealed that the prognosis of BC patients in the high-risk group was worse than that of those in the low-risk group. When compared to clinicopathological variables, multiple regression analysis demonstrated that risk score was a significant independent predictive factor for BC patients. The results of the ssGSEA study revealed a substantial correlation between the predictive traits and the BC patients’ immunological status, low-risk BC patients had more active immune systems, and they responded better to PD1/L1 immunotherapy.

Conclusion: The prognostic signature, which is based on m7G-related LncRNAs, can be utilized to inform patients’ customized treatment plans by independently predicting their prognosis and how well they would respond to immunotherapy.

Long Noncoding RNAs and Circular RNAs Regulate AKT and Its Effectors to Control Cell Functions of Cancer Cells

AKT serine-threonine kinase (AKT) and its effectors are essential for maintaining cell proliferation, apoptosis, autophagy, endoplasmic reticulum (ER) stress, mitochondrial morphogenesis (fission/fusion), ferroptosis, necroptosis, DNA damage response (damage and repair), senescence, and migration of cancer cells. Several lncRNAs and circRNAs also regulate the expression of these functions by numerous pathways. However, the impact on cell functions by lncRNAs and circRNAs regulating AKT and its effectors is poorly understood. This review provides comprehensive information about the relationship of lncRNAs and circRNAs with AKT on the cell functions of cancer cells. the roles of several lncRNAs and circRNAs acting on AKT effectors, such as FOXO, mTORC1/2, S6K1/2, 4EBP1, SREBP, and HIF are explored. To further validate the relationship between AKT, AKT effectors, lncRNAs, and circRNAs, more predicted AKT- and AKT effector-targeting lncRNAs and circRNAs were retrieved from the LncTarD and circBase databases. Consistently, using an in-depth literature survey, these AKT- and AKT effector-targeting database lncRNAs and circRNAs were related to cell functions. Therefore, some lncRNAs and circRNAs can regulate several cell functions through modulating AKT and AKT effectors. This review provides insights into a comprehensive network of AKT and AKT effectors connecting to lncRNAs and circRNAs in the regulation of cancer cell functions.

Development and validation of cuproptosis-related gene signature in the prognostic prediction of liver cancer

Liver cancer is a generic term referring to several cancer types arising from the liver. Every year, liver cancer causes lots of deaths and other burdens to the people all over the world. Though the techniques in the diagnosis and therapy of liver cancer have undergone significant advances, the current status of treating liver cancer is not satisfactory enough. The improvement of techniques for the prognosis of liver cancer patients will be a great supplement for the treatment of liver cancer. Cuproptosis is a newly identified regulatory cell death type, which may have a close connection to liver cancer pathology. Here, we developed a prognostic model for liver cancer based on the cuproptosis-related mRNAs and lncRNAs. This model can not only effectively predict the potential survival of liver cancer patients, but also be applied to evaluate the infiltration of immune cell, tumor mutation burden, and sensitivity to anti-tumor drugs in liver cancer. In addition, this model has been successfully validated in lots of liver cancer patients’ data. In summary, we wish this model can become a helpful tool for clinical use in the therapy of liver cancer.

Overweight/obesity-related transcriptomic signature as a correlate of clinical outcome, immune microenvironment, and treatment response in hepatocellular carcinoma

BACKGROUNDS

The pandemic of overweight and obesity (quantified by body mass index (BMI) ≥ 25) has rapidly raised the patient number of non-alcoholic fatty hepatocellular carcinoma (HCC), and several clinical trials have shown that BMI is associated with the prognosis of HCC. However, whether overweight/obesity is an independent prognostic factor is arguable, and the role of overweight/obesity-related metabolisms in the progression of HCC is scarcely known.

MATERIALS AND METHODS

In the present study, clinical information, mRNA expression profile, and genomic data were downloaded from The Cancer Genome Atlas (TCGA) as a training cohort (TCGA-HCC) for the identification of overweight/obesity-related transcriptome. Machine learning and the Cox regression analysis were conducted for the construction of the overweight/obesity-associated gene (OAG) signature. The Kaplan-Meier curve, receiver operating characteristic (ROC) curve, and the Cox regression analysis were performed to assess the prognostic value of the OAG signature, which was further validated in two independent retrospective cohorts from the International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO). Subsequently, functional enrichment, genomic profiling, and tumor microenvironment (TME) evaluation were utilized to characterize biological activities associated with the OAG signature. GSE109211 and GSE104580 were retrieved to evaluate the underlying response of sorafenib and transcatheter arterial chemoembolization (TACE) treatment, respectively. The Genomics of Drug Sensitivity in Cancer (GDSC) database was employed for the evaluation of chemotherapeutic response.

RESULTS

Overweight/obesity-associated transcriptome was mainly involved in metabolic processes and noticeably and markedly correlated with prognosis and TME of HCC. Afterward, a novel established OAG signature (including 17 genes, namely, GAGE2D, PDE6A, GABRR1, DCAF8L1, DPYSL4, SLC6A3, MMP3, RIBC2, KCNH2, HTRA3, PDX1, ATHL1, PRTG, SHC4, C21orf29, SMIM32, and C1orf133) divided patients into high and low OAG score groups with distinct prognosis (median overall survival (OS): 24.87 vs. 83.51 months, p < 0.0001), and the values of area under ROC curve (AUC) in predicting 1-, 2-, 3-, and 4-year OS were 0.81, 0.80, 0.83, and 0.85, respectively. Moreover, the OAG score was independent of clinical features and also exhibited a good ability for prognosis prediction in the ICGC-LIHC-JP cohort and GSE54236 dataset. Expectedly, the OAG score was also highly correlated with metabolic processes, especially oxidative-related signaling pathways. Furthermore, abundant enrichment of chemokines, receptors, MHC molecules, and other immunomodulators as well as PD-L1/PD-1 expression among patients with high OAG scores indicated that they might have better responses to immunotherapy. However, probably exclusion of T cells from infiltrating tumors resulting in lower infiltration of effective T cells would restrict immunotherapeutic effects. In addition, the OAG score was significantly associated with the response of sorafenib and TACE treatment.

CONCLUSIONS

Overall, this study comprehensively disclosed the relationship between BMI-guided transcriptome and HCC. Moreover, the OAG signature had the potential clinical applications in the future to promote clinical management and precision medicine of HCC.