Identification of a robust signature for clinical outcomes and immunotherapy response in gastric cancer: based on N6-methyladenosine related long noncoding RNAs

N6-methylation in the development, diagnosis, and treatment of gastric cancer

Gastric cancer (GC) ranks third among cancers in terms of mortality rate worldwide. A clear understanding of the mechanisms underlying the genesis and progression of GC will contribute to clinical decision making. N6-methyladenosine (m6A) is the most abundant among diverse mRNA modification types and regulates multiple facets of RNA metabolism. In recent years, emerging studies have shown that m6A modifications are involved in gastric carcinoma tumorigenesis and progression and can potentially be valuable new prospects for diagnosis and prognosis. This article reviews the recent progress regarding m6A in GC.

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.

Novel insights into mutual regulation between N6-methyladenosine modification and LncRNAs in tumors

N⁶-methyladenosine (m⁶A), one of the most common RNA methylation modifications, has emerged in recent years as a new layer of the regulatory mechanism controlling gene expression in eukaryotes. As a reversible epigenetic modification, m⁶A not only occurs on mRNAs but also on Long non-coding RNAs (LncRNAs). As we all known, despite LncRNAs cannot encode proteins, they affect the expression of proteins by interacting with mRNAs or miRNAs, thus playing important roles in the occurrence and development of a variety of tumors. Up to now, it has been widely accepted that m⁶A modification on LncRNAs affects the fate of the corresponding LncRNAs. Interestingly, levels and functions of m⁶A modifications are also mediated by LncRNAs through affecting the m⁶A methyltransferases (METTL3, METTL14, WTAP, METTL16, etc.), demethylases (FTO, ALKBH5) and methyl-binding proteins (YTHDFs, YTHDCs, IGF2BPs, HNRNPs, etc.), which are collectively referred to as "m⁶A regulators". In this review, we summarized the mutual regulation mechanisms between N⁶-methyladenosine modification and LncRNAs in cancer progression, metastasis, invasion and drug resistance. In detail, we focus on the specific mechanisms of m⁶A modification, which is mediated by methyltransferases and demethylases, involves in the regulation of LncRNA levels and functions in the first part. And section two intensively displays the mediation roles of LncRNAs in m⁶A modification via changing the regulatory proteins. At last part, we described the interaction effects between LncRNAs and methyl-binding proteins of m⁶A modification during various tumor occurrence and development.

NSUN2-mediated m5 C RNA methylation dictates retinoblastoma progression through promoting PFAS mRNA stability and expression

BACKGROUND

The precise temporal and spatial regulation of N⁵ -methylcytosine (m⁵ C) RNA modification plays essential roles in RNA metabolism, and is necessary for the maintenance of epigenome homeostasis. Howbeit, the mechanism underlying the m⁵ C modification in carcinogenesis remains to be fully addressed.

METHODS

Global and mRNA m⁵ C levels were determined by mRNA isolation and anti-m⁵ C dot blot in both retinoblastoma (RB) cells and clinical samples. Orthotopic intraocular xenografts were established to examine the oncogenic behaviours of RB. Genome-wide multiomics analyses were performed to identify the functional target of NSUN2, including proteomic analysis, transcriptome screening and m⁵ C-methylated RNA immunoprecipitation sequencing (m⁵ C-meRIP-seq). Organoid-based single-cell analysis and gene-correlation analysis were performed to verify the NSUN2/ALYREF/m⁵ C-PFAS oncogenic cascade.

RESULTS

Herein, we report that NSUN2-mediated m⁵ C RNA methylation fuels purine biosynthesis during the oncogenic progression of RB. First, we discovered that global and mRNA m⁵ C levels were significantly enriched in RBs compared to normal retinas. In addition, tumour-specific NSUN2 expression was noted in RB samples and cell lines. Therapeutically, targeted correction of NSUN2 exhibited efficient therapeutic efficacy in RB both in vitro and in vivo. Through multiomics analyses, we subsequently identified phosphoribosylformylglycinamidine synthase (PFAS), a vital enzyme in purine biosynthesis, as a downstream candidate target of NSUN2. The reintroduction of PFAS largely reversed the inhibitory phenotypes in NSUN2-deficient RB cells, indicating that PFAS was a functional downstream target of NSUN2. Mechanistically, we found that the m⁵ C reader protein ALYREF was responsible for the recognition of the m⁵ C modification of PFAS, increasing its expression by enhancing its RNA stability.

CONCLUSIONS

Conclusively, we initially demonstrated that NSUN2 is necessary for oncogenic gene activation in RB, expanding the current understanding of dynamic m⁵ C function during tumour progression. As the NSUN2/ALYREF/m⁵ C-PFAS oncogenic cascade is an important RB trigger, our study suggests that a targeted m⁵ C reprogramming therapeutic strategy may be a novel and efficient anti-tumour therapy approach.

Construction of an m6A-related lncRNA model for predicting prognosis and immunotherapy in patients with lung adenocarcinoma

N6-methyladenosine (m6A)-related lncRNAs could be involved in the development of multiple tumors with an unknown role in lung adenocarcinoma (LUAD). Hence, gene expression data and clinical data of LUAD patients were acquired from The Cancer Genome Atlas Database. The prognostic m6A-related lncRNAs were identified through differential lncRNA expression analysis and Spearman's correlation analysis. The least absolute shrinkage and selection operator regression was used to establish the prognostic risk model, so as to evaluate and validate the predictive performance with survival analysis and receiver operating characteristic curve analysis. The expression of immune checkpoints, immune cell infiltration and drug sensitivity of patients in different risk groups were analyzed separately. A total of 19 prognostic m6A-related lncRNAs were identified to set up the prognostic risk model. The patients were divided into high- and low-risk groups based on the median value of the risk scores. Compared with the patients in the low-risk group, the prognosis of the patients in the high-risk group was relatively worse. The receiver operating characteristic curves indicated that this model had excellent sensitivity and specificity. Multivariate Cox regression analysis demonstrated that the risk score could be supposed as an independent prognostic risk factor. We highlighted that the risk scores were correlated with immune cell infiltration and drug sensitivity for constructing a prognostic risk model in LUAD patients based on m6A-related lncRNAs.

Research progress of PBX1 in developmental and regenerative medicine

Pre-B-cell leukemia transcription factor 1 (PBX1) proteins are a subfamily of evolutionarily conserved atypical homeodomain transcription factors belonging to the superfamily of triple amino acid loop extension homeodomain proteins. PBX family members play crucial roles in the regulation of various pathophysiological processes. This article reviews the research progress on PBX1 in terms of structure, developmental function, and regenerative medicine. The potential mechanisms of development and research targets in regenerative medicine are also summarized. It also suggests a possible link between PBX1 in the two domains, which is expected to open up a new field for future exploration of cell homeostasis, as well as the regulation of endogenous danger signals. This would provide a new target for the study of diseases in various systems.

Identification and validation of a new pyroptosis-associated lncRNA signature to predict survival outcomes, immunological responses and drug sensitivity in patients with gastric cancer

BACKGROUND

Gastric cancer (GC) ranks fifth in prevalence among carcinomas worldwide. Both pyroptosis and long noncoding RNAs (lncRNAs) play crucial roles in the occurrence and development of gastric cancer. Therefore, we aimed to construct a pyroptosis-associated lncRNA model to predict the outcomes of patients with gastric cancer.

METHODS

Pyroptosis-associated lncRNAs were identified through co-expression analysis. Univariate and multivariate Cox regression analyses were performed using the least absolute shrinkage and selection operator (LASSO). Prognostic values were tested through principal component analysis, a predictive nomogram, functional analysis and Kaplan‒Meier analysis. Finally, immunotherapy and drug susceptibility predictions and hub lncRNA validation were performed.

RESULTS

Using the risk model, GC individuals were classified into two groups: low-risk and high-risk groups. The prognostic signature could distinguish the different risk groups based on principal component analysis. The area under the curve and the conformance index suggested that this risk model was capable of correctly predicting GC patient outcomes. The predicted incidences of the one-, three-, and five-year overall survivals exhibited perfect conformance. Distinct changes in immunological markers were noted between the two risk groups. Finally, greater levels of appropriate chemotherapies were required in the high-risk group. AC005332.1, AC009812.4 and AP000695.1 levels were significantly increased in gastric tumor tissue compared with normal tissue.

CONCLUSIONS

We created a predictive model based on 10 pyroptosis-associated lncRNAs that could accurately predict the outcomes of GC patients and provide a promising treatment option in the future.

An innovative pyroptosis-related long-noncoding-RNA signature predicts the prognosis of gastric cancer via affecting immune cell infiltration landscape

Background: Gastric cancer (GC) is a worldwide popular malignant tumor. However, the survival rate of advanced GC remains low. Pyroptosis and long non-coding RNAs (lncRNAs) are important in cancer progression. Thus, we aimed to find out a pyroptosis-related lncRNAs (PRLs) signature and use it to build a practical risk model with the purpose to predict the prognosis of patients with GC. Methods: Univariate Cox regression analysis was used to identify PRLs linked to GC patient's prognosis. Subsequently, to construct a PRLs signature, the least absolute shrinkage and selection operator regression, and multivariate Cox regression analysis were used. Kaplan-Meier analysis, principal component analysis, and receiver operating characteristic curve analysis were performed to assess our novel lncRNA signature. The correlation between risk signature and clinicopathological features was also examined. Finally, the relationship of pyroptosis and immune cells were evaluated through the CIBERSORT tool and single-sample lncRNA set enrichment analysis (ssGSEA). Results: A PRLs signature comprising eight lncRNAs was discerned as a self-determining predictor of prognosis. GC patients were sub-divided into high-risk and low-risk groups via this risk-model. Stratified analysis of different clinical factors also displayed that the PRLs signature was a good prognosis factor. According to the risk score and clinical characteristics, a nomogram was established. Moreover, the difference between the groups is significance in immune cells and immune pathways. Conclusion: This study established an effective prognostic signature consist of eight PRLs in GC, and constructed an efficient nomogram model. Further, the PRLs correlated with immune cells and immune pathways.

The role of N6-methyladenosine-modified non-coding RNAs in the pathological process of human cancer

Non-coding RNAs (ncRNAs) account for the majority of the widespread transcripts of mammalian genomes. They rarely encode proteins and peptides, but their regulatory role is crucial in numerous physiological and pathological processes. The m6A (N6-methyladenosine) modification is one of the most common internal RNA modifications in eukaryotes and is associated with all aspects of RNA metabolism. Accumulating researches have indicated a close association between m6A modification and ncRNAs, and suggested m6A-modified ncRNAs played a crucial role in tumor progression. The correlation between m6A modification and ncRNAs offers a novel perspective for investigating the potential mechanisms of cancer pathological processes, which suggests that both m6A modification and ncRNAs are critical prognostic markers and therapeutic targets in numerous malignancies. In the present report, we summarized the interaction between m6A modification and ncRNA, emphasizing how their interaction regulates pathological processes in cancer.

The regulation of PBXs and their emerging role in cancer

Pre‐B‐cell leukaemia transcription factor (PBX) proteins are a subfamily of evolutionarily conserved, atypical homeodomain transcription factors that belong to the superfamily of three amino acid loop extension (TALE) homeodomain proteins. Members of the PBX family play crucial roles in regulating multiple pathophysiological processes, such as the development of organs, congenital cardiac defects and carcinogenesis. The dysregulation of PBXs has been shown to be closely associated with many diseases, particularly cancer. However, the detailed mechanisms of PBX dysregulation in cancer progression are still inconclusive. In this review, we summarize the recent advances in the structures, functions and regulatory mechanisms of PBXs, and discuss their underlying mechanisms in cancer progression. We also highlight the great potential of PBXs as biomarkers for the early diagnosis and prognostic evaluation of cancer as well as their therapeutic applications. The information reviewed here may expand researchers’ understanding of PBXs and could strengthen the clinical implication of PBXs in cancer treatment.

Potential Prognostic Value of a Seven m6A-Related LncRNAs Signature and the Correlative Immune Infiltration in Colon Adenocarcinoma

Long non-coding RNAs (lncRNAs) and their N6-methyladenosine (m6A) modifications play an essential role in tumorigenesis and cancer progression. This study was designed to explore the value of m6A-related lncRNAs in prognosis and therapeutic applications of immune infiltration of colon adenocarcinoma (COAD). We downloaded the COAD gene expression and clinical data from The Cancer Genome Atlas project. By co-expression analysis, Lasso Cox regression analysis, and univariate and multivariate Cox regression, we constructed an independent prognostic signature of seven m6A-related lncRNAs. The prognostic lncRNAs were divided into two clusters by consistent clustering analysis, as well as into two groups of low–high risk based on the signature. Then we identified the relationship between the different groups with clinical features and immune cell infiltration. Cluster 2 had a higher risk score with a lower survival rate. The risk score was higher in groups with advanced clinical features, such as stage III–IV, N1-3, and M1. The expression of AC156455.1 was increased in tumor tissues and cluster 2, and the lncRNA ZEB1−AS1 was notably higher in the high-risk group. Five types of immune cells showed differences in two clusters, and most were upregulated in type 2. The expression of memory B cells was positively correlated with the risk score. The prognostic model was verified by the Gene Expression Omnibus (GEO) dataset. Besides, we found that the expression of these seven lncRNAs in tumor tissues was significantly higher than that in normal tissues, which verified the feasibility of the model. Thus, the signature of seven m6A-related lncRNAs can independently predict the prognosis of COAD. This signature is also closely associated with immune cell infiltration, and new therapeutic targets can be explored from this field.

Identification and Validation of a Novel 2-LncRNAs Signature Associated with m6A Regulation in Colorectal Cancer

Colorectal cancer (CRC) is one of the most common tumors in the digestive system, and it is urgent to identify a new biomarker for the diagnosis and treatment of CRC. N6-methyladenosine (m6A) is an abundant mRNA modification and is almost involved in every aspect of physiological processes. In this study, we constructed a novel m6A-related 2-lncRNAs signature that can predict the prognosis of CRC. We obtained m6A-related lncRNAs and identified prognostic lncRNAs through univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis, then constructed a prognostic model based on the risk score, and we also verified the stability of the model. In addition, differential expression analysis between the high- and low-risk subgroups was performed. A total of 1,894 m6A-related lncRNAs were screened from various sources. Using univariate Cox regression analysis and survival analysis, two lncRNAs (AL135999.1 and AL049840.4) were identified (P < 0.05), and the coefficients of lncRNAs were calculated by LASSO. The high-risk group had worse clinical outcomes and overall survival (OS) than the low-risk group, and the risk score can serve as an independent prognostic factor in CRC. In addition, different stages of CRC also showed a different level of risk score. Finally, we found that two lncRNAs were differentially expressed (P < 0.01) in CRC patients, and AL135999.1 may be relevant to m6A modification mediated by methyltransferase-like 3 (METTL3) in CRC. In summary, we constructed a reliable 2-lncRNAs signature based on the risk score, and we identified two m6A-related prognostic lncRNAs, AL135999.1 and AL049840.4. The novel 2-lncRNAs signature plays an essential role in predicting the prognosis of CRC.

Long non-coding RNAs: Biogenesis, functions, and clinical significance in gastric cancer

Gastric cancer (GC) is one of the most prevalent malignant tumor types and the third leading cause of cancer-related death worldwide. Its morbidity and mortality are very high due to a lack of understanding about its pathogenesis and the slow development of novel therapeutic strategies. Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs with a length of more than 200 nt. They play crucial roles in a wide spectrum of physiological and pathological processes by regulating the expression of genes involved in proliferation, differentiation, apoptosis, cell cycle, invasion, metastasis, DNA damage, and carcinogenesis. The aberrant expression of lncRNAs has been found in various cancer types. A growing amount of evidence demonstrates that lncRNAs are involved in many aspects of GC pathogenesis, including its occurrence, metastasis, and recurrence, indicating their potential role as novel biomarkers in the diagnosis, prognosis, and therapeutic targets of GC. This review systematically summarizes the biogenesis, biological properties, and functions of lncRNAs and highlights their critical role and clinical significance in GC. This information may contribute to the development of better diagnostics and treatments for GC.

Angiogenesis-related lncRNAs predict the prognosis signature of stomach adenocarcinoma

Background

Stomach adenocarcinoma (STAD), which accounts for approximately 95% of gastric cancer types, is a malignancy cancer with high morbidity and mortality. Tumor angiogenesis plays important roles in the progression and pathogenesis of STAD, in which long noncoding RNAs (lncRNAs) have been verified to be crucial for angiogenesis. Our study sought to construct a prognostic signature of angiogenesis-related lncRNAs (ARLncs) to accurately predict the survival time of STAD.

Methods

The RNA-sequencing dataset and corresponding clinical data of STAD were acquired from The Cancer Genome Atlas (TCGA). ARLnc sets were obtained from the Ensemble genome database and Molecular Signatures Database (MSigDB, Angiogenesis M14493, INTegrin pathway M160). A ARLnc-related prognostic signature was then constructed via univariate Cox and multivariate Cox regression analysis in the training cohort. Survival analysis and Cox regression were performed to assess the performance of the prognostic signature between low- and high-risk groups, which was validated in the validation cohort. Furthermore, a nomogram that combined the clinical pathological characteristics and risk score conducted to predict the overall survival (OS) of STAD. In addition, ARLnc-mRNA coexpression pairs were constructed with Pearson’s correlation analysis and visualized to infer the functional annotation of the ARLncs by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The expression of four ARLncs in STAD and their correlation with the angiogenesis markers, CD34 and CD105, were also validated by RT–qPCR in a clinical cohort.

Results

A prognostic prediction signature including four ARLncs (PVT1, LINC01315, AC245041.1, and AC037198.1) was identified and constructed. The OS of patients in the high-risk group was significantly lower than that of patients in the low-risk group (p < 0.001). The values of the time-dependent area under the curve (AUC) for the ARLnc signature for 1-, 3-, and 5- year OS were 0.683, 0.739, and 0.618 in the training cohort and 0.671, 0.646, and 0.680 in the validation cohort, respectively. Univariate and multivariate Cox regression analyses indicated that the ARLnc signature was an independent prognostic factor for STAD patients (p < 0.001). Furthermore, the nomogram and calibration curve showed accurate prediction of the survival time based on the risk score. In addition, 262 mRNAs were screened for coexpression with four ARLncs, and GO analysis showed that mRNAs were mainly involved in biological processes, including angiogenesis, cell adhesion, wound healing, and extracellular matrix organization. Furthermore, correlation analysis showed that there was a positive correlation between risk score and the expression of the angiogenesis markers, CD34 and CD105, in TCGA datasets and our clinical sample cohort.

Conclusion

Our study constructed a prognostic signature consisting of four ARLnc genes, which was closely related to the survival of STAD patients, showing high efficacy of the prognostic signature. Thus, the present study provided a novel biomarker and promising therapeutic strategy for patients with STAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08987-y.

Identification of an Immune-Related LncRNA Signature in Gastric Cancer to Predict Survival and Response to Immune Checkpoint Inhibitors

Immune microenvironment in gastric cancer is closely associated with patient’s prognosis. Long non-coding RNAs (lncRNAs) are emerging as key regulators of immune responses. In this study, we aimed to construct a prognostic model based on immune-related lncRNAs (IRLs) to predict the overall survival and response to immune checkpoint inhibitors (ICIs) of gastric cancer (GC) patients. The IRL signature was constructed through a bioinformatics method, and its predictive capability was validated. A stratification analysis indicates that the IRL signature can distinguish different risk patients. A nomogram based on the IRL and other clinical variables efficiently predicted the overall survival of GC patients. The landscape of tumor microenvironment and mutation status partially explain this signature’s predictive capability. We found the level of cancer-associated fibroblasts, endothelial cells, M2 macrophages, and stroma cells was high in the high-risk group, while the number of CD8⁺ T cells and T follicular helper cells was high in the low-risk group. Immunophenoscore (IPS) is validated for ICI response, and the IRL signature low-risk group received higher IPS, representing a more immunogenic phenotype that was more inclined to respond to ICIs. In addition, we found RNF144A-AS1 was highly expressed in GC patients and promoted the proliferation, migration, and invasive capacity of GC cells. We concluded that the IRL signature represents a novel useful model for evaluating GC survival outcomes and could be implemented to optimize the selection of patients to receive ICI treatment.