Hypoxia-Related lncRNA Correlates With Prognosis and Immune Microenvironment in Lower-Grade Glioma

Deciphering the role of acetylation-related gene NAT10 in colon cancer progression and immune evasion: implications for overcoming drug resistance

BACKGROUND

Colon cancer (CC) is one of the most common and lethal cancers worldwide, with rising incidence rates in both developed and developing countries. Although advances in treatments such as surgery, chemotherapy, and targeted therapies have been made, prognosis for advanced colon cancer, particularly with metastasis, remains poor. Recent studies highlight the significant role of post-transcriptional modifications like acetylation in cancer biology, affecting processes like gene transcription, metabolism, and tumor progression.

METHODS

This study applied multi-omics analyses, including single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and Mendelian randomization. Data were obtained from public datasets like GSE132465, UCSC Xena, and GeneCards. We focused on acetylation-related genes, specifically NAT10 and GNE, using scoring methods, cell-cell interaction models, and survival analyses to investigate their role in colon cancer development, metastasis, and immune evasion.

RESULTS

This study identifies that NAT10 is highly expressed in epithelial cells of colorectal cancer (CC) and is closely associated with tumor progression and metastasis. Single-cell RNA sequencing analysis revealed that NAT10-positive epithelial cells exhibited strong interactions with myeloid cells and T cells, with significant differences in cell-cell communication (p < 0.05). Based-on-summary-data Mendelian randomization (SMR) analysis further supports a causal relationship between NAT10 and colorectal cancer. In the MR analysis, a significant positive correlation was observed between NAT10 and colorectal cancer risk using summary data from genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies (β_SMR = 0.004, p_SMR = 0.041, p_HEIDI = 0.737). These findings suggest that NAT10 may serve as a pathogenic factor in colorectal cancer development, providing additional genetic evidence that links this acetylation-related gene to colorectal cancer. Survival analysis further demonstrated that NAT10-positive epithelial cells are associated with poorer prognosis. In the TCGA dataset, patients with NAT10-positive epithelial cells exhibited a significantly shorter disease-free survival (DFS) (p = 0.012). Unlike GNE-positive cells, NAT10-positive epithelial cells exhibited immune escape characteristics, and TIDE analysis indicated that NAT10-positive epithelial cells were associated with a lower response to immune checkpoint blockade therapy (p = 1.3e-5), suggesting that they may impair the efficacy of immunotherapy by promoting immune evasion. In contrast, GNE was also significantly expressed in epithelial cells of colorectal cancer, but its role differs from that of NAT10. GNE-positive epithelial cells demonstrated strong communication with immune cells, particularly in interactions between myeloid cells and T cells through receptor-ligand pairs. Despite the important role of GNE-positive epithelial cells in the tumor microenvironment, their association with immune escape is weaker compared to NAT10. Survival analysis revealed that GNE-positive epithelial cells were associated with a better prognosis (p = 0.015). In the TCGA dataset, patients with GNE-positive epithelial cells displayed longer disease-free survival (DFS), contrary to the results from the SMR analysis.

CONCLUSIONS

Leveraging SMR and multi-omics analysis, this study highlights the significant role of acetylation-related genes, particularly NAT10, in colon cancer. The findings suggest that acetylation modifications in epithelial cells contribute to immune evasion and cancer progression. NAT10 could serve as a promising biomarker and therapeutic target for early diagnosis and targeted therapy, offering new avenues for improving colon cancer treatment and patient outcomes.

Clinical significance and immune infiltration analyses of the coagulation factor V gene in brain lower grade glioma

BACKGROUND

Coagulation factor V (FV) is an essential cofactor in the coagulation cascade. However, the precise function of FV in lower grade glioma (LGG) is little known.

METHODS

We first performed a pan-cancer investigation of FV expression and prognosis using TCGA and GTEx databases. Single-cell RNA sequencing confirmed FV expression in LGG tissues. We then investigated the mRNA expression level, prognostic value, and DNA methylation of FV in LGG using bioinformatics tools. The relationship between FV expression and tumor immune invasion was investigated using TIMER.

RESULTS

FV was highly expressed in a variety of tumors, including LGG, and was associated with tumor prognosis. By combining a series of in silico analysis (including expression and survival analysis), we found that the hsa-miR-665 was the most potent upstream miRNA of FV in LGG. Tumors with high FV expression had less infiltration of lymphocytes and myeloid cells, and FV level was negatively correlated with immune checkpoint expression.

CONCLUSION

Our findings suggest that FV was a potential biomarker for evaluating the prognosis and therapeutics in LGG.

Hypoxia-related lncRNA correlates with prognosis and immune microenvironment in uveal melanoma

BACKGROUND

Hypoxia-related genes are linked to the prognosis of various solid malignant tumors. However, the role of hypoxia-related long non-coding RNAs (HRLs) in uveal melanoma (UVM) remains unclear. This study aimed to identify HRLs associated with UVM prognosis and develop a novel risk signature to predict patient outcomes.

METHODS

Data from 80 UVM samples were obtained from The Cancer Genome Atlas. Prognostic HRLs were screened using Cox univariate and Pearson correlation analyses. HRL signature were constructed using Lasso analysis, and gene enrichment analysis was performed to explore the association between HRLs and immune features. Cell Counting Kit-8 assay was used to measure the propagation of human uveal melanoma (MuM2B) cells, while tumor invasion and migration were evaluated using Transwell and wound-healing experiments. Inflammatory factors and macrophage polarization were evaluated using quantitative PCR.

RESULTS

In total, 621 prognostic HRLs were screened and constructed in 12 HRLs. The risk score showed a significant correlation with the survival time of patients with UVM. Additionally, HRL correlated with diverse key immune checkpoints, revealing possible targets for immunotherapy. Immune-related pathways were highly enriched in the high-risk group. LINC02367, a protective HRL, was associated with the tumor microenvironment and survival time of patients with UVM. In vitro, LINC02367 significantly influenced MuM2B proliferation and migration. It also modulated macrophage polarization by regulating inflammatory factor levels, thereby affecting the immune microenvironment.

CONCLUSIONS

We developed a novel HRL signature to predict prognosis in patients with UVM. HRLs are potential biomarkers and therapeutic targets for the treatment of UVM.

Beyond Cancer Cells: How the Tumor Microenvironment Drives Cancer Progression

Liver cancer represents a substantial global health challenge, contributing significantly to worldwide morbidity and mortality. It has long been understood that tumors are not composed solely of cancerous cells, but also include a variety of normal cells within their structure. These tumor-associated normal cells encompass vascular endothelial cells, fibroblasts, and various inflammatory cells, including neutrophils, monocytes, macrophages, mast cells, eosinophils, and lymphocytes. Additionally, tumor cells engage in complex interactions with stromal cells and elements of the extracellular matrix (ECM). Initially, the components of what is now known as the tumor microenvironment (TME) were thought to be passive bystanders in the processes of tumor proliferation and local invasion. However, recent research has significantly advanced our understanding of the TME's active role in tumor growth and metastasis. Tumor progression is now known to be driven by an intricate imbalance of positive and negative regulatory signals, primarily influenced by specific growth factors produced by both inflammatory and neoplastic cells. This review article explores the latest developments and future directions in understanding how the TME modulates liver cancer, with the aim of informing the design of novel therapies that target critical components of the TME.

CIA-II is associated with lower-grade glioma survival and cell proliferation

BACKGROUND

The role of CIA-II has been clarified in several types of tumors; however, whether dysregulated CIA-II expression is also involved in the pathophysiology of lower-grade glioma (LGG) remains undisclosed.

METHODS

A comprehensive pan-cancer analysis of the expression patterns and prognostic significance of CIA-II in miscellaneous tumors was undertaken. Subsequently, a detailed bioinformatics analysis was executed to identify putative correlations between CIA-II expression and clinical features, prognosis, biological functions, immunological characteristics, genomic alterations, and chemotherapeutics in LGG. In vitro studies were implemented to examine the potential roles of CIA-II in LGG.

RESULTS

CIA-II expression was found to be abnormally elevated in a variety of tumors, including LGG. Additionally, patients with LGG with higher CIA-II expression owned worse prognosis. Importantly, the results declared that CIA-II expression was an independent prognostic indicator for LGG. Moreover, the expression of CIA-II was tightly interrelated with immune cell infiltration, gene mutations, and chemotherapeutics in LGG. In vitro studies revealed that CIA-II was increased and strongly related to the cell proliferation in LGG.

CONCLUSION

CIA-II may be an independent prognostic factor and a serviceable therapeutic target in LGG.

Complement and coagulation cascades are associated with prognosis and the immune microenvironment of lower-grade glioma

Background

Abnormal coagulation is a common feature of glioma. There is a strong correlation between coagulation and the complement system, named complement and coagulation cascades (CCC). However, the role of CCC genes in lower-grade glioma (LGG) remains unclear. This study aimed to investigate the role of CCC genes in LGG.

Methods

In total, 5,628 differential expressed genes were identified between 498 LGG tissues from The Cancer Genome Atlas (TCGA) and 207 normal brain tissues from Genotype-Tissue Expression Project (GTEx). Among them, 20 overlapped CCC genes were identified as differentially expressed CCC genes. Then, comprehensive bioinformatics analysis was used to investigate the role of CCC genes in LGG; 271 LGG tissues from the Chinese Glioma Genome Atlas (CGGA) were used as the validation dataset. Cell Counting Kit-8 (CCK8) proliferation assay, colony formation assay, and wound healing assay were conducted to explore the anti-glioma effect of the sensitive drugs we predicted.

Results

We constructed a risk signature consisting of six CCC genes, including F2R, SERPINA1, TFPI, C1QC, C2, and C3AR1. The CCC gene-based risk signature could accurately predict the prognosis of patients with LGG. In addition, we found that the JAK-STAT, NOD-like receptor, Notch, PI3K-Akt, and Rap1 signaling pathways might be activated and had crosstalk with CCC in the high-risk group. Our findings analyses demonstrated that samples in high- and low-risk groups had different immune landscapes. Moreover, patients in the high-risk group might have greater resistance to immunotherapy. We validated the accuracy of the risk signature in predicting immunotherapy response in two public immunotherapy cohorts, GSE135222 and GSE78220. By means of oncoPredict, MG-132, BMS-536924, PLX-4720, and AZD6482 were identified as potential sensitive drugs for high-risk patients, of which MG-132 was particularly recommended for high-risk patients. We performed in vitro experiments to explore the anti-glioma effect of MG-132, and the results demonstrated MG-132 could inhibit the proliferation and migration of glioma cells.

Conclusions

Our findings show that CCC genes are associated with the prognosis and immune infiltration of LGG and provide possible immunotherapeutic and novel chemotherapeutic strategies for patients with LGG based on the risk signature.

Selection of M7G-related lncRNAs in kidney renal clear cell carcinoma and their putative diagnostic and prognostic role

BACKGROUND

Kidney renal clear cell carcinoma (KIRC) is a common malignant tumor of the urinary system. This study aims to develop new biomarkers for KIRC and explore the impact of biomarkers on the immunotherapeutic efficacy for KIRC, providing a theoretical basis for the treatment of KIRC patients.

METHODS

Transcriptome data for KIRC was obtained from the The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Weighted gene co-expression network analysis identified KIRC-related modules of long noncoding RNAs (lncRNAs). Intersection analysis was performed differentially expressed lncRNAs between KIRC and normal control samples, and lncRNAs associated with N(7)-methylguanosine (m7G), resulting in differentially expressed m7G-associated lncRNAs in KIRC patients (DE-m7G-lncRNAs). Machine Learning was employed to select biomarkers for KIRC. The prognostic value of biomarkers and clinical features was evaluated using Kaplan-Meier (K-M) survival analysis, univariate and multivariate Cox regression analysis. A nomogram was constructed based on biomarkers and clinical features, and its efficacy was evaluated using calibration curves and decision curves. Functional enrichment analysis was performed to investigate the functional enrichment of biomarkers. Correlation analysis was conducted to explore the relationship between biomarkers and immune cell infiltration levels and common immune checkpoint in KIRC samples.

RESULTS

By intersecting 575 KIRC-related module lncRNAs, 1773 differentially expressed lncRNAs, and 62 m7G-related lncRNAs, we identified 42 DE-m7G-lncRNAs. Using XGBoost and Boruta algorithms, 8 biomarkers for KIRC were selected. Kaplan-Meier survival analysis showed significant survival differences in KIRC patients with high and low expression of the PTCSC3 and RP11-321G12.1. Univariate and multivariate Cox regression analyses showed that AP000696.2, PTCSC3 and clinical characteristics were independent prognostic factors for patients with KIRC. A nomogram based on these prognostic factors accurately predicted the prognosis of KIRC patients. The biomarkers showed associations with clinical features of KIRC patients, mainly localized in the cytoplasm and related to cytokine-mediated immune response. Furthermore, immune feature analysis demonstrated a significant decrease in immune cell infiltration levels in KIRC samples compared to normal samples, with a negative correlation observed between the biomarkers and most differentially infiltrating immune cells and common immune checkpoints.

CONCLUSION

In summary, this study discovered eight prognostic biomarkers associated with KIRC patients. These biomarkers showed significant correlations with clinical features, immune cell infiltration, and immune checkpoint expression in KIRC patients, laying a theoretical foundation for the diagnosis and treatment of KIRC.

The coagulation-related genes for prognosis and tumor microenvironment in pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a malignancy characterized by challenging early diagnosis and poor prognosis. It is believed that coagulation has an impact on the tumor microenvironment of PDAC. The aim of this study is to further distinguish coagulation-related genes and investigate immune infiltration in PDAC.

METHODS

We gathered two subtypes of coagulation-related genes from the KEGG database, and acquired transcriptome sequencing data and clinical information on PDAC from The Cancer Genome Atlas (TCGA) database. Using an unsupervised clustering method, we categorized patients into distinct clusters. We investigated the mutation frequency to explore genomic features and performed enrichment analysis, utilizing Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) to explore pathways. CIBERSORT was used to analyze the relationship between tumor immune infiltration and the two clusters. A prognostic model was created for risk stratification, and a nomogram was established to assist in determining the risk score. The response to immunotherapy was assessed using the IMvigor210 cohort. Finally, PDAC patients were recruited, and experimental samples were collected to validate the infiltration of neutrophils using immunohistochemistry. In addition, and identify the ITGA2 expression and function were identified by analyzing single cell sequencing data.

RESULTS

Two coagulation-related clusters were established based on the coagulation pathways present in PDAC patients. Functional enrichment analysis revealed different pathways in the two clusters. Approximately 49.4% of PDAC patients experienced DNA mutation in coagulation-related genes. Patients in the two clusters displayed significant differences in terms of immune cell infiltration, immune checkpoint, tumor microenvironment and TMB. We developed a 4-gene prognostic stratified model through LASSO analysis. Based on the risk score, the nomogram can accurately predict the prognosis in PDAC patients. We identified ITGA2 as a hub gene, which linked to poor overall survival (OS) and short disease-free survival (DFS). Single-cell sequencing analysis demonstrated that ITGA2 was expressed by ductal cells in PDAC.

CONCLUSIONS

Our study demonstrated the correlation between coagulation-related genes and the tumor immune microenvironment. The stratified model can predict the prognosis and calculate the benefits of drug therapy, thus providing the recommendations for clinical personalized treatment.

Decreased SPTBN2 expression regulated by the ceRNA network is associated with poor prognosis and immune infiltration in low‑grade glioma

The majority of low-grade gliomas (LGGs) in adults invariably progress to glioblastoma over time. Spectrin β non-erythrocytic 2 (SPTBN2) is detected in numerous tumors and is involved in tumor occurrence and metastasis. However, the specific roles and detailed mechanisms of SPTBN2 in LGG are largely unknown. The present study performed pan-cancer analysis for the expression and prognosis of SPTBN2 in LGG using The Cancer Genome Atlas and The Genotype-Tissue Expression. Western blotting was used to detect the amount of SPTBN2 between glioma tissues and normal brain tissues. Subsequently, based on expression, prognosis, correlation and immune infiltration, non-coding RNAs (ncRNAs) were identified that regulated SPTBN2 expression. Finally, tumor immune infiltrates associated with SPTBN2 and prognosis were performed. Lower expression of SPTBN2 was correlated with an unfavorable outcome in LGG. A significant correlation between the low SPTBN2 mRNA expression and poor clinicopathological features was observed, including wild-type isocitrate dehydrogenase status (P<0.001), 1p/19q non-codeletion (P<0.001) and elders (P=0.019). The western blotting results revealed that, compared with normal brain tissues, the amount of SPTBN2 was significantly lower in LGG tissues (P=0.0266). Higher expression of five microRNAs (miRs/miRNAs), including hsa-miR-15a-5p, hsa-miR-15b-5p, hsa-miR-16-5p, hsa-miR-34c-5p and hsa-miR-424-5p, correlated with poor prognosis by targeting SPTBN2 in LGG. Subsequently, four long ncRNAs (lncRNAs) [ARMCX5-GPRASP2, BASP1-antisense RNA 1 (AS1), EPB41L4A-AS1 and LINC00641] were observed in the regulation of SPTBN2 via five miRNAs. Moreover, the expression of SPTBN2 was significantly correlated with tumor immune infiltration, immune checkpoint expression and biomarkers of immune cells. In conclusion, SPTBN2 was lowly expressed and correlated with an unfavorable prognosis in LGG. A total of six miRNAs and four lncRNAs were identified as being able to modulate SPTBN2 in a lncRNA-miRNA-mRNA network of LGG. Furthermore, the current findings also indicated that SPTBN2 possessed anti-tumor roles by regulating tumor immune infiltration and immune checkpoint expression.

lncRNA TRHDE-AS1 Correlated with Genomic Landscape and Clinical Outcome in Glioma

The role of lncRNA in cancer development has received more and more attention in research. A variety of lncRNAs are associated with the occurrence and development of glioma. However, the role of TRHDE-AS1 in glioma is still unknown. In this study, we explored the role of TRHDE-AS1 in glioma through bioinformatic methods. We first identified an association between TRHDE-AS1 and tumor prognosis in pan-cancer analysis. Subsequently, the expression levels of TRHDE-AS1 in various clinical types of glioma were compared, and significant differences were found in pathological classification, WHO classification, molecular classification, IDH mutation, and age stratification. We analyzed the genes co-expressed with TRHDE-AS1 in glioma. In the functional analysis of TRHDE-AS1, we found that TRHDE-AS1 may be involved in the regulation of synapse-related functions. In glioma cancer driver gene correlation analysis, it was also found that TRHDE-AS1 was significantly correlated with the expression levels of multiple driver genes such as TP53, BRAF, and IDH1. By comparing the mutant profiles of the high and low TRHDE-AS1 groups, we also found that there may be differences in TP53 and CIC gene mutations in low-grade gliomas. Subsequent correlation analysis between TRHDE-AS1 and glioma immune microenvironment showed that the expression level of TRHDE-AS1 was correlated with a variety of immune cells. Therefore, we believe that TRHDE-AS1 is involved in the occurrence and development of glioma and has the ability to predict the prognosis of glioma as a biomarker of glioma.

Immune cell death-related lncRNA signature as a predictive factor of clinical outcomes and immune checkpoints in gastric cancer

Background: Immune cell death (ICD) is a type of tumor cell death that has recently been shown to activate and regulate tumor immunity. However, the role of ICD-related long non-coding RNAs (lncRNAs) in gastric cancer remains to be clarified. Methods: We obtained 375 tumor samples from the Cancer Genome Atlas (TCGA) database and randomly assigned them to training and verification groups. LASSO and Cox regression analysis were utilized to identify ICD-related lncRNAs and establish a risk model. The changes in the immune microenvironment of the two groups were compared by examining the tumor-infiltrating immune cells. Results: We established a tumor signature based on nine ICD-related lncRNAs. In light of the receiver operating characteristic and Kaplan-Meier curves, the prognostic values of this risk model were verified. Multivariate regression analysis showed that the risk score was an independent risk factor for the prognosis of patients in both the training cohort (HR 2.52; 95% CI: 1.65-3.87) and validation cohort (HR 2.70; 95% CI: 1.54-4.8). A nomogram was developed to predict the 1-, 3-, and 5-year survival of patients with gastric cancer, and the signature was linked to high levels of immunological checkpoint expression (B7-H3, VSIR). Conclusions: An ICD-related lncRNA signature could predict the immune response and prognosis of patients with gastric cancer. This prognostic signature could be employed to independently monitor the efficacy of immunotherapy for gastric cancer patients.

circMMD reduction following tumor treating fields inhibits glioblastoma progression through FUBP1/FIR/DVL1 and miR-15b-5p/FZD6 signaling

BACKGROUND

Tumor treating fields (TTF) is the latest treatment for GBM. Circular RNA (circRNA) has been demonstrated to play critical roles in tumorigenesis. However, the molecular mechanism of TTF remained largely unknown and the role of circRNA in TTF was not reported. The aim of this study was to elucidate the role and mechanism of circMMD in TTF treatment of GBM.

METHODS

Divergent primer was designed to verify the existence of circMMD in GBM cells. The prognostic role of circMMD was explored in glioma specimens. The knockdown and overexpressed plasmids were used to evaluate the effect of circMMD on GBM cell proliferation and TTF efficacy. RNA pull-down and RNA immunoprecipitation were performed to identify binding proteins of circMMD. Subcutaneous and intracranial tumor models were established to validate findings in vivo.

RESULTS

The expression of circMMD was elevated in GBM and its high expression indicated poor prognoses. TTF intervention could reduce circMMD synthesis, which suppressed GBM proliferation and increased TTF-mediated apoptosis. The reduction of circMMD promoted the interaction between FUBP1 and FIR, which decreased DVL1 transcription. Meanwhile, decreased circMMD would promote the activity of miR-15b-5p to degrade FZD6. Finally, the diminished expression of DVL1 and FZD6 expression suppressed the activation of Wnt/β-catenin pathway.

CONCLUSIONS

Our study revealed a novel mechanism of TTF that TTF-mediated reduction of circMMD could inhibit Wnt/β-catenin pathway to suppress GBM proliferation.

A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for low grade glioma

Background

As a common primary intracranial tumor, the diagnosis and therapy of low-grade glioma (LGG) remains a pivotal barrier. Cuproptosis, a new way induces cell death, has attracted worldwide attention. However, the relationship between cuproptosis and LGG remains unknown. Our study is all about finding out if there are any genes related to coproptosis that can be used to predict the outcome of LGG.

Methods

RNA data and clinical information were selected from Cancer Genome Atlas (TCGA) datasets and the Genotype-Tissue Expression (GTEx), 5 lncRNAs (GAS5.AS1, MYLK.AS1, AC142472.1, AC011346.1, AL359643.3) were identified by Cox univariate and multivariate regression, as well as LASSO Cox regression. In the training and test sets, a dual validation of the predictive signature comprised of these 5 lncRNAs was undertaken. The findings demonstrate that the risk model is able to predict the survival regression of LGG patients and has a good performance in either the KM curve approach or the ROC curve. GO, GSEA and KEGG were carried out to explore the possible molecular processes that affecting the prognosis of LGG. The characteristics of immune microenvironment were investigated by using CIBERSORT, ESTIMATE and ssGSEA.

Results

We identified five lncRNAs related with cuproptosis that were closely associated with the prognosis of LGG and used these five lncRNAs to develop a risk model. Using this risk model, LGG patients were then divided into high-risk and low-risk groups. The two patient groups had significantly distinct survival characteristics. Analyses of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that the differential genes of the two patient groups were primarily concentrated in neural active ligand-receptor interaction and cytokine-cytokine receptor interaction. The ssGSEA score determined the information related to immune infiltration, and the two groups were differentially expressed in immune subpopulations such as T cells and B cells as well.

Conclusion

Our study discovered 5 cuproptosis-related lncRNAs which contribute to predicting patients' survival of LGG and provide ideas for the exploration of new targets for LGG in the future.

The integrative analysis based on super-enhancer related genes for predicting different subtypes and prognosis of patient with lower-grade glioma

Objective: Emerging evidence revealed that super-enhancer plays a crucial role in the transcriptional reprogramming for many cancers. The purpose aimed to explored how the super-enhancer related genes affects the prognosis and tumor immune microenvironment (TIME) of patients with low-grade glioma (LGG). Methods: In this study, the differentially expressed genes (DEGs) between LGG cohorts and normal brain tissue cohort were identified by the comprehensive analysis of the super-enhancer (SE) related genes. Then non-negative matrix factorization was performed to seek the optimal classification based on the DEGs, while investigating prognostic and clinical differences between different subtypes. Subsequently, a prognostic related signature (SERS) was constructed for the comprehensive evaluation in term of individualized prognosis, clinical characteristics, cancer markers, genomic alterations, and immune microenvironment of patients with LGG. Results: Based on the expression profiles of 170 DEGs, we identified three SE subtypes, and the three subtypes showed significant differences in prognostic, clinicopathological features. Then, nine optimal SE-related genes were selected to construct the SERS through the least absolute shrinkage and selection operator Cox regression analysis. Survival analysis showed that SERS had strong and stable predictive ability for the prognosis of LGG patients in the The Cancer Genome Atlas, China Glioma Genome Atlas, and Remdrandt cohorts, respectively. We also found that SERS was highly correlated with clinicopathological features, tumor immune microenvironment, cancer hallmarks, and genomic alterations in LGG patients. In addition, the predictive power of SERS for immune checkpoint inhibitor treatment is also superior. The qRT-PCR results and immunohistochemical results also confirmed the difference in the expression of four key genes in normal cells and tumors, as well as in normal tissues and tumor tissues. Conclusion: The SERS could be suitable to utilize individualized prognosis prediction and immunotherapy options for LGG patients in clinical application.

Machine learning-based identification of SOX10 as an immune regulator of macrophage in gliomas

Gliomas, originating from the glial cells, are the most lethal type of primary tumors in the central nervous system. Standard treatments like surgery have not significantly improved the prognosis of glioblastoma patients. Recently, immune therapy has become a novel and effective option. As a conserved group of transcriptional regulators, the Sry-type HMG box (SOX) family has been proved to have a correlation with numerous diseases. Based on the large-scale machine learning, we found that the SOX family, with significant immune characteristics and genomic profiles, can be divided into two distinct clusters in gliomas, among which SOX10 was identified as an excellent immune regulator of macrophage in gliomas. The high expression of SOX10 is related to a shorter OS in LGG, HGG, and pan-cancer groups but benefited from the immunotherapy. It turned out in single-cell sequencing that SOX10 is high in neurons, M1 macrophages, and neural stem cells. Also, macrophages are found to be elevated in the SOX10 high-expression group. SOX10 has a positive correlation with macrophage cytokine production and negative regulation of macrophages' chemotaxis and migration. In conclusion, our study demonstrates the outstanding cluster ability of the SOX family, indicating that SOX10 is an immune regulator of macrophage in gliomas, which can be an effective target for glioma immunotherapy.

Integrated Analysis of TME and Hypoxia Identifies a Classifier to Predict Prognosis and Therapeutic Biomarkers in Soft Tissue Sarcomas

Soft tissue sarcoma (STS) is one of the rarest but most aggressive cancer. It is important to note that intratumoral hypoxia and tumor microenvironment (TME) infiltration play a significant role in the growth and therapeutic resistance of STS. The goal of this study was therefore to determine whether linking hypoxia-related parameters to TME cells could provide a more accurate prediction of prognosis and therapeutic response. An analysis of 109 hypoxia-related genes and 64 TME cells was conducted in STS. Hypoxia-TME classifier was constructed based on 6 hypoxia prognostic genes and 8 TME cells. As a result, we evaluated the prognosis, tumor, and immune characteristics, as well as the effectiveness of therapies in Hypoxia-TME-defined subgroups. The Lowplus group showed a better prognosis and therapeutic response than any other subgroup. It is possible to unravel these differences based on immune-related molecules and somatic mutations in tumors. Further validation of Hypoxia-TME was done in an additional cohort of 225 STS patients. Additionally, we identified five key genes through differential analysis and RT-qPCR, namely, ACSM5, WNT7B, CA9, MMP13, and RAC3, which could be targeted for therapy. As a whole, the Hypoxia-TME classifier demonstrated a pretreatment predictive value for prognosis and therapeutic outcome, providing new approaches to therapy strategizing for patients.

Molecular subtypes of cuproptosis regulators and their correlation with clinical prognosis and immune response in glioma

Cuproptosis is a newly described form of cell death. However, nothing is known about the roles of cuproptosis regulators in glioma. First, we explored the characteristics of cuproptosis molecular subtypes and relevant tumor microenvironment (TME) immune cell infiltration patterns in glioma. Using unsupervised clustering analysis, we identified two cuproptosis subtypes and three gene clusters that exhibited different clinical characteristics and TME cell infiltration patterns. Then, we developed and validated a cuproptosis-related prognostic model for predicting the overall survival of glioma patients. We established a risk score tool based on a nomogram to assess the clinical applicability of the cuproptosis model. A high cuproptosis risk score with high immune cell infiltration level, tumor mutation burden, gene alterations, and immunity activation had an unfavorable overall survival. Next, we identified possible competing endogenous ribonucleic acid regulatory networks based on significantly differentially expressed genes between high-risk and low-risk groups and screened several candidate small molecular compounds that may improve chemotherapy. Data from IMvigor and GSE78200 showed that the cuproptosis score affected the prognosis of patients who received immunotherapy. Our study indicated that cuproptosis regulators are involved in TME immune infiltration and impact the clinical prognosis in glioma. It is necessary for clinical practice to develop different therapeutic strategies according to the different phenotypes associated with immune response. The present findings provide new insight for improving immunotherapy strategies and individualized treatment in glioma.

Prognosis and immune response of a cuproptosis-related lncRNA signature in low grade glioma

Cuproptosis is a newly discovered new mechanism of programmed cell death, and its unique pathway to regulate cell death is thought to have a unique role in understanding cancer progression and guiding cancer therapy. However, this regulation has not been studied in low grade glioma (LGG) at present. In this study, data on low grade glioma patients were downloaded from the TCGA database. We screened the genes related to cuproptosis from the published papers and confirmed the lncRNAs related to them. We applied univariate/multivariate, and LASSO regression algorithms, finally identified 11 lncRNAs for constructing prognosis prediction models, and constructed a risk scoring model. The reliability and validity test of the model indicated that the model could well distinguish the prognosis and survival of LGG patients. Furthermore, the analyses of immunotherapy, immune microenvironment, as well as functional enrichment were also performed. Finally, we verified the expression of these six prognostic key lncRNAs using real-time polymerase chain reaction (RT-PCR). In conclusion, this study is the first analysis based on cuproptosis-related lncRNAs in LGG and aims to open up new directions for LGG therapy.

A Hypoxia-Related lncRNA Signature Correlates with Survival and Tumor Microenvironment in Colorectal Cancer

The hypoxic tumor microenvironment and long noncoding RNAs (lncRNAs) are pivotal in cancer progression and correlate with the survival outcome of patients. However, the role of hypoxia-related lncRNAs (HRLs) in colorectal cancer (CRC) development remains largely unknown. Herein, we developed a hypoxia-related lncRNA signature to predict patients' survival and immune infiltration. The RNA-sequencing data of 500 CRC patients were obtained from The Cancer Genome Atlas (TCGA) dataset, and HRLs were selected using Pearson's analysis. Next, the Cox regression analysis was applied to construct a risk signature consisting of 9 HRLs. This signature could predict the overall survival (OS) of CRC patients with high accuracy in training, validation, and entire cohort. This signature was an independent risk factor and exerted predictive ability in different subgroups. Functional analysis revealed different molecular features between high- and low-risk groups. A series of drugs including cisplatin showed different sensitivities between the two groups. The expression pattern of immune checkpoints was also distinct between the two clusters in this model. Furthermore, the high-risk group had higher immune, stromal, and ESTIMATE score and a more repressive immune microenvironment than the low-risk group. Moreover, MYOSLID, one of the lncRNAs in this signature, could significantly regulate the proliferation, invasion, and metastasis of CRC.

A novel hypoxic lncRNA, HRL-SC, promotes the proliferation and migration of human dental pulp stem cells through the PI3K/AKT signaling pathway

Background

Human dental pulp stem cells (hDPSCs) are critical for pulp generation. hDPSCs proliferate faster under hypoxia, but the mechanism by which long noncoding RNA (lncRNA) regulates this process is not fully understood.

Methods

Novel lncRNAs were obtained by reanalysis of transcriptome datasets from RNA-Seq under hypoxia compared with normoxia, and a differential expression analysis of target genes was performed. Bioinformatics analyses, including gene ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis and gene set enrichment analysis, were used to understand the function of key novel lncRNAs. hDPSCs were isolated from dental pulp tissue. EdU and scratch wound healing assays were used to detect the proliferation and migration of hDPSCs. qRT-PCR was used to detect changes in the RNA expression of selected genes. RNA fluorescence in situ hybridization, small interfering RNA, qRT-PCR and Western blot analysis were used to explore the function of key novel lncRNAs.

Results

We identified 496 novel lncRNAs in hDPSCs under hypoxia, including 45 differentially expressed novel lncRNAs. Of these, we focused on a key novel lncRNA, which we designated HRL-SC (hypoxia-responsive lncRNA in stem cells). Functional annotation revealed that HRL-SC was associated with hypoxic conditions and the PI3K/AKT signaling pathway. HRL-SC was mainly located in the cytoplasm of hDPSCs and had stable high expression under hypoxia. Knockdown of HRL-SC inhibited the proliferation and migration of hDPSCs and the expression levels of PI3K/AKT-related marker proteins. Furthermore, the AKT activator SC79 partially offset the inhibitory effect caused by the knockdown, indicating that HRL-SC promoted hDPSCs through the PI3K/AKT signaling pathway.

Conclusions

Hypoxia-responsive lncRNA HRL-SC promotes the proliferation and migration of hDPSCs through the PI3K/AKT signaling pathway, and this understanding may facilitate the regenerative application of hDPSCs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13287-022-02970-5.

A Whole Exon Screening-Based Score Model Predicts Prognosis and Immune Checkpoint Inhibitor Therapy Effects in Low-Grade Glioma

Objective

This study aims to identify prognostic factors for low-grade glioma (LGG) via different machine learning methods in the whole genome and to predict patient prognoses based on these factors. We verified the results through in vitro experiments to further screen new potential therapeutic targets.

Method

A total of 940 glioma patients from The Cancer Genome Atlas (TCGA) and The Chinese Glioma Genome Atlas (CGGA) were included in this study. Two different feature extraction algorithms – LASSO and Random Forest (RF) – were used to jointly screen genes significantly related to the prognosis of patients. The risk signature was constructed based on these screening genes, and the K-M curve and ROC curve evaluated it. Furthermore, we discussed the differences between the high- and low-risk groups distinguished by the signature in detail, including differential gene expression (DEG), single-nucleotide polymorphism (SNP), copy number variation (CNV), immune infiltration, and immune checkpoint. Finally, we identified the function of a novel molecule, METTL7B, which was highly correlated with PD-L1 expression on tumor cell, as verified by in vitro experiments.

Results

We constructed an accurate prediction model based on seven genes (AUC at 1, 3, 5 years= 0.91, 0.85, 0.74). Further analysis showed that extracellular matrix remodeling and cytokine and chemokine release were activated in the high-risk group. The proportion of multiple immune cell infiltration was upregulated, especially macrophages, accompanied by the high expression of most immune checkpoints. According to the in vitro experiment, we preliminarily speculate that METTL7B affects the stability of PD-L1 mRNA by participating in the modification of m6A.

Conclusion

The seven gene signatures we constructed can predict the prognosis of patients and identify the potential benefits of immune checkpoint inhibitors (ICI) therapy for LGG. More importantly, METTL7B, one of the risk genes, is a crucial molecule that regulates PD-L1 and could be used as a new potential therapeutic target.

The Involvement of Long Non-Coding RNAs in Glioma: From Early Detection to Immunotherapy

Glioma is a brain tumor that arises in the central nervous system and is categorized according to histology and molecular genetic characteristics. Long non-coding RNAs (lncRNAs) are RNAs longer than 200 nucleotides in length. They have been reported to influence significant events such as carcinogenesis, progression, and increased treatment resistance on glioma cells. Long non-coding RNAs promote cell proliferation, migration, epithelial-to-mesenchymal transition and invasion in glioma cells. Various significant advancements in transcriptomic profiling studies have enabled the identification of immune-related long non-coding RNAs as immune cell-specific gene expression regulators that mediates both stimulatory and suppressive immune responses, implying lncRNAs as potential candidates for improving immunotherapy efficacy against tumors and due to the lack of different diagnostic and treatments for glioma, lncRNAs are potential candidates to be used as future diagnostic, prognostic biomarker and treatment tools for glioma. This review’s primary purpose is to concentrate on the role of long non-coding RNAs in early glioma identification, treatment, and immunotherapy.

Machine Learning-Based Integration Develops a Pyroptosis-Related lncRNA Model to Enhance the Predicted Value of Low-Grade Glioma Patients

Background

Molecular features have been included in the categorization of gliomas because they may be excellent predictors of tumor prognosis. Lower-grade glioma (LGGs, which comprise grade 2 and grade 3 gliomas) patients have a wide variety of outcomes. The goal of this research is to investigate a pyroptosis-based long noncoding RNA (lncRNA) profile and see whether it can be used to predict LGG prognosis.

Methods

The Genotype-Tissue Expression (GTEx) and Cancer Genome Atlas (TCGA) datasets were utilized to get RNA data and clinical information for this research. Six considerably related lncRNAs (AL355574.1, AL355974.2, Z97989.1, SNAI3-AS1, LINC02593, and CYTOR) were selected using Cox regression (univariate and multivariate) and LASSO Cox regression. A variety of statistical techniques, including ROC curves, nomogram, and Kaplan-Meier curves, were utilized to verify the risk score's accuracy. Following that, bioinformatics studies were carried out to investigate the possible molecular processes that influence LGG prognosis. The variations in pathway enrichment were investigated using GSEA. The immune microenvironment inconsistencies were investigated using CIBERSORT, ESTIMATE, MCPcounter, TIMER algorithms, and ssGSEA.

Results

We discovered six lncRNAs with distinct expression patterns that are linked to LGG prognosis. Kaplan-Meier studies showed a signature of high-risk lncRNAs associated with a poor prognosis for LGG. Furthermore, the AUC of the lncRNA signature was 0.763, indicating that they may be used to predict LGG prognosis. In predicting LGG prognosis, our risk assessment approach outperformed conventional clinicopathological characteristics. In the high-risk group of people, GSEA identified tumor-related pathways and immune-related pathways. Furthermore, T cell-related activities such as T cell coinhibition and costimulation, check point, APC coinhibition and costimulation, CCR, and inflammatory promoting were shown to be substantially different between the two groups in TCGA analysis. Immune checkpoints including PD-1, CTLA4, and PD-L1 were expressed differentially in the two groups as well.

Conclusion

This study found that pyroptosis-based lncRNAs were useful in predicting LGG patients' survival, suggesting that they may be used as a therapeutic target in the future.

Construction of a Prognostic Model for Hypoxia-Related LncRNAs and Prediction of the Immune Landscape in the Digestive System Pan-Cancer

Digestive system pan-cancer is a general term for digestive system tumors including colorectal carcinoma (CRC), esophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), and liver hepatocellular carcinoma (LIHC). Since the anatomical location, function and metabolism are closely related, there may be similarities in development and progression of these tumors. Hypoxia is the consequence of an imbalance between oxygen demand and supply, and intracellular hypoxia is associated with malignant progression, treatment resistance, and poor prognosis in tumors. Therefore, an urgent and challenging task is to investigate the molecular mechanisms associated with hypoxia in digestive system pan-cancer for the prognosis and treatment of digestive tract tumors. In this study, we identified 18 hypoxia-related lncRNAs (HRlncRNAs) by co-expression analysis between hypoxia genes and lncRNAs from digestive system pan-cancer. Six HRlncRNAs were then obtained using lasso regression and multivariate cox analysis to construct a prognostic model. Next, the Akaike information criterion (AIC) values for 3-year receiver operating curve (ROC) were counted to determine the cut-off point and establish an optimal model to distinguish between high- or low-risk groups among patients with digestive system pan-cancer. To evaluate the stability of the prognosis model, we validated it in terms of survival outcomes, clinicopathological stage, tumor-infiltrating immune cells, immune checkpoint inhibitors (ICIs) and anticancer drugs sensitivity. The results suggested that high- risk group had a worse prognosis and a more positive association with tumor-infiltrating immune cells such as B cells, cancer-associated fibroblasts, endothelial cells, monocytes, macrophages and bone marrow dendritic cells in digestive system pan-cancer. Immune checkpoint inhibitors (ICIs) related biomarkers discovered that high-risk group was positively correlated with high expression of HAVCR2 in digestive system pan-cancer. The anticancer drugs sensitivity analysis showed that the high-risk group was associated with the lower half-inhibitory centration (IC50) of Imatinib in digestive system pan-cancer. In conclusion, the prognostic model of HRlncRNAs showed a promising clinical prediction value and may provide a useful reference for the diagnosis and treatment of the digestive system tumors.

N(6)-adenosine-methyltransferase-14 promotes glioma tumorigenesis by repressing argininosuccinate synthase 1 expression in an m6A-dependent manner

Glioma is one of the leading causes of tumor-related deaths worldwide, but its potential mechanism remains unclear. This study aimed to explore the biological role and potential mechanism of argininosuccinate synthase 1 (ASS1) in glioma. The relative expression levels of ASS1 in glioma specimens and cell lines were calculated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and Western blotting. The biological functions of ASS1 were demonstrated using the 5-ethynyl-2'-deoxyuridine (EdU) assay, transwell assay, and in vivo experiments. In addition, methylated RNA immunoprecipitation (MeRIP), RNA immunoprecipitation (RIP), and luciferase reporter assays were performed to explore the molecular mechanism of ASS1 in glioma. ASS1 expression levels were found to be downregulated in glioma specimens and cell lines. Functionally, we confirmed that ASS1 inhibited glioma cell proliferation, migration, invasion, and growth both. Furthermore, we found that ASS1 was a target of N(6)-adenosine-methyltransferase-14 (METTL14)-mediated N6-methyladenosine (m6A) modification. Overexpression of METTL14 markedly elevated ASS1 mRNA m6A modification and suppressed ASS1 mRNA expression. We also revealed that METTL14-mediated ASS1 mRNA degradation relied on the YTH m6A RNA-binding protein 2 (YTHDF2)-dependent pathway. We confirmed that decreased ASS1 expression promoted the cell proliferation, migration, and invasion in glioma, and that the METTL14/ASS1/YTHDF2 regulatory axis may be an effective therapeutic target for glioma.