Functional implications of aging-related lncRNAs for predicting prognosis and immune status in glioma patients

A novel aging-associated lncRNA signature for predicting prognosis in osteosarcoma

Osteosarcoma (OS) is one of the most prevalent bone tumors in adolescents, and the correlation between aging and OS remains unclear. Currently, few accurate and reliable biomarkers have been determined for OS prognosis. To address this issue, we carried out a detailed bioinformatics analysis based on OS with data from the Cancer Genome Atlas data portal and Human Aging Genomic Resources database, as well as in vitro experiments. A total of 88 OS samples with gene expression profiles and corresponding clinical characteristics were obtained. Through univariate Cox regression analysis and survival analysis, 10 aging-associated survival lncRNAs (AASRs) were identified to be associated with the overall survival of OS patients. Based on the expression levels of the 10 AASRs, the OS patients were classified into two clusters (Cluster A and Cluster B). Cluster A had a worse prognosis, while Cluster B had a better prognosis. Then, 5 AASRs were ultimately included in the signature through least absolute shrinkage and selection operator-Cox regression analysis. Kaplan‒Meier survival analysis verified that the high-risk group exhibited a worse prognosis than the low-risk group. Furthermore, univariate and multivariate Cox regression analyses confirmed that the riskScore was an independent prognostic factor for OS patients. Subsequently, we discovered that the risk signature was correlated with the properties of the tumor microenvironment and immune cell infiltration. Specifically, there was a positive association between the risk model and naïve B cells, resting dendritic cells and gamma delta T cells, while it was negatively related to CD8+ T cells. Finally, in vitro experiments, we found that UNC5B-AS1 inhibited OS cells from undergoing cellular senescence and apoptosis, thereby promoting OS cells proliferation. In conclusion, we constructed and verified a 5 AASR-based signature, that exhibited excellent performance in evaluating the overall survival of OS patients. In addition, we found that UNC5B-AS1 might inhibit the senescence process, thus leading to the development and progression of OS. Our findings may provide novel insights into the treatment of OS patients.

Integrative analyses reveal biological function and prognostic role of m7G methylation regulators in high-grade glioma

Based on 29 m7G regulators, glioma patients were categorized into three groups using data from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets. Distinct characteristics were observed in immune cell infiltration, functional enrichment, and clinical prognosis for every glioma subtype. Analyzing the differentially expressed genes (DEGs) confirmed the distinction among the three m7G clusters. A predictive tool for overall survival (OS) in high-grade glioma patients was developed and confirmed, consisting of 13 m7G regulators forming a prognostic signature. Elevated m7G levels were found to be associated with increased tumor mutation burden and immune activation, indicating a tumor microenvironment characterized by inflammation and a lower overall survival rate. In contrast, reduced m7G scores were linked to a deficiency in immune infiltration, a low burden of mutations, and a non-inflamed phenotype, suggesting a more positive clinical outlook. Additionally, the m7G risk scores were found to impact chemotherapy sensitivity. The m7G predictive pattern shows potential as a marker for the overall survival of patients with high-grade glioma. By significantly improving our comprehension of the functional role of m7G regulators in the advancement of glioma and their impact on clinical results, this study offers valuable perspectives for precision therapy in the management of high-grade glioma.

FDX1 serves as a prognostic biomarker and promotes glioma progression by regulating the immune response

The present study investigates the prognostic value of the FDX1 gene and its association with immune infiltration in gliomas. Gene expression profiles and corresponding clinical parameters of glioma patients were obtained from the Cancer Genome Atlas and Chinese Glioma Genome Atlas databases. In vitro experiments were also performed to validate its impact on malignant phenotypes of glioma cells. Kaplan-Meier analysis demonstrated that high FDX1 expression was associated with poor prognosis in glioma. Function and pathway enrichment for FDX1 predominantly demonstrated immunomodulatory function. In addition, the high-FDX1 expression group had higher Estimation of Stromal and Immune cells in malignant tumor tissues using Expression data, stromal, and immune scores (p<0.001). On evaluation of immunotherapy response, TIDE and dysfunction scores were higher in the low-FDX1 group, while the exclusion score demonstrated an opposite trend. In vitro tests showed that FDX1 silencing-induced inhibition of cell invasion and migration inactivated the nucleotide oligomerization domain (NOD)-like receptor signaling pathway by regulating PD-L1 expression. Notably, NOD1 expression was reversed in FDX1-knockdown cells after treatment with NOD1 agonists. In conclusion, FDX1 may play an important role in the diagnosis and treatment of gliomas. Regulating its expression may therefore help improve immunotherapy for these tumors.

LncRNA SLC25A21-AS1 increases the chemosensitivity and inhibits the progression of ovarian cancer by upregulating the expression of KCNK4

Owing to high mortality rate, ovarian cancer seriously threatens women's health. Extensive abdominal metastasis and chemoresistance are the leading causes of ovarian cancer deaths. Through lncRNA sequencing, our previous study identified lncRNA SLC25A21-AS1, which was significantly downregulated in chemoresistant ovarian cancer cells. In this study, we aimed to evaluate the role and mechanism of SLC25A21-AS1 in ovarian cancer. The expression of SLC25A21-AS1 was analyzed by qRT-PCR and online database GEPIA. The biological functions of SLC25A21-AS1 and KCNK4 were analyzed by CCK-8, transwell, and flow cytometry. The specific mechanism was analyzed by RNA-sequencing, RNA binding protein immunoprecipitation, rescue experiments, and bioinformatic analysis. SLC25A21-AS1 was decreased in ovarian cancer tissues and cell lines. Overexpression of SLC25A21-AS1 enhanced the sensitivity of ovarian cancer cells to paclitaxel and cisplatin, and inhibited cell proliferation, invasion, and migration, while SLC25A21-AS1-silencing showed the opposite effect. Potassium channel subfamily K member 4 (KCNK4) was significantly up-regulated upon enforced expression of SLC25A21-AS1. Overexpression of KCNK4 inhibited cell proliferation, invasion, migration ability, and enhanced the sensitivity of ovarian cancer cells to paclitaxel and cisplatin. Meanwhile, KNCK4-overexpression rescued the promotive effect of SLC25A21-AS1-silencing on cell proliferation, invasion and migration. In addition, SLC25A21-AS1 could interact with the transcription factor Enhancer of Zeste Homolog 2 (EZH2), while EZH2 knockdown increased the expression of KCNK4 in some of the ovarian cancer cell lines. SLC25A21-AS1 enhanced the chemosensitivity and inhibited the proliferation, migration, and invasion ability of ovarian cancer cells at least partially by blocking EZH2-mediated silencing of KCNK4.

A risk stratification and prognostic prediction model for lung adenocarcinoma based on aging-related lncRNA

To create a risk model of aging-related long non-coding RNAs (arlncRNAs) and determine whether they might be useful as markers for risk stratification, prognosis prediction, and targeted therapy guidance for patients with lung adenocarcinoma (LUAD). Data on aging genes and lncRNAs from LUAD patients were obtained from Human Aging Genomic Resources 3 and The Cancer Genome Atlas, and differential co-expression analysis of established differentially expressed arlncRNAs (DEarlncRNAs) was performed. They were then paired with a matrix of 0 or 1 by cyclic single pairing. The risk coefficient for each sample of LUAD individuals was obtained, and a risk model was constructed by performing univariate regression, least absolute shrinkage and selection operator regression analysis, and univariate and multivariate Cox regression analysis. Areas under the curve were calculated for the 1-, 3-, and 5-year receiver operating characteristic curves to determine Akaike information criterion-based cutoffs to identify high- and low-risk groups. The survival rate, correlation of clinical characteristics, malignant-infiltrating immune-cell expression, ICI-related gene expression, and chemotherapeutic drug sensitivity were contrasted with the high- and low-risk groups. We found that 99 DEarlncRNAs were upregulated and 12 were downregulated. Twenty pairs of DEarlncRNA pairs were used to create a prognostic model. The 1-, 3-, and 5-year survival curve areas of LUAD individuals were 0.805, 0.793, and 0.855, respectively. The cutoff value to classify patients into two groups was 0.992. The mortality rate was higher in the high-risk group. We affirmed that the LUAD outcome-related independent predictor was the risk score (p < 0.001). Validation of tumor-infiltrating immune cells and ICI-related gene expression differed substantially between the groups. The high-risk group was highly sensitive to docetaxel, erlotinib, gefitinib, and paclitaxel. Risk models constructed from arlncRNAs can be used for risk stratification in patients with LUAD and serve as prognostic markers to identify patients who might benefit from targeted and chemotherapeutic agents.

Comprehensive analysis of necroptosis-related long noncoding RNA to predict prognosis, immune status, and immunotherapeutic response in clear cell renal cell carcinoma

Background

Necroptosis has been found to be associated with tumorigenesis and tumor progression. However, the prognostic effect of long noncoding RNAs (lncRNAs) associated with necroptosis in clear cell renal cell carcinoma (ccRCC) is still unclear.

Methods

Pearson correlation analysis was used to identify necroptosis-related genes and lncRNAs obtained from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset. Least absolute shrinkage and selection operator (LASSO) regression and Cox regression analyses were used to identify a novel necroptosis-associated lncRNAs signature that significantly correlated with survival of ccRCC. Next, single sample gene set enrichment analysis (ssGSEA) was employed to assess the extent of infiltration with immune cells. Analyses to predict the half-maximal inhibitory concentration (IC50) of patients in different risk groups were also conducted. Moreover, follow-up data of an immunotherapy cohort were used to test for differences in the immunotherapeutic efficiency between two risk groups. Finally, patients with ccRCC were divided into two groups based on 6 prognostic lncRNAs.

Results

We developed a signature of necroptosis-related lncRNAs, which was verified as an independent prognostic factor that can predict prognosis up to 7 years. Patients with higher risk scores were shown to have higher immune suppressive cell infiltration levels and expression of immune checkpoint genes, which suggests that these patients were in a state of immunosuppression. Patients in the low-risk group were found to have an increased response to immunotherapy. A prognostic prediction nomogram was conducted to predict long-term survival of patients. Cluster A tumors were considered hot tumors, since they were correlated with higher levels of immune infiltration and were more sensitive to immunotherapy.

Conclusions

A comprehensive bioinformatics analysis was conducted, which found that the necroptosis-associated lncRNA signature might be a potent prognostic factor for patients with ccRCC, which could contribute to improved prognosis of these 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.