A risk signature of four aging-related genes has clinical prognostic value and is associated with a tumor immune microenvironment in glioma

An Aging-Related lncRNA Signature Establishing for Breast Cancer Prognosis and Immunotherapy Responsiveness Prediction

Purpose

Emerging evidence demonstrates the vital role of aging and long non-coding RNAs (lncRNAs) in breast cancer (BC) progression. Our study intended to develop a prognostic risk model based on aging-related lncRNAs (AG-lncs) to foresee BC patients' outcomes.

Patients and Methods

307 aging-related genes (AGs) were sequenced from the TCGA project. Then, 697 AG-lncs were identified by the co-expression analysis with AGs. Using multivariate and univariate Cox regression analysis, and LASSO, 6 AG-lncs, including al136531.1, mapt-as1, al451085.2, otud6b-as1, tnfrsf14-as1, and linc01871, were validated to compute the risk score and establish a risk signature. Expression levels of al136531.1, mapt-as1, al451085.2, tnfrsf14-as1, and linc01871 were higher in low-risk BC patients, whereas otud6b-as1 expression was higher in high-risk BC patients. In the training and testing set, high-risk patients performed shorter PFI, OS, and DFS than low-risk patients.

Results

Our risk signature had the highest concordance index among other established prognostic signatures and displayed ideal predictive ability for 1-, 3- and 5-year patient OS in the nomogram. Additionally, BC patients with different risk score levels showed different immune statuses and responses to immunotherapy via GSEA, ssGSEA, ESTIMATE algorithm, and TIDE algorithm analysis. Of note, the qRT-PCR analysis validated that these 6 AG-lncs expressed quite differentially in BC tissues at various clinical stages.

Conclusion

The risk signature of 6 AG-lncs might offer a novel prognostic biomarker and promisingly enhance BC immunotherapy's effectiveness.

Integrative analyses reveal prognostic and immunogenic characteristics of m7G methylation regulators in patients with glioma

OBJECTIVES

The expression profiles, biological mechanisms, and clinical relevance of m7G regulators in glioma were studied in this research.

METHODS

Based on the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets, glioma patients, can be categorized into three groups according to 29 m7G regulators, and different subtypes of glioma show different immune cell infiltration characteristics, function enrichment, and clinical prognosis. Three gene clusters were confirmed by utilizing the differentially expressed genes (DEGs) across the three m7G clusters.

RESULTS

A prognostic signature based on 12 m7G regulators was established and validated, producing an effective tool for predicting overall survival (OS) in glioma patients. High m7G scores indicated elevated tumor mutation burden and activation of immunity, suggesting an inflamed tumor microenvironment phenotype with poor overall survival. Low m7G scores characterized by a lack of immune infiltration and low mutation burden indicated a non-inflamed phenotype with a favorable clinical prognosis. It was also found that the m7G risk scores can affect chemotherapy sensitivity and prognosis of patients who received immunotherapy. The hub gene EIF4E1B of m7G regulators can inhibit the in vitro progression of glioma cells by regulating PD-L1 expression through p53 signaling pathway-related inactivation.

CONCLUSIONS

The m7G prognostic signature can be a biomarker of the overall survival of patients with glioma. An initial in-vitro experiment suggested the potential biological mechanisms of immune regulation, with m7G regulators affecting glioma progression by modulating immune responses. The present research provides a better understanding of how m7G regulators function in glioma progression as well as the impact on clinical outcomes, which can provide new insights that might be beneficial for precision therapy of glioma.

The prognostic significance of PD-L1 expression in patients with glioblastoma: A meta-analysis

Background

Glioblastoma (GBM) is a malignant brain tumor associated with high morbidity and mortality rates with a poor prognosis. In recent years, studies on prognostic markers such as programmed death ligand 1 (PD-L1) have increased; however, their conclusions remain controversial. Here, relevant literature was reviewed and a meta-analysis was performed to clarify the correlation between PD-L1 expression and overall survival (OS) in GBM.

Methods

The non-foundational literature on PD-L1 expression associated with OS in GBM up to February 2022 was searched in the PubMed, Metstr, Cochrane, and Web of Science databases. Literature was rigorously screened according to inclusion and exclusion criteria, the total hazard ratio (HR), and corresponding 95% confidence intervals (CIs).

Results

Calculating the combined HR value and corresponding 95% CI of HR=1.124 (95% CI: 1.047-1.201, P=0.000, I2 (I-squared)=48.8%), it was shown that PD-L1 expression was significantly associated with low OS in GBM patients. Although I2 = 48.8% < 50%, to make the results more credible, in the cutoff values ≥10% subgroup HR=1.37 (95% CI: 1.07-1.67, P=0.000, I2 = 0%), which was also the result found in the first meta-analysis. In contrast, in the cutoff value ≥5% subgroup HR=1.14 (95% CI: 0.98-1.30, P=0.000, I2 = 59.8%) and in the cutoff value median PD-L1 expression levels subgroup HR=1.05 (95% CI: 0.92-1.18, P=0.000, I2 = 0%), indicating that PD-L1 expression was not associated with low OS in GBM. Furthermore, in four studies, we found no significant correlation between PD-L1 expression and the progression-free survival of GBM (HR=1.14, 95% CI:0.40-1.88, P=0.03, I2 = 29.3%).

Conclusion

PD-L1 expression was significantly associated with low OS in GBM patients; however, this result needs to be interpreted with caution and requires a large, multicenter clinical study in patients with similar baseline data for further evaluation.

Mining Database to Identify Aging-Related Molecular Subtype and Prognostic Signature in Lung Adenocarcinoma

Background. Lung cancer is emerging as one of most deadly diseases, and the mortality rate was still high with 5-year overall survival rate less than 20%. Aging is referred as protumorigenic state, and it plays a significant role in cancer development. Methods. Molecular subtype of lung cancer was identified by consensus cluster analysis. Prognostic signature was constructed using LASSO cox regression analysis. CeRNA network was constructed to explore lncRNA-miRNA-mRNA regulatory axis. Results. A total of 27 differentially expressed aging-related genes (ARGs) were obtained in LUAD. Three clusters of TCGA-LUAD patients with significant difference in prognosis, immune infiltration, chemotherapy, and targeted therapy were identified. We also developed an aging-related prognostic signature that had a better performance in predicting the1-year, 3-year, and 5-year overall survival of LUAD. Further analysis suggested a significant correlation between prognostic signature gene expression and clinical stage, immune infiltration, tumor mutation burden, microsatellite instability, and drug sensitivity. We also identified the lncRNA UCA1/miR-143-3p/CDK1 regulatory axis in LUAD. Conclusion. Our study identified three clusters of TCGA-LUAD patients with significant difference in prognosis, immune infiltration, chemotherapy, and targeted therapy. We also developed an aging-related prognostic signature that had a good performance in the prognosis of LUAD.

Integrated analysis of senescence-associated genes in pancreatic ductal adenocarcinoma

Background: Cellular senescence plays a critical role in the occurrence and development, and immune modulation of cancer. This research primarily investigated the role of senescence-associated genes (SAGs) in the survival and tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC).

Methods: From the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) database, the gene expression profiles and clinical data of PDAC samples were downloaded. SAGs in the TCGA cohort were used to build a novel prognostic model and validated in the ICGC cohort. The relationship of signature with the immune landscape, tumor mutational burden (TMB), as well as the sensitivity of different therapies, was explored. Moreover, a nomogram was developed to predict the overall survival of PDAC patients.

Results: A prognostic signature was constructed on basis of three SAGs, and patients in the low-risk score group had a longer survival time. The accuracy of the signature to distinguish different score groups was confirmed through principal component analysis (PCA) and the Receiver operator curves curve. The mRNA expression of the three signature genes was also verified in normal pancreatic and PDAC cell lines by RT-qPCR. The signature could independently predict the prognosis of PDAC patients and had broad applicability. Meanwhile, the nomogram predicted that 1- and 3-years survival rates were in good agreement with the observed overall survival rates. Low-risk patients had lower tumor mutational burden, and low-TMB patients had a better prognosis. Low- and high-risk patients exhibit distinct immune cell infiltration and immune checkpoint changes. By further analyzing the risk score, patients in the low-risk group were more responsive to immunotherapy and a variety of commonly used chemotherapeutic drugs.

Conclusion: The prognostic signature can well predict the prognosis and assess the possibility of immunotherapy in personalized PDAC treatment.

Identification of the Tumor Immune Microenvironment and Therapeutic Biomarkers by a Novel Molecular Subtype Based on Aging-Related Genes in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most prevalent malignant tumors with poor prognosis. Increasing evidence has revealed that immune cells and checkpoints in the tumor microenvironment (TME) and aging are associated with the prognosis of HCC. However, the association between aging and the tumor immune microenvironment (TIME) in HCC is still unclear.

Methods

RNA expression profiles and clinical data concerning HCC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Based on differentially expressed aging-related genes (DEAGs), unsupervised clustering was used to identify a novel molecular subtype in HCC. The features of immune cell infiltration and checkpoints were further explored through CIBERSORTx. Enrichment analysis and both univariate and multivariate Cox analyses were conducted to construct a 3-gene model for predicting prognosis and chemosensitivity. Finally, the mRNA and protein expression levels of the 3 genes were verified in HCC and other cancers through database searches and experiments.

Results

Eleven differentially expressed AGs (GHR, APOC3, FOXM1, PON1, TOP2A, FEN1, HELLS, BUB1B, PPARGC1A, PRKDC, and H2AFX) correlated with the prognosis of HCC were used to divide HCC into two subtypes in which the prognosis was different. In cluster 2, which had a poorer prognosis, the infiltration of naive B cells and monocytes was lower in the TCGA and GEO cohorts, while the infiltration of M0 macrophages was higher. In addition, the TCGA cohort indicated that the microenvironment of cluster 2 had more immunosuppression through immune checkpoints. Enrichment analysis suggested that the MYC and E2F targets were positively associated with cluster 2 in the TCGA and GEO cohorts. Additionally, 3 genes (HMGCS2, SLC22A1, and G6PD) were screened to construct the prognostic model through univariate/multivariate Cox analysis. Then, the model was validated through the TCGA validation set and GEO dataset (GSE54236). Cox analysis indicated that the risk score was an independent prognostic factor and that patients in the high-risk group were sensitive to multiple targeted drugs (sorafenib, gemcitabine, rapamycin, etc.). Finally, significantly differential expression of the 3 genes was detected across cancers.

Conclusion

We systematically described the immune differences in the TME between the molecular subtypes based on AGs and constructed a novel three-gene signature to predict prognosis and chemosensitivity in patients with HCC.

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

This study is aimed to establish a new glioma prognosis model by integrating the aging-related lncRNA expression profiles and clinical parameters of glioma patients enrolled in the Chinese Glioma Genome Atlas and The Cancer Genome Atlas. The aging-related lncRNAs were explored using Pearson correlation analysis (|R|> 0.6, P < 0.001), and the prognostic signature in glioma patients was screened using univariate cox regression and least absolute shrinkage/selection operator regression. Based on the fifteen lncRNAs screened out, we divided the glioma patients into three subtypes, and developed a prognostic model. Kaplan-Meier survival curve analysis showed that low-risk patients survived longer time than high-risk patients. Principal component analysis indicated that the signature of aging-related lncRNAs was clearly distinct between the high- and low-risk groups. We also found the fifteen lncRNAs were closely correlated with 119 genes by establishing a co-expression network. Kyoto Encyclopedia of Genes and Genomes analysis displayed that the high- and low-risk groups were enriched in different functions and pathways. Different missense mutations were observed in the two groups, and the most frequent variant types were single nucleotide polymorphism. This study demonstrates that the novel aging-related lncRNAs signature has an important prognosis prediction ability and may contribute to individualized treatment for glioma.

Identification of an Aging-Related Gene Signature in Predicting Prognosis and Indicating Tumor Immune Microenvironment in Breast Cancer

Breast cancer (BC) is the most commonly diagnosed malignancy accompanied by high invasion and metastasis features. Importantly, emerging studies have supported that aging is a key clue that participates in the immune state and development of BC. Nevertheless, there are no studies concerning the aging-related genes (AGs) in constructing the prognosis signature of BC. Here, to address this issue, we initially performed a systematic investigation of the associations between AGs and BC prognosis and accordingly constructed a prognosis risk model with 10 AGs including PLAU, JUND, IL2RG, PCMT1, PTK2, HSPA8, NFKBIA, GCLC, PIK3CA, and DGAT1 by using the least absolute shrinkage and selection operator (LASSO) regression and Cox regression analysis. Meanwhile, our analysis further confirmed that the nomogram possessed a robust performance signature for predicting prognosis compared to clinical characteristics of BC patients, including age, clinical stage, and TNM staging. Moreover, the risk score was confirmed as an independent prognostic index of BC patients and was potentially correlated with immune scores, estimate score, immune cell infiltration level, tumor microenvironment, immunotherapy effect, and drug sensitivity. Furthermore, in the external clinical sample validation, AGs were expressed differentially in patients from different risk groups, and tumor-associated macrophage markers were elevated in high-risk BC tissues with more co-localization of AGs. In addition, the proliferation, transwell, and wound healing assays also confirmed the promoting effect of DGAT1 in BC cell proliferation and migration. Therefore, this well-established risk model could be used for predicting prognosis and immunotherapy in BC, thus providing a powerful instrument for combating BC.

Key protein-coding genes related to microglia in immune regulation and inflammatory response induced by epilepsy

Several studies have shown a link between immunity, inflammatory processes, and epilepsy. Active neuroinflammation and marked immune cell infiltration occur in epilepsy of diverse etiologies. Microglia, as the first line of defense in the central nervous system, are the main effectors of neuroinflammatory processes. Discovery of new biomarkers associated with microglia activation after epileptogenesis indicates that targeting specific molecules may help control seizures. In this research, we used a combination of several bioinformatics approaches, including RNA sequencing, to explore differentially expressed genes (DEGs) in epileptic lesions and control samples, and to construct a protein-protein interaction (PPI) network for DEGs, which was examined utilizing plug-ins in Cytoscape software. Finally, we aimed to identify 10 hub genes in immune and inflammation-related sub-networks, which were subsequently validated in real-time quantitative polymerase chain reaction analysis in a mouse model of kainic acid-induced epilepsy. The expression patterns of nine genes were consistent with sequencing outcomes. Meanwhile, several genes, including CX3CR1, CX3CL1, GPR183, FPR1, P2RY13, P2RY12 and LPAR5, were associated with microglial activation and migration, providing novel candidate targets for immunotherapy in epilepsy and laying the foundation for further research.