Cellular Senescence-Related Genes: Predicting Prognosis in Gastric Cancer

Prognostic cellular senescence-related lncRNAs patterns to predict clinical outcome and immune response in colon cancer

Background

Cellular senescence (CS) is believed to be a major factor in the evolution of cancer. However, CS-related lncRNAs (CSRLs) involved in colon cancer regulation are not fully understood. Our goal was to create a novel CSRLs prognostic model for predicting prognosis and immunotherapy and exploring its potential molecular function in colon cancer.

Methods

The mRNA sequencing data and relevant clinical information of GDC TCGA Colon Cancer (TCGA-COAD) were obtained from UCSC Xena platform, and CS-associated genes was acquired from the CellAge website. Pearson correlation analysis was used to identify CSRLs. Then we used Kaplan-Meier survival curve analysis and univariate Cox analysis to acquire prognostic CSRL. Next, we created a CSRLs prognostic model using LASSO and multivariate Cox analysis, and evaluated its prognostic power by Kaplan-Meier and ROC curve analysis. Besides, we explored the difference in tumor microenvironment, somatic mutation, immunotherapy, and drug sensitivity between high-risk and low-risk groups. Finally, we verified the functions of MYOSLID in cell experiments.

Results

Three CSRLs (AC025165.1, LINC02257 and MYOSLID) were identified as prognostic CSRLs. The prognostic model exhibited a powerful predictive ability for overall survival and clinicopathological features in colon cancer. Moreover, there was a significant difference in the proportion of immune cells and the expression of immunosuppressive point biomarkers between the different groups. The high-risk group benefited from the chemotherapy drugs, such as Teniposide and Mitoxantrone. Finally, cell proliferation and CS were suppressed after MYOSLID knockdown.

Conclusion

CSRLs are promising biomarkers to forecast survival and therapeutic responses in colon cancer patients. Furthermore, MYOSLID, one of 3-CSRLs in the prognostic model, could dramatically regulate the proliferation and CS of colon cancer.

Cellular Aging and Senescence in Cancer: A Holistic Review of Cellular Fate Determinants

This comprehensive review navigates the complex relationship between cellular aging, senescence, and cancer, unraveling the determinants of cellular fate. Beginning with an overview of cellular aging's significance in cancer, the review explores processes, changes, and molecular pathways influencing senescence. The review explores senescence as a dual mechanism in cancer, acting as a suppressor and contributor, focusing on its impact on therapy response. This review highlights opportunities for cancer therapies that target cellular senescence. The review further examines the senescence-associated secretory phenotype and strategies to modulate cellular aging to influence tumor behavior. Additionally, the review highlights the mechanisms of senescence escape in aging and cancer cells, emphasizing their impact on cancer prognosis and resistance to therapy. The article addresses current advances, unexplored aspects, and future perspectives in understanding cellular aging and senescence in cancer.

Cell Senescence-Related Genes as Biomarkers for Prognosis and Immunotherapeutic Response in Colon Cancer

Colon adenocarcinoma (COAD) stands out as the most prevalent malignancy diagnosed within the gastrointestinal tract, bearing substantial incidence and mortality rates. The processes of ageing and senescence intricately intertwine with tumorigenesis and immune regulation, concurrently exerting influence on the remodelling of the tumor microenvironment (TME). This phenomenon, in turn, significantly impacts the efficacy of immunotherapeutic interventions. Despite this awareness, the comprehensive understanding of the intricate interplay between cellular senescence and TME in the context of COAD remains elusive. Further inquiry is imperative to comprehensively gauge the relevance of cellular senescence-related genes (CSGs) in the realms of immune infiltration and the prognostication of COAD. Differentially expressed cell senescence-related genes (DE-CSGs) within COAD tumors and normal specimens were discerned through analysis of the TCGA-COAD dataset. Leveraging univariate, LASSO, and multivariate Cox regression analyses, we formulated a prognostic risk signature. Subsequent validation utilised two independent GEO datasets. Furthermore, a nomogram was devised to gauge the prognostic significance of this signature. Additionally, the immune landscape of the Cell Senescence-related Signature (CSS) was characterised using CIBERSORT and TIMER algorithms. The expression levels of CSGs were quantified through RT-PCR in COAD specimens. Drawing upon mRNA expression profiles of 191 DE-CSGs, we successfully established a 9-gene CSS, demonstrating its autonomy as a prognostic determinant for COAD patients. Those assigned high-risk scores exhibited an immunosuppressive phenotype, marked by elevated proportions of resting CD4+memory T cells and macrophages M0, correlating with diminished overall survival. Subsequent analyses uncovered that the amalgamation of CSS with the expression profiles of immune checkpoint key genes effectively predicted patient prognosis. Furthermore, patients with low-risk scores demonstrated a potential association with more favourable therapeutic outcomes in the context of immunotherapy. This study has culminated in the development of a prognostic risk signature grounded in cell senescence-related genes for COAD. We posit that the CSS plays a regulatory role in immune infiltration, emerging as a robust biomarker for prognosis and a predictive indicator for immunotherapeutic responsiveness within the COAD landscape.

Integrative analysis of potential diagnostic markers and therapeutic targets for glomerulus-associated diabetic nephropathy based on cellular senescence

Introduction

Diabetic nephropathy (DN), distinguished by detrimental changes in the renal glomeruli, is regarded as the leading cause of death from end-stage renal disease among diabetics. Cellular senescence plays a paramount role, profoundly affecting the onset and progression of chronic kidney disease (CKD) and acute kidney injuries. This study was designed to delve deeply into the pathological mechanisms between glomerulus-associated DN and cellular senescence.

Methods

Glomerulus-associated DN datasets and cellular senescence-related genes were acquired from the Gene Expression Omnibus (GEO) and CellAge database respectively. By integrating bioinformatics and machine learning methodologies including the LASSO regression analysis and Random Forest, we screened out four signature genes. The receiver operating characteristic (ROC) curve was performed to evaluate the diagnostic performance of the selected genes. Rigorous experimental validations were subsequently conducted in the mouse model to corroborate the identification of three signature genes, namely LOX, FOXD1 and GJA1. Molecular docking with chlorogenic acids (CGA) was further established not only to validate LOX, FOXD1 and GJA1 as diagnostic markers but also reveal their potential therapeutic effects.

Results and discussion

In conclusion, our findings pinpointed three diagnostic markers of glomerulus-associated DN on the basis of cellular senescence. These markers could not only predict an increased risk of DN progression but also present promising therapeutic targets, potentially ushering in innovative treatments for DN in the elderly population.

Prognosis signature for predicting the survival and immunotherapy response in esophageal carcinoma based on cellular senescence-related genes

Background

Cellular senescence occurs throughout life and can play beneficial roles in a variety of physiological processes, including embryonic development, tissue repair, and tumor suppression. However, the relationship between cellular senescence-related genes (CSRGs) and immunotherapy in esophageal carcinoma (ECa) remains poorly defined.

Methods

The data set used in the analysis was retrieved from TCGA (Research Resource Identifier (RRID): SCR_003193), GEO (RRID: SCR_005012), and CellAge databases. Data processing, statistical analysis, and diagram formation were conducted in R software (RRID: SCR_001905) and GraphPad Prism (RRID: SCR_002798). Based on CSRGs, we used the TCGA database to construct a prognostic signature for ECa and then validated it in the GEO database. The predictive efficiency of the signature was evaluated using receiver operating characteristic (ROC) curves, Cox regression analysis, nomogram, and calibration curves. According to the median risk score derived from CSRGs, patients with ECa were divided into high- and low-risk groups. Immune infiltration and immunotherapy were also analyzed between the two risk groups. Finally, the hub genes of the differences between the two risk groups were identified by the STRING (RRID: SCR_005223) database and Cytoscape (RRID: SCR_003032) software.

Results

A six-gene risk signature (DEK, RUNX1, SMARCA4, SREBF1, TERT, and TOP1) was constructed in the TCGA database. Patients in the high-risk group had a worse overall survival (OS) was disclosed by survival analysis. As expected, the signature presented equally prognostic significance in the GSE53624 cohort. Next, the Area Under ROC Curve (AUC=0.854) and multivariate Cox regression analysis (HR=3.381, 2.073-5.514, P<0.001) also proved that the risk signature has a high predictive ability. Furthermore, we can more accurately predict the prognosis of patients with ECa by nomogram constructed by risk score. The result of the TIDE algorithm showed that ECa patients in the high-risk group had a greater possibility of immune escape. At last, a total of ten hub genes (APOA1, MUC5AC, GC, APOA4, AMBP, FABP1, APOA2, SOX2, MUC8, MUC17) between two risk groups with the highest interaction degrees were identified. By further analysis, four hub genes (APOA4, AMBP, FABP1, and APOA2) were related to the survival differences of ECa.

Conclusions

Our study reveals comprehensive clues that a novel signature based on CSRGs may provide reliable prognosis prediction and insight into new therapy for patients with ECa.

Proteasome-dependent senescent tumor cells mediate immunosuppression through CCL20 secretion and M2 polarization in pancreatic ductal adenocarcinoma

The outcome of pancreatic ductal adenocarcinoma (PDAC) remains poor due to few therapeutic options available and challenges with precision therapy to target each tumour's specific characteristics. In this study, a biologically meaningful patient stratification-prognostic model with therapeutic suggestion value based on tumor senescence was developed and validated in multiple independent cohorts. Further mechanistic investigation based on single-cell transcriptomic data and in vitro experiments revealed that complement derived from non-senescent tumor cells stimulates M1 differentiation and antigen presentation, while senescent tumor cells secrete CCL20 to favor immunosuppressive M2 polarization. Also, senescent phenotype depends on proteasome function, suggesting that high-risk, high-senescence patients may benefit from proteasome inhibitors, which reverse senescence-mediated resistance to conventional chemotherapy and improve outcome. In conclusion, the current study identified senescence as a tumor-specific, hazardous factor associated with immunosuppression in PDAC. Mechanistically, senescence abrogates complement-induced M1 activation and antigen presentation, and upregulates CCL20 to favor M2 polarization. The senescence-related risk model is prognostic and therapeutic-suggestive. In light of the reliance of senescent cells on proteasomal functions, proteasome inhibitors are promising agents for high-risk patients with senescent PDAC.

Cellular senescence-related gene signature as a valuable predictor of prognosis in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a lethal tumor. Its prognosis prediction remains a challenge. Meanwhile, cellular senescence, one of the hallmarks of cancer, and its related prognostic genes signature can provide critical information for clinical decision-making.

METHOD

Using bulk RNA sequencing and microarray data of HCC samples, we established a senescence score model via multi-machine learning algorithms to predict the prognosis of HCC. Single-cell and pseudo-time trajectory analyses were used to explore the hub genes of the senescence score model in HCC sample differentiation.

RESULT

A machine learning model based on cellular senescence gene expression profiles was identified in predicting HCC prognosis. The feasibility and accuracy of the senescence score model were confirmed in external validation and comparison with other models. Moreover, we analyzed the immune response, immune checkpoints, and sensitivity to immunotherapy drugs of HCC patients in different prognostic risk groups. Pseudo-time analyses identified four hub genes in HCC progression, including CDCA8, CENPA, SPC25, and TTK, and indicated related cellular senescence.

CONCLUSIONS

This study identified a prognostic model of HCC by cellular senescence-related gene expression and insight into novel potential targeted therapies.

Comprehensive genomics analysis of aging related gene signature to predict the prognosis and drug resistance of colon adenocarcinoma

Background: Colon adenocarcinoma (COAD) is a heterogeneous tumor and senescence is crucial in the occurrence of cancer. This study aimed to identify senescence-based subtypes and construct a prognostic signature to predict the prognosis and guide immunotherapy or chemotherapy decisions for COAD patients. Methods: Based on the single-cell RNA sequencing (scRNA-seq) data of 13 samples from the Gene Expression Omnibus (GEO) database, we assessed cellular senescence characteristics. Transcriptome data, copy number variations (CNVs) and single nucleotide variations (SNVs) data were obtained from The Cancer Genome Atlas (TCGA) database. GSE39582 and GSE17537 were used for validation. Senescence subtypes were identified using unsupervised consensus clustering analysis, and a prognostic signature was developed using univariate Cox analysis and least absolute shrinkage and selection operator (LASSO). Response of risk groups to chemotherapy was predicted using the half-maximal inhibitory concentration (IC50) values. We further analyzed the relationship between risk gene expression and methylation level. The prediction performance was assessed by nomogram. Results: Senescence-related pathways were highly enriched in malignant cells and bulk RNA-seq verified cellular senescence. Three senescence subtypes were identified, in which patients in clust3 had poorest prognosis and higher T stage, accompanied with higher tumor mutation burden (TMB) and mutations, activated inflammatory response, more immune cell infiltration, and higher immune escape tendency. A senescence-based signature using 11 genes (MFNG, GPRC5B, TNNT1, CCL22, NOXA1, PABPC1L, PCOLCE2, MID2, CPA3, HSPA1A, and CALB1) was established, and accurately predicted a lower prognosis in high risk patients. Its robustness was validated by external cohort. Low risk patients were more sensitive to small molecule drugs including Erlotinib, Sunitinib, MG-132, CGP-082996, AZ628, Sorafenib, VX-680, and Z-LLNle-CHO. Risk score was an independent prognostic factor and nomogram confirmed its reliability. Four risk genes (CALB1, CPA3, NOXA1, and TNNT1) had significant positive correlation with their methylation level, while six genes (CCL22, GPRC5B, HSPA1A, MFNG, PABPC1L, and PCOLCE2) were negatively correlated with their methylation level. Conclusion: This study provides novel understanding of heterogeneity in COAD from the perspective of senescence, and develops signatures for prognosis prediction in COAD.

Landscape of Druggable Molecular Pathways Downstream of Genomic CDH1/Cadherin-1 Alterations in Gastric Cancer

Loss of CDH1/Cadherin-1 is a common step towards the acquisition of an abnormal epithelial phenotype. In gastric cancer (GC), mutation and/or downregulation of CDH1/Cadherin-1 is recurrent in sporadic and hereditary diffuse GC type. To approach the molecular events downstream of CDH1/Cadherin-1 alterations and their relevance in gastric carcinogenesis, we queried public databases for genetic and DNA methylation data in search of molecular signatures with a still-uncertain role in the pathological mechanism of GC. In all GC subtypes, modulated genes correlating with CDH1/Cadherin-1 aberrations are associated with stem cell and epithelial-to-mesenchymal transition pathways. A higher level of genes upregulated in CDH1-mutated GC cases is associated with reduced overall survival. In the diffuse GC (DGC) subtype, genes downregulated in CDH1-mutated compared to cases with wild type CDH1/Cadherin-1 resulted in being strongly intertwined with the DREAM complex. The inverse correlation between hypermethylated CpGs and CDH1/Cadherin-1 transcription in diverse subtypes implies a common epigenetic program. We identified nonredundant protein-encoding isoforms of 22 genes among those differentially expressed in GC compared to normal stomach. These unique proteins represent potential agents involved in cell transformation and candidate therapeutic targets. Meanwhile, drug-induced and CDH1/Cadherin-1 mutation-related gene expression comparison predicts FIT, GR-127935 hydrochloride, amiodarone hydrochloride in GC and BRD-K55722623, BRD-K13169950, and AY 9944 in DGC as the most effective treatments, providing cues for the design of combined pharmacological treatments. By integrating genetic and epigenetic aspects with their expected functional outcome, we unveiled promising targets for combinatorial pharmacological treatments of GC.

Effects of botanical drugs in the treatment of cancer-related fatigue in patients with gastric cancer: A meta-analysis and prediction of potential pharmacological mechanisms

Objective: To systematically review the efficacy and safety of botanical drugs in the treatment of cancer-related fatigue (CRF) caused by gastric cancer (GC) and to determine the underlying pharmacological mechanisms using a network analysis. Methods: Databases such as China National Knowledge Infrastructure (CNKI), SinoMed, Wanfang, Pubmed, Embase, Cochrane Library, and Web of Science were searched for randomized controlled trials (RCTs) from inception to 18 April 2022. Methodological quality assessment was performed using the collaborative tool Cochrane, and data analysis were carried out using RevMan 5.4 and STATA 16 software. The botanical drugs with the highest frequency of use in the included studies was selected. The chemical composition, targets of action, disease targets, and shared targets of these botanical drugs were screened based on network analysis to explore the potential mechanisms of treating CRF in patients with gastric cancer (GC). Results: A total of 13 studies that included 986 patients with gastric CRF met the inclusion criteria. The results showed that botanical drugs could improve the CRF scores of gastric CRF, including the total scores of CRF dichotomous data [Odds Ratio (OR) = 4.22; 95% confidence interval (CI) 1.67-10.68; p = 0.002], the total scores of CRF continuous data [Standardized Mean Difference (SMD) = -0.98; 95% CI -1.36 to -0.60; p < 0.00001], the affective subscales of Piper Fatigue Scale (PFS) scores [Weighted Mean Difference (MD) = -0.79; 95%CI -0.92 to -0.65; p < 0.00001], the sensory subscales of PFS scores (MD = -0.57; 95%CI -0.77 to -0.37; p < 0.00001), the behavioral subscales of PFS scores (MD = -1.05; 95% CI -1.29 to -0.82; p < 0.00001), Quality of Life Questionnaire Core 30 (QLQ-C30) (MD = 10.53, 95% CI 8.26 to12.80; p < 0.00001), and the Karnofsky Performance Status scale (KPS) (MD = 5.18, 95% CI 2.60 to 7.76; p < 0.0001). The botanical drugs group had milder adverse effects than the control group. A total of 44 chemical components and 241 potential targets were obtained from the online database and 121 drug targets overlapped with the disease targets of CRF in patients with GC. Moreover, five key active ingredients, namely quercetin, Stigmasterol, luteolin, kaempferol, and isorhamnetin, as well as five key targets including AKT1, TP53, TNF, VEGFA, and CASP3, were screened. In addition, five key signaling pathways, including cancer, Hepatitis B, Prostate cancer, Hepatitis C, and Pancreatic cancer pathways, were obtained through enrichment analysis. Conclusion: The results of the study showed that botanical drugs have positive effects on CRF in patients with GC. However, more well-designed, multicenter, and large sample-sized Randomized Controlled Trials are required to evaluate the effectiveness of botanical drugs on CRF in patients with GC.