Comprehensive assessment of cellular senescence and aging in the tumor microenvironment of sarcoma

Comprehensive assessment of cellular senescence and aging in the tumor microenvironment of sarcoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comprehensive assessment of cellular senescence and aging in the tumor microenvironment of sarcoma Pengfei Zan, Yi Zhang, Yiwei Zhang, Qingjing Chen, Zhengwei Duan, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3661711/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract SARC (sarcoma) is a heterogeneous group of stromal tumors originating from mesenchymal tissues with poor prognosis. There is growing evidence that senescent cells in the tumor microenvironments (TME) are associated with the development and metastasis of cancer. The impact of senescence on sarcomas has been initially recognized, but not fully understood. Here, we revealed that senescence level and age were both associated with TME, immune treatment indicators and prognosis in SARC. WGCNA and least-selection absolute regression algorithm (LASSO) were used to track senescence-related genes and create a senescence predictor. Consequently, the three genes (RAD54, PIK3IP1, TRIP13) were selected to construct a multiple linear regression model. Through validation cohorts, IHC and qPCR, the predictors conducted by the three genes were proved to have prognostic and pathological significance. The senescence predictor may provide a novel insight into the study of molecular mechanisms and candidate biomarkers for the prognosis, resulting in effective treatments for SARC. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction SARC (sarcoma) is a heterogeneous group of stromal tumors originating from mesenchymal tissues. 1 Although SARC occurs in all age groups, the relative incidence of this malignant tumor is much higher in children and adolescents. 2 As the third most common type of childhood malignancy with poor prognosis, SARC represents a major clinical challenge. 2,3 To date, active treatment strategies (e.g., surgery combined with radiation and chemotherapy) have been adopted to treat SARC, but the recurrence and survival rates have still not been significantly improved 4 . In the 1960s, Hayflick proposed the "Hayflick limit", which states that even given optimal conditions for cell growth, cell division will fail at a certain number of generations, thus bringing the cell cycle to an "irreversible" standstill. 5 Based on this phenomenon, cellular senescence (CS) is defined as the gradual decline of cells' normal physiological functions and proliferation capacity over time or in response to external stresses, whereby cell cycle arrest occurs. 6–8 The presence of senescent cells in tumor microenvironments (TME) of various tumors has been observed in previous studies. There is growing evidence that senescent cells in the TME are associated with the development and metastasis of cancer. 9–11 Senescence and aging are two easily confused concepts. Aging, broadly defined as the time-dependent functional decline in an organism, is a significant risk factor for the development of many tumors 12 . CS is a concept at the cellular level and is identified as a hallmark of aging. 12 The terms "senescence" and "aging" are often used indifferently, but there are differences and relative independence between them, and both play important roles in tumorigenesis 13 . Aging can be directly measured by age, but CS lacks a comprehensive and reliable measurement standard, which has caused many obstacles to the research of CS on tumors. Wang et al. 8 creatively proposed a comprehensive evaluation index for cell senescence in TME—CS score, which provides a new pathway for tumor senescence research. The therapeutic potential of CS in sarcomas has been initially recognized, but its potential cannot be fully exploited due to the current insufficient understanding 14 . This study set out to explore the influence of CS and aging on SARC TME. To enhance the clinical translational utility of CS scores, a CS predictor constructed by three key CS-related genes was proposed and validated. This study provided insights into how CS and aging might affect patient survival and immunotherapy, which may assist in developing new therapeutic strategies. Methods Data and resources RNA-seq data, associated clinical data and somatic mutation data of TCGA-SARC and TCGA-Pan-Cancer cohorts were downloaded from UCSC Xena ( http://xena.ucsc.edu/public/ ). GSE datasets (GSE39262 and GSE21122), as the validation cohorts, were downloaded from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ). Calculating CS score and evaluating its robustness CS score was calculated using the methods in the published literature 8 . First, the previously curated 525 CS-positive related genes and 734 CS-negative related genes were obtained. Next, the activities of the two gene sets in individual samples were calculated respectively using GSVA. Finally, CS score was obtained by subtracting negative-related NES (negative-CS-related activities) from positive -related NES (positive-CS-related activities). According to the median value of the CS score, patients are divided into high-CS and low-CS groups. To evaluate the robustness of the CS score, the GSVA score of TCGA-SARC cases for C6 (oncogenic signature gene sets) of the Molecular Signatures Database (MSigDB) were computed, and their Spearman correlations with CS scores were calculated. In addition, CS score was compared with classical CS molecular markers (p21 and p16) in two main CS molecular features (cell-cycle arrest and SASP) 6,15 . Cell-cycle arrest can be represented by the stemness scores (DNAss and RNAss), and SASP can be portrayed by several core SASP genes (IL-1α, IL-6, IL8, IL-1β, CXCL1, CXCL2) 16 . Genomic variation analysis. The total mutation events of TCGA-SARC were obtained through R package "TCGABiolinks", and "maftools" package set by default parameters was used for analysis and visualization. GI score, LOH and WGD were collected from available data from previous studies 17 . GI score is calculated as the fraction of genomic regions that does not fit the tumor ploidy statuses. WGD statuses were obtained from the fraction of genome with LOH and ploidy. Identifying immune characteristics ESTIMATE, an algorithm based on gene expression data, was used to evaluate the infiltration level of immune cells in tumor tissue 18 . The composition immune infiltration cell was characterized using CIBERSORT 19 , with LM22 signature as inputs. The stemness score (RNAss and DNAss), TCR Shannon, and immune subtypes of TCGA-SARC were downloaded from the UCSC Xena ( https://xenabrowser.net ). Immune cytolytic activity (CYT) score, calculated as the geometric mean of GZMA and PRF1, is a simple and effective quantitative measure of T-cell cytotoxicity. 20 The Tumor Immune Dysfunction and Exclusion (TIDE, http://tide.dfci.harvard.edu/ ) 21,22 , a creative computational method of predicting immunotherapy response, was used to predict responses to immunotherapy of TCGA-SARC cases. DEG identification and WGCNA The DESeq2 package was used to identify differentially expressed genes (DEGs) among different groups of TCGA-SARC according to the threshold false discovery rate (FDR) 1. R package WGCNA 23 was used to identify the co-expressed gene modules most relevant to the CS score. First of all, outliers were filtered using hierarchical cluster analysis. Subsequently, the Pearson correlation coefficient was used to calculate the squared Euclidean distance between each gene. Next, the weighted gene co-expression network was constructed with the soft threshold 5. Based on topological overlap measure (TOM), genes were clustered into modules with a minimum of 50 genes. Modules that are close to each other were merged to get the final modules with the setting of height = 0.25. Cluster classification analysis of TCGA-SARC Based on the transcriptome data, the unsupervised consensus clustering algorithm in 'ConsensusClusterPlus' package was implemented on TCGA-SARC. The optimal clustering number was determined to be 3 and patients were divided into 3 clusters with 1000 iterations. PCA and tSNE were used to display the differences among different clusters. The relationship among clusters, age-and-CS groups, and immune subtypes was exhibit by the Sankey plots. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) Tumor and para-cancer tissue of eight osteosarcoma patients were collected from Shanghai Tongji Hospital (approved by the Ethics Committee of this hospital, Ethical Approval Number: 2021- KYSB-061) Tissues were cut into 1 mm 3 and total RNA were extracted by TRIzol reagent (Invitrogen, USA). Reverse transcription was performed and mRNA expression level was verified using QuantStudio Dx real-time PCR system (Thermo Fisher) based on the manufacturer's protocol. GAPDH (Glyceraldehyde-3-Phosphate Dehydrogenase) was set as the internal reference. Primer sequences for the three genes were as follows (5' -> 3'): ( 1 ) RAD54L: forward 5'-GTTGGCCTGGGTACAAGCATT-3' and Reverse 5’-GGTTTCGGCAGTAACTGTGATT-3'; ( 2 ) PIK3IP1: Forward 5’-ACTGTTGCACTTCACATTTTCCA-3' and Reverse 5’-TCGAGGAGATGGGATTTGACT-3'; ( 3 ) TRIP13: Forward 5’-AGGCAGGTCCTGTGATGATGA-3' and Reverse 5'- TCAAAGGTTTCCGAAAAGGAGAC-3'. The relative expression levels of mRNAs were calculated using 2–ΔΔCt method. Immunohistochemistry (IHC) The incubated slides were deparaffinized in xylene and then rehydrated with graded alcohol. Sequentially, each slide was covered with 10% goat serum in phosphate buffered saline for 10 min at room temperature and then incubated with primary antibodies at 4°overnight. Goat Anti-Rabbit lgG (H + L) (Jackson Cat no.111035003) was used as the secondary antibody. The primary antibodies were the following: anti-RAD54L (1:100, PU726446, Abmart Shanghai Co.,Ltd.), anti-PIK3IP1 (1:100, ab185388, abcam), anti-TRIP13 (1:100, ab204331, abcam) . Statistical analysis Data analysis and graph generation were all performed using R (version 4.1.2). For comparisons of two groups, Student's t-test was applied for normally distributed data, and the Wilcoxon rank-sum test was adopted for non-normally distributed data. ANOVA were used to compare more than two groups. The correlation coefficients were calculated by Spearman correlation method. Survival analyses were conducted and visualized by Kaplan-Meier survival curve with the R package 'survival' and 'survminer' packages. Significant differences were determined using the log-rank test. Receiver operating characteristic (ROC) curves for 1-, 3-, and 5-years survival were delineated using package "timeROC" 24 . Univariate and multivariate Cox regression analysis were used to evaluate the correlation between features and clinical survival. Least absolute shrinkage and selection operator (LASSO) regression were utilized to select the key prognosis-related genes representing CS score. The selected 3 significant genes were fitted with CS score using multiple linear regression. p 0.05, ∗ <0.05, ∗∗ <0.01, ∗∗∗ <0.001 and ∗∗∗∗ <0.0001. Results Delineate the relationship between CS and age To verify the independent prognostic factors of SARC, univariate and multivariate Cox regression analyses and Kaplan-Meier survival analysis were performed on overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), progression-free interval (PFI) of the TCGA-SARC cohort. The results showed that CS score was a protective factor for all four survival indicators, while age was a risk factor for OS and DSS only. (Supplementary Table 1 and Supplementary Figure S1 A-H) Next, we validated the correlation between age and CS score at the pan-cancer level as well as at the level of multiple normal tissues (GTEx). The results showed that the correlation between age and CS score was tissue-specific and cancer type-specific (Fig. 1 A, B). CS scores were significantly correlated with age for only some tissues and tumors, and almost normal tissues with correlations showed a positive correlation except uterus (Fig. 1 A, B). This finding is consistent with that of López-Otín (2013) 12 , who had concluded that cellular senescence is not a generalized property of all tissues in aged organisms. By comparing with classical CS molecular markers (p21 and p16), CS score was found to correlate well with two molecular features of CS (cell-cycle arrest and SASP), which is consistent with the good reliability of CS scores in representing senescence status in pan-cancer. 8 (Fig. 1 C,D) In the present study, the cases of TCGA-SARC were divided into four groups based on CS score and median diagnosis age: "Old + High CS", "Old + Low CS", "Young + High CS", and "Young + Low CS" (Fig. 1 E, F). As is shown in Fig. 1 E, the differences in CS score between "Young" and "Old" and age between "Low CS" and "High CS" were both insignificant. Kaplan-Meier survival analysis demonstrated that patients in the "Young + High CS" group had better survival performance in all four indicators compared with the other three groups. (Figure G and Supplementary Figure S1 I-K) Furthermore, lower CS scores were observed in patients with metastasis, indicating that senescent SARC may maintain fewer malignant properties. (Fig. 1 H) Notably, CS score and age both exhibited positive correlations with P53- and Rb-related pathways, which agree with the current notion that P53 and Rb are both critical tumor suppressor genes and the most important signaling pathway components mediating the senescence phenomenon. (Supplementary Figure S1 L, M) In addition, CS and age display a negative correlation with E2F- and polycomb group protein-associated signatures (i.e., BMI1, MEL18). (Supplementary Figure S1 L, M) Altogether, these results suggest that senescence level and age both significantly affect the progression of SARC. Senescence level and aging are associated with genomic variations of SARC Previous studies at the pan-cancer level have shown that increased genomic variation is a common hallmark of aging and cancer. 17 To understand the role of CS score and age on the genomic variations of SARC, we explored the associations of CS score and age with SARC genomic characteristics. Using the algorithm of Chatsirisupachai et al 17 , the quantified metrics of genomic variations of TCGA-SARC were obtained, including GI score (genomic instability score), LOH (loss of heterozygosity), and WGD (whole-genome duplication). What stands out in Fig. 2A-F is that GI score and LOH were both positively correlated with age, while they were negatively correlated with CS score. WGD plays an important role in cancer evolution and is associated with poor prognosis 25,26 . As shown in Fig. 2G, H, cases with WGD events had significantly higher age and significantly lower CS score than cases without WGD events, indicating that SARC from older and lower-CS patients are more likely to double their genome. The mutation burden increases with age has been well-established 17 . The current study confirmed that older patients had higher total mutation burden (TMB), but tumors with higher CS level exhibited lower TMB. (Fig. 2I-K) To determine the differences in driver mutations between the groups, the TOP10 mutated genes in each group were separately explored. (Fig. 2L, M) The TMB of each group was mainly contributed by missense SNVs, especially C > T mutations (Supplementary Figure S2 A-D). Further clustering analysis of mutant loci revealed that MUC16 mutations were more frequent in the old group, while TP53 mutations were in the Low-CS group. In addition, PATA31D1, HERC2, and SHROOM2 mutations were unique in the old group, while COL5A3, DOCK3, AHNAK2, and GABRR1 mutations uniquely occurred in the low-CS group (Supplementary Figure S2 E, F). Taken together, these results revealed that Lower-CS level SARC from older patients tends to harbor more intensive genomic variations than younger and senescent SARC. This also accords with the above finding, which showed significant negative correlations of CS score with tumor stemness indices in SARC (Fig. 1 D). Fig. 2. Senescence and age both change the tumor immune microenvironment of SARC Tumor immune microenvironment (TIME) plays an important role in determining tumor development and prognosis. 27 The immune infiltration levels of SARC were evaluated using ESTIMATE algorithm, including stromal score (the level of stromal cells present), immune score (the proportion of immune cells in tumor tissue), and estimate score (the proportion of nontumor components). Correlation analysis demonstrated that both CS scores and age were significantly and positively correlated with the three scores (Fig. 3 A-I). The CIBERSORT algorithm was implemented to further analyze the specific composition of immune cells in the tumor samples. Interestingly, Old + High-CS group has the highest growth-promoting macrophages (M2-like macrophages) proportion and the least M0 Macrophages proportion (Fig. 3 J-L). These results suggest that there is an immunostimulatory microenvironment in senescent and aging SARC, but with the specificity of macrophages. Senescent cells release a number of SASP factors to contribute to tumor immunity, which induce changes in the TIME 28 . The associations of core SASP genes with TIME were explored. As evident from Fig. 3 M, similar to CS and age, the expression of several SASP genes expression were positively correlated to ESTIMATE score (indicating a negative correlation with tumor purity). Notably, the tumor growth-promoting macrophages (M2) were also positively related to CS score, age and SASP. This result corroborates the idea of previous studies that SASP has a role in recruiting monocytes to the TIME and promoting their differentiation into macrophages 29 . Senescence level predicts active immune response in SARC Because of the observed significant TIME alterations in senescent and aging SARC, it is hypothesized that CS score and age could be used as predictors of immunotherapy response. Immune molecular features related to age and CS scores were explored. Previous study had demonstrated that the expression of immune checkpoint associated genes significantly affect the efficacy of immune checkpoint blockade (ICB) therapies. As shown in Fig. 4 A and Supplementary Figure S3 A-C, CD86 and HAVCR2 were both positively correlated with CS score and age. Immune cytolytic activity (CYT) score is a simple and effective quantitative measure of T-cell cytotoxicity. 20 As can be seen from Supplementary Figure S3 D-F, there was a linear positive correlation between CYT and CS score and age. In addition, TCR clonality measure also increase with CS score age for SARC samples (Supplementary Figure S3 G-I). Overall, it is reasonable to suspect that senescent and aging SARC tumors would be more susceptible to immunotherapy. This hypothesis can be partially validated by the TIDE prediction (Supplementary Figure S4 J,K), which showed that the TIDE score of High CS group (mean: 1.776) is significantly lower than that of Low CS group (mean: 1. 991). CS and age in SARC were validated to have impact on clinical indices (Supplementary Table 1, Fig. 1 G-H). To further explore the intrinsic association between age and CS, the consensus clustering was used to divide TCGA-SARC into three groups. (Supplementary Fig. 3L-P) It is apparent from Fig. 4 B-D that Cluster 2 was substantially different from the other two clusters and had the best prognostic outcome, while Cluster 3 had the worst prognosis. The results of GSVA on KEGG immune pathways revealed that overall immune activation in high-CS group and cluster 1, while clusters 2 and 3 and low-CS group exhibited immunosuppression (Fig. 4 E). This result was validated by further immune-subtype analysis which showed that immunosuppressive phenotypes (C1 and C4) were mainly consisted of low-CS group, whereas C3 and C6 comprised mainly of the high-CS group (Fig. 4 F). In addition, cluster 3 contained more low-CS cases than high-CS cases, which confirmed the robustness of CS score-based groups. However, these three clusters each contain four age-and-CS groups, which may be due to the influence of unknown factors and need to be studied in the future. Overall, these analyses demonstrated that the higher CS level may indicate better survival and more active immune responses.. Construct a senescence predictor in SARC To translate the significance of SARC CS score into clinical application, the quest for hub genes reflecting CS score was implemented. (Fig. 5A) WGCNA was applied to identify genes related to CS score. Genes were divided into modules and merged with different colors, where the green modules were significantly and strongly correlated with CS score (negative correlation of -0.89 and p < 0.001) (Fig. 5B and Supplementary Figure S4 A-C). Additionally, DEGs among the four CS-and-age based groups were identified using "DESeq2" package (Fig. 5C). Surprisingly, a large proportion of the common DEGs belong to the green module genes. (Fig. 5C,D) GO and KEGG analysis revealed that these 76 genes were mainly enriched in functions and pathways related to cell division and cellular senescence (Supplementary Figure S4 D,E). Subsequently, univariate cox regression was performed on the 76 genes in the intersection, and the genes with prognostic impact were subjected to the next step of LASSO prognostic model construction (Fig. 5A,E,F). Finally, the three genes [RAD54 Like (RAD54L), Phosphoinositide-3-Kinase Interacting Protein 1 (PIK3IP1), Thyroid Hormone Receptor Interactor 13 (TRIP13)] were selected to construct multiple linear regression model. CS predictor was defined as follows: CS predictor = -0.290 × (expression of RAD54L) − 0.132 × (expression of PIK3IP1) -0.098 × (expression of TRIP13). As expected, the CS predictor showed significant associations with the CS score (Fig. 5G). According to the median value of the CS predictor, patients are divided into high-pred-CS and low-pred-CS groups, and the high pred-CS group was found to have a significantly better prognosis (Fig. 5H). Additionally, in the TCGA-SARC cohort, CS predictors displayed excellent predictive performance at 1, 3, and 5 years, at least as good as CS score. (Supplementary Figure S4 F,G) These results suggest that the CS predictor composed of three genes can represent CS levels and have potential as prognostic biomarkers in SARC. Fig. 5. Validating the validity of the constructed senescence predictor To verify the universality of the model, the model was extended to the pan-cancer level and founded that the CS predictor was significantly associated with CS score in all 33 cancers (Fig. 6 A). Even across the all pan-cancer cases, the high pred-CS group had significantly better prognosis with slower cancer progression, consistent with SARC (Fig. 6 B). Moreover, the predictive ability of the CS predictor for the survival of pan-cancer cases is even stronger than that of CS score (Fig. 6 C,D). In addition, it was found that with the increase of pathologic tumor stage at pan-cancer level, the expressions of RAD54L and TRIP13 gradually increased, while PI3KIP1 gradually decreased, resulting in a decrease in CS score and predictor (Fig. 6 E). Consistently, in TCGA-SARC cohort, metastatic (more malignant) tumors showed higher RAD54L and lower PI3KIP1, CS score and CS predictor (Fig. 6 F). Next, the 3 genes expression difference between SARC and normal tissue was explored at the tissue and cell line level. As shown in Fig. 6 G-I, SARC has significantly higher RAD54L and TRIP13 and lower PI3KIP1 compared to normal tissue in three cohort (the insignificant of PI3KIP1 in the TCGA-SARC cohort may be due to too few normal controls (only two cases)). To validate the common phenomenon found by bioinformatic analysis in multiple datasets, tumor and para-cancerous tissues were collected from 8 osteosarcoma patients, and qPCR and IHC was performed for RAD54L, PIK3IP1, and TRIP13. Based on the results, it was found that RAD54L and TRIP13 were significantly higher and PIK3IP1 was lower in tumor tissue than in para-cancerous tissue. (Fig. 6 J,K) Taken together, these results demonstrate that CS predictors are reliable and applicable, and that the expression levels of the 3 genes are significantly correlated with the malignant properties of the tissues. Discussion CS is an important cellular process in aging and tumor development, whereas the senescence landscape and related characteristics of sarcomas have been poorly studied. To gain insight into this, the current study was conducted to revealed the association of CS and age with genomic characteristics, TIME, and clinical outcomes of SARC. Additionally, three CS genes significantly predicted patient survival in various cohorts, suggesting their potential as prognostic biomarkers. Thus, based on the computational metrics of CS levels, our findings provide a framework for better understanding CS-related modulations in the tumor microenvironment and could guide further experiments and biomarker identification. CS score, a metric to measure tumor senescence across the cancer spectrum, was innovatively proposed by Wang et al 8 . Its reliability has been validated at the pan-cancer level in the original article and was also validated in the current study (Fig. 1 C,D). In addition, the relationship between age and CS was investigated in this study. One interesting finding is that the CS score of uterus was significantly negatively correlated with age, whereas most normal tissues' CS score had positive or nonsignificant correlations with age. This result support previous research which showed that genes up-regulated with age in the uterus were enriched in cell cycle terms 30 . Abnormal cell proliferation in aging uterus often leads to atypical hyperplasia and may result in endometrial cancer 31 . In SARC, although CS is not associated with age (possibly due to its high heterogeneity), both affect patient survival. One interesting finding is that the degree of genomic variation is positively correlated with age but negatively correlated with CS. Genomic instability is a well-known hallmark of aging, and the increased SNVs that accompany aging has been reported 12,17,32 . The current study validated that age affects genomic instability, TIME, and immune checkpoints at SARC-level, but there are no significant differences in immunotherapy response predictors between age groups. A possible explanation for this might be that aging, as a biological process that affects the whole body, altered other unstudied factors that exert impact on disease pathology and prognosis. Another finding of this study is that cases with lower CS score (or predictor) generally have higher malignancy and worse prognosis. In other words, genomic variations occur more frequently in less-senescent and higher-aggressive SARC than in senescent SARC, which indicate that genomic variations during SARC tumorigenesis is more intense than during senescence. This result corroborates the view of the previous study that the primary purpose of SC is to prevent the reproduction of damaged cells and trigger their death 12 . This study showed that CS and age influence SARC TIME. First of all, it was demonstrated that CS (including core SASP gene expression due to CS) and age were positively correlated with total immune infiltration level. Secondly, analysis of immune cell subsets corroborates earlier finding that senescent tumor cells are subjected to strict immune surveillance and are efficiently cleared by phagocytosis 33,34 . In addition, senescent or aging SARC exhibited activated immune properties, including increased CYT score and TCR and activated KEGG immune pathways. However, increasing age and CS score have opposite effects on the prognosis of SARC patients. However, while age and CS had similar effects on TIME, they had opposite effects on prognosis in patients with SARC (Supplementary Table 1). There are several possible explanations for these relationships. On the one hand, increasing age may accompany comorbidities affecting the patient's overall physical condition. This idea was presented in some real-world studies of ICB in NSCLC 35,36 . On the other hand, although aging increases immune infiltration quantitatively, it also affects the function of the entire immune system. The phenomenon that the function of the immune system gradually decline with aging is summarized as "immunosenescence", which is manifested as decreased phagocytosis and cell chemotaxis, changes in the proportion of immune cells subsets, and decreased ability to produce specific antibodies, etc 37 . Notably, three key genes were validated to construct a predictor representing CS scores, and have been documented to have an impact on tumor development. RAD54L playing an important role in homologous recombination related repair of DNA double-strand breaks 38 . PIK3IP1 negatively regulates the PI3K pathway. and has an inhibitory effect on the development of hepatocellular carcinoma 39,40 . TRIP13 regulates cell division by interfering with spindle assembly checkpoint, and its high expression is closely associated with proliferation, invasion and metastasis of malignant cells 41 . In current study, through data from different database and qPCR experimental verification, it was demonstrated that RAD54L and TRIP13 were up-regulated and PIK3IP1 down-regulated in SARC with malignant properties. Several limitations still remained in this study. First of all, metrics that can measure CS are too scarce to not included in the study to implement a comparative study with CS score. Secondly, the absence of SARC immunotherapy cohort prevents clinical validation of patients' immunotherapy responses. In conclusion, our study revealed that senescence level and age were both associated with TME, immune treatment indicators and prognosis in SARC. In addition, three genes, RAD54L, PIK3IP1 and TRIP13, were used to construct the predictor for predicting the senescence level, which had predictive value for the malignancy degree and prognosis of SARC Declarations Informed consent statement: Informed written consent was obtained from the patient for publication of this report and any accompanying images. Conflict-of-interest statement: The authors report no conflicts of interest concerning the materials or methods used in this study or the findings specified in this paper. Funding: This work was funded by the National Natural Science Foundation of China (NSFC, No. 82000839) and Natural Science Foundation of Zhejiang Province (No. LQ2H060009) † These authors contributed equally to the work. Author Contributions Research design: Zihua Li and Anquan Shang; Bioinformatic analysis: Pengfei Zan, Yi Zhang, Data Validation: Qingjing Chen, Zhengwei Duan, Yonghao Guan; Data analyses and interpretation: Yi Zhang; Project supervision: Anquan Shang; Manuscript writing and reviewing: Pengfei Zan, Kaiyuan Liu. References Skoda, J. & Veselska, R. Cancer stem cells in sarcomas: Getting to the stemness core. Biochim. Biophys. Acta BBA - Gen. Subj. 1862, 2134–2139 (2018). Anderson, J. L., Denny, C. T., Tap, W. D. & Federman, N. Pediatric sarcomas: translating molecular pathogenesis of disease to novel therapeutic possibilities. Pediatr. Res. 72, 112–121 (2012). Dancsok, A. R., Asleh-Aburaya, K. & Nielsen, T. O. Advances in sarcoma diagnostics and treatment. Oncotarget 8, 7068–7093 (2017). Hingorani, P. et al. Current state of pediatric sarcoma biology and opportunities for future discovery: A report from the sarcoma translational research workshop. Cancer Genet. 209, 182–194 (2016). 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Supplementary Files SupplementaryFigure1.png SupplementaryFigure2.png SupplementaryFigure3.png SupplementaryFigure4.png SupplementaryTable1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3661711","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":253483466,"identity":"1aaf45b0-a43c-40ed-8067-c2fc438831c2","order_by":0,"name":"Pengfei Zan","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Pengfei","middleName":"","lastName":"Zan","suffix":""},{"id":253483467,"identity":"7da4fde0-a5d0-45e0-b5da-1e6b44e5e91c","order_by":1,"name":"Yi Zhang","email":"","orcid":"","institution":"Tongji University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Zhang","suffix":""},{"id":253483468,"identity":"d6085df9-f8bc-4fdf-8ca2-10101b9dd753","order_by":2,"name":"Yiwei Zhang","email":"","orcid":"","institution":"Tongji University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yiwei","middleName":"","lastName":"Zhang","suffix":""},{"id":253483469,"identity":"a464b374-7bde-45dc-87b2-7b1c449ddd1e","order_by":3,"name":"Qingjing Chen","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qingjing","middleName":"","lastName":"Chen","suffix":""},{"id":253483470,"identity":"9982febc-6e03-4f9e-b755-4a1f8abebe27","order_by":4,"name":"Zhengwei Duan","email":"","orcid":"","institution":"Tongji University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhengwei","middleName":"","lastName":"Duan","suffix":""},{"id":253483471,"identity":"0b8ef55c-a83d-441b-991b-fbd021d8003a","order_by":5,"name":"Yonghao Guan","email":"","orcid":"","institution":"Tongji University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yonghao","middleName":"","lastName":"Guan","suffix":""},{"id":253483472,"identity":"af113d5e-a449-4e89-8dad-5a283384a81e","order_by":6,"name":"Kaiyuan Liu","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kaiyuan","middleName":"","lastName":"Liu","suffix":""},{"id":253483473,"identity":"a9e55c8f-fa81-4522-a646-6286fd299cc6","order_by":7,"name":"Anquan Shang","email":"","orcid":"","institution":"The Second People's Hospital of Lianyungang \u0026 The Oncology Hospital of Lianyungang \u0026 The Second People's Hospital of Lianyungang, Xuzhou Medical University, Jiangsu University \u0026 The Second People's Hospital of Lianyungang, Bengbu Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Anquan","middleName":"","lastName":"Shang","suffix":""},{"id":253483474,"identity":"f1809939-f8b5-44be-9f49-f670c97e517b","order_by":8,"name":"Zihua Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYDCCA8wNBxgMDjAwsDcAeQUMDBKEtTBCtfAA8QEDIrWASKDSBCK18N0+2HjwR8GdxP6Zj49JfzCwkZNsYH746AYeLZLnEhsOSBg8S5xxOy1N4oBBmrE0A5uxcQ4eLQZngH4xMDic2HA7xwyo5XDiPAYeNmmCWhKAKuffPEOKFpDKDTd4IFpmE9IiCdRysMHgsPHGM2nJFmeAfpFsJuAXvjPMhz/++HNYdt7xwwdvVFTYyEkcb374GJ8WLICZNOWjYBSMglEwCrAAAK41ViSttVhNAAAAAElFTkSuQmCC","orcid":"","institution":"Tongji University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zihua","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2023-11-25 03:14:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3661711/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3661711/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":47392532,"identity":"ea31d6d9-6f3c-43bc-8f88-86206b0f8b24","added_by":"auto","created_at":"2023-11-30 18:42:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":512478,"visible":true,"origin":"","legend":"\u003cp\u003eDelineate the relationship between CS and age. (A, B) The correlation between age and CS score was tissue-specific and cancer type-specific. (C, D) The correlation between CS score and molecular features of CS. (E) The cases of TCGA-SARC were divided into four groups based on CS score and median diagnosis age. (F) The PLS-DA diagram of the four groups. (G) The \"Young+High CS\" group had better survival performance than the other three groups. (H) Patients with metastasis have lower CS score.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3661711/v1/59a2b5e66adc57ca82f27985.png"},{"id":47393602,"identity":"d1b93e79-6ae0-48bf-9abd-371b16031e0d","added_by":"auto","created_at":"2023-11-30 18:50:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":373957,"visible":true,"origin":"","legend":"\u003cp\u003eSenescence level and aging are associated with genomic variations of SARC. (A-H) The associations of CS score and age with SARC genomic characteristics: A-C, GI score; D-F, LOH; G-H, WGD. (I-K) The associations of CS score and age with TMB. (L, M) The TOP10 mutated genes in each group.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3661711/v1/3302009902cd3e3623e7320b.png"},{"id":47392533,"identity":"817531b1-a2cb-4c23-8b97-b374c406a786","added_by":"auto","created_at":"2023-11-30 18:42:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":566134,"visible":true,"origin":"","legend":"\u003cp\u003eSenescence and age both change the tumor immune microenvironment of SARC. (A-I) The associations of CS score and age with immune infiltration levels: A-C, stromal score; D-F, immune score; G-H, estimate score. (J-L) The associations of CS score and age with immune cells. (M) The associations of core SASP genes with TIME.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3661711/v1/3272a66d2da0ce2a80f992a6.png"},{"id":47393603,"identity":"e7690b6c-7f56-4520-9404-1f372a492912","added_by":"auto","created_at":"2023-11-30 18:50:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":562204,"visible":true,"origin":"","legend":"\u003cp\u003eSenescence level predicts active immune response in SARC. (A) The associations of CS score and age with immune checkpoint-associated genes. (B, C) The PCA and T-SNE of the three clusters. (D) The survival analysis of the three clusters. (E) GSVA on KEGG immune pathways. (F) The immune-subtype analysis of the three clusters.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3661711/v1/384b1d9fc074e119c7c12931.png"},{"id":47392536,"identity":"545cd311-6df7-4d0f-bd66-be6b971644d1","added_by":"auto","created_at":"2023-11-30 18:42:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":457894,"visible":true,"origin":"","legend":"\u003cp\u003eConstruct a senescence predictor in SARC. (A) The flow chart of looking for hub genes reflects the CS score. (B) Age and CS score related modules obtained by WGCNA analysis. (C, D) Venn diagrams of different groups. (E, F) Plots for Lasso Cox regression analysis of genes identified by univariate Cox regression analysis. (G) The correlation between different indicators. (H) The survival analysis of high/low-pred-CS groups.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3661711/v1/d8147e5825f4b1b9dcf0c2c7.png"},{"id":47392541,"identity":"3c063262-7d7a-4cf4-9738-005973f6dd16","added_by":"auto","created_at":"2023-11-30 18:42:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":654589,"visible":true,"origin":"","legend":"\u003cp\u003eValidating the validity of the constructed senescence predictor. (A) The correlation between CS predictor and CS score in 33 cancers. (B) The survival analysis of high/low-pred-CS groups in the pan-cancer level. (C, D) The predictive performance of CS predictors and CS score at 1, 3, and 5 years in the pan-cancer level. (E-I) The expression levels of RAD54L, PIK3IP1, and TRIP13 in different cohorts. (J, K) qPCR results representative IHC staining image of tumor tissues and para-cancerous tissues.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3661711/v1/762839a99210bce597f1f754.png"},{"id":47824087,"identity":"1de0a27a-cf16-4435-a9fe-67df48a3a0f3","added_by":"auto","created_at":"2023-12-08 02:22:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2187853,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3661711/v1/b8fc2b60-1613-4754-9cba-aedae79d22bc.pdf"},{"id":47392535,"identity":"ffbc6516-e400-4f83-a1e9-4602f11a4b7e","added_by":"auto","created_at":"2023-11-30 18:42:59","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1106376,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3661711/v1/e6cf63681ba50879f9c8ecef.png"},{"id":47395052,"identity":"6b91c2c9-4bb3-4ca4-887e-c0173dd60004","added_by":"auto","created_at":"2023-11-30 18:58:59","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":623875,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3661711/v1/38941ce153357d401915346b.png"},{"id":47392538,"identity":"3ab6bb60-f448-48f2-b90e-bcb1834ef49d","added_by":"auto","created_at":"2023-11-30 18:42:59","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1113117,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3661711/v1/795362b5ab7265bd5ca803dd.png"},{"id":47392543,"identity":"7e7b4e78-7045-4543-b4ac-92beb3d37311","added_by":"auto","created_at":"2023-11-30 18:42:59","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":975063,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3661711/v1/0c97b221e23227a924c77703.png"},{"id":47395876,"identity":"fdeefdfe-a59a-4640-b105-e720226d93e9","added_by":"auto","created_at":"2023-11-30 19:06:59","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":15650,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3661711/v1/6be5edb0eea87ed048456b25.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive assessment of cellular senescence and aging in the tumor microenvironment of sarcoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSARC (sarcoma) is a heterogeneous group of stromal tumors originating from mesenchymal tissues.\u003csup\u003e1\u003c/sup\u003e Although SARC occurs in all age groups, the relative incidence of this malignant tumor is much higher in children and adolescents.\u003csup\u003e2\u003c/sup\u003e As the third most common type of childhood malignancy with poor prognosis, SARC represents a major clinical challenge. \u003csup\u003e2,3\u003c/sup\u003e To date, active treatment strategies (e.g., surgery combined with radiation and chemotherapy) have been adopted to treat SARC, but the recurrence and survival rates have still not been significantly improved\u003csup\u003e4\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the 1960s, Hayflick proposed the \"Hayflick limit\", which states that even given optimal conditions for cell growth, cell division will fail at a certain number of generations, thus bringing the cell cycle to an \"irreversible\" standstill.\u003csup\u003e5\u003c/sup\u003e Based on this phenomenon, cellular senescence (CS) is defined as the gradual decline of cells' normal physiological functions and proliferation capacity over time or in response to external stresses, whereby cell cycle arrest occurs.\u003csup\u003e6\u0026ndash;8\u003c/sup\u003e The presence of senescent cells in tumor microenvironments (TME) of various tumors has been observed in previous studies. There is growing evidence that senescent cells in the TME are associated with the development and metastasis of cancer. \u003csup\u003e9\u0026ndash;11\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSenescence and aging are two easily confused concepts. Aging, broadly defined as the time-dependent functional decline in an organism, is a significant risk factor for the development of many tumors\u003csup\u003e12\u003c/sup\u003e. CS is a concept at the cellular level and is identified as a hallmark of aging.\u003csup\u003e12\u003c/sup\u003e The terms \"senescence\" and \"aging\" are often used indifferently, but there are differences and relative independence between them, and both play important roles in tumorigenesis\u003csup\u003e13\u003c/sup\u003e. Aging can be directly measured by age, but CS lacks a comprehensive and reliable measurement standard, which has caused many obstacles to the research of CS on tumors. Wang et al.\u003csup\u003e8\u003c/sup\u003e creatively proposed a comprehensive evaluation index for cell senescence in TME\u0026mdash;CS score, which provides a new pathway for tumor senescence research. The therapeutic potential of CS in sarcomas has been initially recognized, but its potential cannot be fully exploited due to the current insufficient understanding\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study set out to explore the influence of CS and aging on SARC TME. To enhance the clinical translational utility of CS scores, a CS predictor constructed by three key CS-related genes was proposed and validated. This study provided insights into how CS and aging might affect patient survival and immunotherapy, which may assist in developing new therapeutic strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData and resources\u003c/h2\u003e \u003cp\u003eRNA-seq data, associated clinical data and somatic mutation data of TCGA-SARC and TCGA-Pan-Cancer cohorts were downloaded from UCSC Xena (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://xena.ucsc.edu/public/\u003c/span\u003e\u003cspan address=\"http://xena.ucsc.edu/public/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). GSE datasets (GSE39262 and GSE21122), as the validation cohorts, were downloaded from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCalculating CS score and evaluating its robustness\u003c/h2\u003e \u003cp\u003eCS score was calculated using the methods in the published literature\u003csup\u003e8\u003c/sup\u003e. First, the previously curated 525 CS-positive related genes and 734 CS-negative related genes were obtained. Next, the activities of the two gene sets in individual samples were calculated respectively using GSVA. Finally, CS score was obtained by subtracting negative-related NES (negative-CS-related activities) from positive -related NES (positive-CS-related activities). According to the median value of the CS score, patients are divided into high-CS and low-CS groups. To evaluate the robustness of the CS score, the GSVA score of TCGA-SARC cases for C6 (oncogenic signature gene sets) of the Molecular Signatures Database (MSigDB) were computed, and their Spearman correlations with CS scores were calculated. In addition, CS score was compared with classical CS molecular markers (p21 and p16) in two main CS molecular features (cell-cycle arrest and SASP)\u003csup\u003e6,15\u003c/sup\u003e. Cell-cycle arrest can be represented by the stemness scores (DNAss and RNAss), and SASP can be portrayed by several core SASP genes (IL-1α, IL-6, IL8, IL-1β, CXCL1, CXCL2)\u003csup\u003e16\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGenomic variation analysis.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe total mutation events of TCGA-SARC were obtained through R package \"TCGABiolinks\", and \"maftools\" package set by default parameters was used for analysis and visualization. GI score, LOH and WGD were collected from available data from previous studies\u003csup\u003e17\u003c/sup\u003e. GI score is calculated as the fraction of genomic regions that does not fit the tumor ploidy statuses. WGD statuses were obtained from the fraction of genome with LOH and ploidy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eIdentifying immune characteristics\u003c/h2\u003e \u003cp\u003eESTIMATE, an algorithm based on gene expression data, was used to evaluate the infiltration level of immune cells in tumor tissue\u003csup\u003e18\u003c/sup\u003e. The composition immune infiltration cell was characterized using CIBERSORT\u003csup\u003e19\u003c/sup\u003e, with LM22 signature as inputs. The stemness score (RNAss and DNAss), TCR Shannon, and immune subtypes of TCGA-SARC were downloaded from the UCSC Xena (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Immune cytolytic activity (CYT) score, calculated as the geometric mean of GZMA and PRF1, is a simple and effective quantitative measure of T-cell cytotoxicity.\u003csup\u003e20\u003c/sup\u003e The Tumor Immune Dysfunction and Exclusion (TIDE, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e21,22\u003c/sup\u003e, a creative computational method of predicting immunotherapy response, was used to predict responses to immunotherapy of TCGA-SARC cases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDEG identification and WGCNA\u003c/h2\u003e \u003cp\u003eThe DESeq2 package was used to identify differentially expressed genes (DEGs) among different groups of TCGA-SARC according to the threshold false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |Fold Change|\u0026gt;1. R package WGCNA\u003csup\u003e23\u003c/sup\u003e was used to identify the co-expressed gene modules most relevant to the CS score. First of all, outliers were filtered using hierarchical cluster analysis. Subsequently, the Pearson correlation coefficient was used to calculate the squared Euclidean distance between each gene. Next, the weighted gene co-expression network was constructed with the soft threshold 5. Based on topological overlap measure (TOM), genes were clustered into modules with a minimum of 50 genes. Modules that are close to each other were merged to get the final modules with the setting of height\u0026thinsp;=\u0026thinsp;0.25.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCluster classification analysis of TCGA-SARC\u003c/h2\u003e \u003cp\u003eBased on the transcriptome data, the unsupervised consensus clustering algorithm in 'ConsensusClusterPlus' package was implemented on TCGA-SARC. The optimal clustering number was determined to be 3 and patients were divided into 3 clusters with 1000 iterations. PCA and tSNE were used to display the differences among different clusters. The relationship among clusters, age-and-CS groups, and immune subtypes was exhibit by the Sankey plots.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative reverse transcription polymerase chain reaction (qRT-PCR)\u003c/h2\u003e \u003cp\u003eTumor and para-cancer tissue of eight osteosarcoma patients were collected from Shanghai Tongji Hospital (approved by the Ethics Committee of this hospital, Ethical Approval Number: 2021- KYSB-061) Tissues were cut into 1 mm\u003csup\u003e3\u003c/sup\u003e and total RNA were extracted by TRIzol reagent (Invitrogen, USA). Reverse transcription was performed and mRNA expression level was verified using QuantStudio Dx real-time PCR system (Thermo Fisher) based on the manufacturer's protocol. GAPDH (Glyceraldehyde-3-Phosphate Dehydrogenase) was set as the internal reference. Primer sequences for the three genes were as follows (5' -\u0026gt; 3'): (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) RAD54L: forward 5'-GTTGGCCTGGGTACAAGCATT-3' and Reverse 5\u0026rsquo;-GGTTTCGGCAGTAACTGTGATT-3'; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) PIK3IP1: Forward 5\u0026rsquo;-ACTGTTGCACTTCACATTTTCCA-3' and Reverse 5\u0026rsquo;-TCGAGGAGATGGGATTTGACT-3'; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) TRIP13: Forward 5\u0026rsquo;-AGGCAGGTCCTGTGATGATGA-3' and Reverse 5'- TCAAAGGTTTCCGAAAAGGAGAC-3'. The relative expression levels of mRNAs were calculated using 2\u0026ndash;ΔΔCt method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry (IHC)\u003c/h2\u003e \u003cp\u003eThe incubated slides were deparaffinized in xylene and then rehydrated with graded alcohol. Sequentially, each slide was covered with 10% goat serum in phosphate buffered saline for 10 min at room temperature and then incubated with primary antibodies at 4\u0026deg;overnight. Goat Anti-Rabbit lgG (H\u0026thinsp;+\u0026thinsp;L) (Jackson Cat no.111035003) was used as the secondary antibody. The primary antibodies were the following: anti-RAD54L (1:100, PU726446, Abmart Shanghai Co.,Ltd.), anti-PIK3IP1 (1:100, ab185388, abcam), anti-TRIP13 (1:100, ab204331, abcam) .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData analysis and graph generation were all performed using R (version 4.1.2). For comparisons of two groups, Student's t-test was applied for normally distributed data, and the Wilcoxon rank-sum test was adopted for non-normally distributed data. ANOVA were used to compare more than two groups. The correlation coefficients were calculated by Spearman correlation method. Survival analyses were conducted and visualized by Kaplan-Meier survival curve with the R package 'survival' and 'survminer' packages. Significant differences were determined using the log-rank test. Receiver operating characteristic (ROC) curves for 1-, 3-, and 5-years survival were delineated using package \"timeROC\" \u003csup\u003e24\u003c/sup\u003e. Univariate and multivariate Cox regression analysis were used to evaluate the correlation between features and clinical survival. Least absolute shrinkage and selection operator (LASSO) regression were utilized to select the key prognosis-related genes representing CS score. The selected 3 significant genes were fitted with CS score using multiple linear regression. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant, and significance were denoted as follows: ns\u0026thinsp;\u0026gt;\u0026thinsp;0.05, \u0026lowast; \u0026lt;0.05, \u0026lowast;\u0026lowast; \u0026lt;0.01, \u0026lowast;\u0026lowast;\u0026lowast; \u0026lt;0.001 and \u0026lowast;\u0026lowast;\u0026lowast;\u0026lowast; \u0026lt;0.0001.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eDelineate the relationship between CS and age\u003c/h2\u003e\n \u003cp\u003eTo verify the independent prognostic factors of SARC, univariate and multivariate Cox regression analyses and Kaplan-Meier survival analysis were performed on overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), progression-free interval (PFI) of the TCGA-SARC cohort. The results showed that CS score was a protective factor for all four survival indicators, while age was a risk factor for OS and DSS only. (Supplementary Table 1 and Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003eA-H)\u003c/p\u003e\n \u003cp\u003eNext, we validated the correlation between age and CS score at the pan-cancer level as well as at the level of multiple normal tissues (GTEx). The results showed that the correlation between age and CS score was tissue-specific and cancer type-specific (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). CS scores were significantly correlated with age for only some tissues and tumors, and almost normal tissues with correlations showed a positive correlation except uterus (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). This finding is consistent with that of L\u0026oacute;pez-Ot\u0026iacute;n (2013)\u003csup\u003e12\u003c/sup\u003e, who had concluded that cellular senescence is not a generalized property of all tissues in aged organisms.\u003c/p\u003e\n \u003cp\u003eBy comparing with classical CS molecular markers (p21 and p16), CS score was found to correlate well with two molecular features of CS (cell-cycle arrest and SASP), which is consistent with the good reliability of CS scores in representing senescence status in pan-cancer. \u003csup\u003e8\u003c/sup\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC,D)\u003c/p\u003e\n \u003cp\u003eIn the present study, the cases of TCGA-SARC were divided into four groups based on CS score and median diagnosis age: \u0026quot;Old\u0026thinsp;+\u0026thinsp;High CS\u0026quot;, \u0026quot;Old\u0026thinsp;+\u0026thinsp;Low CS\u0026quot;, \u0026quot;Young\u0026thinsp;+\u0026thinsp;High CS\u0026quot;, and \u0026quot;Young\u0026thinsp;+\u0026thinsp;Low CS\u0026quot; (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE, F). As is shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE, the differences in CS score between \u0026quot;Young\u0026quot; and \u0026quot;Old\u0026quot; and age between \u0026quot;Low CS\u0026quot; and \u0026quot;High CS\u0026quot; were both insignificant. Kaplan-Meier survival analysis demonstrated that patients in the \u0026quot;Young\u0026thinsp;+\u0026thinsp;High CS\u0026quot; group had better survival performance in all four indicators compared with the other three groups. (Figure G and Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003eI-K) Furthermore, lower CS scores were observed in patients with metastasis, indicating that senescent SARC may maintain fewer malignant properties. (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eH)\u003c/p\u003e\n \u003cp\u003eNotably, CS score and age both exhibited positive correlations with P53- and Rb-related pathways, which agree with the current notion that P53 and Rb are both critical tumor suppressor genes and the most important signaling pathway components mediating the senescence phenomenon. (Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003eL, M) In addition, CS and age display a negative correlation with E2F- and polycomb group protein-associated signatures (i.e., BMI1, MEL18). (Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003eL, M) Altogether, these results suggest that senescence level and age both significantly affect the progression of SARC.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eSenescence level and aging are associated with genomic variations of SARC\u003c/h2\u003e\n \u003cp\u003ePrevious studies at the pan-cancer level have shown that increased genomic variation is a common hallmark of aging and cancer.\u003csup\u003e17\u003c/sup\u003e To understand the role of CS score and age on the genomic variations of SARC, we explored the associations of CS score and age with SARC genomic characteristics. Using the algorithm of Chatsirisupachai et al\u003csup\u003e17\u003c/sup\u003e, the quantified metrics of genomic variations of TCGA-SARC were obtained, including GI score (genomic instability score), LOH (loss of heterozygosity), and WGD (whole-genome duplication). What stands out in Fig.\u0026nbsp;2A-F is that GI score and LOH were both positively correlated with age, while they were negatively correlated with CS score. WGD plays an important role in cancer evolution and is associated with poor prognosis\u003csup\u003e25,26\u003c/sup\u003e. As shown in Fig.\u0026nbsp;2G, H, cases with WGD events had significantly higher age and significantly lower CS score than cases without WGD events, indicating that SARC from older and lower-CS patients are more likely to double their genome.\u003c/p\u003e\n \u003cp\u003eThe mutation burden increases with age has been well-established\u003csup\u003e17\u003c/sup\u003e. The current study confirmed that older patients had higher total mutation burden (TMB), but tumors with higher CS level exhibited lower TMB. (Fig. 2I-K) To determine the differences in driver mutations between the groups, the TOP10 mutated genes in each group were separately explored. (Fig. 2L, M) The TMB of each group was mainly contributed by missense SNVs, especially C\u0026thinsp;\u0026gt;\u0026thinsp;T mutations (Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003eA-D). Further clustering analysis of mutant loci revealed that MUC16 mutations were more frequent in the old group, while TP53 mutations were in the Low-CS group. In addition, PATA31D1, HERC2, and SHROOM2 mutations were unique in the old group, while COL5A3, DOCK3, AHNAK2, and GABRR1 mutations uniquely occurred in the low-CS group (Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003eE, F). Taken together, these results revealed that Lower-CS level SARC from older patients tends to harbor more intensive genomic variations than younger and senescent SARC. This also accords with the above finding, which showed significant negative correlations of CS score with tumor stemness indices in SARC (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). \u003cstrong\u003eFig. 2.\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eSenescence and age both change the tumor immune microenvironment of SARC\u003c/h2\u003e\n \u003cp\u003eTumor immune microenvironment (TIME) plays an important role in determining tumor development and prognosis.\u003csup\u003e27\u003c/sup\u003e The immune infiltration levels of SARC were evaluated using ESTIMATE algorithm, including stromal score (the level of stromal cells present), immune score (the proportion of immune cells in tumor tissue), and estimate score (the proportion of nontumor components). Correlation analysis demonstrated that both CS scores and age were significantly and positively correlated with the three scores (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA-I). The CIBERSORT algorithm was implemented to further analyze the specific composition of immune cells in the tumor samples. Interestingly, Old\u0026thinsp;+\u0026thinsp;High-CS group has the highest growth-promoting macrophages (M2-like macrophages) proportion and the least M0 Macrophages proportion (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eJ-L). These results suggest that there is an immunostimulatory microenvironment in senescent and aging SARC, but with the specificity of macrophages.\u003c/p\u003e\n \u003cp\u003eSenescent cells release a number of SASP factors to contribute to tumor immunity, which induce changes in the TIME\u003csup\u003e28\u003c/sup\u003e. The associations of core SASP genes with TIME were explored. As evident from Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eM, similar to CS and age, the expression of several SASP genes expression were positively correlated to ESTIMATE score (indicating a negative correlation with tumor purity). Notably, the tumor growth-promoting macrophages (M2) were also positively related to CS score, age and SASP. This result corroborates the idea of previous studies that SASP has a role in recruiting monocytes to the TIME and promoting their differentiation into macrophages\u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eSenescence level predicts active immune response in SARC\u003c/h2\u003e\n \u003cp\u003eBecause of the observed significant TIME alterations in senescent and aging SARC, it is hypothesized that CS score and age could be used as predictors of immunotherapy response. Immune molecular features related to age and CS scores were explored. Previous study had demonstrated that the expression of immune checkpoint associated genes significantly affect the efficacy of immune checkpoint blockade (ICB) therapies. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA and Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003eA-C, CD86 and HAVCR2 were both positively correlated with CS score and age. Immune cytolytic activity (CYT) score is a simple and effective quantitative measure of T-cell cytotoxicity.\u003csup\u003e20\u003c/sup\u003e As can be seen from Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003eD-F, there was a linear positive correlation between CYT and CS score and age. In addition, TCR clonality measure also increase with CS score age for SARC samples (Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003eG-I). Overall, it is reasonable to suspect that senescent and aging SARC tumors would be more susceptible to immunotherapy. This hypothesis can be partially validated by the TIDE prediction (Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003eJ,K), which showed that the TIDE score of High CS group (mean: 1.776) is significantly lower than that of Low CS group (mean: 1. 991).\u003c/p\u003e\n \u003cp\u003eCS and age in SARC were validated to have impact on clinical indices (Supplementary Table 1, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eG-H). To further explore the intrinsic association between age and CS, the consensus clustering was used to divide TCGA-SARC into three groups. (Supplementary Fig. 3L-P) It is apparent from Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB-D that Cluster 2 was substantially different from the other two clusters and had the best prognostic outcome, while Cluster 3 had the worst prognosis. The results of GSVA on KEGG immune pathways revealed that overall immune activation in high-CS group and cluster 1, while clusters 2 and 3 and low-CS group exhibited immunosuppression (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE). This result was validated by further immune-subtype analysis which showed that immunosuppressive phenotypes (C1 and C4) were mainly consisted of low-CS group, whereas C3 and C6 comprised mainly of the high-CS group (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF). In addition, cluster 3 contained more low-CS cases than high-CS cases, which confirmed the robustness of CS score-based groups. However, these three clusters each contain four age-and-CS groups, which may be due to the influence of unknown factors and need to be studied in the future. Overall, these analyses demonstrated that the higher CS level may indicate better survival and more active immune responses..\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eConstruct a senescence predictor in SARC\u003c/h2\u003e\n \u003cp\u003eTo translate the significance of SARC CS score into clinical application, the quest for hub genes reflecting CS score was implemented. (Fig. 5A) WGCNA was applied to identify genes related to CS score. Genes were divided into modules and merged with different colors, where the green modules were significantly and strongly correlated with CS score (negative correlation of -0.89 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig. 5B and Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003eA-C). Additionally, DEGs among the four CS-and-age based groups were identified using \u0026quot;DESeq2\u0026quot; package (Fig. 5C). Surprisingly, a large proportion of the common DEGs belong to the green module genes. (Fig. 5C,D) GO and KEGG analysis revealed that these 76 genes were mainly enriched in functions and pathways related to cell division and cellular senescence (Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003eD,E). Subsequently, univariate cox regression was performed on the 76 genes in the intersection, and the genes with prognostic impact were subjected to the next step of LASSO prognostic model construction (Fig. 5A,E,F). Finally, the three genes [RAD54 Like (RAD54L), Phosphoinositide-3-Kinase Interacting Protein 1 (PIK3IP1), Thyroid Hormone Receptor Interactor 13 (TRIP13)] were selected to construct multiple linear regression model. CS predictor was defined as follows: CS predictor = -0.290 \u0026times; (expression of RAD54L)\u0026thinsp;\u0026minus;\u0026thinsp;0.132 \u0026times; (expression of PIK3IP1) -0.098 \u0026times; (expression of TRIP13). As expected, the CS predictor showed significant associations with the CS score (Fig. 5G). According to the median value of the CS predictor, patients are divided into high-pred-CS and low-pred-CS groups, and the high pred-CS group was found to have a significantly better prognosis (Fig. 5H). Additionally, in the TCGA-SARC cohort, CS predictors displayed excellent predictive performance at 1, 3, and 5 years, at least as good as CS score. (Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003eF,G) These results suggest that the CS predictor composed of three genes can represent CS levels and have potential as prognostic biomarkers in SARC. \u003cstrong\u003eFig. 5.\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eValidating the validity of the constructed senescence predictor\u003c/h2\u003e\n \u003cp\u003eTo verify the universality of the model, the model was extended to the pan-cancer level and founded that the CS predictor was significantly associated with CS score in all 33 cancers (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). Even across the all pan-cancer cases, the high pred-CS group had significantly better prognosis with slower cancer progression, consistent with SARC (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB). Moreover, the predictive ability of the CS predictor for the survival of pan-cancer cases is even stronger than that of CS score (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC,D). In addition, it was found that with the increase of pathologic tumor stage at pan-cancer level, the expressions of RAD54L and TRIP13 gradually increased, while PI3KIP1 gradually decreased, resulting in a decrease in CS score and predictor (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eE). Consistently, in TCGA-SARC cohort, metastatic (more malignant) tumors showed higher RAD54L and lower PI3KIP1, CS score and CS predictor (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eF). Next, the 3 genes expression difference between SARC and normal tissue was explored at the tissue and cell line level. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eG-I, SARC has significantly higher RAD54L and TRIP13 and lower PI3KIP1 compared to normal tissue in three cohort (the insignificant of PI3KIP1 in the TCGA-SARC cohort may be due to too few normal controls (only two cases)). To validate the common phenomenon found by bioinformatic analysis in multiple datasets, tumor and para-cancerous tissues were collected from 8 osteosarcoma patients, and qPCR and IHC was performed for RAD54L, PIK3IP1, and TRIP13. Based on the results, it was found that RAD54L and TRIP13 were significantly higher and PIK3IP1 was lower in tumor tissue than in para-cancerous tissue. (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eJ,K) Taken together, these results demonstrate that CS predictors are reliable and applicable, and that the expression levels of the 3 genes are significantly correlated with the malignant properties of the tissues.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCS is an important cellular process in aging and tumor development, whereas the senescence landscape and related characteristics of sarcomas have been poorly studied. To gain insight into this, the current study was conducted to revealed the association of CS and age with genomic characteristics, TIME, and clinical outcomes of SARC. Additionally, three CS genes significantly predicted patient survival in various cohorts, suggesting their potential as prognostic biomarkers. Thus, based on the computational metrics of CS levels, our findings provide a framework for better understanding CS-related modulations in the tumor microenvironment and could guide further experiments and biomarker identification.\u003c/p\u003e \u003cp\u003eCS score, a metric to measure tumor senescence across the cancer spectrum, was innovatively proposed by Wang et al\u003csup\u003e8\u003c/sup\u003e. Its reliability has been validated at the pan-cancer level in the original article and was also validated in the current study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC,D). In addition, the relationship between age and CS was investigated in this study. One interesting finding is that the CS score of uterus was significantly negatively correlated with age, whereas most normal tissues' CS score had positive or nonsignificant correlations with age. This result support previous research which showed that genes up-regulated with age in the uterus were enriched in cell cycle terms\u003csup\u003e30\u003c/sup\u003e. Abnormal cell proliferation in aging uterus often leads to atypical hyperplasia and may result in endometrial cancer\u003csup\u003e31\u003c/sup\u003e. In SARC, although CS is not associated with age (possibly due to its high heterogeneity), both affect patient survival.\u003c/p\u003e \u003cp\u003eOne interesting finding is that the degree of genomic variation is positively correlated with age but negatively correlated with CS. Genomic instability is a well-known hallmark of aging, and the increased SNVs that accompany aging has been reported\u003csup\u003e12,17,32\u003c/sup\u003e. The current study validated that age affects genomic instability, TIME, and immune checkpoints at SARC-level, but there are no significant differences in immunotherapy response predictors between age groups. A possible explanation for this might be that aging, as a biological process that affects the whole body, altered other unstudied factors that exert impact on disease pathology and prognosis. Another finding of this study is that cases with lower CS score (or predictor) generally have higher malignancy and worse prognosis. In other words, genomic variations occur more frequently in less-senescent and higher-aggressive SARC than in senescent SARC, which indicate that genomic variations during SARC tumorigenesis is more intense than during senescence. This result corroborates the view of the previous study that the primary purpose of SC is to prevent the reproduction of damaged cells and trigger their death\u003csup\u003e12\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study showed that CS and age influence SARC TIME. First of all, it was demonstrated that CS (including core SASP gene expression due to CS) and age were positively correlated with total immune infiltration level. Secondly, analysis of immune cell subsets corroborates earlier finding that senescent tumor cells are subjected to strict immune surveillance and are efficiently cleared by phagocytosis\u003csup\u003e33,34\u003c/sup\u003e. In addition, senescent or aging SARC exhibited activated immune properties, including increased CYT score and TCR and activated KEGG immune pathways. However, increasing age and CS score have opposite effects on the prognosis of SARC patients. However, while age and CS had similar effects on TIME, they had opposite effects on prognosis in patients with SARC (Supplementary Table\u0026nbsp;1). There are several possible explanations for these relationships. On the one hand, increasing age may accompany comorbidities affecting the patient's overall physical condition. This idea was presented in some real-world studies of ICB in NSCLC\u003csup\u003e35,36\u003c/sup\u003e. On the other hand, although aging increases immune infiltration quantitatively, it also affects the function of the entire immune system. The phenomenon that the function of the immune system gradually decline with aging is summarized as \"immunosenescence\", which is manifested as decreased phagocytosis and cell chemotaxis, changes in the proportion of immune cells subsets, and decreased ability to produce specific antibodies, etc\u003csup\u003e37\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNotably, three key genes were validated to construct a predictor representing CS scores, and have been documented to have an impact on tumor development. RAD54L playing an important role in homologous recombination related repair of DNA double-strand breaks\u003csup\u003e38\u003c/sup\u003e. PIK3IP1 negatively regulates the PI3K pathway. and has an inhibitory effect on the development of hepatocellular carcinoma\u003csup\u003e39,40\u003c/sup\u003e. TRIP13 regulates cell division by interfering with spindle assembly checkpoint, and its high expression is closely associated with proliferation, invasion and metastasis of malignant cells\u003csup\u003e41\u003c/sup\u003e. In current study, through data from different database and qPCR experimental verification, it was demonstrated that RAD54L and TRIP13 were up-regulated and PIK3IP1 down-regulated in SARC with malignant properties.\u003c/p\u003e \u003cp\u003eSeveral limitations still remained in this study. First of all, metrics that can measure CS are too scarce to not included in the study to implement a comparative study with CS score. Secondly, the absence of SARC immunotherapy cohort prevents clinical validation of patients' immunotherapy responses. In conclusion, our study revealed that senescence level and age were both associated with TME, immune treatment indicators and prognosis in SARC. In addition, three genes, RAD54L, PIK3IP1 and TRIP13, were used to construct the predictor for predicting the senescence level, which had predictive value for the malignancy degree and prognosis of SARC\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eInformed consent statement: Informed written consent was obtained from the patient for publication of this report and any accompanying images.\u003c/p\u003e\n\u003cp\u003eConflict-of-interest statement: The authors report no conflicts of interest concerning the materials or methods used in this study or the findings specified in this paper.\u003c/p\u003e\n\u003cp\u003eFunding: This work was funded by the National Natural Science Foundation of China (NSFC, No. 82000839) and Natural Science Foundation of Zhejiang Province (No. LQ2H060009)\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e†\u003c/sup\u003eThese authors contributed equally to the work.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eResearch design: Zihua Li and Anquan Shang; Bioinformatic analysis: Pengfei Zan, Yi Zhang, Data Validation: Qingjing Chen, Zhengwei Duan, Yonghao Guan; Data analyses and interpretation: Yi Zhang; Project supervision: Anquan Shang; Manuscript writing and reviewing: Pengfei Zan, Kaiyuan Liu.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSkoda, J. \u0026amp; Veselska, R. 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Nucleic Acids Res. 43, 3180\u0026ndash;3196 (2015).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3661711/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3661711/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSARC (sarcoma) is a heterogeneous group of stromal tumors originating from mesenchymal tissues with poor prognosis. There is growing evidence that senescent cells in the tumor microenvironments (TME) are associated with the development and metastasis of cancer. The impact of senescence on sarcomas has been initially recognized, but not fully understood. Here, we revealed that senescence level and age were both associated with TME, immune treatment indicators and prognosis in SARC. WGCNA and least-selection absolute regression algorithm (LASSO) were used to track senescence-related genes and create a senescence predictor. Consequently, the three genes (RAD54, PIK3IP1, TRIP13) were selected to construct a multiple linear regression model. Through validation cohorts, IHC and qPCR, the predictors conducted by the three genes were proved to have prognostic and pathological significance. The senescence predictor may provide a novel insight into the study of molecular mechanisms and candidate biomarkers for the prognosis, resulting in effective treatments for SARC.\u003c/p\u003e","manuscriptTitle":"Comprehensive assessment of cellular senescence and aging in the tumor microenvironment of sarcoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-30 18:42:53","doi":"10.21203/rs.3.rs-3661711/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"647d0857-bb38-494d-82e7-5638b13fcdcd","owner":[],"postedDate":"November 30th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-12-08T02:14:23+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-30 18:42:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3661711","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3661711","identity":"rs-3661711","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}