Divergent Roles of Circadian Regulators CLOCK and CRY1 in Driving Pro-Tumoral Stemness and Immunoevasion in Osteosarcoma

Divergent Roles of Circadian Regulators CLOCK and CRY1 in Driving Pro-Tumoral Stemness and Immunoevasion in Osteosarcoma | 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 Divergent Roles of Circadian Regulators CLOCK and CRY1 in Driving Pro-Tumoral Stemness and Immunoevasion in Osteosarcoma Sukanya Bhoumik, Yool Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7167173/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 The circadian clock is a cell-autonomous regulatory system that influences diverse cancer-related processes, including cell proliferation, metabolism, and immune regulation. While core clock regulators are known to affect tumor biology, their distinct tumor-intrinsic and microenvironmental roles in osteosarcoma (OS) remain poorly defined. Here, we report that the expression of CLOCK and CRY1, but not their canonical partners BMAL1 and CRY2, is significantly associated with poor survival in OS and linked to oncogenic programs. Integrative transcriptomic and immune analyses reveal that CLOCK and CRY1 are positively correlated with cancer stem cell (CSC) markers, epithelial-mesenchymal transition (EMT) drivers, metabolic and metastatic genes, and immunosuppressive factors such as (e.g., MYC, SLC16A1, HK1, TNC, CD276, ITGA4, WISP1, POSTN, VEGFA). Knockdown of CLOCK or CRY1 in 143B OS stem-like cells significantly reduces the expression of these genes, supporting a functional role in maintaining tumor-promoting phenotypes. Moreover, high CLOCK and CRY1 expression correlates with reduced infiltration of CD4⁺ T cells and dendritic cells, elevated cancer-associated fibroblasts (CAFs) and myeloid-derived suppressor cells (MDSCs), and increased markers of immune exclusion and dysfunction. In contrast, BMAL1 and CRY2 show minimal or inverse associations with these parameters. These findings uncover an unexpected divergence among circadian regulators, positioning CLOCK and CRY1 as potential drivers of OS aggressiveness via both tumor-intrinsic and immune-evasive mechanisms, and suggest their therapeutic targeting as a promising strategy for disrupting circadian-linked oncogenic circuits in OS. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Osteosarcoma (OS) is the most common malignant bone tumor in children and adolescents and remains a clinical challenge due to its high heterogeneity, metastatic potential, and resistance to therapy [ 1 , 2 ]. Accumulating evidence implicates cancer stem cells (CSCs), a subpopulation with self-renewal, plasticity, and tumor-initiating properties, in driving therapeutic failure and disease progression in OS and other cancers [ 3 , 4 ]. These stem-like cells interface dynamically with metabolic cues, extracellular matrix (ECM) remodeling, and immune surveillance mechanisms, shaping a tumor microenvironment (TME) that fosters immune evasion and recurrence [ 5 – 9 ]. However, the upstream regulators orchestrating these tumor-intrinsic and microenvironmental interactions remain incompletely understood. The circadian clock is a cell-autonomous timing system that regulates 24-hour molecular rhythms through interconnected transcriptional feedback loops between the positive regulators brain and muscle ARNT-like 1 (BMAL1) and circadian locomotor output cycles kaput (CLOCK) and their repressors, period 1 and 2 (PER1/2) and cryptochrome 1 and 2 (CRY1/2) (Fig. 1 A). This molecular clockwork regulates key physiological processes, including differentiation, proliferation, and metabolism, shaping normal and cancer cell behavior [ 10 , 11 ]. We have recently shown that human OS cells (U-2OS, 143B), as well as melanoma cells, exhibit functional circadian rhythms [ 12 – 14 ], and circadian disruption impacts tumor growth by affecting cell cycle [ 15 , 16 ] and anti-tumor immune responses [ 17 ]. Emerging preclinical cancer model studies suggest that circadian clock components regulate their targeted CSCs and targeted cancer therapy [ 18 – 20 ]. However, most of these studies, including our recent work in OS [ 20 ], have primarily focused on the roles of circadian regulators in tumor-intrinsic mechanisms, such as cell proliferation and metabolic regulation [ 21 ]. Increasing evidence suggests that tumor cells, particularly CSCs, engage in dynamic crosstalk through diverse molecular and cellular interactions with non-tumor stromal and immune cells within the tumor microenvironment (TME). This crosstalk plays a critical role in influencing tumor progression and treatment response and is closely associated with overall survival in OS [ 22 – 24 ]. The TME is composed of a heterogeneous mix of anti-tumor immune cells, such as CD8⁺ cytotoxic T lymphocytes, CD4⁺ helper T cells, and dendritic cells, as well as pro-tumor immunosuppressive cells, including regulatory T cells (Tregs), cancer-associated fibroblasts (CAFs), and myeloid-derived suppressor cells (MDSCs) [ 25 , 26 ]. However, it remains unclear how circadian regulators contribute to CSC-driven remodeling of the TME during OS progression and prognosis. In this study, integrative gene and immune profiling revealed that CLOCK and CRY1 promote OS progression by activating CSC/EMT programs, pro-tumoral metabolism (e.g., glycolysis, lipid synthesis), and immunosuppressive microenvironmental remodeling. Functional validation in 143B OS CSC models confirmed that CLOCK and CRY1, but not BMAL1 or CRY2, maintain the expression of oncogenic and immunomodulatory factors. Correspondingly, CLOCK/CRY1 expression was linked to increased CAF and MDSC infiltration, reduced numbers of CD4⁺ T cells and dendritic cells, impaired T cell function, greater exclusion, and higher tumor purity. BMAL1 and CRY2 expression showed minimal or opposite correlations. These findings suggest that circadian regulators distinctly shape OS prognosis through both tumor-intrinsic and immune-evasive mechanisms. Results In a recent study, we showed that core clock factors (BMAL1, CLOCK, CRY1, CRY2) play critical roles in regulating OS stemness and invasiveness [ 20 ]. Building on these findings, we conducted additional outcome analyses using the Tumor Immune Estimation Resource (TIMER), a web-based platform for comprehensive immune-genomic analysis across multiple cancer types from The Cancer Genome Atlas (TCGA), with a specific focus on sarcoma (SARC) patients. The results showed that elevated CLOCK and CRY1 expression were significantly associated with increased risk and poorer overall survival ( Z > 0, p < 0.05), while higher BMAL1 or CRY2 expression correlated with minimal or reduced risk and improved survival outcomes ( p < 0.05) (Fig. 1 B). Similarly, Kaplan-Meier survival curves for SARC displayed distinct prognostic patterns, with CLOCK (HR = 1.2) and CRY1 (HR = 1.35) showing overall higher hazard ratios (HR) than BMAL1 (HR = 0.95) and CRY2 (HR = 0.8) (Fig. 1 C–F). This suggests that roles beyond the tumor-intrinsic circadian functions of core clock genes may contribute to OS patient survival. CSCs and their EMT properties are increasingly recognized as key determinants of therapy response and clinical outcomes [ 27 – 30 ]. To explore the potential link between core clock gene expression and stemness in SARC, we conducted gene co-expression analysis with established CSC/EMT markers using GEPIA. CLOCK and CRY1 showed significant or moderate positive correlations with several stemness and EMT markers, including CD133 ( R = 0.13, p < 0.05 for both) and CDH2 ( R = 0.19 and 0.12; p < 0.01 and < 0.05, respectively), with particularly strong associations observed for ZEB1 ( R = 0.44 and 0.45; p < 0.0001 for both) and ZEB2 ( R = 0.22 and 0.36; p < 0.001 for both). In contrast, BMAL1 and CRY2 exhibited relatively weaker or even negative correlations with markers such as MYC ( CRY2 : R = − 0.15, p < 0.05), CD44 (CRY1: R = − 0.06, p = not significant), and VIM (CRY2: R = − 0.13, p < 0.05), although SOX2 showed a positive association with both BMAL1 and CRY1 ( R = 0.19 and 0.18; p < 0.01 and < 0.01, respectively) (Fig. 2 A, B; Fig. S1 ). These patterns support the notion that CLOCK and CRY1 are more strongly linked to transcriptional programs driving CSC maintenance and EMT, which is consistent with their association with poor patient prognosis. In line with the correlations observed with CSC/EMT marker genes, CLOCK and CRY1 also showed a strong positive association with lipid metabolism factors ( ACSL4 , R = 0.18 and 0.13; p < 0.01 and < 0.05, respectively; FASN for CRY1 , R = 0.14, p < 0.05) and, in particular, glycolysis factors ( SLC2A1 [a.k.a. GLUT1 ], SLC16A1 [a.k.a. MCT1 ], and HK1 for CLOCK ; SLC16A1 and HK1 for CRY1 ; R = 0.30–0.38, p < 0.01 or lower), which are well-known to be preferred by cells with CSC/EMT property. This aligns with previous findings showing that CSCs and EMT cells rely on glycolysis (Warburg effect) for rapid ATP production, for biosynthetic precursors, and to adapt to hypoxic tumor environments [ 31 ]. In contrast, BMAL1 showed only small or modest positive correlations with these metabolic factors (e.g., ACSL4 , FASN , SLC2A1 , LDHA , PFKFB3 ; R > 0, p > 0.05), while CRY2 was moderately or significantly negatively correlated with key lipogenic and glycolytic enzymes, such as HK2 and PKM ( R = − 0.21, p < 0.01) (Fig. 3 A, B; Fig. S2 ). Nonetheless, all core clock genes were generally positively associated with mitochondrial metabolism genes, such as PRKAA1 , TFAM , and SIRT3 ( R = 0.29–0.52, p < 0.001 or lower), suggesting a shared role in supporting mitochondrial function. Notably, a recent single-cell RNA-sequencing (scRNA-seq) study of patient OS samples [ 32 ] identified 15 hub genes associated with metastasis, a process known to be facilitated by CSC/EMT characteristics. Further correlation analysis revealed that CLOCK and CRY1 were moderately to significantly positively correlated with several metastasis-associated genes, including SKP2 ( CLOCK : R = 0.35; CRY1 : R = 0.37), MCM3 ( CLOCK : R = 0.38; CRY1 : R = 0.42), and CCNA2 ( CLOCK : R = 0.34; CRY1 : R = 0.45), all with p < 0.00001 (Fig. 4 A, B; Fig. S3 ). In contrast, BMAL1 showed no significant associations with most of these genes, while CRY2 exhibited moderate to significant negative correlations with multiple genes, including CENPM ( R = − 0.20, p < 0.01), TK1 ( R = − 0.19, p < 0.01), CKS2 ( R = − 0.19, p < 0.01), and PHB ( R = − 0.15, p < 0.05). Moreover, since many metastasis-associated genes are involved in cell cycle regulation, we further analyzed canonical cell cycle progression genes and found that CLOCK and CRY1 were moderately to significantly positively correlated with nearly all of them, including CCNA1, CCNA2 , CCNB1 , CCNB2 , CCND1 , CCND2 , CCND3 , CCNE1 , CCNE2 , CDK2 , CDK4 , CDK6 , MKI67 , and MTOR ( R > 0, p < 0.05 to 0 or R 0.05) or significant negative correlations (e.g., CCNB : R = − 0.17, p = 0.0073) with several cell cycle regulators ( Fig. S4A, B ). Notably, tumor suppressor and cell cycle inhibitory genes (e.g., TP53 , PTEN , RB1 , CDKN1A , CDKN1B , CDKN2A , CDKN2B ) did not show consistent or clock gene-specific correlation patterns ( Fig. S4A, B ). These findings suggest that CLOCK and CRY1 , but not BMAL1 and CRY2 , may contribute to a tumor-intrinsic gene expression program driven by CSC/EMT pathways, promoting proliferation and metastasis. Beyond tumor-intrinsic pathways, accumulating evidence suggests that cancer cells, particularly CSC populations, remodel the tumor microenvironment (TME) to promote immune evasion and suppression by releasing ECM-associated proteins, immunomodulatory molecules, and soluble factors that inhibit T cell responses and recruit immunosuppressive cells, such as Tregs, MDSCs, and CAFs [ 33 – 37 ]. To explore the relationship between clock gene expression and CSC-driven TME remodeling in OS, we performed correlation analyses and found that CLOCK and CRY1 were mildly to significantly positively correlated ( R > 0, p < 0.05) with several key mediators of an immunosuppressive and immune-evasive TME (Fig. 5 A, B; Fig. S5 ). These genes included chemokines and cytokines (e.g., WISP1 [ 38 , 39 ], POSTN [ 38 , 40 ], VEGFA [ 41 – 43 ]), ECM-associated factors (e.g., TNC [ 44 ], HAS3 [ 45 ]), immune-interacting surface molecules (e.g., ITGA4 [ 46 ], ADORA2B [ 47 ] ), and immune checkpoint molecules (e.g., CD276 [ 48 , 49 ]). In contrast, BMAL1 and CRY2 showed weak or non-significant correlations ( R > 0 or R 0.05) with most of these genes (Fig. 5 A, B; Fig. S5 ). Similarly, further correlation analysis using previously published RNA sequencing data (GSE99671) from 18 OS patients [ 50 ] revealed that CLOCK and CRY1 showed significantly positive correlation patterns with the majority of pro-tumorigenic immunomodulatory genes, with CRY1 being associated with a greater number of genes than CLOCK (Fig. 6 A, B). In contrast, BMAL1 and, especially, CRY2 exhibited predominantly non-significant or negative correlations with these genes (Fig. 6 A; Fig. S6A, B ). These findings suggest that, unlike BMAL1 and CRY2, CLOCK and CRY1 engage distinct gene expression networks associated with pro-tumoral immunosuppressive factors and tumor-intrinsic CSC/EMT pathways, with CRY1 showing broader gene correlations than CLOCK. To explore CRY1's distinct role in shaping a pro-tumorigenic and immunomodulatory gene expression landscape, we analyzed publicly available RNA sequencing data from human carcinoma cells with doxycycline-inducible CRY1 knockdown mediated by short hairpin RNA ( sh-CRY1 ) [ 51 ]. Remarkably, CRY1 knockdown led to extensive downregulation of many of the aforementioned CSC/EMT markers, associated lipogenic and glycolytic factors, metastasis-promoting factors, cell cycle and tumor progression elements, and immunosuppressive factors compared to control cells ( sh-CTL ) (Fig. 7 A). These findings are consistent with our own CRY1 RNAi results, which showed marked reductions in the expressions of CSC/EMT markers and lipid metabolism genes [ 20 ]. To further validate CRY1's role alongside CLOCK in OS, we employed specific siRNAs targeting either CRY1 or CLOCK in xenograft-derived 143B OS CSCs, as established in our recent study [ 20 ] ( Fig. S7 ). Consistent with the transcriptome data (Fig. 7 A), our qPCR analysis revealed that knockdown of CRY1 , as well as CLOCK , moderately to significantly reduced the expression of several oncogenic, metabolic, and immunomodulatory genes (e.g., MYC, SLC16A1, HK1, TNC, CD276, ITGA4, WISP1, POSTN, VEGFA ), with the exception of VEGFA in CRY1 knockdown cells and POSTN in CLOCK knockdown cells (Fig. 7 B). Collectively, these data reinforce our earlier gene association findings (Figs. 2 – 5 ), highlighting a pivotal role for CRY1 and CLOCK in driving a pro-tumorigenic transcriptional landscape through both intrinsic and extrinsic immunomodulatory mechanisms in OS. To explore the contribution of core circadian clock genes to tumor immunity, we examined the relationship between their expression and the presence of tumor-infiltrating immune cells (TIICs) [ 52 , 53 ]. The expressions of CLOCK and CRY1 were significantly negatively (Spearman's Rho ( ρ ) < 0, p < 0.05) correlated with infiltration of CD4 + T cells [ 54 ] and dendritic cells, key cell types essential for antigen presentation and effective T cell activation [ 55 ] (Fig. 8 A, B). Moreover, both genes showed significant positive correlations ( ρ > 0, p < 0.05) with MDSC infiltration and CAF abundance, two critical components of the tumor stroma known to support immunosuppression and tumor progression [ 56 , 57 ] (Fig. 8 A, B), exhibiting clear immunosuppressive and stromal remodeling signatures. Notably, similar immunosuppressive infiltration patterns were observed for key CSC/EMT-associated factors (e.g., SOX2 , CD133 , ZEB1 , SLC16A1 ) and immunomodulatory genes (e.g., WISP1 , VEGFA , TNC ) in subsequent immune profiling analyses, further supporting a functional link between CLOCK / CRY1 expression and a pro-tumoral, immune-evasive tumor microenvironment ( Fig. S8 ). However, BMAL1 expression showed no significant associations with any major immune or stromal cell types, suggesting a limited role in modulating the tumor immune landscape (Fig. 8 A). In addition, CRY2 was positively associated with CAFs ( ρ = 0.235) but negatively correlated with MDSCs ( ρ = −0.157) and lacked significant correlations with other immune cell subsets, suggesting a stromal-modulatory but less immunosuppressive role (Fig. 8 A). To further evaluate the functional and spatial impact of circadian genes on anti-tumor immunity, we analyzed their associations with key immune activation and exclusion markers [ 58 ] (Fig. 9 A, B). CLOCK and CRY1 showed significant negative correlations ( ρ < 0, p < 0.05) with the expression of interferon-gamma ( IFNG ), a cytokine produced by activated T cells and natural killer cells, and T cell dysfunction scores, indicating impaired T cell activation and increased exhaustion, which is characteristic of “immune-cold” tumors [ 59 , 60 ]. Moreover, both genes were significantly positively correlated ( ρ > 0, p < 0.05) with T cell exclusion scores and tumor purity, suggesting enhanced immune evasion and reduced immune infiltration (Fig. 9 A, B), likely mediated by CAFs and MDSCs, as shown in Fig. 8 . In contrast, BMAL1 and CRY2 exhibited weaker or more selective associations, with CRY2 showing links to stromal exclusion but not direct immunosuppression (Fig. 9 A). Together, these findings highlight distinct immunomodulatory roles of circadian clock genes, with CLOCK and CRY1 contributing to an immune-evasive program in OS. Discussion While BMAL1 and CLOCK form the canonical positive arm of the circadian transcriptional loop, and CRY1 and CRY2 act as repressors, our integrative genomic and immune profiling of human sarcoma reveals a functional divergence between these pairs in sarcoma. In our analyses, high CLOCK and CRY1 expression is consistently linked to poor survival, characterized by elevated expression of CSC/EMT markers, metabolic and metastatic factors, and immunosuppressive signals, as well as reduced anti-tumor immune infiltration and activity. In contrast, BMAL1 and CRY2 expression exhibits neutral or protective associations with patient survival and shows minimal impact on tumor-intrinsic and immune microenvironmental signaling pathways (Figs. 1 – 9 ). This uncoupling of function suggests that CLOCK and CRY1 may form a distinct pro-tumorigenic axis, operating independently from the canonical BMAL1/CLOCK activator and CRY1/2 feedback complexes. Tumorigenic CSC populations, particularly glioblastoma stem cells (GSCs) and leukemia stem cells (LSCs), have been shown to exhibit robust circadian rhythms in culture, with both CLOCK and BMAL1 proven to be essential for their survival in brain and blood cancers [ 18 , 61 , 62 ]. This aligns with our recent 3D spheroid culture and cell migration/invasion analyses that demonstrated a similar down-regulation of CSC/EMT marker genes (e.g., POU5F1 [ OCT4 ], CDH2 , ZEB1 ) following knockdown of CLOCK or BMAL1 by RNAi in human OSC model studies [ 20 ]. Notably, the divergent functions of these two dimeric partner molecules are reminiscent of previous studies suggesting that (1) CLOCK and BMAL1 have differential binding affinities and interactions with other transcriptional regulators, leading to heterogeneous transcriptional outputs [ 63 , 64 ]; (2) CLOCK possesses intrinsic histone acetyltransferase (HAT) activity [ 65 ], enabling it to act as a pioneer-like factor that directly remodels chromatin and facilitates transcriptional activation, whereas BMAL1 primarily functions as a rhythmic scaffold that integrates metabolic cues and coordinates transcriptional timing through interactions with repressors, such as CRY and PER [ 63 ]; (3) their distinct post-translational modifications, such as stable acetylation of CLOCK versus rhythmic acetylation and sumoylation of BMAL1 [ 66 – 69 ], further refine their contributions to circadian transcriptional dynamics. Such divergence of these two factors may be particularly relevant in cancer contexts, such as sarcoma, where CLOCK may promote tumorigenic transcriptional programs through direct chromatin remodeling, while BMAL1 may play a more nuanced role by interacting with other transcriptional and post-translational regulators. A similar functional divergence between the circadian repressor partners CRY1 and CRY2 has been suggested in previous studies, suggesting that CRY1 and CRY2 may exert distinct, even opposing, roles, particularly in the regulation of MYC, a master oncogenic regulator [ 70 , 71 ]. CRY2 facilitates MYC degradation via the SCF (FBXL3) complex, thereby suppressing cell proliferation [ 72 ], whereas CRY1 lacks this function and is even associated with MYC upregulation. In a more recent study, MYC expression was shown to be downregulated in CRY1-deficient embryonic stem cells (ESCs), with loss of CRY1 impairing self-renewal, colony formation, and glycolytic reprogramming, reflecting stemness-associated deficits [ 73 ]. These evidence resonate with our previous and present data indicating marked downregulation of MYC , OCT4 , CDH3 , and ZEB1 , and pro-tumoral immunomodulatory genes upon CRY1 knockdown (shRNA/siRNA) (Fig. 7 ) [ 20 ]. These findings also underscore a previously underrecognized functional divergence between CRY1 and CRY2 in cancer biology, highlighting CRY1 as a potential oncogenic driver of stemness and metabolic reprogramming in osteosarcoma. Beyond their roles in intrinsic gene regulation, our data reveal that CLOCK and CRY1 expression correlates with multiple hallmarks of immune evasion. Specifically, both factors are associated with increased abundance of CAFs and MDSCs, along with reduced infiltration of CD4⁺ T cells and dendritic cells (Fig. 8 ). Additionally, CLOCK and CRY1 exhibit negative correlations with IFNG expression and T cell function markers, and positive correlations with T cell exclusion scores and tumor purity (Fig. 9 ), collectively indicating a shift toward an “immune-cold” TME [ 74 ]. In contrast, BMAL1 and CRY2 show minimal or context-dependent associations, with CRY2 displaying stromal involvement but limited evidence of immunosuppressive activity. These immune profiling results align with our transcriptomic and functional analyses (Figs. 6 and 7 ), suggesting that CLOCK and CRY1 may contribute to immune resistance in OS by regulating ECM components (e,g., TNC , ITGA4 ), immunomodulatory chemokines (e.g., WISP1 , POSTN ), and immune checkpoint molecules such as CD276, likely through cooperation with CAFs and MDSCs. Together, these findings support a model in which CLOCK and CRY1 serve as tumor-intrinsic orchestrators of immune exclusion and dysfunction. We speculate that these mechanisms may represent a broader paradigm in OS and potentially other sarcomas, and propose that combinatorial strategies targeting CLOCK/CRY1 in conjunction with immune checkpoint inhibitors merit further investigation. In summary, our findings reveal the distinct roles of core circadian regulators in OS prognosis. CLOCK and CRY1 emerge as critical drivers of tumor aggressiveness through their modulation of CSC properties, metabolic pathways, and immune microenvironment dynamics. Furthermore, our results suggest that selective targeting of CLOCK and/or CRY1 may provide therapeutic benefits in improving outcomes for patients with OS. Further studies are warranted to elucidate the precise mechanisms by which these circadian regulators influence tumor biology and to explore their potential as therapeutic targets in OS. Materials and Methods Survival analysis The prognostic significance of clock gene expression ( BMAL1 , CLOCK , CRY1 , CRY2 ) was assessed using the Gene_Outcome module in the TIMER2.0 database ( http://timer.cistrome.org/ ; accessed on 1 October 2023) [ 53 ]. Kaplan-Meier (KM) survival curves were generated using Cox proportional hazards models. Analyses were performed across multiple TCGA cancer types, with particular focus on sarcoma (SARC). Patients were stratified into high and low expression cohorts using median cutoffs (cutoff-high and cutoff-low = 50%). Hazard ratios (HR), p -values from Cox models, and log-rank test results were used to assess survival significance. In addition, Z -score-based outcome risk plots were used to compare the indicated clock genes across cancer types. Our analyses were also adjusted for clinical variables including age, tumor stage, and tumor purity. Gene correlation analysis To explore the gene networks associated with circadian regulators, Pearson correlation coefficients were calculated between clock genes and established markers of cancer stemness, EMT, oncogenic metabolism, metastasis, and immune modulation, as shown in the corresponding figures. Analyses were conducted using the Correlation module of the Gene Expression Profiling Interactive Analysis (GEPIA; http://gepia.cancer-pku.cn/ ; accessed on 1 June 2024) platform [ 75 ]. Further correlation analyses were performed on OS patient RNA-seq data (GSE99671) using GraphPad Prism to examine associations between core clock genes and immunosuppressive or immune-evasive molecules [ 50 ]. Tumor-immune profiling analysis To assess the tumor immune microenvironment, the immune module of TIMER2.0 was used to examine correlations between CRY1 / CRY2 expression and tumor-infiltrating immune cells (TIICs), including CD8 + T cells, CD4 + T cells, Tregs, CAFs, and MDSCs. Purity-adjusted Spearman correlation coefficients and associated p -values were calculated. Five deconvolution algorithms, CIBERSORT (Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts), EPIC (Estimating the Proportions of Immune and Cancer cells), MCP-counter (Microenvironment Cell Populations-counter), TIDE (Tumor Immune Dysfunction and Exclusion), and quanTIseq (Quantification of the Tumor Immune Contexture), were employed for robust immune cell estimation. Analyses were conducted on SARC samples to identify tumor subtype-specific trends In addition, associations between circadian regulators and immune activation and exclusion markers, such as interferon-gamma ( IFNG ) expression, T cell dysfunction scores, T cell exclusion scores, and tumor purity, were determined using the Immune Infiltration Analysis Module in TIMER3.0 ( https://compbio.cn/timer3/ ; accessed on 15 May 2025) [ 58 ]. Spearman correlation coefficients were calculated with tumor purity adjustments. Functional validation via CRY1 and CLOCK knockdown in OS CSCs To functionally validate the gene correlation findings, we performed siRNA-mediated knockdown of CLOCK and CRY1 in xenograft-derived 143B OS CSCs, as described in our recent study [ 20 ]. Gene expression changes were assessed by quantitative PCR (qPCR) using validated primers targeting representative CSC/EMT and immune/metabolic modulatory genes (listed in the Supplementary Table; see Supplementary material). In addition, CRY1 knockdown effects on gene expression profiles were validated using publicly available RNA-seq datasets (GSE144961) derived from human carcinoma cells engineered to stably express doxycycline-inducible sh-CRY1 constructs [ 51 ]. Transfection of siRNA Specific siRNAs targeting human CLOCK (SI00069790) and CRY1 (SI02757370) were obtained from Qiagen (Germantown, MD, USA). 143B CSCs were transfected with 100 pmol of siRNA per well using Lipofectamine RNAiMAX Transfection Reagent (13778075, Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s instructions. RNA isolation, cDNA synthesis, and quantitative real-time qPCR Total RNA was isolated from 143B CSCs using the RNeasy Plus Mini Kit (Qiagen, 74134, Germantown, MD, USA), according to the manufacturer’s instructions. Equal amounts of RNA were then reverse-transcribed to generate complementary DNA (cDNA) using the SuperScript™ First-Strand Synthesis System for RT-PCR (11904018, Thermo Fisher Scientific), with 2 µL of random hexamers and 9 µL of RNA, following the supplied protocol. Real-time qPCR was performed using SYBR Green PCR Master Mix (1708880, Bio-Rad, Hercules, CA, USA) and 10 µM forward and reverse primers (listed in the Supplementary Table). Thermal cycling conditions were as follows: 50°C for 2 minutes, 95°C for 10 minutes, followed by 40 cycles of 95°C for 15 seconds and 60°C for 1 minute. Melting curve analysis was conducted to verify amplification specificity. Gene expression levels were analyzed using the comparative Ct (ΔΔCt) method and normalized to GAPDH . Reactions were carried out on a ViiA7 Real-Time PCR System (Thermo Fisher Scientific, Waltham, MA, USA). Statistical Analysis Statistical analyses were performed using tools integrated within the TIMER and GEPIA platforms. Spearman or Pearson correlations were applied as appropriate. For survival analyses, Cox regression and log-rank tests were used. Results with p -values < 0.05 were considered statistically significant. Other statistical analyses were conducted using GraphPad Software (version 10). Significance was assessed using two-way ANOVA, one-way ANOVA, or unpaired Student’s t -tests, as appropriate. When p < 0.05, it was considered statistically significant. Declarations Ethics approval and consent to participate Ethical review and approval were waived for this study because it utilized publicly available RNA-seq data from GSE99671, GEPIA, TIMER2, and TIMER3 that is fully anonymized and deidentified. No additional human subjects were involved, and ethical approval was obtained by the original study authors. Consent for publication Not applicable Availability of data and materials The datasets analyzed in this study are publicly available. Gene expression and immune profiling data were accessed via GEPIA (http://gepia.cancer-pku.cn/), TIMER2.0 (http://timer.cistrome.org/), and TIMER3.0 (https://compbio.cn/timer3/). RNA-seq datasets from osteosarcoma patients were obtained from GEO under accession number GSE99671. Publicly available RNA-seq data for CRY1 knockdown experiments were accessed under GEO accession number GSE144961. All other data supporting the findings of this study are included in the article and its Supplementary Materials Competing interests The authors declare no conflicts of interest. Funding This work was supported by the Hennings Cancer gift from the Elson S. Floyd College of Medicine (GF003245 to Y.L.). Authors' contributions Y.L. conceptualized the study, developed the methodology, performed software analysis, and curated the data. Y.L. and S.B. conducted the investigation, validation, formal analysis, and provided resources. Y.L. wrote the original draft, prepared the visualizations, supervised the project, managed project administration, and acquired funding. Y.L. and S.B. reviewed and edited the manuscript. All authors read and approved the final manuscript. Acknowledgements We thank Amy Sullivan, Ph.D. from Obrizus Communications for assisting with the helpful editing of this article. References Taran SJ, Taran R, Malipatil NB. Pediatric Osteosarcoma: An Updated Review. Indian J Med Paediatr Oncol. 2017;38:33–43. Yan GN, Lv YF, Guo QN. Advances in osteosarcoma stem cell research and opportunities for novel therapeutic targets. Cancer Lett. 2016;370:268–74. Menéndez ST, Gallego B, Murillo D, Rodríguez A, Rodríguez R. Cancer Stem Cells as a Source of Drug Resistance in Bone Sarcomas. J Clin Med 2021, 10. Fujiwara S, Kawamoto T, Kawakami Y, Koterazawa Y, Hara H, Takemori T, Kitayama K, Yahiro S, Kakutani K, Matsumoto T, et al. Acquisition of cancer stem cell properties in osteosarcoma cells by defined factors. Stem Cell Res Ther. 2020;11:429. Martins-Neves SR, Sampaio-Ribeiro G, Gomes CMF. Self-Renewal and Pluripotency in Osteosarcoma Stem Cells' Chemoresistance: Notch, Hedgehog, and Wnt/β-Catenin Interplay with Embryonic Markers. Int J Mol Sci 2023, 24. Feng Z, Ou Y, Hao L. The roles of glycolysis in osteosarcoma. Front Pharmacol. 2022;13:950886. Liu H, Zhang Z, Song L, Gao J, Liu Y. Lipid metabolism of cancer stem cells. Oncol Lett. 2022;23:119. Zhang S, Yang X, Wang L, Zhang C. Interplay between inflammatory tumor microenvironment and cancer stem cells. Oncol Lett. 2018;16:679–86. Zhu T, Han J, Yang L, Cai Z, Sun W, Hua Y, Xu J. Immune Microenvironment in Osteosarcoma: Components, Therapeutic Strategies and Clinical Applications. Front Immunol. 2022;13:907550. Lee Y, Wisor JP. Multi-Modal Regulation of Circadian Physiology by Interactive Features of Biological Clocks. Biology (Basel) 2021, 11. Lee Y. Roles of circadian clocks in cancer pathogenesis and treatment. Exp Mol Med 2021. Anafi RC, Lee Y, Sato TK, Venkataraman A, Ramanathan C, Kavakli IH, Hughes ME, Baggs JE, Growe J, Liu AC, et al. Machine learning helps identify CHRONO as a circadian clock component. PLoS Biol. 2014;12:e1001840. Lee Y, Jang AR, Francey LJ, Sehgal A, Hogenesch JB. KPNB1 mediates PER/CRY nuclear translocation and circadian clock function. Elife 2015, 4. Lee Y, Shen Y, Francey LJ, Ramanathan C, Sehgal A, Liu AC, Hogenesch JB. The NRON complex controls circadian clock function through regulated PER and CRY nuclear translocation. Sci Rep. 2019;9:11883. Lee Y, Lahens NF, Zhang S, Bedont J, Field JM, Sehgal A. G1/S cell cycle regulators mediate effects of circadian dysregulation on tumor growth and provide targets for timed anticancer treatment. PLoS Biol. 2019;17:e3000228. Lee Y, Fong SY, Shon J, Zhang SL, Brooks R, Lahens NF, Chen D, Dang CV, Field JM, Sehgal A. Time-of-day specificity of anticancer drugs may be mediated by circadian regulation of the cell cycle. Sci Adv 2021, 7. Zhang X, Pant SM, Ritch CC, Tang HY, Shao H, Dweep H, Gong YY, Brooks R, Brafford P, Wolpaw AJ, et al. Cell state dependent effects of Bmal1 on melanoma immunity and tumorigenicity. Nat Commun. 2024;15:633. Dong Z, Zhang G, Qu M, Gimple RC, Wu Q, Qiu Z, Prager BC, Wang X, Kim LJY, Morton AR, et al. Targeting Glioblastoma Stem Cells through Disruption of the Circadian Clock. Cancer Discov. 2019;9:1556–73. Puram RV, Kowalczyk MS, de Boer CG, Schneider RK, Miller PG, McConkey M, Tothova Z, Tejero H, Heckl D, Järås M, et al. Core Circadian Clock Genes Regulate Leukemia Stem Cells in AML. Cell. 2016;165:303–16. Bhoumik S, Lee Y. Core Molecular Clock Factors Regulate Osteosarcoma Stem Cell Survival and Behavior via CSC/EMT Pathways and Lipid Droplet Biogenesis. Cells 2025, 14. Lee Y, Tanggono AS. Potential Role of the Circadian Clock in the Regulation of Cancer Stem Cells and Cancer Therapy. Int J Mol Sci 2022, 23. Wu B, Shi X, Jiang M, Liu H. Cross-talk between cancer stem cells and immune cells: potential therapeutic targets in the tumor immune microenvironment. Mol Cancer. 2023;22:38. Li YR, Fang Y, Lyu Z, Zhu Y, Yang L. Exploring the dynamic interplay between cancer stem cells and the tumor microenvironment: implications for novel therapeutic strategies. J Transl Med. 2023;21:686. Wang G, Xu D, Zhang Z, Li X, Shi J, Sun J, Liu HZ, Zhou M, Zheng T. The pan-cancer landscape of crosstalk between epithelial-mesenchymal transition and immune evasion relevant to prognosis and immunotherapy response. NPJ Precis Oncol. 2021;5:56. Sharma P, Aaroe A, Liang J, Puduvalli VK. Tumor microenvironment in glioblastoma: Current and emerging concepts. Neurooncol Adv. 2023;5:vdad009. Jain S, Rick JW, Joshi RS, Beniwal A, Spatz J, Gill S, Chang AC, Choudhary N, Nguyen AT, Sudhir S et al. Single-cell RNA sequencing and spatial transcriptomics reveal cancer-associated fibroblasts in glioblastoma with protumoral effects. J Clin Invest 2023, 133. Shibue T, Weinberg RA. EMT, CSCs, and drug resistance: the mechanistic link and clinical implications. Nat Rev Clin Oncol. 2017;14:611–29. Patrașcu AV, Țarcă E, Lozneanu L, Ungureanu C, Moroșan E, Parteni DE, Jehac A, Bernic J, Cojocaru E. The Role of Epithelial-Mesenchymal Transition in Osteosarcoma Progression: From Biology to Therapy. Diagnostics (Basel) 2025, 15. Chu X, Tian W, Ning J, Xiao G, Zhou Y, Wang Z, Zhai Z, Tanzhu G, Yang J, Zhou R. Cancer stem cells: advances in knowledge and implications for cancer therapy. Signal Transduct Target Ther. 2024;9:170. Prager BC, Xie Q, Bao S, Rich JN. Cancer Stem Cells: The Architects of the Tumor Ecosystem. Cell Stem Cell. 2019;24:41–53. Zhao J, Jin D, Huang M, Ji J, Xu X, Wang F, Zhou L, Bao B, Jiang F, Xu W, et al. Glycolysis in the tumor microenvironment: a driver of cancer progression and a promising therapeutic target. Front Cell Dev Biol. 2024;12:1416472. Li J, Bai Y, Zhang H, Chen T, Shang G. Single-cell RNA sequencing reveals the communications between tumor microenvironment components and tumor metastasis in osteosarcoma. Front Immunol. 2024;15:1445555. Chen P, Hsu WH, Han J, Xia Y, DePinho RA. Cancer Stemness Meets Immunity: From Mechanism to Therapy. Cell Rep. 2021;34:108597. Müller L, Tunger A, Plesca I, Wehner R, Temme A, Westphal D, Meier F, Bachmann M, Schmitz M. Bidirectional Crosstalk Between Cancer Stem Cells and Immune Cell Subsets. Front Immunol. 2020;11:140. Chulpanova DS, Rizvanov AA, Solovyeva VV. The Role of Cancer Stem Cells and Their Extracellular Vesicles in the Modulation of the Antitumor Immunity. Int J Mol Sci 2022, 24. Donini C, Rotolo R, Proment A, Aglietta M, Sangiolo D, Leuci V. Cellular Immunotherapy Targeting Cancer Stem Cells: Preclinical Evidence and Clinical Perspective. Cells 2021, 10. Annett S, Robson T. Targeting cancer stem cells in the clinic: Current status and perspectives. Pharmacol Ther. 2018;187:13–30. Li C, Xue Y, Yinwang E, Ye Z. The Recruitment and Immune Suppression Mechanisms of Myeloid-Derived Suppressor Cells and Their Impact on Bone Metastatic Cancer. Cancer Rep (Hoboken). 2025;8:e70044. Tsai HC, Tzeng HE, Huang CY, Huang YL, Tsai CH, Wang SW, Wang PC, Chang AC, Fong YC, Tang CH. WISP-1 positively regulates angiogenesis by controlling VEGF-A expression in human osteosarcoma. Cell Death Dis. 2017;8:e2750. Wang Z, Xiong S, Mao Y, Chen M, Ma X, Zhou X, Ma Z, Liu F, Huang Z, Luo Q, Ouyang G. Periostin promotes immunosuppressive premetastatic niche formation to facilitate breast tumour metastasis. J Pathol. 2016;239:484–95. Yang J, Yang D, Sun Y, Sun B, Wang G, Trent JC, Araujo DM, Chen K, Zhang W. Genetic amplification of the vascular endothelial growth factor (VEGF) pathway genes, including VEGFA, in human osteosarcoma. Cancer. 2011;117:4925–38. Ribatti D. Immunosuppressive effects of vascular endothelial growth factor. Oncol Lett. 2022;24:369. Zhang Y, Brekken RA. Direct and indirect regulation of the tumor immune microenvironment by VEGF. J Leukoc Biol. 2022;111:1269–86. Tanaka M, Yamazaki T, Araki N, Yoshikawa H, Yoshida T, Sakakura T, Uchida A. Clinical significance of tenascin-C expression in osteosarcoma: tenascin-C promotes distant metastases of osteosarcoma. Int J Mol Med. 2000;5:505–10. Tofuku K, Yokouchi M, Murayama T, Minami S, Komiya S. HAS3-related hyaluronan enhances biological activities necessary for metastasis of osteosarcoma cells. Int J Oncol. 2006;29:175–83. Zhang J, Wang G, Liu J, Tang F, Wang S, Li Y. ITGA4 as a potential prognostic and immunotherapeutic biomarker in human cancer and its clinical significance in gastric cancer: an integrated analysis and validation. Front Oncol. 2025;15:1513622. Li P, Pang KL, Chen SJ, Yang D, Nai AT, He GC, Fang Z, Yang Q, Cai MB, He JY. ADORA2B promotes proliferation and migration in head and neck squamous cell carcinoma and is associated with immune infiltration. BMC Cancer. 2025;25:673. McEachron TA, Triche TJ, Sorenson L, Parham DM, Carpten JD. Profiling targetable immune checkpoints in osteosarcoma. Oncoimmunology. 2018;7:e1475873. Liu L, Yao Z, Liu Y, Li Y, Ding Y, Hu J, Liu Z, Shi P, Chen K, Zhang W, Hou Y. A Pan-Cancer Analysis of the Oncogenic Role of CD276 in Human Tumors. Genes (Basel) 2024, 15. Ho XD, Phung P, Le Q, Nguyen VH, Reimann V, Prans E, Kõks E, Maasalu G, Le K, Trinh NTH. Whole transcriptome analysis identifies differentially regulated networks between osteosarcoma and normal bone samples. Exp Biol Med (Maywood). 2017;242:1802–11. Shafi AA, McNair CM, McCann JJ, Alshalalfa M, Shostak A, Severson TM, Zhu Y, Bergman A, Gordon N, Mandigo AC, et al. The circadian cryptochrome, CRY1, is a pro-tumorigenic factor that rhythmically modulates DNA repair. Nat Commun. 2021;12:401. Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B, Liu XS. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 2017;77:e108–10. Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, Li B, Liu XS. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48:W509–14. Montauti E, Oh DY, Fong L. CD4. Trends Cancer. 2024;10:969–85. Del Prete A, Salvi V, Soriani A, Laffranchi M, Sozio F, Bosisio D, Sozzani S. Dendritic cell subsets in cancer immunity and tumor antigen sensing. Cell Mol Immunol. 2023;20:432–47. Gabrilovich DI. Myeloid-Derived Suppressor Cells. Cancer Immunol Res. 2017;5:3–8. Feng L, Chen Y, Jin W. Research progress on cancer-associated fibroblasts in osteosarcoma. Oncol Res. 2025;33:1091–103. Cui H, Zhao G, Lu Y, Zuo S, Duan D, Luo X, Zhao H, Li J, Zeng Z, Chen Q, Li T. TIMER3: an enhanced resource for tumor immune analysis. Nucleic Acids Res 2025. Wu B, Zhang B, Li B, Wu H, Jiang M. Cold and hot tumors: from molecular mechanisms to targeted therapy. Signal Transduct Target Ther. 2024;9:274. Sun CY, Zhang Z, Tao L, Xu FF, Li HY, Zhang HY, Liu W. T cell exhaustion drives osteosarcoma pathogenesis. Ann Transl Med. 2021;9:1447. Puram RV, Kowalczyk MS, de Boer CG, Schneider RK, Miller PG, McConkey M, Tothova Z, Tejero H, Heckl D, Jaras M, et al. Core Circadian Clock Genes Regulate Leukemia Stem Cells in AML. Cell. 2016;165:303–16. Sulli G, Rommel A, Wang X, Kolar MJ, Puca F, Saghatelian A, Plikus MV, Verma IM, Panda S. Pharmacological activation of REV-ERBs is lethal in cancer and oncogene-induced senescence. Nature. 2018;553:351–5. Trott AJ, Menet JS. Regulation of circadian clock transcriptional output by CLOCK:BMAL1. PLoS Genet. 2018;14:e1007156. Lee Y, Lee J, Kwon I, Nakajima Y, Ohmiya Y, Son GH, Lee KH, Kim K. Coactivation of the CLOCK-BMAL1 complex by CBP mediates resetting of the circadian clock. J Cell Sci. 2010;123:3547–57. Doi M, Hirayama J, Sassone-Corsi P. Circadian regulator CLOCK is a histone acetyltransferase. Cell. 2006;125:497–508. Hirayama J, Sahar S, Grimaldi B, Tamaru T, Takamatsu K, Nakahata Y, Sassone-Corsi P. CLOCK-mediated acetylation of BMAL1 controls circadian function. Nature. 2007;450:1086–90. Cardone L, Hirayama J, Giordano F, Tamaru T, Palvimo JJ, Sassone-Corsi P. Circadian clock control by SUMOylation of BMAL1. Science. 2005;309:1390–4. Lee J, Lee Y, Lee MJ, Park E, Kang SH, Chung CH, Lee KH, Kim K. Dual modification of BMAL1 by SUMO2/3 and ubiquitin promotes circadian activation of the CLOCK/BMAL1 complex. Mol Cell Biol. 2008;28:6056–65. Lee Y, Chun SK, Kim K. Sumoylation controls CLOCK-BMAL1-mediated clock resetting via CBP recruitment in nuclear transcriptional foci. Biochim Biophys Acta. 2015;1853:2697–708. Dhanasekaran R, Deutzmann A, Mahauad-Fernandez WD, Hansen AS, Gouw AM, Felsher DW. The MYC oncogene - the grand orchestrator of cancer growth and immune evasion. Nat Rev Clin Oncol. 2022;19:23–36. Meškytė EM, Keskas S, Ciribilli Y. MYC as a Multifaceted Regulator of Tumor Microenvironment Leading to Metastasis. Int J Mol Sci 2020, 21. Huber AL, Papp SJ, Chan AB, Henriksson E, Jordan SD, Kriebs A, Nguyen M, Wallace M, Li Z, Metallo CM, Lamia KA. CRY2 and FBXL3 Cooperatively Degrade c-MYC. Mol Cell. 2016;64:774–89. Sato S, Hishida T, Kinouchi K, Hatanaka F, Li Y, Nguyen Q, Chen Y, Wang PH, Kessenbrock K, Li W, et al. The circadian clock CRY1 regulates pluripotent stem cell identity and somatic cell reprogramming. Cell Rep. 2023;42:112590. Wherry EJ, Kurachi M. Molecular and cellular insights into T cell exhaustion. Nat Rev Immunol. 2015;15:486–99. Tang Z, Li C, Kang B, Gao G, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45:W98–102. Additional Declarations No competing interests reported. Supplementary Files SupplementarymaterialBMCcancer.pdf 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-7167173","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":489422782,"identity":"82490769-ff9c-42ff-8063-12f490decb1a","order_by":0,"name":"Sukanya Bhoumik","email":"","orcid":"","institution":"Washington State University","correspondingAuthor":false,"prefix":"","firstName":"Sukanya","middleName":"","lastName":"Bhoumik","suffix":""},{"id":489422783,"identity":"3e4a0091-7475-4c1a-9d29-4d1b0c8ea7dc","order_by":1,"name":"Yool Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYHACxgMMBQwM/BAOM3F6DjAYMDBINpCsxeAAsVoMjjcfAGqxkTO+kf5MgqHCOrGBoJYzxxKAWtKMzW7kmEkwnEknQsuNHKCTDA4nbrudwybB2HaYaC3/6zfPBjqM8R/xWg4kGEgnmEkwNhChRRLklwSDZMMZ998YWyQcSzcmqIXvePPBBx8q7OT5e44/vPGhxlqWoBaFA0AiAcZLwKUMGcgTNHQUjIJRMApGAQCa50FTIVNRygAAAABJRU5ErkJggg==","orcid":"","institution":"Washington State University","correspondingAuthor":true,"prefix":"","firstName":"Yool","middleName":"","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2025-07-20 02:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7167173/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7167173/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87523782,"identity":"75916590-1509-4a2a-83a6-a5e4710bbbe8","added_by":"auto","created_at":"2025-07-24 18:41:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":151335,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCore clock regulators exhibit differential associations with overall survival in sarcoma (SARC) patients.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Schematic representation of the core circadian clock machinery, illustrating the transcriptional feedback loop between the CLOCK/BMAL1 activator and PER/CRY repressor dimers. (\u003cstrong\u003eB\u003c/strong\u003e) The table (top) and bar graph (bottom) show the relative Z-scores and corresponding \u003cem\u003ep\u003c/em\u003e-values quantifying the associations between the expression levels of the indicated clock genes and survival risk in SARC, based on data from The Cancer Genome Atlas (TCGA). \u0026nbsp;Positive \u003cem\u003eZ\u003c/em\u003e-scores (\u003cem\u003eZ\u003c/em\u003e \u0026gt; 0), indicating increased risk and worse survival, are shown in red shades, while negative \u003cem\u003eZ\u003c/em\u003e-scores (\u003cem\u003eZ\u003c/em\u003e \u0026lt; 0), indicating a protective effect and better survival, are shown in blue shades. Analyses were conducted using a Cox proportional hazards model, with clinical covariates (age, stage, and tumor purity) optionally included. Statistical significance is denoted as \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05. (\u003cstrong\u003eC–F\u003c/strong\u003e) Kaplan-Meier survival curves for SARC patients based on the expressions of \u003cem\u003eBMAL1\u003c/em\u003e (C), \u003cem\u003eCLOCK\u003c/em\u003e (D), \u003cem\u003eCRY1\u003c/em\u003e (E), and \u003cem\u003eCRY2\u003c/em\u003e(F). Hazard ratios (HR) and \u003cem\u003ep\u003c/em\u003e-values from the Cox model are indicated on each plot. All data were obtained using the Gene_Outcome module in the TIMER database (http://timer.cistrome.org/).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7167173/v1/a1d613f8041ca7ecb507a875.png"},{"id":87523225,"identity":"2e41135b-b2ff-45fd-83aa-7cbe3532d31a","added_by":"auto","created_at":"2025-07-24 18:25:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":213607,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential gene correlations between core clock regulators and CSC/EMT-associated factors in human sarcoma. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Bar graph showing the correlations between the expressions of \u003cem\u003eBMAL1\u003c/em\u003e, \u003cem\u003eCLOCK\u003c/em\u003e, \u003cem\u003eCRY1\u003c/em\u003e, and \u003cem\u003eCRY2\u003c/em\u003e and genes associated with cancer stem cell (CSC) properties (\u003cem\u003eMYC\u003c/em\u003e, \u003cem\u003eSOX2\u003c/em\u003e, \u003cem\u003eCD44\u003c/em\u003e, \u003cem\u003eCD133 \u003c/em\u003e[\u003cem\u003ePROM1\u003c/em\u003e]\u003cem\u003e, NANOG\u003c/em\u003e, \u003cem\u003ePOU5F1\u003c/em\u003e [\u003cem\u003eOCT4\u003c/em\u003e]) or epithelial-mesenchymal transition (EMT) (\u003cem\u003eCDH2\u003c/em\u003e, \u003cem\u003eZEB1\u003c/em\u003e, \u003cem\u003eZEB2\u003c/em\u003e, \u003cem\u003eVIM\u003c/em\u003e) in sarcoma patient samples. The Pearson’s correlation coefficient (R) was used to assess linear relationships between the gene pairs. Significant positive correlations (\u003cem\u003eR\u003c/em\u003e\u0026gt; 0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) are marked with red asterisks, and significant negative correlations (\u003cem\u003eR\u003c/em\u003e \u0026lt; 0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) are marked with blue asterisks. (\u003cstrong\u003eB\u003c/strong\u003e) Representative pairwise gene correlation plots illustrating distinct correlation patterns between \u003cem\u003eBMAL1\u003c/em\u003e, \u003cem\u003eCLOCK\u003c/em\u003e, \u003cem\u003eCRY1\u003c/em\u003e, and \u003cem\u003eCRY2\u003c/em\u003e and selected CSC (\u003cem\u003eSOX2\u003c/em\u003e, \u003cem\u003eCD133\u003c/em\u003e) and EMT (\u003cem\u003eCDH2\u003c/em\u003e) markers, as shown in (A). Significant positive correlations are boxed in bold red (\u003cem\u003eR\u003c/em\u003e \u0026gt; 0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), and significant negative correlations are boxed in bold blue (\u003cem\u003eR\u003c/em\u003e \u0026lt; 0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). The data were sourced from GEPIA (http://gepia.cancer-pku.cn/). TPM; transcripts per million, MYC: proto-oncogene protein c-Myc, STAT3: signal transducer and activator of transcription 3, SOX2: SRY (sex determining region Y)-box 2, CD44: cluster of differentiation 44, CD133: prominin-1, NANOG: nanog homeobox, POU5F1 (OCT4): octamer-binding transcription factor 4, CDH2: cadherin 2 (also known as N-cadherin), ZEB1: zinc finger E-box-binding homeobox 1, ZEB2: zinc finger E-box-binding homeobox 2, VIM: vimentin.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7167173/v1/c5dd70d3740b9d2c7e827910.png"},{"id":87522732,"identity":"eebd7ebe-740b-4be3-b279-628ce5b81a6b","added_by":"auto","created_at":"2025-07-24 18:17:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":252399,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential gene correlations between core clock regulators and onco-metabolic genes in human sarcoma. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Bar graph showing the correlations between the expressions of \u003cem\u003eBMAL1\u003c/em\u003e, \u003cem\u003eCLOCK\u003c/em\u003e, \u003cem\u003eCRY1\u003c/em\u003e, and \u003cem\u003eCRY2\u003c/em\u003e and genes associated with lipid metabolism, glycolysis, or mitochondrial metabolism in sarcoma patient samples. The Pearson’s correlation coefficient (\u003cem\u003eR\u003c/em\u003e) was used to assess linear relationships between the gene pairs. Significant positive correlations (\u003cem\u003eR\u003c/em\u003e \u0026gt; 0, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05) are marked with red asterisks, and significant negative correlations (\u003cem\u003eR\u003c/em\u003e \u0026lt; 0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) are marked with blue asterisks. (\u003cstrong\u003eB\u003c/strong\u003e) Representative pairwise gene correlation plots illustrating distinct correlation patterns between \u003cem\u003eBMAL1\u003c/em\u003e, \u003cem\u003eCLOCK\u003c/em\u003e, \u003cem\u003eCRY1\u003c/em\u003e, and \u003cem\u003eCRY2\u003c/em\u003eand selected glycolytic genes (\u003cem\u003eSCL16A1\u003c/em\u003e, \u003cem\u003eLDHA, HK1\u003c/em\u003e), as shown in (A). Significant positive correlations are boxed in bold red (\u003cem\u003eR\u003c/em\u003e \u0026gt; 0, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05), and significant negative correlations are boxed in bold blue (\u003cem\u003eR\u003c/em\u003e\u0026lt; 0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). The data were sourced from GEPIA (http://gepia.cancer-pku.cn/). TPM; transcripts per million, DGAT1: diacylglycerol O-acyltransferase 1, DGAT2: diacylglycerol O-acyltransferase 2, ACSL4: acyl-CoA synthetase long chain family member 4, FABP7: fatty acid binding protein 7, FASN: fatty acid synthase, CHKA: choline kinase alpha, SREBF1: sterol regulatory element binding transcription factor 1, SLC16A1: solute carrier family 16 member 1 (also known as MCT1, monocarboxylate transporter 1), LDHA: lactate dehydrogenase A, HK1: hexokinase 1, HK2: hexokinase 2, PFKFB3: 6-phosphofructokinase fructose 2,6-bisphosphatase 3, PKM: pyruvate kinase M, HIF1A: hypoxia inducible factor 1 subunit alpha, PRKAA1: protein kinase AMP-activated catalytic subunit alpha 1, PRKAA2: protein kinase AMP-activated catalytic subunit alpha 2, PPARGC1A: peroxisome proliferator activated receptor gamma coactivator 1 alpha, TFAM: mitochondrial transcription factor A, SIRT3: sirtuin 3.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7167173/v1/0f951535615b1f58f898f914.png"},{"id":87523324,"identity":"9f4b1eb2-d861-48b8-980c-a0b53c734bc3","added_by":"auto","created_at":"2025-07-24 18:33:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":253786,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential gene correlations between core clock regulators and metastasis-associated genes in human sarcoma. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Bar graph showing the correlations between the expressions of \u003cem\u003eBMAL1\u003c/em\u003e, \u003cem\u003eCLOCK\u003c/em\u003e, \u003cem\u003eCRY1\u003c/em\u003e, and \u003cem\u003eCRY2\u003c/em\u003e and the indicated pro-metastatic genes in sarcoma patient samples. The Pearson’s correlation coefficient (\u003cem\u003eR\u003c/em\u003e) was used to assess linear relationships between the gene pairs. Significant positive correlations (\u003cem\u003eR\u003c/em\u003e \u0026gt; 0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) are marked with red asterisks, and significant negative correlations (\u003cem\u003eR\u003c/em\u003e \u0026lt; 0, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05) are marked with blue asterisks. (\u003cstrong\u003eB\u003c/strong\u003e) Representative pairwise gene correlation plots illustrating distinct correlation patterns between \u003cem\u003eBMAL1\u003c/em\u003e, \u003cem\u003eCLOCK\u003c/em\u003e, \u003cem\u003eCRY1\u003c/em\u003e, and \u003cem\u003eCRY2\u003c/em\u003e and selected metastasis-associated genes (\u003cem\u003eSKP2\u003c/em\u003e, \u003cem\u003eCCNA2, TK1\u003c/em\u003e), as shown in (A). Significant positive correlations are boxed in bold red (\u003cem\u003eR\u003c/em\u003e \u0026gt; 0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), and significant negative correlations are boxed in bold blue (\u003cem\u003eR\u003c/em\u003e \u0026lt; 0, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05). Data were sourced from GEPIA (http://gepia.cancer-pku.cn/). TPM; transcripts per million, HSP90AB1: heat shock protein 90 alpha family class B member 1, MCM3: minichromosome maintenance complex component 3, JUN: Jun proto-oncogene, AP-1 transcription factor subunit, KIF20A: kinesin family member 20A, SKP2: S-phase kinase associated protein 2, CCNF: cyclin F, CCNA2: cyclin A2, CKS1B: CDC28 protein kinase regulatory subunit 1B, KIF4A: kinesin family member 4A, TRIP13: thyroid hormone receptor interactor 13, TROAP: trophinin associated protein, PHB: prohibitin, CKS2: CDC28 protein kinase regulatory subunit 2, TK1: thymidine kinase 1, CENPM: centromere protein M.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7167173/v1/22002d5089d8a9311d7f85a6.png"},{"id":87523226,"identity":"662df40f-61a7-4219-9235-d6c2db5a5085","added_by":"auto","created_at":"2025-07-24 18:25:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":252648,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential gene correlations between core clock regulators and pro-tumoral immunomodulatory genes in human sarcoma. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Bar graph showing the correlations between the expressions of \u003cem\u003eBMAL1\u003c/em\u003e, \u003cem\u003eCLOCK\u003c/em\u003e, \u003cem\u003eCRY1\u003c/em\u003e, and \u003cem\u003eCRY2\u003c/em\u003e and the indicated pro-tumoral immunomodulatory genes in sarcoma patient samples. The Pearson’s correlation coefficient (\u003cem\u003eR\u003c/em\u003e) was used to assess linear relationships between the gene pairs. Significant positive correlations (\u003cem\u003eR \u003c/em\u003e\u0026gt; 0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) are marked with red asterisks, and significant negative correlations (\u003cem\u003eR\u003c/em\u003e \u0026lt; 0, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05) are marked with blue asterisks. (\u003cstrong\u003eB\u003c/strong\u003e) Representative pairwise gene correlation plots illustrating distinct correlation patterns between \u003cem\u003eBMAL1\u003c/em\u003e, \u003cem\u003eCLOCK\u003c/em\u003e, \u003cem\u003eCRY1\u003c/em\u003e, and \u003cem\u003eCRY2\u003c/em\u003e and selected pro-tumoral immunomodulatory genes (\u003cem\u003eWISP1\u003c/em\u003e, \u003cem\u003eVEGFA, TNC\u003c/em\u003e), as shown in (A). Significant positive correlations are boxed in bold red (\u003cem\u003eR\u003c/em\u003e \u0026gt; 0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), and significant negative correlations are boxed in bold blue (\u003cem\u003eR\u003c/em\u003e \u0026lt; 0, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05). Data were sourced from GEPIA (http://gepia.cancer-pku.cn/). TPM; transcripts per million, WISP1: WNT1 inducible signaling pathway protein 1, MICB: MHC class I polypeptide-related chain B, POSTN: periostin, VEGFA: vascular endothelial growth factor A, TNC: tenascin C, ITGA4: integrin subunit alpha 4, HAS3: hyaluronan synthase 3, CD276: cluster of differentiation 276 (also known as B7-H3), ADORA2B: adenosine receptor A2B.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7167173/v1/52aff607a197493838febdaf.png"},{"id":87522740,"identity":"6d917a20-c83b-4392-b497-4f2b6969c445","added_by":"auto","created_at":"2025-07-24 18:17:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":232589,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential gene correlations between core clock regulators and pro-tumoral immunomodulatory genes in human osteosarcoma. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Table showing the correlations between the expressions of \u003cem\u003eBMAL1\u003c/em\u003e, \u003cem\u003eCLOCK\u003c/em\u003e, \u003cem\u003eCRY1\u003c/em\u003e, and \u003cem\u003eCRY2\u003c/em\u003e and the indicated pro-tumoral immunomodulatory genes in human osteosarcoma (OS) patient samples. The Spearman’s correlation coefficient (\u003cem\u003eR\u003c/em\u003e) was used to evaluate the linear relationships between the selected gene expression pairs in publicly available RNA sequencing data (GSE99671) by calculating the differential expression ratios of the core clock components and immunomodulatory genes in tumor (T) versus normal (N) tissues. Positive correlations (\u003cem\u003eR\u003c/em\u003e \u0026gt; 0) are shown in red shades, while negative correlations (\u003cem\u003eR\u003c/em\u003e \u0026lt; 0) are in blue shades. Significant correlations (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05) are indicated with red or blue \u003cem\u003ep\u003c/em\u003e-values in scientific notation, while non-significant correlations are displayed with black \u003cem\u003ep\u003c/em\u003e-values in general notation. (\u003cstrong\u003eB, C\u003c/strong\u003e) Representative pairwise gene correlation plots illustrating distinct correlation patterns between \u003cem\u003eCLOCK\u003c/em\u003e (B) or \u003cem\u003eCRY1\u003c/em\u003e(C) and the pro-tumoral immunomodulatory genes shown in (A). Significant positive correlations are boxed in bold red (\u003cem\u003eR\u003c/em\u003e \u0026gt; 0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), and significant negative correlations are boxed in bold blue (\u003cem\u003eR\u003c/em\u003e\u0026lt; 0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). WISP1: WNT1 inducible signaling pathway protein 1, MICB: MHC class I polypeptide-related chain B, POSTN: periostin, VEGFA: vascular endothelial growth factor A, TNC: tenascin C, ITGA4: integrin subunit alpha 4, HAS3: hyaluronan synthase 3, CD276: cluster of differentiation 276 (also known as B7-H3), ADORA2B: adenosine receptor A2B.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7167173/v1/eadaa178221452a62e052c13.png"},{"id":87522739,"identity":"39f9ca4b-ea4b-4dbd-8aae-d082b803ae08","added_by":"auto","created_at":"2025-07-24 18:17:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":262960,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCRY1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e knockdown downregulates the expression of CSC/EMT markers and associated pro-tumoral and immunosuppressive genes in human osteosarcoma. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eHeat map showing the effects of \u003cem\u003eCRY1\u003c/em\u003e knockdown on the expressions of CSC/EMT, pro-metastatic, oncogenic metabolic pathway (lipogenesis, glycolysis), cell cycle progression/suppression, and immune suppression/evasion genes. This analysis was performed using publicly available RNA-seq datasets (GSE144961) from human carcinoma cells engineered to stably express doxycycline inducible sh-CRY1 constructs [58]. (\u003cstrong\u003eB\u003c/strong\u003e) qPCR analysis showing the effects of siRNA-mediated knockdown of \u003cem\u003eCLOCK\u003c/em\u003e (\u003cem\u003esi-CLOCK\u003c/em\u003e) or \u003cem\u003eCRY1\u003c/em\u003e (\u003cem\u003esi-CRY1\u003c/em\u003e) on the expressions of selected genes in 143B OS CSCs, compared to control siRNA-treated cells (\u003cem\u003esi-CTL\u003c/em\u003e). Statistical significance was determined by one-way ANOVA followed by Tukey’s multiple comparisons test (*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0005, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001; n.s., not significant).\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7167173/v1/5d85d993d8fd206ff414672d.png"},{"id":87523227,"identity":"d5e0ee9f-b196-4c10-9b2b-7b6c1f103dc9","added_by":"auto","created_at":"2025-07-24 18:25:23","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":476625,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCore clock regulators differentially influence immune cell infiltration in the tumor-immune microenvironment of human osteosarcoma. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Table showing the associations between the expressions of \u003cem\u003eBMAL1\u003c/em\u003e, \u003cem\u003eCLOCK\u003c/em\u003e, \u003cem\u003eCRY1\u003c/em\u003e, and \u003cem\u003eCRY2\u003c/em\u003e and immune cell (CD4\u003csup\u003e+\u003c/sup\u003e T cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells, dendritic cells, macrophages, Tregs, CAFs, and MDSCs) infiltration in human sarcoma (SARC). (\u003cstrong\u003eB\u003c/strong\u003e) Representative Kaplan-Meier (KM) curves illustrating the significantly positive (boxed in red) and negative (boxed in blue) correlations between the expression of \u003cem\u003eCLOCK\u003c/em\u003e or \u003cem\u003eCRY1\u003c/em\u003e and the infiltration of the indicated immunomodulatory cells (CD4\u003csup\u003e+\u003c/sup\u003e T cells, dendritic cells, CAFs, MDSCs) in SARC. The purity-adjusted Spearman's rank correlation test was applied to determine both the \u003cem\u003ep\u003c/em\u003e-values and partial correlation values. Significantly positive and negative correlations were determined based on purity-adjusted Spearman's rho values (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). The data were sourced from the TIMER database (http://timer.cistrome.org/). CD8\u003csup\u003e+ \u003c/sup\u003eT cells: cytotoxic t lymphocytes, CD4\u003csup\u003e+ \u003c/sup\u003eT cells: helper T lymphocytes, Tregs: regulatory T cells, CAFs: cancer-associated fibroblasts, MDSCs: myeloid-derived suppressor cells.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7167173/v1/00411f5fa7e500994b34c9f3.png"},{"id":87522340,"identity":"e9bd622f-76d6-489c-bf3a-d8d2d31c2a12","added_by":"auto","created_at":"2025-07-24 18:09:23","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":508446,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCore circadian clock genes differentially associate with immune activity, T cell exclusion, and tumor purity in human sarcoma. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Table summarizing the correlations between \u003cem\u003eBMAL1\u003c/em\u003e, \u003cem\u003eCLOCK\u003c/em\u003e, \u003cem\u003eCRY1\u003c/em\u003e, and \u003cem\u003eCRY2 \u003c/em\u003eexpression and key immune functional parameters, including interferon gamma (\u003cem\u003eIFNG\u003c/em\u003e) expression, T cell dysfunction, T cell exclusion, and tumor purity, in human sarcoma (SARC). (\u003cstrong\u003eB\u003c/strong\u003e) Representative Kaplan-Meier (KM) curves illustrating significantly positive (boxed in red) and negative (boxed in blue) correlations between the expression of \u003cem\u003eCLOCK\u003c/em\u003e or \u003cem\u003eCRY1\u003c/em\u003e and the indicated immune parameter scores (\u003cem\u003eIFNG\u003c/em\u003e, T cell Dysfunction, T Cell Exclusion, and Tumor Purity) in SARC are shown. The purity-adjusted Spearman's rank correlation test was applied to determine both the \u003cem\u003ep\u003c/em\u003e-values and partial correlation values. Significantly positive and negative correlations were determined based on purity-adjusted Spearman's rho values (\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05). The data were sourced from the TIMER database (http://timer.cistrome.org/). T cell dysfunction: expression-based score indicating T cell exhaustion, T cell exclusion: spatial exclusion of cytotoxic T cells from the tumor parenchyma, tumor purity: the proportion of tumor cells relative to immune and stromal components.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7167173/v1/a7186e0b07ee604e0dfdb114.png"},{"id":93190178,"identity":"18833ec8-4c2e-41fc-a2f5-be4f01107c97","added_by":"auto","created_at":"2025-10-10 03:54:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3178471,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7167173/v1/48470b7a-69b1-4aad-8577-8bfd06cf760d.pdf"},{"id":87522331,"identity":"ac9f1f75-a5ad-4eb7-9028-21334d9e835a","added_by":"auto","created_at":"2025-07-24 18:09:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1034008,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialBMCcancer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7167173/v1/efc8983eace308fe56442816.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Divergent Roles of Circadian Regulators CLOCK and CRY1 in Driving Pro-Tumoral Stemness and Immunoevasion in Osteosarcoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteosarcoma (OS) is the most common malignant bone tumor in children and adolescents and remains a clinical challenge due to its high heterogeneity, metastatic potential, and resistance to therapy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Accumulating evidence implicates cancer stem cells (CSCs), a subpopulation with self-renewal, plasticity, and tumor-initiating properties, in driving therapeutic failure and disease progression in OS and other cancers [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These stem-like cells interface dynamically with metabolic cues, extracellular matrix (ECM) remodeling, and immune surveillance mechanisms, shaping a tumor microenvironment (TME) that fosters immune evasion and recurrence [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the upstream regulators orchestrating these tumor-intrinsic and microenvironmental interactions remain incompletely understood.\u003c/p\u003e\u003cp\u003eThe circadian clock is a cell-autonomous timing system that regulates 24-hour molecular rhythms through interconnected transcriptional feedback loops between the positive regulators brain and muscle ARNT-like 1 (BMAL1) and circadian locomotor output cycles kaput (CLOCK) and their repressors, period 1 and 2 (PER1/2) and cryptochrome 1 and 2 (CRY1/2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). This molecular clockwork regulates key physiological processes, including differentiation, proliferation, and metabolism, shaping normal and cancer cell behavior [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. We have recently shown that human OS cells (U-2OS, 143B), as well as melanoma cells, exhibit functional circadian rhythms [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and circadian disruption impacts tumor growth by affecting cell cycle [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and anti-tumor immune responses [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Emerging preclinical cancer model studies suggest that circadian clock components regulate their targeted CSCs and targeted cancer therapy [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, most of these studies, including our recent work in OS [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], have primarily focused on the roles of circadian regulators in tumor-intrinsic mechanisms, such as cell proliferation and metabolic regulation [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIncreasing evidence suggests that tumor cells, particularly CSCs, engage in dynamic crosstalk through diverse molecular and cellular interactions with non-tumor stromal and immune cells within the tumor microenvironment (TME). This crosstalk plays a critical role in influencing tumor progression and treatment response and is closely associated with overall survival in OS [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The TME is composed of a heterogeneous mix of anti-tumor immune cells, such as CD8⁺ cytotoxic T lymphocytes, CD4⁺ helper T cells, and dendritic cells, as well as pro-tumor immunosuppressive cells, including regulatory T cells (Tregs), cancer-associated fibroblasts (CAFs), and myeloid-derived suppressor cells (MDSCs) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, it remains unclear how circadian regulators contribute to CSC-driven remodeling of the TME during OS progression and prognosis.\u003c/p\u003e\u003cp\u003eIn this study, integrative gene and immune profiling revealed that CLOCK and CRY1 promote OS progression by activating CSC/EMT programs, pro-tumoral metabolism (e.g., glycolysis, lipid synthesis), and immunosuppressive microenvironmental remodeling. Functional validation in 143B OS CSC models confirmed that CLOCK and CRY1, but not BMAL1 or CRY2, maintain the expression of oncogenic and immunomodulatory factors. Correspondingly, CLOCK/CRY1 expression was linked to increased CAF and MDSC infiltration, reduced numbers of CD4⁺ T cells and dendritic cells, impaired T cell function, greater exclusion, and higher tumor purity. BMAL1 and CRY2 expression showed minimal or opposite correlations. These findings suggest that circadian regulators distinctly shape OS prognosis through both tumor-intrinsic and immune-evasive mechanisms.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn a recent study, we showed that core clock factors (BMAL1, CLOCK, CRY1, CRY2) play critical roles in regulating OS stemness and invasiveness [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Building on these findings, we conducted additional outcome analyses using the Tumor Immune Estimation Resource (TIMER), a web-based platform for comprehensive immune-genomic analysis across multiple cancer types from The Cancer Genome Atlas (TCGA), with a specific focus on sarcoma (SARC) patients. The results showed that elevated \u003cem\u003eCLOCK\u003c/em\u003e and \u003cem\u003eCRY1\u003c/em\u003e expression were significantly associated with increased risk and poorer overall survival (\u003cem\u003eZ\u003c/em\u003e \u0026gt; 0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), while higher \u003cem\u003eBMAL1\u003c/em\u003e or \u003cem\u003eCRY2\u003c/em\u003e expression correlated with minimal or reduced risk and improved survival outcomes (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Similarly, Kaplan-Meier survival curves for SARC displayed distinct prognostic patterns, with \u003cem\u003eCLOCK\u003c/em\u003e (HR = 1.2) and \u003cem\u003eCRY1\u003c/em\u003e (HR = 1.35) showing overall higher hazard ratios (HR) than \u003cem\u003eBMAL1\u003c/em\u003e (HR = 0.95) and \u003cem\u003eCRY2\u003c/em\u003e (HR = 0.8) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC–F). This suggests that roles beyond the tumor-intrinsic circadian functions of core clock genes may contribute to OS patient survival.\u003c/p\u003e\u003cp\u003eCSCs and their EMT properties are increasingly recognized as key determinants of therapy response and clinical outcomes [\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e–\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. To explore the potential link between core clock gene expression and stemness in SARC, we conducted gene co-expression analysis with established CSC/EMT markers using GEPIA. \u003cem\u003eCLOCK\u003c/em\u003e and \u003cem\u003eCRY1\u003c/em\u003e showed significant or moderate positive correlations with several stemness and EMT markers, including \u003cem\u003eCD133\u003c/em\u003e (\u003cem\u003eR\u003c/em\u003e = 0.13, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 for both) and \u003cem\u003eCDH2\u003c/em\u003e (\u003cem\u003eR\u003c/em\u003e = 0.19 and 0.12; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 and \u0026lt; 0.05, respectively), with particularly strong associations observed for \u003cem\u003eZEB1\u003c/em\u003e (\u003cem\u003eR\u003c/em\u003e = 0.44 and 0.45; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001 for both) and \u003cem\u003eZEB2\u003c/em\u003e (\u003cem\u003eR\u003c/em\u003e = 0.22 and 0.36; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 for both). In contrast, \u003cem\u003eBMAL1\u003c/em\u003e and \u003cem\u003eCRY2\u003c/em\u003e exhibited relatively weaker or even negative correlations with markers such as \u003cem\u003eMYC\u003c/em\u003e (\u003cem\u003eCRY2\u003c/em\u003e: \u003cem\u003eR\u003c/em\u003e = − 0.15, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), \u003cem\u003eCD44\u003c/em\u003e (CRY1: \u003cem\u003eR\u003c/em\u003e = − 0.06, \u003cem\u003ep\u003c/em\u003e = not significant), and \u003cem\u003eVIM\u003c/em\u003e (CRY2: \u003cem\u003eR\u003c/em\u003e = − 0.13, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), although \u003cem\u003eSOX2\u003c/em\u003e showed a positive association with both \u003cem\u003eBMAL1\u003c/em\u003e and \u003cem\u003eCRY1\u003c/em\u003e (\u003cem\u003eR\u003c/em\u003e = 0.19 and 0.18; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 and \u0026lt; 0.01, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B; \u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). These patterns support the notion that \u003cem\u003eCLOCK\u003c/em\u003e and \u003cem\u003eCRY1\u003c/em\u003e are more strongly linked to transcriptional programs driving CSC maintenance and EMT, which is consistent with their association with poor patient prognosis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn line with the correlations observed with CSC/EMT marker genes, \u003cem\u003eCLOCK\u003c/em\u003e and \u003cem\u003eCRY1\u003c/em\u003e also showed a strong positive association with lipid metabolism factors (\u003cem\u003eACSL4\u003c/em\u003e, \u003cem\u003eR\u003c/em\u003e = 0.18 and 0.13; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 and \u0026lt; 0.05, respectively; \u003cem\u003eFASN\u003c/em\u003e for \u003cem\u003eCRY1\u003c/em\u003e, \u003cem\u003eR\u003c/em\u003e = 0.14, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) and, in particular, glycolysis factors (\u003cem\u003eSLC2A1\u003c/em\u003e [a.k.a. \u003cem\u003eGLUT1\u003c/em\u003e], \u003cem\u003eSLC16A1\u003c/em\u003e [a.k.a. \u003cem\u003eMCT1\u003c/em\u003e], and \u003cem\u003eHK1\u003c/em\u003e for \u003cem\u003eCLOCK\u003c/em\u003e; \u003cem\u003eSLC16A1\u003c/em\u003e and \u003cem\u003eHK1\u003c/em\u003e for \u003cem\u003eCRY1\u003c/em\u003e; \u003cem\u003eR\u003c/em\u003e = 0.30–0.38, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 or lower), which are well-known to be preferred by cells with CSC/EMT property. This aligns with previous findings showing that CSCs and EMT cells rely on glycolysis (Warburg effect) for rapid ATP production, for biosynthetic precursors, and to adapt to hypoxic tumor environments [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In contrast, \u003cem\u003eBMAL1\u003c/em\u003e showed only small or modest positive correlations with these metabolic factors (e.g., \u003cem\u003eACSL4\u003c/em\u003e, \u003cem\u003eFASN\u003c/em\u003e, \u003cem\u003eSLC2A1\u003c/em\u003e, \u003cem\u003eLDHA\u003c/em\u003e, \u003cem\u003ePFKFB3\u003c/em\u003e; \u003cem\u003eR\u003c/em\u003e \u0026gt; 0, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05), while \u003cem\u003eCRY2\u003c/em\u003e was moderately or significantly negatively correlated with key lipogenic and glycolytic enzymes, such as \u003cem\u003eHK2\u003c/em\u003e and \u003cem\u003ePKM\u003c/em\u003e (\u003cem\u003eR\u003c/em\u003e = − 0.21, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B; \u003cb\u003eFig. S2\u003c/b\u003e). Nonetheless, all core clock genes were generally positively associated with mitochondrial metabolism genes, such as \u003cem\u003ePRKAA1\u003c/em\u003e, \u003cem\u003eTFAM\u003c/em\u003e, and \u003cem\u003eSIRT3\u003c/em\u003e (\u003cem\u003eR\u003c/em\u003e = 0.29–0.52, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 or lower), suggesting a shared role in supporting mitochondrial function.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNotably, a recent single-cell RNA-sequencing (scRNA-seq) study of patient OS samples [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] identified 15 hub genes associated with metastasis, a process known to be facilitated by CSC/EMT characteristics. Further correlation analysis revealed that \u003cem\u003eCLOCK\u003c/em\u003e and \u003cem\u003eCRY1\u003c/em\u003e were moderately to significantly positively correlated with several metastasis-associated genes, including \u003cem\u003eSKP2\u003c/em\u003e (\u003cem\u003eCLOCK\u003c/em\u003e: \u003cem\u003eR\u003c/em\u003e = 0.35; \u003cem\u003eCRY1\u003c/em\u003e: \u003cem\u003eR\u003c/em\u003e = 0.37), \u003cem\u003eMCM3\u003c/em\u003e (\u003cem\u003eCLOCK\u003c/em\u003e: \u003cem\u003eR\u003c/em\u003e = 0.38; \u003cem\u003eCRY1\u003c/em\u003e: \u003cem\u003eR\u003c/em\u003e = 0.42), and \u003cem\u003eCCNA2\u003c/em\u003e (\u003cem\u003eCLOCK\u003c/em\u003e: \u003cem\u003eR\u003c/em\u003e = 0.34; \u003cem\u003eCRY1\u003c/em\u003e: \u003cem\u003eR\u003c/em\u003e = 0.45), all with \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.00001 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B; \u003cb\u003eFig. S3\u003c/b\u003e). In contrast, \u003cem\u003eBMAL1\u003c/em\u003e showed no significant associations with most of these genes, while \u003cem\u003eCRY2\u003c/em\u003e exhibited moderate to significant negative correlations with multiple genes, including \u003cem\u003eCENPM\u003c/em\u003e (\u003cem\u003eR\u003c/em\u003e = − 0.20, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), \u003cem\u003eTK1\u003c/em\u003e (\u003cem\u003eR\u003c/em\u003e = − 0.19, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), \u003cem\u003eCKS2\u003c/em\u003e (\u003cem\u003eR\u003c/em\u003e = − 0.19, \u003cem\u003ep \u0026lt;\u003c/em\u003e 0.01), and \u003cem\u003ePHB\u003c/em\u003e (\u003cem\u003eR\u003c/em\u003e = − 0.15, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Moreover, since many metastasis-associated genes are involved in cell cycle regulation, we further analyzed canonical cell cycle progression genes and found that \u003cem\u003eCLOCK\u003c/em\u003e and \u003cem\u003eCRY1\u003c/em\u003e were moderately to significantly positively correlated with nearly all of them, including \u003cem\u003eCCNA1, CCNA2\u003c/em\u003e, \u003cem\u003eCCNB1\u003c/em\u003e, \u003cem\u003eCCNB2\u003c/em\u003e, \u003cem\u003eCCND1\u003c/em\u003e, \u003cem\u003eCCND2\u003c/em\u003e, \u003cem\u003eCCND3\u003c/em\u003e, \u003cem\u003eCCNE1\u003c/em\u003e, \u003cem\u003eCCNE2\u003c/em\u003e, \u003cem\u003eCDK2\u003c/em\u003e, \u003cem\u003eCDK4\u003c/em\u003e, \u003cem\u003eCDK6\u003c/em\u003e, \u003cem\u003eMKI67\u003c/em\u003e, and \u003cem\u003eMTOR\u003c/em\u003e (\u003cem\u003eR\u003c/em\u003e \u0026gt; 0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 to \u0026lt; 1e − 7) (\u003cb\u003eFig. S4A, B\u003c/b\u003e). In contrast, BMAL1 showed weak or negligible correlations with most of these genes, while \u003cem\u003eCRY2\u003c/em\u003e exhibited either weak (\u003cem\u003eR\u003c/em\u003e \u0026gt; 0 or \u003cem\u003eR\u003c/em\u003e \u0026lt; 0, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05) or significant negative correlations (e.g., \u003cem\u003eCCNB\u003c/em\u003e: \u003cem\u003eR\u003c/em\u003e = − 0.17, \u003cem\u003ep\u003c/em\u003e = 0.0073) with several cell cycle regulators (\u003cb\u003eFig. S4A, B\u003c/b\u003e). Notably, tumor suppressor and cell cycle inhibitory genes (e.g., \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003ePTEN\u003c/em\u003e, \u003cem\u003eRB1\u003c/em\u003e, \u003cem\u003eCDKN1A\u003c/em\u003e, \u003cem\u003eCDKN1B\u003c/em\u003e, \u003cem\u003eCDKN2A\u003c/em\u003e, \u003cem\u003eCDKN2B\u003c/em\u003e) did not show consistent or clock gene-specific correlation patterns (\u003cb\u003eFig. S4A, B\u003c/b\u003e). These findings suggest that \u003cem\u003eCLOCK\u003c/em\u003e and \u003cem\u003eCRY1\u003c/em\u003e, but not \u003cem\u003eBMAL1\u003c/em\u003e and \u003cem\u003eCRY2\u003c/em\u003e, may contribute to a tumor-intrinsic gene expression program driven by CSC/EMT pathways, promoting proliferation and metastasis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBeyond tumor-intrinsic pathways, accumulating evidence suggests that cancer cells, particularly CSC populations, remodel the tumor microenvironment (TME) to promote immune evasion and suppression by releasing ECM-associated proteins, immunomodulatory molecules, and soluble factors that inhibit T cell responses and recruit immunosuppressive cells, such as Tregs, MDSCs, and CAFs [\u003cspan additionalcitationids=\"CR34 CR35 CR36\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e–\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. To explore the relationship between clock gene expression and CSC-driven TME remodeling in OS, we performed correlation analyses and found that \u003cem\u003eCLOCK\u003c/em\u003e and \u003cem\u003eCRY1\u003c/em\u003e were mildly to significantly positively correlated (\u003cem\u003eR\u003c/em\u003e \u0026gt; 0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) with several key mediators of an immunosuppressive and immune-evasive TME (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B; \u003cb\u003eFig. S5\u003c/b\u003e). These genes included chemokines and cytokines (e.g., \u003cem\u003eWISP1\u003c/em\u003e [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], \u003cem\u003ePOSTN\u003c/em\u003e [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], \u003cem\u003eVEGFA\u003c/em\u003e [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e–\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]), ECM-associated factors (e.g., \u003cem\u003eTNC\u003c/em\u003e [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], \u003cem\u003eHAS3\u003c/em\u003e [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]), immune-interacting surface molecules (e.g., \u003cem\u003eITGA4\u003c/em\u003e [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], \u003cem\u003eADORA2B\u003c/em\u003e [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] ), and immune checkpoint molecules (e.g., \u003cem\u003eCD276\u003c/em\u003e [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]). In contrast, \u003cem\u003eBMAL1\u003c/em\u003e and \u003cem\u003eCRY2\u003c/em\u003e showed weak or non-significant correlations (\u003cem\u003eR\u003c/em\u003e \u0026gt; 0 or \u003cem\u003eR\u003c/em\u003e \u0026lt; 0, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05) with most of these genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B; \u003cb\u003eFig. S5\u003c/b\u003e). Similarly, further correlation analysis using previously published RNA sequencing data (GSE99671) from 18 OS patients [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] revealed that \u003cem\u003eCLOCK\u003c/em\u003e and \u003cem\u003eCRY1\u003c/em\u003e showed significantly positive correlation patterns with the majority of pro-tumorigenic immunomodulatory genes, with \u003cem\u003eCRY1\u003c/em\u003e being associated with a greater number of genes than \u003cem\u003eCLOCK\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B). In contrast, \u003cem\u003eBMAL1\u003c/em\u003e and, especially, \u003cem\u003eCRY2\u003c/em\u003e exhibited predominantly non-significant or negative correlations with these genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA; \u003cb\u003eFig. S6A, B\u003c/b\u003e). These findings suggest that, unlike BMAL1 and CRY2, CLOCK and CRY1 engage distinct gene expression networks associated with pro-tumoral immunosuppressive factors and tumor-intrinsic CSC/EMT pathways, with CRY1 showing broader gene correlations than CLOCK.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo explore CRY1's distinct role in shaping a pro-tumorigenic and immunomodulatory gene expression landscape, we analyzed publicly available RNA sequencing data from human carcinoma cells with doxycycline-inducible \u003cem\u003eCRY1\u003c/em\u003e knockdown mediated by short hairpin RNA (\u003cem\u003esh-CRY1\u003c/em\u003e) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Remarkably, \u003cem\u003eCRY1\u003c/em\u003e knockdown led to extensive downregulation of many of the aforementioned CSC/EMT markers, associated lipogenic and glycolytic factors, metastasis-promoting factors, cell cycle and tumor progression elements, and immunosuppressive factors compared to control cells (\u003cem\u003esh-CTL\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). These findings are consistent with our own \u003cem\u003eCRY1\u003c/em\u003e RNAi results, which showed marked reductions in the expressions of CSC/EMT markers and lipid metabolism genes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. To further validate CRY1's role alongside CLOCK in OS, we employed specific siRNAs targeting either \u003cem\u003eCRY1\u003c/em\u003e or \u003cem\u003eCLOCK\u003c/em\u003e in xenograft-derived 143B OS CSCs, as established in our recent study [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] (\u003cb\u003eFig. S7\u003c/b\u003e). Consistent with the transcriptome data (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), our qPCR analysis revealed that knockdown of \u003cem\u003eCRY1\u003c/em\u003e, as well as \u003cem\u003eCLOCK\u003c/em\u003e, moderately to significantly reduced the expression of several oncogenic, metabolic, and immunomodulatory genes (e.g., \u003cem\u003eMYC, SLC16A1, HK1, TNC, CD276, ITGA4, WISP1, POSTN, VEGFA\u003c/em\u003e), with the exception of \u003cem\u003eVEGFA\u003c/em\u003e in \u003cem\u003eCRY1\u003c/em\u003e knockdown cells and \u003cem\u003ePOSTN\u003c/em\u003e in \u003cem\u003eCLOCK\u003c/em\u003e knockdown cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Collectively, these data reinforce our earlier gene association findings (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e–\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), highlighting a pivotal role for CRY1 and CLOCK in driving a pro-tumorigenic transcriptional landscape through both intrinsic and extrinsic immunomodulatory mechanisms in OS.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo explore the contribution of core circadian clock genes to tumor immunity, we examined the relationship between their expression and the presence of tumor-infiltrating immune cells (TIICs) [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The expressions of \u003cem\u003eCLOCK\u003c/em\u003e and \u003cem\u003eCRY1\u003c/em\u003e were significantly negatively (Spearman's Rho (\u003cem\u003eρ\u003c/em\u003e) \u0026lt; 0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) correlated with infiltration of CD4\u003csup\u003e+\u003c/sup\u003e T cells [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] and dendritic cells, key cell types essential for antigen presentation and effective T cell activation [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, B). Moreover, both genes showed significant positive correlations (\u003cem\u003eρ\u003c/em\u003e \u0026gt; 0, p \u0026lt; 0.05) with MDSC infiltration and CAF abundance, two critical components of the tumor stroma known to support immunosuppression and tumor progression [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, B), exhibiting clear immunosuppressive and stromal remodeling signatures. Notably, similar immunosuppressive infiltration patterns were observed for key CSC/EMT-associated factors (e.g., \u003cem\u003eSOX2\u003c/em\u003e, \u003cem\u003eCD133\u003c/em\u003e, \u003cem\u003eZEB1\u003c/em\u003e, \u003cem\u003eSLC16A1\u003c/em\u003e) and immunomodulatory genes (e.g., \u003cem\u003eWISP1\u003c/em\u003e, \u003cem\u003eVEGFA\u003c/em\u003e, \u003cem\u003eTNC\u003c/em\u003e) in subsequent immune profiling analyses, further supporting a functional link between \u003cem\u003eCLOCK\u003c/em\u003e/\u003cem\u003eCRY1\u003c/em\u003e expression and a pro-tumoral, immune-evasive tumor microenvironment (\u003cb\u003eFig. S8\u003c/b\u003e). However, \u003cem\u003eBMAL1\u003c/em\u003e expression showed no significant associations with any major immune or stromal cell types, suggesting a limited role in modulating the tumor immune landscape (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). In addition, \u003cem\u003eCRY2\u003c/em\u003e was positively associated with CAFs (\u003cem\u003eρ\u003c/em\u003e = 0.235) but negatively correlated with MDSCs (\u003cem\u003eρ\u003c/em\u003e = −0.157) and lacked significant correlations with other immune cell subsets, suggesting a stromal-modulatory but less immunosuppressive role (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further evaluate the functional and spatial impact of circadian genes on anti-tumor immunity, we analyzed their associations with key immune activation and exclusion markers [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, B). \u003cem\u003eCLOCK\u003c/em\u003e and \u003cem\u003eCRY1\u003c/em\u003e showed significant negative correlations (\u003cem\u003eρ\u003c/em\u003e \u0026lt; 0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) with the expression of interferon-gamma (\u003cem\u003eIFNG\u003c/em\u003e), a cytokine produced by activated T cells and natural killer cells, and T cell dysfunction scores, indicating impaired T cell activation and increased exhaustion, which is characteristic of “immune-cold” tumors [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Moreover, both genes were significantly positively correlated (\u003cem\u003eρ\u003c/em\u003e \u0026gt; 0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) with T cell exclusion scores and tumor purity, suggesting enhanced immune evasion and reduced immune infiltration (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, B), likely mediated by CAFs and MDSCs, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. In contrast, \u003cem\u003eBMAL1\u003c/em\u003e and \u003cem\u003eCRY2\u003c/em\u003e exhibited weaker or more selective associations, with \u003cem\u003eCRY2\u003c/em\u003e showing links to stromal exclusion but not direct immunosuppression (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). Together, these findings highlight distinct immunomodulatory roles of circadian clock genes, with CLOCK and CRY1 contributing to an immune-evasive program in OS.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWhile BMAL1 and CLOCK form the canonical positive arm of the circadian transcriptional loop, and CRY1 and CRY2 act as repressors, our integrative genomic and immune profiling of human sarcoma reveals a functional divergence between these pairs in sarcoma. In our analyses, high \u003cem\u003eCLOCK\u003c/em\u003e and \u003cem\u003eCRY1\u003c/em\u003e expression is consistently linked to poor survival, characterized by elevated expression of CSC/EMT markers, metabolic and metastatic factors, and immunosuppressive signals, as well as reduced anti-tumor immune infiltration and activity. In contrast, \u003cem\u003eBMAL1\u003c/em\u003e and \u003cem\u003eCRY2\u003c/em\u003e expression exhibits neutral or protective associations with patient survival and shows minimal impact on tumor-intrinsic and immune microenvironmental signaling pathways (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e–\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). This uncoupling of function suggests that CLOCK and CRY1 may form a distinct pro-tumorigenic axis, operating independently from the canonical BMAL1/CLOCK activator and CRY1/2 feedback complexes.\u003c/p\u003e\u003cp\u003eTumorigenic CSC populations, particularly glioblastoma stem cells (GSCs) and leukemia stem cells (LSCs), have been shown to exhibit robust circadian rhythms in culture, with both CLOCK and BMAL1 proven to be essential for their survival in brain and blood cancers [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. This aligns with our recent 3D spheroid culture and cell migration/invasion analyses that demonstrated a similar down-regulation of CSC/EMT marker genes (e.g., \u003cem\u003ePOU5F1\u003c/em\u003e [\u003cem\u003eOCT4\u003c/em\u003e], \u003cem\u003eCDH2\u003c/em\u003e, \u003cem\u003eZEB1\u003c/em\u003e) following knockdown of \u003cem\u003eCLOCK\u003c/em\u003e or \u003cem\u003eBMAL1\u003c/em\u003e by RNAi in human OSC model studies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Notably, the divergent functions of these two dimeric partner molecules are reminiscent of previous studies suggesting that (1) CLOCK and BMAL1 have differential binding affinities and interactions with other transcriptional regulators, leading to heterogeneous transcriptional outputs [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]; (2) CLOCK possesses intrinsic histone acetyltransferase (HAT) activity [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], enabling it to act as a pioneer-like factor that directly remodels chromatin and facilitates transcriptional activation, whereas BMAL1 primarily functions as a rhythmic scaffold that integrates metabolic cues and coordinates transcriptional timing through interactions with repressors, such as CRY and PER [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]; (3) their distinct post-translational modifications, such as stable acetylation of CLOCK versus rhythmic acetylation and sumoylation of BMAL1 [\u003cspan additionalcitationids=\"CR67 CR68\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e–\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], further refine their contributions to circadian transcriptional dynamics. Such divergence of these two factors may be particularly relevant in cancer contexts, such as sarcoma, where CLOCK may promote tumorigenic transcriptional programs through direct chromatin remodeling, while BMAL1 may play a more nuanced role by interacting with other transcriptional and post-translational regulators.\u003c/p\u003e\u003cp\u003eA similar functional divergence between the circadian repressor partners CRY1 and CRY2 has been suggested in previous studies, suggesting that CRY1 and CRY2 may exert distinct, even opposing, roles, particularly in the regulation of MYC, a master oncogenic regulator [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. CRY2 facilitates MYC degradation via the SCF (FBXL3) complex, thereby suppressing cell proliferation [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], whereas CRY1 lacks this function and is even associated with MYC upregulation. In a more recent study, MYC expression was shown to be downregulated in CRY1-deficient embryonic stem cells (ESCs), with loss of CRY1 impairing self-renewal, colony formation, and glycolytic reprogramming, reflecting stemness-associated deficits [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. These evidence resonate with our previous and present data indicating marked downregulation of \u003cem\u003eMYC\u003c/em\u003e, \u003cem\u003eOCT4\u003c/em\u003e, \u003cem\u003eCDH3\u003c/em\u003e, and \u003cem\u003eZEB1\u003c/em\u003e, and pro-tumoral immunomodulatory genes upon \u003cem\u003eCRY1\u003c/em\u003e knockdown (shRNA/siRNA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These findings also underscore a previously underrecognized functional divergence between CRY1 and CRY2 in cancer biology, highlighting CRY1 as a potential oncogenic driver of stemness and metabolic reprogramming in osteosarcoma.\u003c/p\u003e\u003cp\u003eBeyond their roles in intrinsic gene regulation, our data reveal that \u003cem\u003eCLOCK\u003c/em\u003e and \u003cem\u003eCRY1\u003c/em\u003e expression correlates with multiple hallmarks of immune evasion. Specifically, both factors are associated with increased abundance of CAFs and MDSCs, along with reduced infiltration of CD4⁺ T cells and dendritic cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Additionally, \u003cem\u003eCLOCK\u003c/em\u003e and \u003cem\u003eCRY1\u003c/em\u003e exhibit negative correlations with IFNG expression and T cell function markers, and positive correlations with T cell exclusion scores and tumor purity (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), collectively indicating a shift toward an “immune-cold” TME [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. In contrast, \u003cem\u003eBMAL1\u003c/em\u003e and \u003cem\u003eCRY2\u003c/em\u003e show minimal or context-dependent associations, with CRY2 displaying stromal involvement but limited evidence of immunosuppressive activity. These immune profiling results align with our transcriptomic and functional analyses (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), suggesting that CLOCK and CRY1 may contribute to immune resistance in OS by regulating ECM components (e,g., \u003cem\u003eTNC\u003c/em\u003e, \u003cem\u003eITGA4\u003c/em\u003e), immunomodulatory chemokines (e.g., \u003cem\u003eWISP1\u003c/em\u003e, \u003cem\u003ePOSTN\u003c/em\u003e), and immune checkpoint molecules such as CD276, likely through cooperation with CAFs and MDSCs. Together, these findings support a model in which CLOCK and CRY1 serve as tumor-intrinsic orchestrators of immune exclusion and dysfunction. We speculate that these mechanisms may represent a broader paradigm in OS and potentially other sarcomas, and propose that combinatorial strategies targeting CLOCK/CRY1 in conjunction with immune checkpoint inhibitors merit further investigation.\u003c/p\u003e\u003cp\u003eIn summary, our findings reveal the distinct roles of core circadian regulators in OS prognosis. CLOCK and CRY1 emerge as critical drivers of tumor aggressiveness through their modulation of CSC properties, metabolic pathways, and immune microenvironment dynamics. Furthermore, our results suggest that selective targeting of CLOCK and/or CRY1 may provide therapeutic benefits in improving outcomes for patients with OS. Further studies are warranted to elucidate the precise mechanisms by which these circadian regulators influence tumor biology and to explore their potential as therapeutic targets in OS.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003eSurvival analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe prognostic significance of clock gene expression (\u003cem\u003eBMAL1\u003c/em\u003e, \u003cem\u003eCLOCK\u003c/em\u003e, \u003cem\u003eCRY1\u003c/em\u003e, \u003cem\u003eCRY2\u003c/em\u003e) was assessed using the Gene_Outcome module in the TIMER2.0 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; accessed on 1 October 2023) [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Kaplan-Meier (KM) survival curves were generated using Cox proportional hazards models. Analyses were performed across multiple TCGA cancer types, with particular focus on sarcoma (SARC). Patients were stratified into high and low expression cohorts using median cutoffs (cutoff-high and cutoff-low = 50%). Hazard ratios (HR), \u003cem\u003ep\u003c/em\u003e-values from Cox models, and log-rank test results were used to assess survival significance. In addition, \u003cem\u003eZ\u003c/em\u003e-score-based outcome risk plots were used to compare the indicated clock genes across cancer types. Our analyses were also adjusted for clinical variables including age, tumor stage, and tumor purity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGene correlation analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo explore the gene networks associated with circadian regulators, Pearson correlation coefficients were calculated between clock genes and established markers of cancer stemness, EMT, oncogenic metabolism, metastasis, and immune modulation, as shown in the corresponding figures. Analyses were conducted using the Correlation module of the Gene Expression Profiling Interactive Analysis (GEPIA; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; accessed on 1 June 2024) platform [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Further correlation analyses were performed on OS patient RNA-seq data (GSE99671) using GraphPad Prism to examine associations between core clock genes and immunosuppressive or immune-evasive molecules [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eTumor-immune profiling analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo assess the tumor immune microenvironment, the immune module of TIMER2.0 was used to examine correlations between \u003cem\u003eCRY1\u003c/em\u003e/\u003cem\u003eCRY2\u003c/em\u003e expression and tumor-infiltrating immune cells (TIICs), including CD8\u003csup\u003e+\u003c/sup\u003e T cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells, Tregs, CAFs, and MDSCs. Purity-adjusted Spearman correlation coefficients and associated \u003cem\u003ep\u003c/em\u003e-values were calculated. Five deconvolution algorithms, CIBERSORT (Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts), EPIC (Estimating the Proportions of Immune and Cancer cells), MCP-counter (Microenvironment Cell Populations-counter), TIDE (Tumor Immune Dysfunction and Exclusion), and quanTIseq (Quantification of the Tumor Immune Contexture), were employed for robust immune cell estimation. Analyses were conducted on SARC samples to identify tumor subtype-specific trends In addition, associations between circadian regulators and immune activation and exclusion markers, such as interferon-gamma (\u003cem\u003eIFNG\u003c/em\u003e) expression, T cell dysfunction scores, T cell exclusion scores, and tumor purity, were determined using the Immune Infiltration Analysis Module in TIMER3.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://compbio.cn/timer3/\u003c/span\u003e\u003cspan address=\"https://compbio.cn/timer3/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; accessed on 15 May 2025) [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Spearman correlation coefficients were calculated with tumor purity adjustments.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFunctional validation via\u003c/b\u003e \u003cb\u003eCRY1\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eCLOCK\u003c/b\u003e \u003cb\u003eknockdown in OS CSCs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo functionally validate the gene correlation findings, we performed siRNA-mediated knockdown of \u003cem\u003eCLOCK\u003c/em\u003e and \u003cem\u003eCRY1\u003c/em\u003e in xenograft-derived 143B OS CSCs, as described in our recent study [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Gene expression changes were assessed by quantitative PCR (qPCR) using validated primers targeting representative CSC/EMT and immune/metabolic modulatory genes (listed in the Supplementary Table; see Supplementary material). In addition, \u003cem\u003eCRY1\u003c/em\u003e knockdown effects on gene expression profiles were validated using publicly available RNA-seq datasets (GSE144961) derived from human carcinoma cells engineered to stably express doxycycline-inducible sh-CRY1 constructs [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eTransfection of siRNA\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSpecific siRNAs targeting human \u003cem\u003eCLOCK\u003c/em\u003e (SI00069790) and \u003cem\u003eCRY1\u003c/em\u003e (SI02757370) were obtained from Qiagen (Germantown, MD, USA). 143B CSCs were transfected with 100 pmol of siRNA per well using Lipofectamine RNAiMAX Transfection Reagent (13778075, Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s instructions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRNA isolation, cDNA synthesis, and quantitative real-time qPCR\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTotal RNA was isolated from 143B CSCs using the RNeasy Plus Mini Kit (Qiagen, 74134, Germantown, MD, USA), according to the manufacturer’s instructions. Equal amounts of RNA were then reverse-transcribed to generate complementary DNA (cDNA) using the SuperScript™ First-Strand Synthesis System for RT-PCR (11904018, Thermo Fisher Scientific), with 2 µL of random hexamers and 9 µL of RNA, following the supplied protocol. Real-time qPCR was performed using SYBR Green PCR Master Mix (1708880, Bio-Rad, Hercules, CA, USA) and 10 µM forward and reverse primers (listed in the Supplementary Table). Thermal cycling conditions were as follows: 50°C for 2 minutes, 95°C for 10 minutes, followed by 40 cycles of 95°C for 15 seconds and 60°C for 1 minute. Melting curve analysis was conducted to verify amplification specificity. Gene expression levels were analyzed using the comparative Ct (ΔΔCt) method and normalized to \u003cem\u003eGAPDH\u003c/em\u003e. Reactions were carried out on a ViiA7 Real-Time PCR System (Thermo Fisher Scientific, Waltham, MA, USA).\u003c/p\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using tools integrated within the TIMER and GEPIA platforms. Spearman or Pearson correlations were applied as appropriate. For survival analyses, Cox regression and log-rank tests were used. Results with \u003cem\u003ep\u003c/em\u003e-values \u0026lt; 0.05 were considered statistically significant. Other statistical analyses were conducted using GraphPad Software (version 10). Significance was assessed using two-way ANOVA, one-way ANOVA, or unpaired Student’s \u003cem\u003et\u003c/em\u003e-tests, as appropriate. When \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, it was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical review and approval were waived for this study because it utilized publicly available RNA-seq data from GSE99671, GEPIA, TIMER2, and TIMER3 that is fully anonymized and deidentified. No additional human subjects were involved, and ethical approval was obtained by the original study authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in this study are publicly available. Gene expression and immune profiling data were accessed via GEPIA (http://gepia.cancer-pku.cn/), TIMER2.0 (http://timer.cistrome.org/), and TIMER3.0 (https://compbio.cn/timer3/). RNA-seq datasets from osteosarcoma patients were obtained from GEO under accession number GSE99671. Publicly available RNA-seq data for CRY1 knockdown experiments were accessed under GEO accession number GSE144961. All other data supporting the findings of this study are included in the article and its Supplementary Materials\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Hennings Cancer gift from the Elson S. Floyd College of Medicine (GF003245 to Y.L.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.L. conceptualized the study, developed the methodology, performed software analysis, and curated the data. Y.L. and S.B. conducted the investigation, validation, formal analysis, and provided resources. Y.L. wrote the original draft, prepared the visualizations, supervised the project, managed project administration, and acquired funding. Y.L. and S.B. reviewed and edited the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Amy Sullivan, Ph.D. from Obrizus Communications for assisting with the helpful editing of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTaran SJ, Taran R, Malipatil NB. Pediatric Osteosarcoma: An Updated Review. Indian J Med Paediatr Oncol. 2017;38:33\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan GN, Lv YF, Guo QN. Advances in osteosarcoma stem cell research and opportunities for novel therapeutic targets. Cancer Lett. 2016;370:268\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMen\u0026eacute;ndez ST, Gallego B, Murillo D, Rodr\u0026iacute;guez A, Rodr\u0026iacute;guez R. Cancer Stem Cells as a Source of Drug Resistance in Bone Sarcomas. J Clin Med 2021, 10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFujiwara S, Kawamoto T, Kawakami Y, Koterazawa Y, Hara H, Takemori T, Kitayama K, Yahiro S, Kakutani K, Matsumoto T, et al. Acquisition of cancer stem cell properties in osteosarcoma cells by defined factors. Stem Cell Res Ther. 2020;11:429.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartins-Neves SR, Sampaio-Ribeiro G, Gomes CMF. Self-Renewal and Pluripotency in Osteosarcoma Stem Cells' Chemoresistance: Notch, Hedgehog, and Wnt/β-Catenin Interplay with Embryonic Markers. Int J Mol Sci 2023, 24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFeng Z, Ou Y, Hao L. The roles of glycolysis in osteosarcoma. Front Pharmacol. 2022;13:950886.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu H, Zhang Z, Song L, Gao J, Liu Y. Lipid metabolism of cancer stem cells. Oncol Lett. 2022;23:119.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang S, Yang X, Wang L, Zhang C. Interplay between inflammatory tumor microenvironment and cancer stem cells. Oncol Lett. 2018;16:679\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu T, Han J, Yang L, Cai Z, Sun W, Hua Y, Xu J. Immune Microenvironment in Osteosarcoma: Components, Therapeutic Strategies and Clinical Applications. Front Immunol. 2022;13:907550.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee Y, Wisor JP. Multi-Modal Regulation of Circadian Physiology by Interactive Features of Biological Clocks. Biology (Basel) 2021, 11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee Y. Roles of circadian clocks in cancer pathogenesis and treatment. Exp Mol Med 2021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnafi RC, Lee Y, Sato TK, Venkataraman A, Ramanathan C, Kavakli IH, Hughes ME, Baggs JE, Growe J, Liu AC, et al. Machine learning helps identify CHRONO as a circadian clock component. PLoS Biol. 2014;12:e1001840.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee Y, Jang AR, Francey LJ, Sehgal A, Hogenesch JB. KPNB1 mediates PER/CRY nuclear translocation and circadian clock function. Elife 2015, 4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee Y, Shen Y, Francey LJ, Ramanathan C, Sehgal A, Liu AC, Hogenesch JB. The NRON complex controls circadian clock function through regulated PER and CRY nuclear translocation. Sci Rep. 2019;9:11883.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee Y, Lahens NF, Zhang S, Bedont J, Field JM, Sehgal A. G1/S cell cycle regulators mediate effects of circadian dysregulation on tumor growth and provide targets for timed anticancer treatment. PLoS Biol. 2019;17:e3000228.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee Y, Fong SY, Shon J, Zhang SL, Brooks R, Lahens NF, Chen D, Dang CV, Field JM, Sehgal A. Time-of-day specificity of anticancer drugs may be mediated by circadian regulation of the cell cycle. Sci Adv 2021, 7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang X, Pant SM, Ritch CC, Tang HY, Shao H, Dweep H, Gong YY, Brooks R, Brafford P, Wolpaw AJ, et al. Cell state dependent effects of Bmal1 on melanoma immunity and tumorigenicity. Nat Commun. 2024;15:633.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDong Z, Zhang G, Qu M, Gimple RC, Wu Q, Qiu Z, Prager BC, Wang X, Kim LJY, Morton AR, et al. Targeting Glioblastoma Stem Cells through Disruption of the Circadian Clock. Cancer Discov. 2019;9:1556\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePuram RV, Kowalczyk MS, de Boer CG, Schneider RK, Miller PG, McConkey M, Tothova Z, Tejero H, Heckl D, J\u0026auml;r\u0026aring;s M, et al. Core Circadian Clock Genes Regulate Leukemia Stem Cells in AML. Cell. 2016;165:303\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBhoumik S, Lee Y. Core Molecular Clock Factors Regulate Osteosarcoma Stem Cell Survival and Behavior via CSC/EMT Pathways and Lipid Droplet Biogenesis. Cells 2025, 14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee Y, Tanggono AS. Potential Role of the Circadian Clock in the Regulation of Cancer Stem Cells and Cancer Therapy. Int J Mol Sci 2022, 23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu B, Shi X, Jiang M, Liu H. Cross-talk between cancer stem cells and immune cells: potential therapeutic targets in the tumor immune microenvironment. Mol Cancer. 2023;22:38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi YR, Fang Y, Lyu Z, Zhu Y, Yang L. Exploring the dynamic interplay between cancer stem cells and the tumor microenvironment: implications for novel therapeutic strategies. J Transl Med. 2023;21:686.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang G, Xu D, Zhang Z, Li X, Shi J, Sun J, Liu HZ, Zhou M, Zheng T. The pan-cancer landscape of crosstalk between epithelial-mesenchymal transition and immune evasion relevant to prognosis and immunotherapy response. NPJ Precis Oncol. 2021;5:56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSharma P, Aaroe A, Liang J, Puduvalli VK. Tumor microenvironment in glioblastoma: Current and emerging concepts. Neurooncol Adv. 2023;5:vdad009.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJain S, Rick JW, Joshi RS, Beniwal A, Spatz J, Gill S, Chang AC, Choudhary N, Nguyen AT, Sudhir S et al. Single-cell RNA sequencing and spatial transcriptomics reveal cancer-associated fibroblasts in glioblastoma with protumoral effects. J Clin Invest 2023, 133.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShibue T, Weinberg RA. EMT, CSCs, and drug resistance: the mechanistic link and clinical implications. Nat Rev Clin Oncol. 2017;14:611\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePatrașcu AV, Țarcă E, Lozneanu L, Ungureanu C, Moroșan E, Parteni DE, Jehac A, Bernic J, Cojocaru E. The Role of Epithelial-Mesenchymal Transition in Osteosarcoma Progression: From Biology to Therapy. Diagnostics (Basel) 2025, 15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChu X, Tian W, Ning J, Xiao G, Zhou Y, Wang Z, Zhai Z, Tanzhu G, Yang J, Zhou R. Cancer stem cells: advances in knowledge and implications for cancer therapy. Signal Transduct Target Ther. 2024;9:170.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePrager BC, Xie Q, Bao S, Rich JN. Cancer Stem Cells: The Architects of the Tumor Ecosystem. Cell Stem Cell. 2019;24:41\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao J, Jin D, Huang M, Ji J, Xu X, Wang F, Zhou L, Bao B, Jiang F, Xu W, et al. Glycolysis in the tumor microenvironment: a driver of cancer progression and a promising therapeutic target. Front Cell Dev Biol. 2024;12:1416472.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi J, Bai Y, Zhang H, Chen T, Shang G. Single-cell RNA sequencing reveals the communications between tumor microenvironment components and tumor metastasis in osteosarcoma. Front Immunol. 2024;15:1445555.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen P, Hsu WH, Han J, Xia Y, DePinho RA. Cancer Stemness Meets Immunity: From Mechanism to Therapy. Cell Rep. 2021;34:108597.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eM\u0026uuml;ller L, Tunger A, Plesca I, Wehner R, Temme A, Westphal D, Meier F, Bachmann M, Schmitz M. Bidirectional Crosstalk Between Cancer Stem Cells and Immune Cell Subsets. Front Immunol. 2020;11:140.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChulpanova DS, Rizvanov AA, Solovyeva VV. The Role of Cancer Stem Cells and Their Extracellular Vesicles in the Modulation of the Antitumor Immunity. Int J Mol Sci 2022, 24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDonini C, Rotolo R, Proment A, Aglietta M, Sangiolo D, Leuci V. Cellular Immunotherapy Targeting Cancer Stem Cells: Preclinical Evidence and Clinical Perspective. Cells 2021, 10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnnett S, Robson T. Targeting cancer stem cells in the clinic: Current status and perspectives. Pharmacol Ther. 2018;187:13\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi C, Xue Y, Yinwang E, Ye Z. The Recruitment and Immune Suppression Mechanisms of Myeloid-Derived Suppressor Cells and Their Impact on Bone Metastatic Cancer. Cancer Rep (Hoboken). 2025;8:e70044.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTsai HC, Tzeng HE, Huang CY, Huang YL, Tsai CH, Wang SW, Wang PC, Chang AC, Fong YC, Tang CH. WISP-1 positively regulates angiogenesis by controlling VEGF-A expression in human osteosarcoma. Cell Death Dis. 2017;8:e2750.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Z, Xiong S, Mao Y, Chen M, Ma X, Zhou X, Ma Z, Liu F, Huang Z, Luo Q, Ouyang G. Periostin promotes immunosuppressive premetastatic niche formation to facilitate breast tumour metastasis. J Pathol. 2016;239:484\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang J, Yang D, Sun Y, Sun B, Wang G, Trent JC, Araujo DM, Chen K, Zhang W. Genetic amplification of the vascular endothelial growth factor (VEGF) pathway genes, including VEGFA, in human osteosarcoma. Cancer. 2011;117:4925\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRibatti D. Immunosuppressive effects of vascular endothelial growth factor. Oncol Lett. 2022;24:369.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Brekken RA. Direct and indirect regulation of the tumor immune microenvironment by VEGF. J Leukoc Biol. 2022;111:1269\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTanaka M, Yamazaki T, Araki N, Yoshikawa H, Yoshida T, Sakakura T, Uchida A. Clinical significance of tenascin-C expression in osteosarcoma: tenascin-C promotes distant metastases of osteosarcoma. Int J Mol Med. 2000;5:505\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTofuku K, Yokouchi M, Murayama T, Minami S, Komiya S. HAS3-related hyaluronan enhances biological activities necessary for metastasis of osteosarcoma cells. Int J Oncol. 2006;29:175\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang J, Wang G, Liu J, Tang F, Wang S, Li Y. ITGA4 as a potential prognostic and immunotherapeutic biomarker in human cancer and its clinical significance in gastric cancer: an integrated analysis and validation. Front Oncol. 2025;15:1513622.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi P, Pang KL, Chen SJ, Yang D, Nai AT, He GC, Fang Z, Yang Q, Cai MB, He JY. ADORA2B promotes proliferation and migration in head and neck squamous cell carcinoma and is associated with immune infiltration. BMC Cancer. 2025;25:673.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcEachron TA, Triche TJ, Sorenson L, Parham DM, Carpten JD. Profiling targetable immune checkpoints in osteosarcoma. Oncoimmunology. 2018;7:e1475873.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu L, Yao Z, Liu Y, Li Y, Ding Y, Hu J, Liu Z, Shi P, Chen K, Zhang W, Hou Y. A Pan-Cancer Analysis of the Oncogenic Role of CD276 in Human Tumors. Genes (Basel) 2024, 15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHo XD, Phung P, Le Q, Nguyen VH, Reimann V, Prans E, K\u0026otilde;ks E, Maasalu G, Le K, Trinh NTH. Whole transcriptome analysis identifies differentially regulated networks between osteosarcoma and normal bone samples. Exp Biol Med (Maywood). 2017;242:1802\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShafi AA, McNair CM, McCann JJ, Alshalalfa M, Shostak A, Severson TM, Zhu Y, Bergman A, Gordon N, Mandigo AC, et al. The circadian cryptochrome, CRY1, is a pro-tumorigenic factor that rhythmically modulates DNA repair. Nat Commun. 2021;12:401.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B, Liu XS. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 2017;77:e108\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, Li B, Liu XS. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48:W509\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMontauti E, Oh DY, Fong L. CD4. Trends Cancer. 2024;10:969\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDel Prete A, Salvi V, Soriani A, Laffranchi M, Sozio F, Bosisio D, Sozzani S. Dendritic cell subsets in cancer immunity and tumor antigen sensing. Cell Mol Immunol. 2023;20:432\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGabrilovich DI. Myeloid-Derived Suppressor Cells. Cancer Immunol Res. 2017;5:3\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFeng L, Chen Y, Jin W. Research progress on cancer-associated fibroblasts in osteosarcoma. Oncol Res. 2025;33:1091\u0026ndash;103.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCui H, Zhao G, Lu Y, Zuo S, Duan D, Luo X, Zhao H, Li J, Zeng Z, Chen Q, Li T. TIMER3: an enhanced resource for tumor immune analysis. Nucleic Acids Res 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu B, Zhang B, Li B, Wu H, Jiang M. Cold and hot tumors: from molecular mechanisms to targeted therapy. Signal Transduct Target Ther. 2024;9:274.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun CY, Zhang Z, Tao L, Xu FF, Li HY, Zhang HY, Liu W. T cell exhaustion drives osteosarcoma pathogenesis. Ann Transl Med. 2021;9:1447.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePuram RV, Kowalczyk MS, de Boer CG, Schneider RK, Miller PG, McConkey M, Tothova Z, Tejero H, Heckl D, Jaras M, et al. Core Circadian Clock Genes Regulate Leukemia Stem Cells in AML. Cell. 2016;165:303\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSulli G, Rommel A, Wang X, Kolar MJ, Puca F, Saghatelian A, Plikus MV, Verma IM, Panda S. Pharmacological activation of REV-ERBs is lethal in cancer and oncogene-induced senescence. Nature. 2018;553:351\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTrott AJ, Menet JS. Regulation of circadian clock transcriptional output by CLOCK:BMAL1. PLoS Genet. 2018;14:e1007156.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee Y, Lee J, Kwon I, Nakajima Y, Ohmiya Y, Son GH, Lee KH, Kim K. Coactivation of the CLOCK-BMAL1 complex by CBP mediates resetting of the circadian clock. J Cell Sci. 2010;123:3547\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDoi M, Hirayama J, Sassone-Corsi P. Circadian regulator CLOCK is a histone acetyltransferase. Cell. 2006;125:497\u0026ndash;508.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHirayama J, Sahar S, Grimaldi B, Tamaru T, Takamatsu K, Nakahata Y, Sassone-Corsi P. CLOCK-mediated acetylation of BMAL1 controls circadian function. Nature. 2007;450:1086\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCardone L, Hirayama J, Giordano F, Tamaru T, Palvimo JJ, Sassone-Corsi P. Circadian clock control by SUMOylation of BMAL1. Science. 2005;309:1390\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee J, Lee Y, Lee MJ, Park E, Kang SH, Chung CH, Lee KH, Kim K. Dual modification of BMAL1 by SUMO2/3 and ubiquitin promotes circadian activation of the CLOCK/BMAL1 complex. Mol Cell Biol. 2008;28:6056\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee Y, Chun SK, Kim K. Sumoylation controls CLOCK-BMAL1-mediated clock resetting via CBP recruitment in nuclear transcriptional foci. Biochim Biophys Acta. 2015;1853:2697\u0026ndash;708.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDhanasekaran R, Deutzmann A, Mahauad-Fernandez WD, Hansen AS, Gouw AM, Felsher DW. The MYC oncogene - the grand orchestrator of cancer growth and immune evasion. Nat Rev Clin Oncol. 2022;19:23\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeškytė EM, Keskas S, Ciribilli Y. MYC as a Multifaceted Regulator of Tumor Microenvironment Leading to Metastasis. Int J Mol Sci 2020, 21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuber AL, Papp SJ, Chan AB, Henriksson E, Jordan SD, Kriebs A, Nguyen M, Wallace M, Li Z, Metallo CM, Lamia KA. CRY2 and FBXL3 Cooperatively Degrade c-MYC. Mol Cell. 2016;64:774\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSato S, Hishida T, Kinouchi K, Hatanaka F, Li Y, Nguyen Q, Chen Y, Wang PH, Kessenbrock K, Li W, et al. The circadian clock CRY1 regulates pluripotent stem cell identity and somatic cell reprogramming. Cell Rep. 2023;42:112590.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWherry EJ, Kurachi M. Molecular and cellular insights into T cell exhaustion. Nat Rev Immunol. 2015;15:486\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang Z, Li C, Kang B, Gao G, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45:W98\u0026ndash;102.\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-7167173/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7167173/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe circadian clock is a cell-autonomous regulatory system that influences diverse cancer-related processes, including cell proliferation, metabolism, and immune regulation. While core clock regulators are known to affect tumor biology, their distinct tumor-intrinsic and microenvironmental roles in osteosarcoma (OS) remain poorly defined. Here, we report that the expression of CLOCK and CRY1, but not their canonical partners BMAL1 and CRY2, is significantly associated with poor survival in OS and linked to oncogenic programs. Integrative transcriptomic and immune analyses reveal that CLOCK and CRY1 are positively correlated with cancer stem cell (CSC) markers, epithelial-mesenchymal transition (EMT) drivers, metabolic and metastatic genes, and immunosuppressive factors such as (e.g., MYC, SLC16A1, HK1, TNC, CD276, ITGA4, WISP1, POSTN, VEGFA). Knockdown of CLOCK or CRY1 in 143B OS stem-like cells significantly reduces the expression of these genes, supporting a functional role in maintaining tumor-promoting phenotypes. Moreover, high CLOCK and CRY1 expression correlates with reduced infiltration of CD4⁺ T cells and dendritic cells, elevated cancer-associated fibroblasts (CAFs) and myeloid-derived suppressor cells (MDSCs), and increased markers of immune exclusion and dysfunction. In contrast, BMAL1 and CRY2 show minimal or inverse associations with these parameters. These findings uncover an unexpected divergence among circadian regulators, positioning CLOCK and CRY1 as potential drivers of OS aggressiveness via both tumor-intrinsic and immune-evasive mechanisms, and suggest their therapeutic targeting as a promising strategy for disrupting circadian-linked oncogenic circuits in OS.\u003c/p\u003e","manuscriptTitle":"Divergent Roles of Circadian Regulators CLOCK and CRY1 in Driving Pro-Tumoral Stemness and Immunoevasion in Osteosarcoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-24 18:09:18","doi":"10.21203/rs.3.rs-7167173/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":"1f18403e-363d-4e63-a633-fdc0ebf06e7f","owner":[],"postedDate":"July 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-10T03:53:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-24 18:09:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7167173","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7167173","identity":"rs-7167173","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}