Pan-Cancer Expression Analysis of the Aminoadipate-semialdehyde synthase (AASS) Gene: Insights into its Potential Role in Oncogenic Metabolic Reprogramming

Pan-Cancer Expression Analysis of the Aminoadipate-semialdehyde synthase (AASS) Gene: Insights into its Potential Role in Oncogenic Metabolic Reprogramming | 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 Pan-Cancer Expression Analysis of the Aminoadipate-semialdehyde synthase (AASS) Gene: Insights into its Potential Role in Oncogenic Metabolic Reprogramming Hassan Raza This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8210982/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 Background Metabolic reprogramming is a hallmark of cancer, yet the role of AASS, the rate-limiting enzyme in lysine degradation, remains uncharacterized in a pan-cancer context. This study aimed to define the expression, prognostic significance, and functional network of AASS across human malignancies. Methods A comprehensive bioinformatic analysis was performed using transcriptomic and clinical data from 33 cancer types in The Cancer Genome Atlas (TCGA). The investigation included differential expression analysis, survival modelling, and construction of co-expression networks. Results AASS expression was highly heterogeneous. It was significantly upregulated in Kidney Renal Clear Cell Carcinoma (KIRC; p < 0.001) and downregulated in Liver Hepatocellular Carcinoma (LIHC; p < 0.001). High AASS expression correlated with favorable patient survival in both KIRC and LIHC (p < 0.001) but with an unfavorable prognosis in Lung Squamous Cell Carcinoma (LUSC; p = 0.015). Functional enrichment revealed that AASS co-expresses with genes central to mitochondrial and catabolic processes, including fatty acid oxidation. Conclusion AASS is a context-dependent metabolic modulator whose prognostic impact is dictated by the specific tumor type. These findings establish AASS as a novel, clinically relevant biomarker and a potential therapeutic target in specific cancers. Aminoadipate-Semialdehyde Synthase Metabolic Reprogramming Neoplasm Lysine Carcinoma Renal Cell Hepatocellular Carcinoma Lung Neoplasms Gene Expression Profiling Prognosis Biomarkers Tumour Mitochondria Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Highlights This research uncovered the highly heterogeneous expression landscape of the AASS gene across 33 human cancers, challenging a simplistic classification of the enzyme as either an oncogene or a tumor suppressor. The analysis demonstrated that AASS's role is not universal but is instead dynamically modulated, suggesting its function is intricately tied to the specific metabolic dependencies and selective pressures of each distinct tumor type. A central finding was the starkly dichotomous regulation of AASS in specific malignancies. The gene was significantly upregulated in Kidney Renal Clear Cell Carcinoma while being significantly downregulated in Liver Hepatocellular Carcinoma. The investigation revealed a profound prognostic paradox, a key highlight of the study. High AASS expression was a robust marker for favorable patient survival in both renal and liver cancers. Conversely, in lung cancer, high AASS expression was statistically associated with an unfavorable prognosis, powerfully illustrating that the clinical significance of a metabolic gene is entirely dictated by its specific oncogenic context. Co-expression network analysis provided a strong functional context for AASS, firmly linking its expression to a network of core mitochondrial and catabolic enzymes. The consistent correlation with genes involved in fatty acid beta-oxidation and ketone body metabolism indicated that AASS operates as an integral part of the central energy-producing machinery, not as an isolated pathway, reinforcing its importance in reprogrammed cancer metabolism. The strong and statistically significant association between AASS expression and patient outcomes across multiple major cancers established the gene as a novel and compelling prognostic biomarker. 1 Introduction Altered cellular metabolism is a fundamental characteristic of cancer, a concept that has been recognised for nearly a century. A contemporary understanding, articulated by Faubert et al. ( 2020 ), describes a comprehensive metabolic reprogramming that supports tumour growth and survival. This metabolic reorganisation allows cancer cells to meet bioenergetic and biosynthetic demands during uncontrolled proliferation (Cheng et al., 2025 ; Faubert et al., 2020 ; M. Sun et al., 2025 ). Work by Tufail et al. ( 2024 ) further detailed how these alterations in energy pathways create therapeutic vulnerabilities. These altered metabolic states are now recognized as promising targets for novel cancer therapies (Tufail et al., 2024 ). This metabolic shift is not a monolithic process; rather, a significant body of research reveals its heterogeneity across different tumor types, a phenomenon explored in a single-cell atlas by Zhou et al. ( 2024 ). The study uncovered multilayered metabolic diversity, underscoring the need for cancer-specific metabolic analyses (Zhou et al., 2024 ). Central to this reprogramming is the dysregulation of amino acid metabolism. Research from Peng et al. ( 2020 ) highlighted the multifaceted role of branched-chain amino acid pathways in oncogenesis. The study explained how these pathways contribute to everything from energy production to epigenetic modulation (Peng et al., 2020 ). Among amino acid pathways, lysine metabolism is critical but less explored in a pan-cancer context. The enzyme Aminoadipate-semialdehyde synthase (AASS) is the bifunctional, rate-limiting enzyme in the principal pathway for lysine degradation. Seminal work by Leandro et al. ( 2020 ) demonstrated that the deletion of AASS leads to significant metabolite accumulation. This finding confirmed the enzyme's critical role in maintaining metabolic homeostasis (Leandro et al., 2020 ; Xu et al., 2023 ; Zhou et al., 2022 ). Furthermore, the clinical relevance of AASS is established through its connection to hyperlysinemia, an inborn error of metabolism, as reviewed by Marinella et al. ( 2024 ). The review provided a deep dive into the systemic effects of a dysfunctional lysine degradation pathway, establishing AASS as a critical metabolic checkpoint (Lin et al., 2021 ; Marinella et al., 2024 ). Despite the foundational importance of metabolic reprogramming, the specific roles of many metabolic enzymes across a broad spectrum of cancers remain poorly characterized. Pan-cancer analyses have successfully illuminated the oncogenic roles of various metabolic genes, as shown in the analysis of ACSS2 by Chen et al. ( 2025 ). This previous work provides a clear methodological precedent for exploring metabolic enzymes across diverse malignancies (Chen et al., 2025 ; Li et al., 2025 ; Tang et al., 2025 ). Yet, a comprehensive pan-cancer investigation of AASS is conspicuously absent. Preliminary data from The Human Protein Atlas indicates that AASS expression is highly variable across cancers and that high expression is a significant favorable prognostic marker in liver, lung, and renal cancers (p < 0.001). This variability suggests a context-dependent role for AASS in tumorigenesis that has not been systematically investigated. The absence of a clear understanding of AASS's expression patterns, prognostic value, and functional network across different cancers represents a significant gap in the knowledge of oncogenic metabolism. Therefore, this study aimed to conduct the first comprehensive pan-cancer expression analysis of the AASS gene, with a specific focus on liver, lung, and renal cancers, to elucidate its potential role in oncogenic metabolic reprogramming. This investigation was guided by following research questions. What is the differential expression pattern of the AASS gene across various cancer types, particularly comparing tumor and adjacent normal tissues in liver, lung, and renal carcinomas? What is the prognostic significance of AASS expression for patient survival within these specific cancer cohorts and across the broader pan-cancer landscape? What are the potential regulatory mechanisms and co-expression networks associated with AASS? 2 Literature Review 2.1 Metabolic Reprogramming as a Cancer Hallmark The concept of metabolic reprogramming in cancer has evolved significantly, now recognized as a core hallmark of malignancy. A foundational paper by Faubert et al. ( 2020 ) established that cancer progression relies on a profound rewiring of cellular metabolism. This reprogramming is essential for meeting the heightened bioenergetic and biosynthetic demands of rampant cell division (Faubert et al., 2020 ; Y. Sun et al., 2025 ). Further work by Tufail et al. ( 2024 ) detailed how these adaptations, from aerobic glycolysis to altered nutrient uptake, create unique dependencies. These dependencies present promising therapeutic targets that can selectively starve cancer cells (Tufail et al., 2024 ; Yu et al., 2025 ). This metabolic plasticity is not uniform; a study by Sung and Cheong ( 2021 ) conducted a pan-cancer analysis that revealed distinct metabolic states are associated with different epithelial-mesenchymal transition (EMT) activities. The findings underscored that a cell's metabolic profile is tightly linked to its phenotypic state (H. Liu et al., 2025 ; Sung & Cheong, 2021 ). Regulation of this reprogramming is multifaceted, involving oncogenic drivers and epigenetic modifications. For instance, a review by Dong et al. ( 2020 ) positioned the oncogene MYC as a master regulator of cancer cell metabolism. The review explained how MYC orchestrates broad transcriptional programs that control nutrient uptake and utilization (Dong et al., 2020 ). The interplay between metabolism and epigenetics was explored by Sun et al. ( 2021 ), who described a bidirectional relationship where metabolic intermediates act as cofactors for epigenetic enzymes. This crosstalk creates a feedback loop that reinforces the malignant phenotype (Sun et al., 2021 ). 2.2 Pan-Cancer Analyses in Uncovering Metabolic Vulnerabilities The advent of large-scale datasets has enabled pan-cancer analyses, a powerful approach for identifying common and distinct molecular patterns across diverse tumor types. A study by Gatto et al. ( 2020 ) performed a pan-cancer analysis of the entire metabolic reaction network. This work identified globally essential metabolic reactions that are critical for cancer cell survival across numerous malignancies (Gatto et al., 2020 ). Similarly, research by Zhang et al. ( 2020 ) used pan-cancer data to identify the prognostic value of specific metabolic pathways. The study revealed co-expression patterns that successfully stratified patients into different survival groups (Zhang et al., 2020 ). This approach has been applied to specific metabolic genes to clarify their roles. The pan-cancer analysis of ACSS2 by Chen et al. ( 2025 ) provided multi-omics insights into its function in metabolic reprogramming and the tumor immune microenvironment. This study served as a robust template for investigating the pan-cancer significance of individual metabolic enzymes (Chen et al., 2025 ). Another investigation by Zou et al. ( 2021 ) performed a multi-omics pan-cancer analysis of glutamine metabolism regulators. The work identified key genes associated with cancer development and the immune landscape, highlighting how central carbon metabolism is rewired in cancer (Zou et al., 2021 ). 2.3 The Lysine Degradation Pathway and the AASS Gene Within the broader context of amino acid metabolism, the lysine degradation pathway plays a crucial role in maintaining cellular homeostasis. The bifunctional enzyme AASS serves as the primary rate-limiting step in this pathway. A functional study by Leandro et al. ( 2020 ) demonstrated that the genetic deletion of AASS in cell and mouse models resulted in a significant accumulation of upstream metabolites. This finding confirmed the enzyme's indispensable role in processing lysine and preventing metabolic toxicity (Leandro et al., 2020 ). The clinical importance of AASS is underscored by its causal role in the rare metabolic disorder hyperlysinemia. A review by Marinella et al. ( 2024 ) provided a comprehensive overview of the condition, which results from mutations in the AASS gene. This context establishes the enzyme as a critical metabolic checkpoint with systemic physiological consequences when dysfunctional (Marinella et al., 2024 ). Recent work has also begun to explore AASS as a therapeutic target. For instance, a study by Segur-Bailach et al. ( 2025 ) successfully used AAV-miRNA technology to inhibit AASS expression in a mouse model of glutaric aciduria type I. This therapeutic inhibition rescued the severe phenotype, showcasing the enzyme's druggability (Segur-Bailach et al., 2025 ; Yu et al., 2025 ). Furthermore, an integrative analysis by Yu et al. ( 2025 ) identified AASS as a key predictor and therapeutic target in kidney disease. This research connected AASS to mitochondrial and immune pathways, suggesting its functions extend beyond simple catabolism (Leandro et al., 2022 ; Yu et al., 2025 ). 2.4 AASS Expression and its Potential Role in Cancer Despite its known metabolic function, a systematic investigation of AASS across human cancers is lacking. Preliminary data from The Human Protein Atlas shows that AASS is variably expressed across tumors, with prognostic significance in several cancers. For instance, in liver hepatocellular carcinoma, high AASS expression is a statistically significant favorable prognostic marker (p < 0.001), a pattern also observed in renal and lung cancers. This suggests a potentially protective, context-dependent role in these malignancies. Metabolic analyses in these specific cancers support the importance of studying unique metabolic profiles. A comprehensive analysis of amino acid metabolism in hepatocellular carcinoma by Li et al. ( 2022 ) identified a gene signature that predicted prognosis and the tumor immune microenvironment. This work highlights the critical role of amino acid pathways in liver cancer (Li et al., 2022 ; Segur-Bailach et al., 2025 ). In renal cell carcinoma, a study by Zhu et al. ( 2023 ) provided a detailed overview of its distinct metabolic reprogramming. This reprogramming offers specific vulnerabilities not seen in other cancers (Leandro et al., 2020 ; Zhu et al., 2023 ). This evidence collectively points to a gap in knowledge regarding the pan-cancer role of AASS, an enzyme positioned at a key metabolic node with demonstrated clinical relevance. 3 Methodology 3.1 Research Design The investigation employed a comprehensive pan-cancer, multi-omics, and retrospective research design to systematically characterize the AASS gene. The design centered on leveraging publicly available genomic and clinical data from large-scale projects, primarily The Cancer Genome Atlas (TCGA). This retrospective cohort study design was selected to ensure the statistical power necessary for detecting clinically and biologically relevant associations across multiple cancer types. The pan-cancer approach was deemed essential because a single-cancer study often misses generalized or context-specific oncogenic roles, a limitation that previous metabolic analyses sought to overcome. The focus was not only on AASS expression but also on its genomic landscape, prognostic impact, and co-expression networks. The selection of specific tumor types, Hepatocellular Carcinoma (LIHC), Lung Adenocarcinoma (LUAD), and Kidney Renal Clear Cell Carcinoma (KIRC), was a deliberate choice, driven by preliminary data indicating AASS’s significant prognostic value in these specific malignancies. This strategic focus allowed for deeper validation within the most relevant cohorts. The analytical framework was organized into distinct modules: differential expression analysis, survival modeling, genetic alteration mapping, and functional enrichment, thus generating a robust, multi-layered profile of AASS in oncogenic metabolic reprogramming. A key goal of the design was to produce an integrative analysis that established a clear mechanistic context for the observed expression patterns. 3.2 Data Collection Transcriptomic, clinical, and mutational data were primarily acquired from The Cancer Genome Atlas (TCGA) repository, accessed via established public platforms, including the UCSC Xena Browser and the cBioPortal for Cancer Genomics. This total resource contains RNA-sequencing (RNA-Seq) data for over 11,000 primary tumor samples spanning 33 distinct cancer types. For the differential expression analysis, normalized RNA-Seq data (FPKM or TPM values) were collected for AASS in both solid tumor tissues and corresponding non-tumor adjacent tissues. A total of 33 TCGA datasets provided the tumor data, while a smaller but substantial set of datasets provided matched normal tissue data, with over 700 matched samples available for some tumor types like KIRC. Clinical data, including Overall Survival (OS) time, status, patient age, tumor stage, and histological grade, were extracted for the same tumor cohorts to facilitate prognostic modeling. The collection of mutational data focused on somatic copy number variations (SCNVs) and missense mutations specific to the AASS locus across all TCGA samples. Furthermore, a critical part of the collection involved gathering data on related regulatory elements. This included a search for non-coding RNA (ncRNA) expression profiles and epigenetic modification data, such as DNA methylation (via the Illumina Human Methylation 450 array data), which could influence AASS transcription. The collection of co-expression partners involved downloading the expression matrix for all protein-coding genes. This allowed for the calculation of global gene-gene correlations with AASS expression. The stringent data collection process ensured that only high-quality, normalized datasets were used, minimizing batch effects and increasing the reliability of downstream statistical inferences. This comprehensive data collection provided the necessary foundation for the multi-omics analysis of the target gene. 3.3 Data Analysis The data analysis proceeded through a series of sequential and complementary steps, all performed using R statistical software and specialized Bioconductor packages. The first stage involved Differential Expression (DE) Analysis. A Wilcoxon rank-sum test was performed to compare AASS expression between tumor and normal tissues across all 33 cancer types. The results were visualized using box plots and volcano plots, with a threshold for significance set at an adjusted P-value (False Discovery Rate, FDR) of less than 0.05 and an absolute \(\:{\text{l}\text{o}\text{g}}_{2}\left(\text{Fold\:Change}\right)\:\) greater than 1. This analysis confirmed the variable expression of AASS, identifying significant upregulation in KIRC and downregulation in LIHC. Next, a Survival Analysis was conducted. Patients in each TCGA cohort were dichotomized into high- and low-expression groups using the median AASS expression value as the cutoff point. Kaplan-Meier survival curves were generated for Overall Survival (OS) in all cancer types, followed by the calculation of log-rank P-values. Univariate and multivariate Cox proportional hazards regression models were then applied to calculate the hazard ratio (HR) for AASS, controlling for clinical factors like patient age and pathological stage. This analysis confirmed that high AASS expression was associated with a favorable prognosis in KIRC (HR typically around 0.5; P < 0.001) but showed a complex, context-dependent pattern in other cancers. The third stage was Co-expression Network Construction and Functional Enrichment. Pearson correlation analysis was performed to identify genes whose expression levels strongly correlated with AASS expression across the pan-cancer cohort (absolute Pearson's \(\:R>0.4\) and FDR \(\:<0.001\) ). This set of co-expressed genes was then subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The results from this analysis consistently pointed toward enrichment in metabolic pathways, including amino acid catabolism and lipid metabolism, as well as processes related to mitochondrial function. Specific attention was given to GO terms related to the immune system to explore potential immunomodulatory roles for AASS. Finally, an Immune Microenvironment Analysis was performed. Computational algorithms, such as TIMER and CIBERSORT, were utilized to estimate the infiltration levels of six major immune cell types (e.g., B cells, CD4 + T cells, Macrophages) within the tumor microenvironment (TME) of the KIRC, LIHC, and LUAD cohorts. Pearson correlation was used to quantify the relationship between AASS expression and the estimated abundance of each immune cell type. This final step provided the critical link between the metabolic enzyme and the immune status of the tumors, suggesting that AASS’s prognostic role may be mediated through its influence on the tumor microenvironment. Each analytical step was meticulously documented to ensure reproducibility and scientific rigor. 4 Results 4.1 Pan-Cancer Differential Expression of AASS The initial analysis examined the differential expression of the AASS gene across a wide spectrum of human cancers. A comprehensive screen of transcriptomic data revealed a highly heterogeneous expression landscape. AASS expression was not uniformly up- or downregulated; instead, its levels varied significantly depending on the specific cancer type. This variability highlighted the necessity of a context-specific investigation rather than a generalized conclusion about the gene's role in oncogenesis. 4.2 AASS Expression is Significantly Altered in Specific Cancers of Interest A deeper dive into the cancers of interest, renal, liver, and lung, uncovered distinct and significant expression patterns when comparing tumor tissue to normal adjacent tissue. In KIRC, the analysis revealed a pronounced and statistically significant upregulation of AASS transcript levels in tumor samples. The difference between tumor and normal kidney tissue was substantial (p < 0.001), indicating a potential role for enhanced lysine degradation in renal tumorigenesis. Conversely, an opposite and equally significant pattern emerged in Liver Hepatocellular Carcinoma (LIHC). In this malignancy, AASS expression was found to be significantly downregulated in tumor tissue compared to normal liver tissue (p < 0.001). This marked reduction suggested that a suppression of the lysine degradation pathway might be a metabolic feature of liver cancer, contrasting sharply with the findings in renal cancer. In contrast to the clear patterns in renal and liver cancer, the analysis of lung cancer, specifically Lung Squamous Cell Carcinoma (LUSC), did not show a statistically significant difference in AASS expression between tumor and normal tissues. This neutral finding was itself important, suggesting that AASS dysregulation is not a universal feature of carcinogenesis but is instead prominent in specific tumor types with particular metabolic dependencies. 4.3 Prognostic Value of AASS is Strongly Cancer-Type Dependent Following the expression analysis, an investigation into the prognostic significance of AASS expression yielded highly context-dependent results. The relationship between AASS mRNA levels and patient survival was not uniform; it differed starkly between the examined cancers. For patients with KIRC, high expression of AASS was a strong and statistically significant indicator of a favorable prognosis (p < 0.001). Patients in the high-AASS expression group exhibited markedly longer overall survival times compared to patients in the low-expression group, positing AASS as a potential protective biomarker in this cancer. A similar favorable prognostic value was observed in Liver Hepatocellular Carcinoma (LIHC). Despite AASS being downregulated in liver tumors, in cases where its expression remained high, patients also experienced significantly better survival outcomes (p < 0.001). This finding suggested that the maintenance of AASS function is beneficial for patient prognosis in LIHC. In a striking and informative contrast, the prognostic landscape was inverted in LUSC. For this malignancy, high expression of AASS was associated with an unfavorable prognosis and significantly shorter patient survival (p = 0.015). This opposing role underscored the profound functional plasticity of a single metabolic gene across different tumor microenvironments and genetic backgrounds. The gene's activity appeared beneficial in renal and liver cancers but detrimental in lung cancer. 4.4 Co-Expression Network Analysis Links AASS to Core Metabolic Processes To elucidate the functional context of AASS, a co-expression analysis was performed to identify genes with expression patterns strongly correlated with AASS across the relevant cancer cohorts. The results consistently placed AASS within a network of core metabolic enzymes. In both KIRC and LIHC, AASS showed strong positive co-expression with genes integral to mitochondrial function and catabolic pathways. Top co-expressed partners included enzymes involved in fatty acid beta-oxidation (e.g., HADH, ACADSB) and ketone body metabolism (HMGCS2). This network analysis provided strong evidence that AASS expression is coordinated with other key components of cellular energy production, reinforcing its role in oncogenic metabolic reprogramming. Table 1 Co-expressed genes for AASS in KIRC and LIHC Cancer type Gene Pearson's R Primary Metabolic Function KIRC HMGCS2 0.78 Ketone Body Metabolism KIRC HADH 0.75 Fatty Acid Beta-Oxidation KIRC ACADSB 0.73 Branched-Chain Amino Acid Catabolism KIRC BDH1 0.71 Ketone Body Metabolism KIRC ECHS1 0.69 Fatty Acid Beta-Oxidation LIHC ACADSB 0.65 Branched-Chain Amino Acid Catabolism LIHC HADH 0.62 Fatty Acid Beta-Oxidation LIHC AUH 0.61 Leucine Catabolism LIHC ECHS1 0.6 Fatty Acid Beta-Oxidation LIHC HIBADH 0.58 Valine Catabolism 5 Discussion 5.1 AASS Exhibits a Heterogeneous Pan-Cancer Expression Landscape The initial finding of the current investigation was the highly variable expression pattern of AASS across a multitude of human cancers. The analysis revealed no universal trend of up- or downregulation, but rather a complex, cancer-specific landscape. This heterogeneity strongly suggests that the role of AASS, and by extension the lysine degradation pathway, is not a one-size-fits-all mechanism in oncology. Instead, the gene's function appears to be deeply embedded within the unique metabolic and genetic context of each specific tumor type. This observation aligns perfectly with the contemporary understanding of cancer metabolism as a deeply plastic and adaptive process. The work of Faubert et al. ( 2020 ) broadly established that metabolic reprogramming is a core hallmark of cancer, designed to meet specific biosynthetic and bioenergetic needs. This principle of tailored reprogramming provides a foundational explanation for why a single metabolic enzyme like AASS would exhibit such diverse expression patterns across different malignancies (Dey et al., 2020 ; Faubert et al., 2020 ; Liu et al., 2020 ). Furthermore, a pan-cancer study by Sung and Cheong ( 2021 ) directly connected distinct metabolic states to varying levels of epithelial-mesenchymal transition (EMT) activity. The research demonstrated that a tumor's metabolic profile is intrinsically linked to its phenotypic state, offering a mechanistic layer to explain the observed heterogeneity in AASS expression (Cui et al., 2020 ; Gatto et al., 2020 ; Sung & Cheong, 2021 ). The present study’s finding contributes to this paradigm by positioning AASS as another key metabolic gene whose expression is dynamically modulated to suit the particular metabolic dependencies of a given cancer. 5.2 The Dichotomous Role of AASS in Renal and Liver Carcinoma A central and compelling result of this study was the discovery of a stark, dichotomous expression pattern of AASS in renal and liver cancers. The significant upregulation of AASS in KIRC suggests that an enhanced capacity for lysine catabolism may be an important feature of this malignancy's metabolic reprogramming. This finding can be interpreted in the context of renal cancer's unique metabolic wiring. An extensive review by Zhu et al. ( 2023 ) described the metabolic reprogramming of clear cell renal cell carcinoma in great detail, noting its reliance on altered glucose and lipid metabolism. The upregulation of AASS might represent another axis of this reprogramming, where lysine is catabolized to produce acetyl-CoA, feeding into the TCA cycle or supporting lipid synthesis to fuel tumor growth (Mao et al., 2023 ; Zhu et al., 2023 ). In this scenario, AASS activity would be pro-tumorigenic by providing a critical fuel source. In dramatic contrast, the significant downregulation of AASS in Liver Hepatocellular Carcinoma (LIHC) implies a completely different metabolic strategy. This suppression of lysine degradation could serve to conserve lysine for protein synthesis, a critical need for rapidly proliferating cancer cells. A comprehensive analysis by Li et al. ( 2022 ) identified a distinct gene signature related to amino acid metabolism that was prognostic in hepatocellular carcinoma. The dysregulation of amino acid pathways is a central feature of LIHC, often involving the shunting of amino acids toward anabolic processes rather than catabolism (Li et al., 2022 ; Peng et al., 2020 ; Trisolini et al., 2020 ). The observed downregulation of AASS in the current study fits neatly into that model, suggesting a deliberate metabolic shift away from lysine breakdown to support the anabolic demands of liver cancer. This diametrically opposed regulation in two major cancer types underscores the critical importance of context in metabolic gene function (Xu et al., 2023 ; Zhang et al., 2023 ; Zhou et al., 2024 ). 5.3 Context-Dependent Prognostic Significance of AASS Expression Perhaps the most functionally significant finding of this investigation was the context-dependent prognostic value of AASS expression. The observation that high AASS expression correlated with a favorable prognosis in both KIRC and LIHC, yet an unfavorable prognosis in LUSC, presents a fascinating paradox. In KIRC and LIHC, where high AASS expression predicted better patient survival, the enzyme's activity might represent a more differentiated, less aggressive metabolic state. A functioning lysine degradation pathway could prevent the accumulation of potentially toxic metabolites or indicate a metabolic profile that is less reliant on the aggressive, anabolic pathways often associated with poor outcomes. This idea is supported by the work of Zhanget al. ( 2020 ), which explored the prognostic value of metabolic pathways across cancers. The study demonstrated that the expression patterns of specific metabolic networks, not just individual genes, could effectively stratify patients into different survival groups, reinforcing the idea that AASS expression is a marker of a broader, prognostically relevant metabolic state (Chen et al., 2021 ; Lian et al., 2025 ; Zhang et al., 2020 ). The inverted, unfavorable prognostic role of AASS in LUSC requires a different interpretation. In the specific metabolic context of lung cancer, elevated AASS activity might fuel a pathway that directly promotes tumor aggression or metastasis. For example, the acetyl-CoA produced from lysine degradation could be shunted towards lipid synthesis to support membrane production for proliferating cells or used for histone acetylation, an epigenetic modification that can drive oncogenic gene expression. A parallel can be drawn from the pan-cancer analysis of ACSS2 by Chen et al. ( 2025 ), which revealed a complex, multi-faceted role for another metabolic enzyme in both metabolic reprogramming and the tumor immune response. The study on ACSS2 showed how a single enzyme could have varied implications for cancer progression, providing a strong precedent for the complex prognostic role observed for AASS in the present analysis (Chen et al., 2025 ; Dang et al., 2025 ; Gu et al., 2024 ). The opposing prognostic significance of AASS across these cancers powerfully illustrates that a metabolic enzyme's impact on clinical outcomes is not intrinsic to the enzyme itself but is dictated by the larger cellular and metabolic network in which the enzyme operates. 5.4 AASS Co-Expression Networks Affirm a Core Metabolic Function The functional context of AASS was further elucidated through co-expression network analysis, which robustly placed the gene within a core network of mitochondrial and catabolic enzymes. The consistent co-expression of AASS with genes central to fatty acid beta-oxidation and ketone body metabolism provides strong evidence that lysine degradation is not an isolated pathway but is closely integrated with other major energy-producing processes in cancer cells. This finding suggests that AASS functions as part of a coordinated metabolic program to generate acetyl-CoA from multiple fuel sources, reinforcing the enzyme's role in central carbon metabolism. This concept of integrated amino acid metabolism is well-supported in the literature. A review by Peng et al. ( 2020 ) detailed the multifaceted role of branched-chain amino acid (BCAA) metabolism in cancer. The review explained how BCAA catabolism is intricately linked to other key metabolic pathways and serves diverse functions beyond simple energy production, providing a strong parallel for the integrated role posited for AASS (Peng et al., 2020 ). Furthermore, the integration of AASS into these core networks helps explain its potential importance in oncogenesis. Cancer cells often exhibit metabolic flexibility, allowing them to switch between fuel sources depending on nutrient availability. A study by Zou et al. ( 2021 ) conducted a pan-cancer analysis of glutamine metabolism regulators, a pathway famously exploited by cancer cells for both bioenergetic and biosynthetic purposes. The work demonstrated how a network of genes coordinates glutamine utilization to support cancer development, providing a model for how a similar network, including AASS, might coordinate lysine utilization (Huang et al., 2025 ; Zou et al., 2021 ). The current study’s co-expression results suggest that AASS is a key player in this metabolic flexibility, contributing to a pool of acetyl-CoA that can be used for energy via the TCA cycle or diverted for anabolic processes. This firm placement of AASS within a broader catabolic network elevates its significance from a simple component of a single amino acid pathway to an integral part of the central metabolic engine that drives cancer cell survival and proliferation (Trisolini et al., 2020 ). 5.5 Comparative Critical Discussion A critical comparison of the present AASS investigation with the broader landscape of pan-cancer bioinformatic studies reveals both shared limitations inherent to the methodology and unique strengths that advance the field. Many large-scale computational analyses, while powerful in scope, can present an oversimplified view of a gene's function by averaging effects across dozens of distinct diseases. A pan-cancer analysis of topoisomerase IIα by Wanget al. ( 2022 ), for example, effectively identified a generalized oncogenic role and potential regulatory mechanisms for the gene. A potential shortcoming of such a broad-scope conclusion, however, is the risk of masking the profound, context-dependent functional plasticity that the current AASS study uncovered, where the gene's prognostic meaning inverted from favorable in renal cancer to unfavorable in lung cancer (Huo et al., 2021 ; Wang et al., 2022 ; Wu et al., 2025 ). Similarly, the pan-cancer investigation of the oncogenic role of SND1 by Cui et al. ( 2020 ) robustly correlated its expression with poor prognosis across multiple tumors. A limitation of this otherwise solid correlational approach is that it can stop short of providing a deeper mechanistic hypothesis, a gap the current AASS study sought to address through its detailed co-expression and functional enrichment analyses (Cui et al., 2020 ; Y. Liu et al., 2025 ; Zhang et al., 2023 ). The present AASS analysis attempted to mitigate these common pitfalls by adopting a focused approach within the broader pan-cancer framework, using the global screen to identify and then deeply interrogate the specific malignancies where AASS expression and prognostic value were most dramatically altered. Furthermore, even within the subfield of metabolic pan-cancer studies, there can be a narrow focus that overlooks the integrated nature of cellular metabolism. The pivotal pan-cancer analysis by Zhou et al ( 2021 ) revealed the oncogenic role of HMGCS1, a key enzyme in the mevalonate and ketone metabolism pathways. The study's strength was its clear focus, but a potential critique is that by concentrating on a well-established pathway, such analyses might inadvertently neglect the crosstalk with less-studied metabolic axes, like the lysine degradation pathway represented by AASS (Zhou et al., 2021 ; Zhou et al., 2022 ). The current AASS investigation’s co-expression analysis, which explicitly linked AASS to fatty acid oxidation and ketone body metabolism enzymes like HMGCS2, was a deliberate attempt to build a more integrated metabolic picture, showing how a less-explored amino acid pathway is functionally coordinated with central energy-producing hubs (Dong et al., 2024 ; Mao et al., 2023 ; Xiong et al., 2025 ). This approach moves beyond confirming the importance of known pathways and begins to map the connections between them, providing a more holistic view of the reprogrammed cancer metabolome. Despite these potential limitations in the broader literature, the value and strength of previous pan-cancer studies are undeniable, as such works provided the essential methodological and conceptual foundation for the present AASS investigation. The comprehensive prognostic and immunological analysis of the metabolic gene HKDC1 by Liang et al. ( 2025 ) serves as a prime example of a robust research blueprint. That study’s successful integration of expression, survival, and immune landscape data provided a validated, multi-pronged methodology that the current AASS study emulated to construct a similarly layered and comprehensive profile of its target gene (Liang et al., 2025 ; Mullen & Singh, 2023 ; Zhang et al., 2025 ). The adoption of this established framework ensured a high degree of analytical rigor and comparability with the existing body of literature. The AASS study did not reinvent the analytical wheel but rather applied a proven, powerful toolset to a novel and functionally ambiguous target. Conceptually, the current work stands on the shoulders of high-level reviews and studies that established the very importance of exploring metabolic dependencies from a pan-cancer perspective. A critical review by Mullen & Singh ( 2023 ) persuasively argued that nucleotide metabolism represents a fundamental pan-cancer dependency. This type of overarching work creates the intellectual rationale for interrogating other, less-characterized metabolic pathways, like lysine catabolism, in the search for new vulnerabilities (Liu et al., 2020 ; Mullen & Singh, 2023 ; Sellitto et al., 2021 ). The AASS study directly answers this call by shifting the focus to a non-canonical amino acid pathway, thereby building upon the foundational principle that mapping the full spectrum of metabolic reprogramming is essential for identifying the next generation of therapeutic targets. Moreover, the conceptual framework connecting metabolism to the tumor immune microenvironment, as comprehensively outlined in a review by Xia et al. ( 2021 ), was a direct inspiration for a key analytical arm of the AASS study. The review detailed the intricate molecular mechanisms by which metabolic shifts in cancer cells influence immune cell function and infiltration (Gu et al., 2024 ; Xia et al., 2021 ). This knowledge motivated the correlation analysis between AASS expression and immune cell abundance, a crucial step that moved the investigation beyond cell-intrinsic metabolic function to explore its potential impact on the broader tumor ecosystem. In doing so, the AASS study not only characterized a gene but also generated testable hypotheses about its role in shaping the immune landscape, a strength it owes to the conceptual groundwork laid by prior research. The present analysis represents a synthesis of these positive attributes: it leverages established, robust methodologies to explore a novel gene within a compelling, pre-existing conceptual framework, ultimately pushing the boundaries of that framework by uncovering an unexpected and highly context-dependent biological story. 6 Conclusion This investigation concluded that Aminoadipate-semialdehyde synthase is not a classical oncogene or tumor suppressor but rather a critical, context-dependent modulator of cancer metabolism. The findings demonstrated a stark functional dichotomy, with AASS playing distinct and sometimes opposing roles in different malignancies. The upregulation and favorable prognostic significance in renal cancer contrasted sharply with its unfavorable prognostic role in lung cancer, while its downregulation in liver cancer still pointed to a beneficial role when expressed. This functional plasticity underscores a critical principle: the impact of a metabolic enzyme is determined not by its solitary function but by the broader metabolic and signaling network of the specific cancer cell. This study successfully repositioned AASS from a peripheral enzyme in a single amino acid pathway to a significant and clinically relevant player in the landscape of oncogenic metabolic reprogramming, highlighting the necessity of context-specific research in cancer metabolism. 6.1 Limitations & Strengths of the Study The primary limitation of this research lies in its purely bioinformatic and retrospective nature. The analysis was conducted on publicly available transcriptomic data, meaning the findings are correlational and do not directly measure AASS protein levels or enzymatic activity. Consequently, the study cannot establish causality and requires experimental validation to confirm the functional roles hypothesized. However, the study's principal strength is its comprehensive scope and statistical power. By leveraging the vast datasets of The Cancer Genome Atlas, the investigation provided a robust, pan-cancer view that would be unfeasible in a single experimental study. A further strength was the multi-layered analytical approach, which integrated differential expression, patient survival, and co-expression network data to build a holistic and nuanced profile of AASS. This approach successfully uncovered novel, clinically relevant patterns for a previously understudied metabolic gene, generating a strong foundation for future mechanistic research. 6.2 Future Research Directions Based on the findings of this study, several future research directions are essential. The immediate next step involves experimental validation of the observed correlations. Using in vitro cell line models of renal, liver, and lung cancer, studies should modulate AASS expression via CRISPR or siRNA to directly assess its effects on cell proliferation, migration, and metabolic flux. In vivo studies using xenograft or genetically engineered mouse models are also necessary to confirm the prognostic findings in a physiological context. Further research should investigate the upstream regulatory mechanisms responsible for the differential expression of AASS, including the roles of specific transcription factors and epigenetic modifications like DNA methylation. Finally, given the opposing prognostic roles, future work could explore the therapeutic potential of targeting AASS, investigating whether inhibiting its activity in lung cancer or restoring its expression in liver cancer could represent viable clinical strategies. Declarations Ethical Approval Ethical approval was not applicable for this study. The research was conducted using exclusively anonymized, publicly available secondary data from established repositories such as The Cancer Genome Atlas (TCGA). Therefore, no direct human or animal subjects were involved, and no institutional review board approval was required. Data Availability The datasets analyzed during the current study were derived from publicly available repositories. All transcriptomic and clinical data were obtained from The Cancer Genome Atlas (TCGA) program. This data can be accessed through various public portals, including the UCSC Xena Browser and the cBioPortal for Cancer Genomics. Summary data and visualizations are also publicly accessible via The Human Protein Atlas at: https://www.proteinatlas.org/ENSG00000008311-AASS Conflict Of Intrest The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. 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1","display":"","copyAsset":false,"role":"figure","size":334555,"visible":true,"origin":"","legend":"\u003cp\u003eAASS expression in the TCGA\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8210982/v1/2d1708aba629a629055a81b4.png"},{"id":97130945,"identity":"169b251d-c696-49de-8942-40eb4ca86be8","added_by":"auto","created_at":"2025-12-01 08:41:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":163352,"visible":true,"origin":"","legend":"\u003cp\u003eAASS expression in the TCGA KIRC\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8210982/v1/c5d129d6ecc3b34562fd06fa.png"},{"id":97130948,"identity":"5f35b249-ec90-435a-8041-bdb472d0bdaa","added_by":"auto","created_at":"2025-12-01 08:41:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":106898,"visible":true,"origin":"","legend":"\u003cp\u003eAASS expression in the TCGA LIHC\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8210982/v1/ede079b02482563f826d0323.png"},{"id":97142688,"identity":"05081af1-34e9-4b6b-955d-9e9de77cbebc","added_by":"auto","created_at":"2025-12-01 10:07:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":148549,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival plot for AASS in the KIRC\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8210982/v1/e5a375c9c12f108f31032548.png"},{"id":97142141,"identity":"42587baa-6e00-4733-aa50-d5720f56ab1d","added_by":"auto","created_at":"2025-12-01 10:07:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":154833,"visible":true,"origin":"","legend":"\u003cp\u003eThe Kaplan-Meier plot for AASS in the TCGA LIHC\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8210982/v1/946def44070ba3b8a4a8bf28.png"},{"id":97130954,"identity":"540aadd5-79e5-4f67-ab3b-88b9f229da41","added_by":"auto","created_at":"2025-12-01 08:41:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":135473,"visible":true,"origin":"","legend":"\u003cp\u003eThe Kaplan-Meier plot for AASS in the LUSC\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8210982/v1/a6deccc78183dcba895461ff.png"},{"id":97249540,"identity":"217a692f-f686-4539-a2c5-9e9aa252c05d","added_by":"auto","created_at":"2025-12-02 13:12:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1946606,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8210982/v1/5f1ca391-6049-4bdd-add1-476dc895382b.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePan-Cancer Expression Analysis of the Aminoadipate-semialdehyde synthase (AASS) Gene: Insights into its Potential Role in Oncogenic Metabolic Reprogramming\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Highlights","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003eThis research uncovered the highly heterogeneous expression landscape of the AASS gene across 33 human cancers, challenging a simplistic classification of the enzyme as either an oncogene or a tumor suppressor. The analysis demonstrated that AASS's role is not universal but is instead dynamically modulated, suggesting its function is intricately tied to the specific metabolic dependencies and selective pressures of each distinct tumor type.\u003c/li\u003e\n \u003cli\u003eA central finding was the starkly dichotomous regulation of AASS in specific malignancies. The gene was significantly upregulated in Kidney Renal Clear Cell Carcinoma while being significantly downregulated in Liver Hepatocellular Carcinoma.\u003c/li\u003e\n \u003cli\u003eThe investigation revealed a profound prognostic paradox, a key highlight of the study. High AASS expression was a robust marker for favorable patient survival in both renal and liver cancers. Conversely, in lung cancer, high AASS expression was statistically associated with an unfavorable prognosis, powerfully illustrating that the clinical significance of a metabolic gene is entirely dictated by its specific oncogenic context.\u003c/li\u003e\n \u003cli\u003eCo-expression network analysis provided a strong functional context for AASS, firmly linking its expression to a network of core mitochondrial and catabolic enzymes. The consistent correlation with genes involved in fatty acid beta-oxidation and ketone body metabolism indicated that AASS operates as an integral part of the central energy-producing machinery, not as an isolated pathway, reinforcing its importance in reprogrammed cancer metabolism.\u003c/li\u003e\n \u003cli\u003eThe strong and statistically significant association between AASS expression and patient outcomes across multiple major cancers established the gene as a novel and compelling prognostic biomarker.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1 Introduction","content":"\u003cp\u003eAltered cellular metabolism is a fundamental characteristic of cancer, a concept that has been recognised for nearly a century. A contemporary understanding, articulated by Faubert et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), describes a comprehensive metabolic reprogramming that supports tumour growth and survival. This metabolic reorganisation allows cancer cells to meet bioenergetic and biosynthetic demands during uncontrolled proliferation (Cheng et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Faubert et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; M. Sun et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Work by Tufail et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) further detailed how these alterations in energy pathways create therapeutic vulnerabilities. These altered metabolic states are now recognized as promising targets for novel cancer therapies (Tufail et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This metabolic shift is not a monolithic process; rather, a significant body of research reveals its heterogeneity across different tumor types, a phenomenon explored in a single-cell atlas by Zhou et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The study uncovered multilayered metabolic diversity, underscoring the need for cancer-specific metabolic analyses (Zhou et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCentral to this reprogramming is the dysregulation of amino acid metabolism. Research from Peng et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) highlighted the multifaceted role of branched-chain amino acid pathways in oncogenesis. The study explained how these pathways contribute to everything from energy production to epigenetic modulation (Peng et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Among amino acid pathways, lysine metabolism is critical but less explored in a pan-cancer context. The enzyme Aminoadipate-semialdehyde synthase (AASS) is the bifunctional, rate-limiting enzyme in the principal pathway for lysine degradation. Seminal work by Leandro et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) demonstrated that the deletion of AASS leads to significant metabolite accumulation. This finding confirmed the enzyme's critical role in maintaining metabolic homeostasis (Leandro et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, the clinical relevance of AASS is established through its connection to hyperlysinemia, an inborn error of metabolism, as reviewed by Marinella et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The review provided a deep dive into the systemic effects of a dysfunctional lysine degradation pathway, establishing AASS as a critical metabolic checkpoint (Lin et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Marinella et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite the foundational importance of metabolic reprogramming, the specific roles of many metabolic enzymes across a broad spectrum of cancers remain poorly characterized. Pan-cancer analyses have successfully illuminated the oncogenic roles of various metabolic genes, as shown in the analysis of ACSS2 by Chen et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This previous work provides a clear methodological precedent for exploring metabolic enzymes across diverse malignancies (Chen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Yet, a comprehensive pan-cancer investigation of AASS is conspicuously absent. Preliminary data from The Human Protein Atlas indicates that AASS expression is highly variable across cancers and that high expression is a significant favorable prognostic marker in liver, lung, and renal cancers (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This variability suggests a context-dependent role for AASS in tumorigenesis that has not been systematically investigated. The absence of a clear understanding of AASS's expression patterns, prognostic value, and functional network across different cancers represents a significant gap in the knowledge of oncogenic metabolism. Therefore, this study aimed to conduct the first comprehensive pan-cancer expression analysis of the AASS gene, with a specific focus on liver, lung, and renal cancers, to elucidate its potential role in oncogenic metabolic reprogramming. This investigation was guided by following research questions.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eWhat is the differential expression pattern of the AASS gene across various cancer types, particularly comparing tumor and adjacent normal tissues in liver, lung, and renal carcinomas?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eWhat is the prognostic significance of AASS expression for patient survival within these specific cancer cohorts and across the broader pan-cancer landscape?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eWhat are the potential regulatory mechanisms and co-expression networks associated with AASS?\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"2 Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Metabolic Reprogramming as a Cancer Hallmark\u003c/h2\u003e\u003cp\u003eThe concept of metabolic reprogramming in cancer has evolved significantly, now recognized as a core hallmark of malignancy. A foundational paper by Faubert et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) established that cancer progression relies on a profound rewiring of cellular metabolism. This reprogramming is essential for meeting the heightened bioenergetic and biosynthetic demands of rampant cell division (Faubert et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Y. Sun et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Further work by Tufail et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) detailed how these adaptations, from aerobic glycolysis to altered nutrient uptake, create unique dependencies. These dependencies present promising therapeutic targets that can selectively starve cancer cells (Tufail et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This metabolic plasticity is not uniform; a study by Sung and Cheong (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) conducted a pan-cancer analysis that revealed distinct metabolic states are associated with different epithelial-mesenchymal transition (EMT) activities. The findings underscored that a cell's metabolic profile is tightly linked to its phenotypic state (H. Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sung \u0026amp; Cheong, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Regulation of this reprogramming is multifaceted, involving oncogenic drivers and epigenetic modifications. For instance, a review by Dong et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) positioned the oncogene MYC as a master regulator of cancer cell metabolism. The review explained how MYC orchestrates broad transcriptional programs that control nutrient uptake and utilization (Dong et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The interplay between metabolism and epigenetics was explored by Sun et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who described a bidirectional relationship where metabolic intermediates act as cofactors for epigenetic enzymes. This crosstalk creates a feedback loop that reinforces the malignant phenotype (Sun et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Pan-Cancer Analyses in Uncovering Metabolic Vulnerabilities\u003c/h2\u003e\u003cp\u003eThe advent of large-scale datasets has enabled pan-cancer analyses, a powerful approach for identifying common and distinct molecular patterns across diverse tumor types. A study by Gatto et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) performed a pan-cancer analysis of the entire metabolic reaction network. This work identified globally essential metabolic reactions that are critical for cancer cell survival across numerous malignancies (Gatto et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, research by Zhang et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) used pan-cancer data to identify the prognostic value of specific metabolic pathways. The study revealed co-expression patterns that successfully stratified patients into different survival groups (Zhang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This approach has been applied to specific metabolic genes to clarify their roles. The pan-cancer analysis of ACSS2 by Chen et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) provided multi-omics insights into its function in metabolic reprogramming and the tumor immune microenvironment. This study served as a robust template for investigating the pan-cancer significance of individual metabolic enzymes (Chen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Another investigation by Zou et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) performed a multi-omics pan-cancer analysis of glutamine metabolism regulators. The work identified key genes associated with cancer development and the immune landscape, highlighting how central carbon metabolism is rewired in cancer (Zou et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 The Lysine Degradation Pathway and the AASS Gene\u003c/h2\u003e\u003cp\u003eWithin the broader context of amino acid metabolism, the lysine degradation pathway plays a crucial role in maintaining cellular homeostasis. The bifunctional enzyme AASS serves as the primary rate-limiting step in this pathway. A functional study by Leandro et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) demonstrated that the genetic deletion of AASS in cell and mouse models resulted in a significant accumulation of upstream metabolites. This finding confirmed the enzyme's indispensable role in processing lysine and preventing metabolic toxicity (Leandro et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The clinical importance of AASS is underscored by its causal role in the rare metabolic disorder hyperlysinemia. A review by Marinella et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) provided a comprehensive overview of the condition, which results from mutations in the AASS gene. This context establishes the enzyme as a critical metabolic checkpoint with systemic physiological consequences when dysfunctional (Marinella et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Recent work has also begun to explore AASS as a therapeutic target. For instance, a study by Segur-Bailach et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) successfully used AAV-miRNA technology to inhibit AASS expression in a mouse model of glutaric aciduria type I. This therapeutic inhibition rescued the severe phenotype, showcasing the enzyme's druggability (Segur-Bailach et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, an integrative analysis by Yu et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) identified AASS as a key predictor and therapeutic target in kidney disease. This research connected AASS to mitochondrial and immune pathways, suggesting its functions extend beyond simple catabolism (Leandro et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 AASS Expression and its Potential Role in Cancer\u003c/h2\u003e\u003cp\u003eDespite its known metabolic function, a systematic investigation of AASS across human cancers is lacking. Preliminary data from The Human Protein Atlas shows that AASS is variably expressed across tumors, with prognostic significance in several cancers. For instance, in liver hepatocellular carcinoma, high AASS expression is a statistically significant favorable prognostic marker (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a pattern also observed in renal and lung cancers. This suggests a potentially protective, context-dependent role in these malignancies. Metabolic analyses in these specific cancers support the importance of studying unique metabolic profiles. A comprehensive analysis of amino acid metabolism in hepatocellular carcinoma by Li et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) identified a gene signature that predicted prognosis and the tumor immune microenvironment. This work highlights the critical role of amino acid pathways in liver cancer (Li et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Segur-Bailach et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In renal cell carcinoma, a study by Zhu et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) provided a detailed overview of its distinct metabolic reprogramming. This reprogramming offers specific vulnerabilities not seen in other cancers (Leandro et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This evidence collectively points to a gap in knowledge regarding the pan-cancer role of AASS, an enzyme positioned at a key metabolic node with demonstrated clinical relevance.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Methodology","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Research Design\u003c/h2\u003e\u003cp\u003eThe investigation employed a comprehensive pan-cancer, multi-omics, and retrospective research design to systematically characterize the AASS gene. The design centered on leveraging publicly available genomic and clinical data from large-scale projects, primarily The Cancer Genome Atlas (TCGA). This retrospective cohort study design was selected to ensure the statistical power necessary for detecting clinically and biologically relevant associations across multiple cancer types. The pan-cancer approach was deemed essential because a single-cancer study often misses generalized or context-specific oncogenic roles, a limitation that previous metabolic analyses sought to overcome. The focus was not only on AASS expression but also on its genomic landscape, prognostic impact, and co-expression networks. The selection of specific tumor types, Hepatocellular Carcinoma (LIHC), Lung Adenocarcinoma (LUAD), and Kidney Renal Clear Cell Carcinoma (KIRC), was a deliberate choice, driven by preliminary data indicating AASS\u0026rsquo;s significant prognostic value in these specific malignancies. This strategic focus allowed for deeper validation within the most relevant cohorts. The analytical framework was organized into distinct modules: differential expression analysis, survival modeling, genetic alteration mapping, and functional enrichment, thus generating a robust, multi-layered profile of AASS in oncogenic metabolic reprogramming. A key goal of the design was to produce an integrative analysis that established a clear mechanistic context for the observed expression patterns.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Data Collection\u003c/h2\u003e\u003cp\u003eTranscriptomic, clinical, and mutational data were primarily acquired from The Cancer Genome Atlas (TCGA) repository, accessed via established public platforms, including the UCSC Xena Browser and the cBioPortal for Cancer Genomics. This total resource contains RNA-sequencing (RNA-Seq) data for over 11,000 primary tumor samples spanning 33 distinct cancer types. For the differential expression analysis, normalized RNA-Seq data (FPKM or TPM values) were collected for AASS in both solid tumor tissues and corresponding non-tumor adjacent tissues. A total of 33 TCGA datasets provided the tumor data, while a smaller but substantial set of datasets provided matched normal tissue data, with over 700 matched samples available for some tumor types like KIRC. Clinical data, including Overall Survival (OS) time, status, patient age, tumor stage, and histological grade, were extracted for the same tumor cohorts to facilitate prognostic modeling. The collection of mutational data focused on somatic copy number variations (SCNVs) and missense mutations specific to the AASS locus across all TCGA samples. Furthermore, a critical part of the collection involved gathering data on related regulatory elements. This included a search for non-coding RNA (ncRNA) expression profiles and epigenetic modification data, such as DNA methylation (via the Illumina Human Methylation 450 array data), which could influence AASS transcription. The collection of co-expression partners involved downloading the expression matrix for all protein-coding genes. This allowed for the calculation of global gene-gene correlations with AASS expression. The stringent data collection process ensured that only high-quality, normalized datasets were used, minimizing batch effects and increasing the reliability of downstream statistical inferences. This comprehensive data collection provided the necessary foundation for the multi-omics analysis of the target gene.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Data Analysis\u003c/h2\u003e\u003cp\u003eThe data analysis proceeded through a series of sequential and complementary steps, all performed using R statistical software and specialized Bioconductor packages. The first stage involved Differential Expression (DE) Analysis. A Wilcoxon rank-sum test was performed to compare AASS expression between tumor and normal tissues across all 33 cancer types. The results were visualized using box plots and volcano plots, with a threshold for significance set at an adjusted P-value (False Discovery Rate, FDR) of less than 0.05 and an absolute \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{l}\\text{o}\\text{g}}_{2}\\left(\\text{Fold\\:Change}\\right)\\:\\)\u003c/span\u003e\u003c/span\u003e greater than 1. This analysis confirmed the variable expression of AASS, identifying significant upregulation in KIRC and downregulation in LIHC.\u003c/p\u003e\u003cp\u003eNext, a Survival Analysis was conducted. Patients in each TCGA cohort were dichotomized into high- and low-expression groups using the median AASS expression value as the cutoff point. Kaplan-Meier survival curves were generated for Overall Survival (OS) in all cancer types, followed by the calculation of log-rank P-values. Univariate and multivariate Cox proportional hazards regression models were then applied to calculate the hazard ratio (HR) for AASS, controlling for clinical factors like patient age and pathological stage. This analysis confirmed that high AASS expression was associated with a favorable prognosis in KIRC (HR typically around 0.5; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but showed a complex, context-dependent pattern in other cancers.\u003c/p\u003e\u003cp\u003eThe third stage was Co-expression Network Construction and Functional Enrichment. Pearson correlation analysis was performed to identify genes whose expression levels strongly correlated with AASS expression across the pan-cancer cohort (absolute Pearson's \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:R\u0026gt;0.4\\)\u003c/span\u003e\u003c/span\u003e and FDR \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lt;0.001\\)\u003c/span\u003e\u003c/span\u003e). This set of co-expressed genes was then subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The results from this analysis consistently pointed toward enrichment in metabolic pathways, including amino acid catabolism and lipid metabolism, as well as processes related to mitochondrial function. Specific attention was given to GO terms related to the immune system to explore potential immunomodulatory roles for AASS.\u003c/p\u003e\u003cp\u003eFinally, an Immune Microenvironment Analysis was performed. Computational algorithms, such as TIMER and CIBERSORT, were utilized to estimate the infiltration levels of six major immune cell types (e.g., B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, Macrophages) within the tumor microenvironment (TME) of the KIRC, LIHC, and LUAD cohorts. Pearson correlation was used to quantify the relationship between AASS expression and the estimated abundance of each immune cell type. This final step provided the critical link between the metabolic enzyme and the immune status of the tumors, suggesting that AASS\u0026rsquo;s prognostic role may be mediated through its influence on the tumor microenvironment. Each analytical step was meticulously documented to ensure reproducibility and scientific rigor.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Pan-Cancer Differential Expression of AASS\u003c/h2\u003e\u003cp\u003eThe initial analysis examined the differential expression of the AASS gene across a wide spectrum of human cancers. A comprehensive screen of transcriptomic data revealed a highly heterogeneous expression landscape. AASS expression was not uniformly up- or downregulated; instead, its levels varied significantly depending on the specific cancer type. This variability highlighted the necessity of a context-specific investigation rather than a generalized conclusion about the gene's role in oncogenesis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.2 AASS Expression is Significantly Altered in Specific Cancers of Interest\u003c/h2\u003e\u003cp\u003eA deeper dive into the cancers of interest, renal, liver, and lung, uncovered distinct and significant expression patterns when comparing tumor tissue to normal adjacent tissue. In KIRC, the analysis revealed a pronounced and statistically significant upregulation of AASS transcript levels in tumor samples. The difference between tumor and normal kidney tissue was substantial (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a potential role for enhanced lysine degradation in renal tumorigenesis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eConversely, an opposite and equally significant pattern emerged in Liver Hepatocellular Carcinoma (LIHC). In this malignancy, AASS expression was found to be significantly downregulated in tumor tissue compared to normal liver tissue (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This marked reduction suggested that a suppression of the lysine degradation pathway might be a metabolic feature of liver cancer, contrasting sharply with the findings in renal cancer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn contrast to the clear patterns in renal and liver cancer, the analysis of lung cancer, specifically Lung Squamous Cell Carcinoma (LUSC), did not show a statistically significant difference in AASS expression between tumor and normal tissues. This neutral finding was itself important, suggesting that AASS dysregulation is not a universal feature of carcinogenesis but is instead prominent in specific tumor types with particular metabolic dependencies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Prognostic Value of AASS is Strongly Cancer-Type Dependent\u003c/h2\u003e\u003cp\u003eFollowing the expression analysis, an investigation into the prognostic significance of AASS expression yielded highly context-dependent results. The relationship between AASS mRNA levels and patient survival was not uniform; it differed starkly between the examined cancers. For patients with KIRC, high expression of AASS was a strong and statistically significant indicator of a favorable prognosis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients in the high-AASS expression group exhibited markedly longer overall survival times compared to patients in the low-expression group, positing AASS as a potential protective biomarker in this cancer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA similar favorable prognostic value was observed in Liver Hepatocellular Carcinoma (LIHC). Despite AASS being downregulated in liver tumors, in cases where its expression remained high, patients also experienced significantly better survival outcomes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding suggested that the maintenance of AASS function is beneficial for patient prognosis in LIHC.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn a striking and informative contrast, the prognostic landscape was inverted in LUSC. For this malignancy, high expression of AASS was associated with an unfavorable prognosis and significantly shorter patient survival (p\u0026thinsp;=\u0026thinsp;0.015). This opposing role underscored the profound functional plasticity of a single metabolic gene across different tumor microenvironments and genetic backgrounds. The gene's activity appeared beneficial in renal and liver cancers but detrimental in lung cancer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Co-Expression Network Analysis Links AASS to Core Metabolic Processes\u003c/h2\u003e\u003cp\u003eTo elucidate the functional context of AASS, a co-expression analysis was performed to identify genes with expression patterns strongly correlated with AASS across the relevant cancer cohorts. The results consistently placed AASS within a network of core metabolic enzymes. In both KIRC and LIHC, AASS showed strong positive co-expression with genes integral to mitochondrial function and catabolic pathways. Top co-expressed partners included enzymes involved in fatty acid beta-oxidation (e.g., HADH, ACADSB) and ketone body metabolism (HMGCS2). This network analysis provided strong evidence that AASS expression is coordinated with other key components of cellular energy production, reinforcing its role in oncogenic metabolic reprogramming.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCo-expressed genes for AASS in KIRC and LIHC\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePearson's R\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePrimary Metabolic Function\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKIRC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHMGCS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKetone Body Metabolism\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKIRC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHADH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFatty Acid Beta-Oxidation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKIRC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACADSB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBranched-Chain Amino Acid Catabolism\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKIRC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBDH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKetone Body Metabolism\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKIRC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eECHS1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFatty Acid Beta-Oxidation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLIHC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACADSB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBranched-Chain Amino Acid Catabolism\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLIHC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHADH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFatty Acid Beta-Oxidation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLIHC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLeucine Catabolism\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLIHC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eECHS1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFatty Acid Beta-Oxidation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLIHC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHIBADH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eValine Catabolism\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e5.1 AASS Exhibits a Heterogeneous Pan-Cancer Expression Landscape\u003c/h2\u003e\u003cp\u003eThe initial finding of the current investigation was the highly variable expression pattern of AASS across a multitude of human cancers. The analysis revealed no universal trend of up- or downregulation, but rather a complex, cancer-specific landscape. This heterogeneity strongly suggests that the role of AASS, and by extension the lysine degradation pathway, is not a one-size-fits-all mechanism in oncology. Instead, the gene's function appears to be deeply embedded within the unique metabolic and genetic context of each specific tumor type. This observation aligns perfectly with the contemporary understanding of cancer metabolism as a deeply plastic and adaptive process. The work of Faubert et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) broadly established that metabolic reprogramming is a core hallmark of cancer, designed to meet specific biosynthetic and bioenergetic needs. This principle of tailored reprogramming provides a foundational explanation for why a single metabolic enzyme like AASS would exhibit such diverse expression patterns across different malignancies (Dey et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Faubert et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, a pan-cancer study by Sung and Cheong (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) directly connected distinct metabolic states to varying levels of epithelial-mesenchymal transition (EMT) activity. The research demonstrated that a tumor's metabolic profile is intrinsically linked to its phenotypic state, offering a mechanistic layer to explain the observed heterogeneity in AASS expression (Cui et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gatto et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sung \u0026amp; Cheong, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The present study\u0026rsquo;s finding contributes to this paradigm by positioning AASS as another key metabolic gene whose expression is dynamically modulated to suit the particular metabolic dependencies of a given cancer.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e5.2 The Dichotomous Role of AASS in Renal and Liver Carcinoma\u003c/h2\u003e\u003cp\u003eA central and compelling result of this study was the discovery of a stark, dichotomous expression pattern of AASS in renal and liver cancers. The significant upregulation of AASS in KIRC suggests that an enhanced capacity for lysine catabolism may be an important feature of this malignancy's metabolic reprogramming. This finding can be interpreted in the context of renal cancer's unique metabolic wiring. An extensive review by Zhu et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) described the metabolic reprogramming of clear cell renal cell carcinoma in great detail, noting its reliance on altered glucose and lipid metabolism. The upregulation of AASS might represent another axis of this reprogramming, where lysine is catabolized to produce acetyl-CoA, feeding into the TCA cycle or supporting lipid synthesis to fuel tumor growth (Mao et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this scenario, AASS activity would be pro-tumorigenic by providing a critical fuel source. In dramatic contrast, the significant downregulation of AASS in Liver Hepatocellular Carcinoma (LIHC) implies a completely different metabolic strategy. This suppression of lysine degradation could serve to conserve lysine for protein synthesis, a critical need for rapidly proliferating cancer cells. A comprehensive analysis by Li et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) identified a distinct gene signature related to amino acid metabolism that was prognostic in hepatocellular carcinoma. The dysregulation of amino acid pathways is a central feature of LIHC, often involving the shunting of amino acids toward anabolic processes rather than catabolism (Li et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Trisolini et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The observed downregulation of AASS in the current study fits neatly into that model, suggesting a deliberate metabolic shift away from lysine breakdown to support the anabolic demands of liver cancer. This diametrically opposed regulation in two major cancer types underscores the critical importance of context in metabolic gene function (Xu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Context-Dependent Prognostic Significance of AASS Expression\u003c/h2\u003e\u003cp\u003ePerhaps the most functionally significant finding of this investigation was the context-dependent prognostic value of AASS expression. The observation that high AASS expression correlated with a favorable prognosis in both KIRC and LIHC, yet an unfavorable prognosis in LUSC, presents a fascinating paradox. In KIRC and LIHC, where high AASS expression predicted better patient survival, the enzyme's activity might represent a more differentiated, less aggressive metabolic state. A functioning lysine degradation pathway could prevent the accumulation of potentially toxic metabolites or indicate a metabolic profile that is less reliant on the aggressive, anabolic pathways often associated with poor outcomes. This idea is supported by the work of Zhanget al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which explored the prognostic value of metabolic pathways across cancers. The study demonstrated that the expression patterns of specific metabolic networks, not just individual genes, could effectively stratify patients into different survival groups, reinforcing the idea that AASS expression is a marker of a broader, prognostically relevant metabolic state (Chen et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lian et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe inverted, unfavorable prognostic role of AASS in LUSC requires a different interpretation. In the specific metabolic context of lung cancer, elevated AASS activity might fuel a pathway that directly promotes tumor aggression or metastasis. For example, the acetyl-CoA produced from lysine degradation could be shunted towards lipid synthesis to support membrane production for proliferating cells or used for histone acetylation, an epigenetic modification that can drive oncogenic gene expression. A parallel can be drawn from the pan-cancer analysis of ACSS2 by Chen et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which revealed a complex, multi-faceted role for another metabolic enzyme in both metabolic reprogramming and the tumor immune response. The study on ACSS2 showed how a single enzyme could have varied implications for cancer progression, providing a strong precedent for the complex prognostic role observed for AASS in the present analysis (Chen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Dang et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The opposing prognostic significance of AASS across these cancers powerfully illustrates that a metabolic enzyme's impact on clinical outcomes is not intrinsic to the enzyme itself but is dictated by the larger cellular and metabolic network in which the enzyme operates.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e5.4 AASS Co-Expression Networks Affirm a Core Metabolic Function\u003c/h2\u003e\u003cp\u003eThe functional context of AASS was further elucidated through co-expression network analysis, which robustly placed the gene within a core network of mitochondrial and catabolic enzymes. The consistent co-expression of AASS with genes central to fatty acid beta-oxidation and ketone body metabolism provides strong evidence that lysine degradation is not an isolated pathway but is closely integrated with other major energy-producing processes in cancer cells. This finding suggests that AASS functions as part of a coordinated metabolic program to generate acetyl-CoA from multiple fuel sources, reinforcing the enzyme's role in central carbon metabolism. This concept of integrated amino acid metabolism is well-supported in the literature. A review by Peng et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) detailed the multifaceted role of branched-chain amino acid (BCAA) metabolism in cancer. The review explained how BCAA catabolism is intricately linked to other key metabolic pathways and serves diverse functions beyond simple energy production, providing a strong parallel for the integrated role posited for AASS (Peng et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, the integration of AASS into these core networks helps explain its potential importance in oncogenesis. Cancer cells often exhibit metabolic flexibility, allowing them to switch between fuel sources depending on nutrient availability. A study by Zou et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) conducted a pan-cancer analysis of glutamine metabolism regulators, a pathway famously exploited by cancer cells for both bioenergetic and biosynthetic purposes. The work demonstrated how a network of genes coordinates glutamine utilization to support cancer development, providing a model for how a similar network, including AASS, might coordinate lysine utilization (Huang et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zou et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The current study\u0026rsquo;s co-expression results suggest that AASS is a key player in this metabolic flexibility, contributing to a pool of acetyl-CoA that can be used for energy via the TCA cycle or diverted for anabolic processes. This firm placement of AASS within a broader catabolic network elevates its significance from a simple component of a single amino acid pathway to an integral part of the central metabolic engine that drives cancer cell survival and proliferation (Trisolini et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e5.5 Comparative Critical Discussion\u003c/h2\u003e\u003cp\u003eA critical comparison of the present AASS investigation with the broader landscape of pan-cancer bioinformatic studies reveals both shared limitations inherent to the methodology and unique strengths that advance the field. Many large-scale computational analyses, while powerful in scope, can present an oversimplified view of a gene's function by averaging effects across dozens of distinct diseases. A pan-cancer analysis of topoisomerase IIα by Wanget al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), for example, effectively identified a generalized oncogenic role and potential regulatory mechanisms for the gene. A potential shortcoming of such a broad-scope conclusion, however, is the risk of masking the profound, context-dependent functional plasticity that the current AASS study uncovered, where the gene's prognostic meaning inverted from favorable in renal cancer to unfavorable in lung cancer (Huo et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Similarly, the pan-cancer investigation of the oncogenic role of SND1 by Cui et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) robustly correlated its expression with poor prognosis across multiple tumors. A limitation of this otherwise solid correlational approach is that it can stop short of providing a deeper mechanistic hypothesis, a gap the current AASS study sought to address through its detailed co-expression and functional enrichment analyses (Cui et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Y. Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The present AASS analysis attempted to mitigate these common pitfalls by adopting a focused approach within the broader pan-cancer framework, using the global screen to identify and then deeply interrogate the specific malignancies where AASS expression and prognostic value were most dramatically altered.\u003c/p\u003e\u003cp\u003eFurthermore, even within the subfield of metabolic pan-cancer studies, there can be a narrow focus that overlooks the integrated nature of cellular metabolism. The pivotal pan-cancer analysis by Zhou et al (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) revealed the oncogenic role of HMGCS1, a key enzyme in the mevalonate and ketone metabolism pathways. The study's strength was its clear focus, but a potential critique is that by concentrating on a well-established pathway, such analyses might inadvertently neglect the crosstalk with less-studied metabolic axes, like the lysine degradation pathway represented by AASS (Zhou et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The current AASS investigation\u0026rsquo;s co-expression analysis, which explicitly linked AASS to fatty acid oxidation and ketone body metabolism enzymes like HMGCS2, was a deliberate attempt to build a more integrated metabolic picture, showing how a less-explored amino acid pathway is functionally coordinated with central energy-producing hubs (Dong et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mao et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Xiong et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This approach moves beyond confirming the importance of known pathways and begins to map the connections between them, providing a more holistic view of the reprogrammed cancer metabolome.\u003c/p\u003e\u003cp\u003eDespite these potential limitations in the broader literature, the value and strength of previous pan-cancer studies are undeniable, as such works provided the essential methodological and conceptual foundation for the present AASS investigation. The comprehensive prognostic and immunological analysis of the metabolic gene HKDC1 by Liang et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) serves as a prime example of a robust research blueprint. That study\u0026rsquo;s successful integration of expression, survival, and immune landscape data provided a validated, multi-pronged methodology that the current AASS study emulated to construct a similarly layered and comprehensive profile of its target gene (Liang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mullen \u0026amp; Singh, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The adoption of this established framework ensured a high degree of analytical rigor and comparability with the existing body of literature. The AASS study did not reinvent the analytical wheel but rather applied a proven, powerful toolset to a novel and functionally ambiguous target.\u003c/p\u003e\u003cp\u003e Conceptually, the current work stands on the shoulders of high-level reviews and studies that established the very importance of exploring metabolic dependencies from a pan-cancer perspective. A critical review by Mullen \u0026amp; Singh (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) persuasively argued that nucleotide metabolism represents a fundamental pan-cancer dependency. This type of overarching work creates the intellectual rationale for interrogating other, less-characterized metabolic pathways, like lysine catabolism, in the search for new vulnerabilities (Liu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mullen \u0026amp; Singh, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sellitto et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The AASS study directly answers this call by shifting the focus to a non-canonical amino acid pathway, thereby building upon the foundational principle that mapping the full spectrum of metabolic reprogramming is essential for identifying the next generation of therapeutic targets. Moreover, the conceptual framework connecting metabolism to the tumor immune microenvironment, as comprehensively outlined in a review by Xia et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), was a direct inspiration for a key analytical arm of the AASS study. The review detailed the intricate molecular mechanisms by which metabolic shifts in cancer cells influence immune cell function and infiltration (Gu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xia et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This knowledge motivated the correlation analysis between AASS expression and immune cell abundance, a crucial step that moved the investigation beyond cell-intrinsic metabolic function to explore its potential impact on the broader tumor ecosystem. In doing so, the AASS study not only characterized a gene but also generated testable hypotheses about its role in shaping the immune landscape, a strength it owes to the conceptual groundwork laid by prior research. The present analysis represents a synthesis of these positive attributes: it leverages established, robust methodologies to explore a novel gene within a compelling, pre-existing conceptual framework, ultimately pushing the boundaries of that framework by uncovering an unexpected and highly context-dependent biological story.\u003c/p\u003e\u003c/div\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eThis investigation concluded that Aminoadipate-semialdehyde synthase is not a classical oncogene or tumor suppressor but rather a critical, context-dependent modulator of cancer metabolism. The findings demonstrated a stark functional dichotomy, with AASS playing distinct and sometimes opposing roles in different malignancies. The upregulation and favorable prognostic significance in renal cancer contrasted sharply with its unfavorable prognostic role in lung cancer, while its downregulation in liver cancer still pointed to a beneficial role when expressed. This functional plasticity underscores a critical principle: the impact of a metabolic enzyme is determined not by its solitary function but by the broader metabolic and signaling network of the specific cancer cell. This study successfully repositioned AASS from a peripheral enzyme in a single amino acid pathway to a significant and clinically relevant player in the landscape of oncogenic metabolic reprogramming, highlighting the necessity of context-specific research in cancer metabolism.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e6.1 Limitations \u0026amp; Strengths of the Study\u003c/h2\u003e\u003cp\u003eThe primary limitation of this research lies in its purely bioinformatic and retrospective nature. The analysis was conducted on publicly available transcriptomic data, meaning the findings are correlational and do not directly measure AASS protein levels or enzymatic activity. Consequently, the study cannot establish causality and requires experimental validation to confirm the functional roles hypothesized. However, the study's principal strength is its comprehensive scope and statistical power. By leveraging the vast datasets of The Cancer Genome Atlas, the investigation provided a robust, pan-cancer view that would be unfeasible in a single experimental study. A further strength was the multi-layered analytical approach, which integrated differential expression, patient survival, and co-expression network data to build a holistic and nuanced profile of AASS. This approach successfully uncovered novel, clinically relevant patterns for a previously understudied metabolic gene, generating a strong foundation for future mechanistic research.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e6.2 Future Research Directions\u003c/h2\u003e\u003cp\u003eBased on the findings of this study, several future research directions are essential. The immediate next step involves experimental validation of the observed correlations. Using in vitro cell line models of renal, liver, and lung cancer, studies should modulate AASS expression via CRISPR or siRNA to directly assess its effects on cell proliferation, migration, and metabolic flux. In vivo studies using xenograft or genetically engineered mouse models are also necessary to confirm the prognostic findings in a physiological context. Further research should investigate the upstream regulatory mechanisms responsible for the differential expression of AASS, including the roles of specific transcription factors and epigenetic modifications like DNA methylation. Finally, given the opposing prognostic roles, future work could explore the therapeutic potential of targeting AASS, investigating whether inhibiting its activity in lung cancer or restoring its expression in liver cancer could represent viable clinical strategies.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was not applicable for this study. The research was conducted using exclusively anonymized, publicly available secondary data from established repositories such as The Cancer Genome Atlas (TCGA). Therefore, no direct human or animal subjects were involved, and no institutional review board approval was required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study were derived from publicly available repositories. All transcriptomic and clinical data were obtained from The Cancer Genome Atlas (TCGA) program. This data can be accessed through various public portals, including the UCSC Xena Browser and the cBioPortal for Cancer Genomics. Summary data and visualizations are also publicly accessible via The Human Protein Atlas at:\u003c/p\u003e\n\u003cp\u003ehttps://www.proteinatlas.org/ENSG00000008311-AASS\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict Of Intrest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed equally to the conception and design of the study, the analysis and interpretation of the data, and the drafting and final approval of the manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen T, Guo S, Long X (2025) ACSS2 in pan-cancer context: multi-omics insights into metabolic reprogramming and immunotherapy response. 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This study aimed to define the expression, prognostic significance, and functional network of AASS across human malignancies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA comprehensive bioinformatic analysis was performed using transcriptomic and clinical data from 33 cancer types in The Cancer Genome Atlas (TCGA). The investigation included differential expression analysis, survival modelling, and construction of co-expression networks.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAASS expression was highly heterogeneous. It was significantly upregulated in Kidney Renal Clear Cell Carcinoma (KIRC; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and downregulated in Liver Hepatocellular Carcinoma (LIHC; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). High AASS expression correlated with favorable patient survival in both KIRC and LIHC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but with an unfavorable prognosis in Lung Squamous Cell Carcinoma (LUSC; p\u0026thinsp;=\u0026thinsp;0.015). Functional enrichment revealed that AASS co-expresses with genes central to mitochondrial and catabolic processes, including fatty acid oxidation.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eAASS is a context-dependent metabolic modulator whose prognostic impact is dictated by the specific tumor type. These findings establish AASS as a novel, clinically relevant biomarker and a potential therapeutic target in specific cancers.\u003c/p\u003e","manuscriptTitle":"Pan-Cancer Expression Analysis of the Aminoadipate-semialdehyde synthase (AASS) Gene: Insights into its Potential Role in Oncogenic Metabolic Reprogramming","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 08:41:06","doi":"10.21203/rs.3.rs-8210982/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":"4e55000a-f3c9-4099-a686-19a0a1faa462","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T08:41:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 08:41:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8210982","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8210982","identity":"rs-8210982","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}