Next Article in Journal
Comprehensive Analysis of Immune-Related Mitochondrial Genes in Ischemic Stroke Through Integrated Bioinformatics and Validation
Previous Article in Journal
Epigenetic Therapies for Inflammatory and Immune-Mediated Skin Diseases
Previous Article in Special Issue
Single-Cell Analysis Reveals Epithelial Heterogeneity and Tumor Microenvironment Characteristics During the Malignant Progression of Colorectal Cancer
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Biomedicines
Volume 14
Issue 2
10.3390/biomedicines14020374
biomedicines-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

RPS6KA1 Remodels Fatty Acid Metabolism and Suppresses Malignant Progression in Colorectal Cancer

by
Qixin Liu
1,2 and
Ziheng Peng
2,3,*
1
Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
2
National Clinical Research Center for Geriatric Disease, Xiangya Hospital, Central South University, Changsha 410008, China
3
Department of Gastroenterology, Xiangya Hospital, Central South University, Changsha 410008, China
*
Author to whom correspondence should be addressed.
Biomedicines 2026, 14(2), 374; https://doi.org/10.3390/biomedicines14020374
Submission received: 14 December 2025 / Revised: 22 January 2026 / Accepted: 28 January 2026 / Published: 5 February 2026
(This article belongs to the Special Issue Advancements in the Treatment of Colorectal Cancer)
Browse Figures
Versions Notes

Abstract

Background: Colorectal cancer (CRC), with high incidence but low rates of early diagnosis, poses significant challenges to public health worldwide. Lipid metabolic reprogramming has been closely associated with CRC occurrence and development. This study aimed to identify key fatty acid metabolism-related molecules involved in the development of CRC and to explore potential prognostic biomarkers and therapeutic targets. Methods: Based on The Cancer Genome Atlas (TCGA) data from colon adenocarcinoma (COAD) patients, we applied weighted gene co-expression network analysis (WGCNA), Cox regression, and least absolute shrinkage and selection operator (LASSO) to identify fatty acid metabolism-related signature genes in CRC. Expression validation and prognostic analysis were conducted. Summary-data-based Mendelian randomization (SMR) was used to infer causal relationships between target genes and CRC. Single-cell transcriptomics and immune infiltration analysis elucidated underlying pathogenic mechanisms. Cellular and animal experiments validated tumor-suppressive effects and lipid metabolic regulatory mechanisms. Results: RPS6KA1 and CHGA were identified as fatty acid metabolism-related signature genes in COAD. Only RPS6KA1 was significantly downregulated in COAD and negatively correlated with poor prognosis (p = 0.0069). SMR confirmed its tumor-suppressive role, potentially associated with enhanced antitumor functions of CD8+T cells and follicular helper T cells. In vitro and in vivo experiments demonstrated that RPS6KA1 inhibits malignant progression of colon cancer and modulates fatty acid metabolism. Conclusions: Integrated multi-dimensional bioinformatic and experimental analyses reveal that RPS6KA1 remodels fatty acid metabolism and suppresses malignant progression, indicating its value as a prognostic biomarker in CRC and providing new insights for therapeutic strategies.

1. Introduction

Colorectal cancer (CRC) is a malignant tumor originating from the epithelial cells of the colon or the rectum mucosa [1]. It ranks as the third most prevalent cancer and the second leading cause of cancer-related deaths globally, with over 1.9 million new cases and approximately 900,000 fatalities annually [2]. CRC is also the leading cause of cancer incidence in men under 50 years of age, with its prevalence increasing by 1–4% each year [3]. Despite recent improvements in the diagnosis and treatment strategies for CRC, such as colonoscopy, surgical resection, chemotherapy, and targeted therapy, late-stage diagnosis and treatment resistance remain major challenges for patients and healthcare systems [4,5]. Therefore, a comprehensive understanding of CRC pathogenesis is essential for identifying at-risk populations and developing targeted treatments.
The pathogenesis of CRC involves multiple factors, including genetic susceptibility [6] and metabolic reprogramming [7]. Among these, reprogramming of lipid metabolism is recognized as a hallmark of cancer. Fatty acid (FA) metabolism, a key aspect of lipid metabolism, has been increasingly implicated in cancer initiation and progression [8,9]. Cancer cells exhibit heightened dependence on de novo lipogenesis (DNL) and the uptake of exogenous FAs to meet the energy demands of rapid proliferation and to support oncogenic signaling that promotes tumorigenesis and disease advancement [10,11]. It has been established that reprogramming of lipid metabolism enhances cell proliferation, migration, and invasion by regulating membrane synthesis, energy storage, and cellular homeostasis in CRC [12,13]. Exogenous fatty acids can significantly promote KRAS activation and IL-8 expression, thereby mediating metastasis in KRAS/p53-mutant colorectal cancer [14]. Solute carrier family 44 member 4 inhibits fatty acid oxidation and suppresses CRC progression by promoting the interaction of the E3 ligase MUL1 with carnitine palmitoyltransferase 2, leading to CPT2 degradation [15].
Despite the recognized importance of fatty acid metabolism in CRC, research on this topic remains limited. To address this gap, we conducted multi-dimensional bioinformatic analyses and in vitro/in vivo experiments to identify fatty acid metabolism-related molecules involved in CRC biology and to further investigate their underlying mechanisms. Our findings provide valuable insights for future identification of at-risk populations and the development of targeted therapies modulating fatty acid metabolism in CRC.

2. Methods

The overall workflow of this study is illustrated in Figure 1.

2.1. RNA-Seq Data Acquisition

Gene expression and clinical data of CRC patients were obtained from The Cancer Genome Atlas (TCGA) database, comprising 483 colorectal adenocarcinoma (COAD) patients, 41 normal samples, and 19,937 genes. Among these, 459 patients with clinical information were used for survival analysis.

2.2. Weighted Gene Co-Expression Network Analysis (WGCNA)

The “WGCNA” R package (version 1.72.5) was used to perform WGCNA on TCGA-COAD transcriptomic data. After excluding outliers and low-variance genes, an optimal soft threshold power (β) was selected to satisfy scale-free topology. The adjacency matrix was converted into a topological overlap matrix. Gene modules were identified through hierarchical clustering and dynamic tree cutting, with a minimum module size of 30.

2.3. Differential Expression Analysis of Signature Genes

The “limma” R package (version 3.58.1) was used to generate paired boxplots illustrating differential expression of signature genes in the TCGA-COAD dataset. Protein-level differences between colon cancer and normal tissues were further analyzed using the CPTAC online dataset and immunohistochemistry data from the Human Protein Atlas.

2.4. Survival Analysis

Kaplan–Meier survival curves and log-rank tests were used to compare survival between high- and low-expression groups of signature genes. Additionally, the “survivalROC” R package (version 1.0.3.1) was used to plot ROC curves, and the predictive performance of the model for 1-, 3-, and 5-year survival probabilities was evaluated using the area under the curve (AUC), 95% confidence interval (CI), and p-value.

2.5. Summary-Data-Based Mendelian Randomization (SMR) Analysis

SMR analysis was performed using SMR software (smr-1.3.1-win) developed by Yang’s lab [16]. Results were filtered using SMR (p < 0.05) and HEIDI (p > 0.05) criteria.

2.6. Single-Cell RNA Sequencing Data Analysis (scRNA-Seq)

The scRNA-seq dataset GSE231559 [17] was retrieved from the Gene Expression Omnibus (GEO) database. The R packages “Seurat” (version 5.1.0) and “patchwork” (version 1.2.0) were used to analyze data from 3 normal colon tissue samples and 5 colon cancer patients. Quality control included exclusion of genes detected in fewer than 3 cells, cells with fewer than 200 genes detected, and cells with mitochondrial gene expression exceeding 10%. Expression profiles were normalized, and doublets were detected and removed using the “DoubletFinder” R package (version 2.0.4). PCA was performed on the top 2000 highly variable genes, followed by dimensionality reduction and clustering via UMAP. Cell types were annotated using marker genes and visualized on UMAP plots. A heatmap of gene expression across cell types was generated using the “DoHeatmap” function.

2.7. Cell Culture

Human embryonic kidney cells (293T), normal colon epithelial cells (NCM460), and colon cancer cell lines (SW480, Lovo, SW620, Caco2, HCT116) were obtained from the Central Cell Bank of Central South University. Cells were cultured in DMEM supplemented with 10% bovine calf serum (BCS) and 1% penicillin/streptomycin at 37 °C in a 5% CO2 atmosphere.

2.8. Overexpression Plasmid Construction

Target sequences for RPS6KA1 were synthesized by Tsingke Biotechnology (Beijing, China) and cloned into the PLVX-puro vector.

2.9. Lentiviral Packaging and Stable Cell Line Establishment

RPS6KA1 overexpression plasmids were co-transfected with packaging plasmids (pRSV-Rev, Gag-pol, and pCMV-VSV-G; Addgene, Cambridge, MA, USA, #12253, #14887, #8454) into 293T cells. The viral supernatant was collected after 48 h and filtered through a 0.45 μm filter. Lovo and HCT116 cells were infected and selected using puromycin to establish stable cell lines.

2.10. Quantitative Real-Time PCR (qPCR)

Primers for RPS6KA1 (forward: ATGCAGACCCCAGCAGATTT, reverse: GTGCAGCTTCACCACGAATG) and the reference gene Actin (forward: CATGTACGTTGCTATCCAGGC, reverse: CTCCTTAATGTCACGCACGAT) were synthesized by Tsingke Biotechnology (Beijing, China). Total RNA was extracted and reverse-transcribed into cDNA. qPCR was performed using the SYBR Green qPCR SuperMix Kit (Bimake, B7500, Houston, TX, USA) with at least three technical replicates.

2.11. Subcutaneous Xenograft Tumor Model in Nude Mice

A total of 30 female 4-week-old BALB/c nude mice were purchased from Hua Fu Kang Biotechnology Co., Ltd. (Beijing, China). Experiments were repeated 3 times, with a total of 5 mice in each group. This sample size was chosen to ensure sufficient statistical power (β > 0.8, α = 0.05). After one week of acclimatization, Mice were randomly assigned to subcutaneous injection in the left thoracic wall with 1 × 106 HCT116-RPS6KA1-OE (hereafter referred to as the OE group) or control HCT116-RPS6KA1-NC (hereafter referred to as the NC group) cells suspended in PBS mixed with Matrigel (10%). Tumor volume was measured weekly. After three weeks, mice were euthanized, and tumors were excised and weighed. All animal experiments were approved by the animal ethics committee of Xiangya Hospital (2023111856, approved on 16 November 2023).

2.12. Immunohistochemistry (IHC)

Tumor sections were deparaffinized in xylene, rehydrated, and subjected to antigen retrieval via heat-induced epitope retrieval. Endogenous peroxidase activity was blocked with 3% H2O2, and nonspecific sites were blocked with 5% goat serum. Sections were incubated overnight at 4 °C with rabbit anti-human RPS6KA1 (Epizyme, Shanghai, China, P013408; 1:1000) or Ki67 (Proteintech, Wuhan, China, 27309-1-AP, 1:200), followed by HRP-conjugated secondary antibody and DAB development. Nuclei were counterstained with hematoxylin. The experimental operator was unaware of the grouping before the staining was completed to control confounding factors.

2.13. Oil Red O Staining

Cells grown on coverslips were fixed with 4% formaldehyde and stained using the Oil Red O Stain Kit (Nanjing Jiancheng, Nanjing, China, D027-1-1) according to the manufacturer’s instructions. Nuclei were counterstained with hematoxylin, and images were acquired under a microscope.

2.14. BODIPY 493/503 Staining

Cells on coverslips were fixed with 4% formaldehyde and stained using the BODIPY 493/503 Staining Kit (Beyotime, Shanghai, China, C2053S). Nuclei were stained with Hoechst 33342, and images were captured using a fluorescence microscope.

2.15. ATP Content Assay

Cells cultured in 6-well plates were washed with PBS and lysed. ATP levels were measured using the Enhanced ATP Assay Kit (Beyotime, Shanghai, China, S0027) with a luminometer. A portion of the lysate was used for protein quantification via BCA assay.

2.16. Free Fatty Acid (FFA) Assay

Cells were collected, washed with PBS, and homogenized with steel beads at 4 °C. The homogenate was centrifuged at 12,000 rcf for 5 min at 4 °C, and the supernatant was used for FFA measurement with the Amplex Red Free Fatty Acid Assay Kit (Beyotime, Shanghai, China, S2015S).

2.17. Mitochondrial Membrane Potential Assay

Cells were collected, washed with PBS, and stained using the JC-1 Mitochondrial Membrane Potential Assay Kit (Beyotime, Shanghai, China, C2006). Fluorescence was measured with a microplate reader, and data were analyzed accordingly.

2.18. Statistical Analysis

Statistical analyses were performed using R version 4.3.3, relevant R packages, and GraphPad Prism 8.3.0. Student’s t-test was used to compare two groups, and one-way ANOVA was used for comparisons among three or more groups. A p-value < 0.05 was considered statistically significant, with ns indicating not significant; * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.

3. Results

3.1. Identification of Fatty Acid Metabolism-Related Signature Genes in COAD

In the WGCNA, a soft threshold power of 8 was selected, achieving a scale-free fit index R2 = 0.9 (Figure 2A–C). Five co-expression modules were identified, among which the blue module showed the strongest correlation with tumor (cor = −0.83, p = 4 × 10−133) and normal (cor = 0.83, p = 4 × 10−133) samples (Figure 2D,E). Intersection with fatty acid metabolism-related genes from GeneCards yielded 244 key genes (Figure 2F). GO and KEGG enrichment analyses revealed that these genes were primarily involved in fatty acid metabolic processes (Supplementary Figure S1A–D). Univariate Cox regression identified RPS6KA1 and CHGA as significantly associated with colon cancer prognosis (Figure 2G). Subsequent LASSO and multivariate Cox regression analyses confirmed that both RPS6KA1 (HR = 0.987, 95% CI: 0.977–0.998, p = 0.016) and CHGA (HR = 1.003, 95% CI: 1.001–1.004, p < 0.001) were independent prognostic factors (Figure 2H–K). RPS6KA1 inhibited fatty acid oxidation in adipocytes [18,19], whereas CHGA produced the opposite effect [20]. However, Kaplan–Meier analysis in the TCGA-COAD dataset showed no significant association between CHGA expression and overall survival (p = 0.2061) (Supplementary Figure S2). Therefore, RPS6KA1 was selected for further investigation.

3.2. Expression of RPS6KA1 in COAD

Analysis via Timer2.0 revealed pan-cancer expression of RPS6KA1, with notably lower expression in COAD (Figure 3A). Both CPTAC and paired TCGA-COAD analyses confirmed decreased RPS6KA1 RNA expression in tumor tissues compared to normal colon tissues (Figure 3B,C). Similarly, protein-level analyses using CPTAC and HPA databases indicated reduced RPS6KA1 expression in COAD (Figure 3D). These findings were validated in vitro: qPCR and Western blot analyses demonstrated lower RPS6KA1 expression at both the RNA and protein levels in multiple colon cancer cell lines (HCT116, SW480, Lovo) compared to the normal colon epithelial cell line NCM460 (Figure 3F,G). To explore the mechanism underlying reduced RPS6KA1 expression, we assessed methylation levels and found no significant difference between tumor and normal tissues (p = 0.179) (Figure 3H). However, phosphorylation levels at S230, S372, and S3889 were significantly altered in tumor tissues (p = 1 × 10−7, p = 0.0213, and p = 9 × 10−15, respectively, Figure 3I–K).

3.3. Prognostic Value of RPS6KA1 and Clinical Prediction Model

Survival status distribution and Kaplan–Meier analysis based on TCGA-COAD data indicated that high RPS6KA1 expression was associated with better overall survival (p = 0.0069) (Figure 4A,B). ROC analysis demonstrated good predictive performance of RPS6KA1 for patient survival (AUC = 0.626, Figure 4C), with AUC values of 0.617, 0.634, and 0.657 for 1-, 3-, and 5-year survival, respectively (Figure 4D). RPS6KA1 exhibited superior prognostic performance compared to clinicopathological features such as age, gender, T stage, N stage, M stage, and tumor subdivision, particularly for predicting 3- and 5-year survival (Figure 4E–G). RPS6KA1 expression was correlated with the T stage and M stage (Figure 4H). Multivariate Cox regression confirmed that low RPS6KA1 expression was an independent risk factor for poor prognosis (HR = 1.87, 95% CI: 1.01–3.46, p = 0.045), along with age (HR = 1.04, 95% CI: 1.01–1.07, p = 0.003) and M stage (HR = 4.09, 95% CI: 1.99–8.40, p < 0.001) (Figure 4I).

3.4. Genetic and Causal Analyses Supporting RPS6KA1′S Role in CRC

Chromosomal localization placed RPS6KA1 on chromosome 1 (Figure 5A). Although correlated, it is not clear whether there is a causative association between RPS6KA1 and CRC. SMR and colocalization analyses were used to investigate the causal relationship between RPS6KA1 cis-eQTL and CRC. The SMR plot revealed a significant negative correlation between RPS6KA1 cis-eQTL and CRC GWAS signals (Figure 5B). Locus-specific SMR and colocalization results further supported RPS6KA1 as a potential causal factor suppressing CRC development (Figure 5C,D).

3.5. Single-Cell Expression and Distribution of RPS6KA1 in CRC

After quality control, batch effect correction, doublet removal, cell cycle regression, and PCA, 35 distinct clusters were identified (Figure 6A,B). The distribution of clusters across samples is shown in Figure 6F. Using canonical markers (EPCAM, CD68, CD79A, etc.), seven major cell types were annotated: T cells, B cells, endothelial cells, myeloid cells, plasma cells, fibroblasts, and epithelial cells (Figure 6C,D). RPS6KA1 was predominantly expressed in T cells, B cells, and epithelial cells (Figure 6E). Compared to tumor tissues, normal colon samples (GSM7290762, GSM7290768, GSM7290771) had lower proportions of T cells and higher proportions of endothelial cells (Figure 6F). These findings suggest that RPS6KA1 may participate in immune regulation during CRC pathogenesis.

3.6. Association Between RPS6KA1 and Immune Infiltration in CRC

CIBERSORT analysis revealed differences in tumor immune cell (TIC) composition between high- and low-RPS6KA1-expression groups (Figure 7A). Correlation analysis showed a positive association between CD8+ T cells and follicular helper T cells (r = 0.38), and a negative correlation between CD8+ T cells and resting CD4+ memory T cells (r = –0.48) (Figure 7B). Among 22 immune cell types, CD8+ T cells, follicular helper T cells, Tregs, activated NK cells, monocytes, and eosinophils differed significantly between high- and low-RPS6KA1 groups (Figure 7C). RPS6KA1 expression positively correlated with CD8+ T cells and follicular helper T cells and negatively with eosinophils and neutrophils (Figure 7D).

3.7. In Vitro and In Vivo Functional Validation of RPS6KA1 in CRC

Stable RPS6KA1-overexpressing cell lines were established and validated by Western blot and qPCR (Figure 8A,B). CCK-8 assays showed that RPS6KA1 overexpression significantly inhibited cell proliferation (Figure 8C). Colony formation assays confirmed reduced clonogenic ability (Figure 8D). Wound healing assays demonstrated impaired migration (Figure 8F,G), and Transwell assays showed decreased migration and invasion (Figure 8H,I). In vivo, xenograft tumors derived from RPS6KA1-overexpressing cells exhibited reduced volume and weight (Figure 8K,L). IHC staining revealed lower Ki-67 expression in RPS6KA1-overexpressing tumors (Figure 8M). Collectively, these results indicate that RPS6KA1 suppresses proliferation and invasion in CRC.

3.8. RPS6KA1 Inhibits Fatty Acid Oxidation in CRC

Oil Red O staining indicated that RPS6KA1 overexpression suppressed lipolysis and increased neutral lipid storage in Lovo and HCT116 cells (Figure 9A). BODIPY 493/503 staining yielded consistent results (Figure 9B,C). Intracellular free fatty acid levels, the substrate for fatty acid oxidation [21], were elevated in RPS6KA1-overexpressing cells (Figure 9D). Additionally, assays measuring ATP content, a key product of fatty acid oxidation [22], showed reduced ATP production (Figure 9E). JC-1 staining indicated decreased mitochondrial membrane potential (Figure 9F). These findings suggest that RPS6KA1 inhibits fatty acid oxidation in colon cancer.

4. Discussion

As the most common subtype of CRC, COAD is often challenging to detect early due to tumor heterogeneity. In recent years, the age at first diagnosis of COAD has gradually decreased, underscoring the need to identify reliable biomarkers for early detection of at-risk populations, timely diagnosis, and accurate prognosis prediction. The development of predictive biomarkers is a prerequisite for implementing personalized treatment in cancer patients.
Dysregulated lipid metabolism is one of the most prominent metabolic alterations in cancer. Tumor progression is frequently accompanied by disruptions in lipid metabolism, including changes in lipid uptake, synthesis, and hydrolysis [23]. Although aberrant lipid metabolism is a hallmark of CRC, its underlying regulatory mechanisms remain incompletely understood. In this study, we identified RPS6KA1, a lipid metabolism-related gene that is downregulated in colon cancer and associated with poor prognosis. Through SMR, single-cell analysis, and immune infiltration assessment, RPS6KA1 was identified as a potential immune-related tumor suppressor in colon cancer. Functional experiments in vitro and in vivo confirmed that RPS6KA1 increases fatty acid concentration and suppresses malignant behaviors of CRC.
RPS6KA1, also known as RSK1, belongs to the p90 ribosomal S6 kinase (RSK) family, whose members are serine/threonine kinases acting as downstream effectors of the MAPK pathway [24]. It is involved in various biological processes including development, inflammation, and cancer [24]. Previous reports have implicated RPS6KA1 in multiple cancers, though findings remain inconsistent. For instance, it drives a TRIM28/E2F1 feedback loop promoting prostate cancer progression [25] and mediates hyperinflammation via PI3K/AKT/mTOR and NFκB signaling in myeloid malignancies [26]. Conversely, RPS6KA1 may act as a tumor suppressor in certain contexts: it phosphorylates LKB1/STK11 in Peutz-Jeghers syndrome [27] and suppresses metastasis in lung cancer [28]. However, conflicting studies in the same A549 lung cancer cell line suggest that it may promote metastasis [29], and its role varies across melanoma subtypes [30]. In CRC, existing evidence is limited and contradictory: Guoguo Jin et al. reported RPS6KA1 as an oncogene, where its inhibition upregulated Bax, cleaved caspase-3, and PARP to suppress tumor growth [31], whereas Kenneth K. Wu et al. proposed it as a potential anti-tumor target by inhibiting COX-2 transcription [32]. Leveraging multi-dimensional bioinformatics and integrated validation, our study supports a tumor-suppressive role for RPS6KA1 in CRC. We speculate that tumor heterogeneity may account for these discrepant findings.
Mendelian randomization offers a robust approach to inferring causality by using genetic variants as instrumental variables, mitigating confounding and reverse causality in observational studies [33]. SMR integrates GWAS summary statistics with eQTL data to elucidate mechanisms through which genetic variation influences complex traits and identify therapeutic targets [16]. Applying SMR, we provided genetic evidence supporting a causal protective role of RPS6KA1 in CRC.
Single-cell sequencing and immune infiltration analysis revealed that RPS6KA1 was positively correlated with CD8+ T cells and T follicular helper (TFH) cells. Enrichment of TFH signatures is associated with prolonged survival in lung adenocarcinoma [34]. TFH cells enhance antitumor immunity by supporting B cells and synergizing with CD8+ T cell effector functions [34], suggesting that TFH and CD8+ T cells may mediate the tumor-suppressive effects of RPS6KA1.
Interestingly, these effects of RPS6KA1 are mediated not through direct participation in fatty acid metabolism but via the regulation of key biological processes such as autophagy-related molecules and mitotic clonal expansion [18,19]. Although RPS6KA1 phosphorylation is dispensable for terminal adipocyte differentiation, it contributes to embryonic stem cell commitment toward adipocyte progenitors and modulates de novo lipogenesis [35,36]. Reprogrammed lipid metabolism not only promotes tumor progression but also shapes immune cell functionality, particularly that of CD8+ T cells [9,37]. Targeting fatty acid metabolism has been demonstrated to enhance CD8+ T cell memory [38]. Obesity has been shown to reprogram the tumor microenvironment and suppress antitumor immunity [39]. We speculate that RPS6KA1 may enhance antitumor immunity by promoting lipogenesis to support CD8+ T cell function.
Despite these insights, our study has limitations. First, the regulatory relationship between RPS6KA1, fatty acid biosynthesis, and CD8+ T cells in CRC requires further experimental validation. Second, potential selection bias in database sources, such as ethnicity, geography, molecular differences, and tumor heterogeneity, may introduce systematic deviations. Third, prospective studies and external validation datasets are needed to enhance the reliability of our findings. We aim to address these limitations in future research.

5. Conclusions

This comprehensive study demonstrates the prognostic value of RPS6KA1 in CRC. We found that RPS6KA1 is downregulated in CRC and negatively correlated with poor prognosis and serves as an independent protective factor. Genetic evidence and functional experiments support its tumor-suppressive role. Moreover, RPS6KA1 is closely associated with lipid metabolism and the immune microenvironment in CRC. Collectively, our findings suggest that RPS6KA1 may suppress malignant progression and remodel the immune microenvironment through lipid metabolic reprogramming, offering new insights for the diagnosis and treatment of CRC.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biomedicines14020374/s1, Figure S1: A. Bubble plot of GO enrichment analysis. B. Circle plot of GO enrichment analysis. C. Barplot of KEGG enrichment analysis. D. Cnetplot of KEGG enrichment analysis; Figure S2: KM curves between different CHGA expression groups.

Author Contributions

Conceptualization: Z.P. Data curation: Q.L. Formal analysis: Q.L. Methodology: Q.L. Software: Q.L. Writing—original draft: Q.L. Writing—review and editing: Z.P. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by grants from the Fundamental Research Funds for the Central Universities of Central South University (No: 1053320242289).

Institutional Review Board Statement

The animal study protocol was approved by the Ethics Committee of Xiangya Hospital (protocol code 2023111856 and date 16 November 2023).

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets generated and/or analyzed during the current study are available in The Cancer Genome Atlas database (http://www.cancer.gov/tcga) (accessed on 1 September 2023), Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/) (accessed on 1 September 2023), GeneCards (https://www.genecards.org/) (accessed on 4 September 2023), Timer 2.0 (https://cistrome.shinyapps.io/timer/) (accessed on 10 September 2023), Clinical Proteomic Tumor Analysis Consortium (https://ualcan.path.uab.edu) (accessed on 15 September 2023), Human Protein Atlas (https://www.proteinatlas.org/) (accessed on 19 September 2023), AlphaFold database(https://alphafold.ebi.ac.uk/) (accessed on 27 September 2023) and PubChem (https://pubchem.ncbi.nlm.nih.gov/) (accessed on 4 October 2023).

Acknowledgments

We express our gratitude for valuable data resources provided by the researchers and databases.

Conflicts of Interest

The authors report no conflicts of interest.

References

  1. Dekker, E.; Tanis, P.J.; Vleugels, J.L.A.; Kasi, P.M.; Wallace, M.B. Colorectal cancer. Lancet 2019, 394, 1467–1480. [Google Scholar] [CrossRef]
  2. Bray, F.; Laversanne, M.; Sung, H.; Ferlay, J.; Siegel, R.L.; Soerjomataram, I.; Jemal, A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2024, 74, 229–263. [Google Scholar] [CrossRef]
  3. Siegel, R.L.; Torre, L.A.; Soerjomataram, I.; Hayes, R.B.; Bray, F.; Weber, T.K.; Jemal, A. Global patterns and trends in colorectal cancer incidence in young adults. Gut 2019, 68, 2179–2185. [Google Scholar] [CrossRef]
  4. Li, C.; Wang, Z.; Ding, P.; Zhou, Z.; Chen, R.; Hu, Y.; Zhao, K.; Peng, W.; Yang, X.; Sun, N.; et al. A Digital Score Based on Circulating-Tumor-Cells-Derived mRNA Quantification and Machine Learning for Early Colorectal Cancer Detection. ACS Nano 2025, 19, 18117–18128. [Google Scholar] [CrossRef]
  5. Karthika, C.; Sureshkumar, R.; Zehravi, M.; Akter, R.; Ali, F.; Ramproshad, S.; Mondal, B.; Kundu, M.K.; Dey, A.; Rahman, M.H.; et al. Multidrug Resistance in Cancer Cells: Focus on a Possible Strategy Plan to Address Colon Carcinoma Cells. Life 2022, 12, 811. [Google Scholar] [CrossRef]
  6. Yang, L.; Yang, H.; Chu, Y.; Song, Y.; Ding, L.; Zhu, B.; Zhai, W.; Wang, X.; Kuang, Y.; Ren, F.; et al. CREPT is required for murine stem cell maintenance during intestinal regeneration. Nat. Commun. 2021, 12, 270. [Google Scholar] [CrossRef]
  7. Solanki, S.; Sanchez, K.; Ponnusamy, V.; Kota, V.; Bell, H.N.; Cho, C.-S.; Kowalsky, A.H.; Green, M.; Lee, J.H.; Shah, Y.M. Dysregulated Amino Acid Sensing Drives Colorectal Cancer Growth and Metabolic Reprogramming Leading to Chemoresistance. Gastroenterology 2022, 164, 376–391.e13. [Google Scholar] [CrossRef]
  8. Carracedo, A.; Cantley, L.C.; Pandolfi, P.P. Cancer metabolism: Fatty acid oxidation in the limelight. Nat. Rev. Cancer 2013, 13, 227–232. [Google Scholar] [CrossRef]
  9. Currie, E.; Schulze, A.; Zechner, R.; Walther, T.C.; Farese, R.V. Cellular fatty acid metabolism and cancer. Cell Metab. 2013, 18, 153–161. [Google Scholar] [CrossRef]
  10. Koundouros, N.; Poulogiannis, G. Reprogramming of fatty acid metabolism in cancer. Br. J. Cancer 2019, 122, 4–22. [Google Scholar] [CrossRef]
  11. Yu, X.-H.; Ren, X.-H.; Liang, X.-H.; Tang, Y.-L. Roles of fatty acid metabolism in tumourigenesis: Beyond providing nutrition (Review). Mol. Med. Rep. 2018, 18, 5307–5316. [Google Scholar] [CrossRef]
  12. Dai, W.; Xiang, W.; Han, L.; Yuan, Z.; Wang, R.; Ma, Y.; Yang, Y.; Cai, S.; Xu, Y.; Mo, S.; et al. PTPRO represses colorectal cancer tumorigenesis and progression by reprogramming fatty acid metabolism. Cancer Commun. 2022, 42, 848–867. [Google Scholar] [CrossRef]
  13. Yang, Y.; Luo, D.; Shao, Y.; Shan, Z.; Liu, Q.; Weng, J.; He, W.; Zhang, R.; Li, Q.; Wang, Z.; et al. circCAPRIN1 interacts with STAT2 to promote tumor progression and lipid synthesis via upregulating ACC1 expression in colorectal cancer. Cancer Commun. 2022, 43, 100–122. [Google Scholar] [CrossRef]
  14. Shen, C.-J.; Chan, R.-H.; Lin, B.-W.; Li, N.-C.; Huang, Y.-H.; Chang, W.-C.; Chen, B.-K. Oleic acid-induced metastasis of KRAS/p53-mutant colorectal cancer relies on concurrent KRAS activation and IL-8 expression bypassing EGFR activation. Theranostics 2023, 13, 4650–4666. [Google Scholar] [CrossRef]
  15. Yang, Y.; Zheng, L.; He, J.; Wu, T.; Yang, H.; Zhang, B.; Zhou, S.; Lu, Y.; He, X.; Li, J.; et al. SLC44A2 negatively regulates mitochondrial fatty acid oxidation to suppress colorectal progression by blocking the MUL1-CPT2 interaction. Cell Death Dis. 2025, 16, 468. [Google Scholar] [CrossRef]
  16. Zhu, Z.; Zhang, F.; Hu, H.; Bakshi, A.; Robinson, M.R.; Powell, J.E.; Montgomery, G.W.; Goddard, M.E.; Wray, N.R.; Visscher, P.M.; et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 2016, 48, 481–487. [Google Scholar] [CrossRef]
  17. Hsu, W.-H.; LaBella, K.A.; Lin, Y.; Xu, P.; Lee, R.; Hsieh, C.-E.; Yang, L.; Zhou, A.; Blecher, J.M.; Wu, C.-J.; et al. Oncogenic KRAS Drives Lipofibrogenesis to Promote Angiogenesis and Colon Cancer Progression. Cancer Discov. 2023, 13, 2652–2673. [Google Scholar] [CrossRef]
  18. Han, J.-H.; Jang, K.-W.; Myung, C.-S. Garcinia cambogia attenuates adipogenesis by affecting CEBPB and SQSTM1/p62-mediated selective autophagic degradation of KLF3 through RPS6KA1 and STAT3 suppression. Autophagy 2021, 18, 518–539. [Google Scholar] [CrossRef]
  19. Lee, H.-W.; Karki, R.; Han, J.-H. Inhibition of the RPS6KA1/FoxO1 signaling axis by hydroxycitric acid attenuates HFD-induced obesity through MCE suppression. Phytomedicine 2024, 128, 155551. [Google Scholar] [CrossRef]
  20. Bandyopadhyay, G.K.; Vu, C.U.; Gentile, S.; Lee, H.; Biswas, N.; Chi, N.-W.; O’Connor, D.T.; Mahata, S.K. Catestatin (chromogranin A(352-372)) and novel effects on mobilization of fat from adipose tissue through regulation of adrenergic and leptin signaling. J. Biol. Chem. 2012, 287, 23141–23151. [Google Scholar] [CrossRef]
  21. Corn, K.C.; Windham, M.A.; Rafat, M. Lipids in the tumor microenvironment: From cancer progression to treatment. Prog. Lipid Res. 2020, 80, 101055. [Google Scholar] [CrossRef] [PubMed]
  22. Lee, H.; Woo, S.M.; Jang, H.; Kang, M.; Kim, S.-Y. Cancer depends on fatty acids for ATP production: A possible link between cancer and obesity. Semin. Cancer Biol. 2022, 86, 347–357. [Google Scholar] [CrossRef]
  23. Bian, X.; Liu, R.; Meng, Y.; Xing, D.; Xu, D.; Lu, Z. Lipid metabolism and cancer. J. Exp. Med. 2021, 218, e20201606. [Google Scholar] [CrossRef]
  24. Spirrison, A.N.; Lannigan, D.A. RSK1 and RSK2 as therapeutic targets: An up-to-date snapshot of emerging data. Expert. Opin. Ther. Targets 2024, 28, 1047–1059. [Google Scholar] [CrossRef]
  25. Kim, M.; Liu, J.; Zhang, Y.; Wang, R.; Goettl, R.; Grasso, J.; Allison, D.B.; Wang, C.; Gao, T.; Liu, X.; et al. RSK1-driven TRIM28/E2F1 feedback loop promotes castration-resistant prostate cancer progression. J. Clin. Investig. 2025, 135, e185119. [Google Scholar] [CrossRef]
  26. Kong, T.; Laranjeira, A.B.A.; Letson, C.T.; Yu, L.; Lin, S.; Fowles, J.S.; Fisher, D.A.C.; Ng, S.; Yang, W.; He, F.; et al. RSK1 is an exploitable dependency in myeloproliferative neoplasms and secondary acute myeloid leukemia. Nat. Commun. 2025, 16, 492. [Google Scholar] [CrossRef]
  27. Sapkota, G.P.; Kieloch, A.; Lizcano, J.M.; Lain, S.; Arthur, J.S.; Williams, M.R.; Morrice, N.; Deak, M.; Alessi, D.R. Phosphorylation of the protein kinase mutated in Peutz-Jeghers cancer syndrome, LKB1/STK11, at Ser431 by p90(RSK) and cAMP-dependent protein kinase, but not its farnesylation at Cys(433), is essential for LKB1 to suppress cell vrowth. J. Biol. Chem. 2001, 276, 19469–19482. [Google Scholar] [CrossRef]
  28. Lara, R.; Mauri, F.A.; Taylor, H.; Derua, R.; Shia, A.; Gray, C.; Nicols, A.; Shiner, R.J.; Schofield, E.; Bates, P.A.; et al. An siRNA screen identifies RSK1 as a key modulator of lung cancer metastasis. Oncogene 2011, 30, 3513–3521. [Google Scholar] [CrossRef]
  29. Abdulrahman, N.; Jaballah, M.; Poomakkoth, N.; Riaz, S.; Abdelaziz, S.; Issa, A.; Mraiche, F. Inhibition of p90 ribosomal S6 kinase attenuates cell migration and proliferation of the human lung adenocarcinoma through phospho-GSK-3β and osteopontin. Mol. Cell Biochem. 2016, 418, 21–29. [Google Scholar] [CrossRef]
  30. Salhi, A.; Farhadian, J.A.; Giles, K.M.; Vega-Saenz de Miera, E.; Silva, I.P.; Bourque, C.; Yeh, K.; Chhangawala, S.; Wang, J.; Ye, F.; et al. RSK1 activation promotes invasion in nodular melanoma. Am. J. Pathol. 2015, 185, 704–716. [Google Scholar] [CrossRef]
  31. Jin, G.; Yan, M.; Liu, K.; Yao, K.; Chen, H.; Zhang, C.; Yi, Y.; Reddy, K.; Gorja, D.R.; Laster, K.V.; et al. Discovery of a novel dual-target inhibitor against RSK1 and MSK2 to suppress growth of human colon cancer. Oncogene 2020, 39, 6733–6746. [Google Scholar] [CrossRef]
  32. Wu, K.K. Transcription-based COX-2 inhibition: A therapeutic strategy. Thromb. Haemost. 2006, 96, 417–422. [Google Scholar] [CrossRef]
  33. Davey Smith, G.; Hemani, G. Mendelian randomization: Genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 2014, 23, R89–R98. [Google Scholar] [CrossRef]
  34. Cui, C.; Wang, J.; Fagerberg, E.; Chen, P.-M.; Connolly, K.A.; Damo, M.; Cheung, J.F.; Mao, T.; Askari, A.S.; Chen, S.; et al. Neoantigen-driven B cell and CD4 T follicular helper cell collaboration promotes anti-tumor CD8 T cell responses. Cell 2021, 184, 6101–6118.e13. [Google Scholar] [CrossRef]
  35. Carnevalli, L.S.; Masuda, K.; Frigerio, F.; Le Bacquer, O.; Um, S.H.; Gandin, V.; Topisirovic, I.; Sonenberg, N.; Thomas, G.; Kozma, S.C. S6K1 plays a critical role in early adipocyte differentiation. Dev. Cell 2010, 18, 763–774. [Google Scholar] [CrossRef] [PubMed]
  36. Yi, S.A.; Um, S.H.; Lee, J.; Yoo, J.H.; Bang, S.Y.; Park, E.K.; Lee, M.G.; Nam, K.H.; Jeon, Y.J.; Park, J.W.; et al. S6K1 Phosphorylation of H2B Mediates EZH2 Trimethylation of H3: A Determinant of Early Adipogenesis. Mol. Cell 2016, 62, 443–452. [Google Scholar] [CrossRef] [PubMed]
  37. Li, T.; Bie, Y.-R.; Chen, S.-M.; Shang, Y.; Hu, P.-S. Fatty acid in the tumor microenvironment: Perspectives for immunotherapy. Crit. Rev. Oncol. Hematol. 2025, 215, 104878. [Google Scholar] [CrossRef]
  38. Pearce, E.L.; Walsh, M.C.; Cejas, P.J.; Harms, G.M.; Shen, H.; Wang, L.-S.; Jones, R.G.; Choi, Y. Enhancing CD8 T-cell memory by modulating fatty acid metabolism. Nature 2009, 460, 103–107. [Google Scholar] [CrossRef]
  39. Ringel, A.E.; Drijvers, J.M.; Baker, G.J.; Catozzi, A.; García-Cañaveras, J.C.; Gassaway, B.M.; Miller, B.C.; Juneja, V.R.; Nguyen, T.H.; Joshi, S.; et al. Obesity Shapes Metabolism in the Tumor Microenvironment to Suppress Anti-Tumor Immunity. Cell 2020, 183, 1848–1866.e26. [Google Scholar] [CrossRef] [PubMed]
Biomedicines 14 00374 g001
Figure 1. The flowchart of this study.
Figure 1. The flowchart of this study.
Biomedicines 14 00374 g001
Biomedicines 14 00374 g002
Figure 2. (A). Scale-free topology fit index (R2 > 0.9). (B). Mean connectivity (Soft Threshold = 5). (C). Clustering dendrogram of genes based on topological overlap. (D). Module merging based on eigengene similarity, resulting in five final modules. (E). The genetic correlation of the blue module. (F). Venn diagram. (G). Forest map of univariate Cox regression analysis(Blue: HR < 1.0; Red: HR > 1.0). (H). Coefficient profiles in the LASSO regression model. (I). C-index of the LASSO regression model. (J). Cross-validation for tuning parameter screening in the LASSO regression model. (K). Forest map of multivariate Cox regression analysis(Blue: HR < 1.0; Red: HR > 1.0).
Figure 2. (A). Scale-free topology fit index (R2 > 0.9). (B). Mean connectivity (Soft Threshold = 5). (C). Clustering dendrogram of genes based on topological overlap. (D). Module merging based on eigengene similarity, resulting in five final modules. (E). The genetic correlation of the blue module. (F). Venn diagram. (G). Forest map of univariate Cox regression analysis(Blue: HR < 1.0; Red: HR > 1.0). (H). Coefficient profiles in the LASSO regression model. (I). C-index of the LASSO regression model. (J). Cross-validation for tuning parameter screening in the LASSO regression model. (K). Forest map of multivariate Cox regression analysis(Blue: HR < 1.0; Red: HR > 1.0).
Biomedicines 14 00374 g002
Biomedicines 14 00374 g003
Figure 3. (A). Box plot of pan-cancer analysis. (B). Box plot of RPS6KA1 RNA expression differences in CPTAC. (C). Expression difference in RPS6KA1 in TCGA paired samples. (D). Box plot of RPS6KA1 protein expression differences in CPTAC. (E). Protein expression of RPS6KA1 from the HPA database. (F). mRNA expression profile of RPS6KA1. (G). Protein expression profile of RPS6KA1. (H). Box plot of RPS6KA1 methylation level differences in CPTAC. (I–K). Box plot of RPS6KA1 phosphorylation level differences in CPTAC. ** p < 0.01, *** p < 0.001.
Figure 3. (A). Box plot of pan-cancer analysis. (B). Box plot of RPS6KA1 RNA expression differences in CPTAC. (C). Expression difference in RPS6KA1 in TCGA paired samples. (D). Box plot of RPS6KA1 protein expression differences in CPTAC. (E). Protein expression of RPS6KA1 from the HPA database. (F). mRNA expression profile of RPS6KA1. (G). Protein expression profile of RPS6KA1. (H). Box plot of RPS6KA1 methylation level differences in CPTAC. (I–K). Box plot of RPS6KA1 phosphorylation level differences in CPTAC. ** p < 0.01, *** p < 0.001.
Biomedicines 14 00374 g003
Biomedicines 14 00374 g004
Figure 4. (A). Survival state graph between different RPS6KA1 expression groups. (B). KM curves between different RPS6KA1 expression groups. (C). ROC curve of RPS6KA1. (D). ROC curve for 1, 3, and 5 years. (E–G). The 1-, 3-, and 5-year ROC curves of RPS6KA1 and clinicopathological features. (H). Heat map of correlation between RPS6KA1 and clinicopathological features. (I). Univariate Cox and multivariate Cox regression analysis of RPS6KA1 and clinicopathological features. * p < 0.05.
Figure 4. (A). Survival state graph between different RPS6KA1 expression groups. (B). KM curves between different RPS6KA1 expression groups. (C). ROC curve of RPS6KA1. (D). ROC curve for 1, 3, and 5 years. (E–G). The 1-, 3-, and 5-year ROC curves of RPS6KA1 and clinicopathological features. (H). Heat map of correlation between RPS6KA1 and clinicopathological features. (I). Univariate Cox and multivariate Cox regression analysis of RPS6KA1 and clinicopathological features. * p < 0.05.
Biomedicines 14 00374 g004
Biomedicines 14 00374 g005
Figure 5. (A). Distribution of RPS6KA1 in chromosomes. (B). Single-gene scatter map of gene RPS6KA1. (C). Chromosomal location diagram of gene RPS6KA1. (D). Co-localization analysis.
Figure 5. (A). Distribution of RPS6KA1 in chromosomes. (B). Single-gene scatter map of gene RPS6KA1. (C). Chromosomal location diagram of gene RPS6KA1. (D). Co-localization analysis.
Biomedicines 14 00374 g005
Biomedicines 14 00374 g006
Figure 6. (A). UMAP map of dimensionality reduction clustering. (B). The distribution of a single cell sample. (C). Marker gene map for annotation of each cell subpopulation. (D). The distribution of different cell subtypes. (E). The distribution of RPS6KA1 in different cell subtypes. (F). The proportion of different cell subtypes in different samples.
Figure 6. (A). UMAP map of dimensionality reduction clustering. (B). The distribution of a single cell sample. (C). Marker gene map for annotation of each cell subpopulation. (D). The distribution of different cell subtypes. (E). The distribution of RPS6KA1 in different cell subtypes. (F). The proportion of different cell subtypes in different samples.
Biomedicines 14 00374 g006
Biomedicines 14 00374 g007
Figure 7. (A). The proportion of main kinds of TICs in different RPS6KA1 expression groups. (B). Heatmap showing the correlation between the main TICs. (C). CIBERSORT algorithm analysis of immune cell differences between different RPS6KA1 expression groups. (D). Correlation between RPS6KA1 and immune cells. * p < 0.05, ** p < 0.01.
Figure 7. (A). The proportion of main kinds of TICs in different RPS6KA1 expression groups. (B). Heatmap showing the correlation between the main TICs. (C). CIBERSORT algorithm analysis of immune cell differences between different RPS6KA1 expression groups. (D). Correlation between RPS6KA1 and immune cells. * p < 0.05, ** p < 0.01.
Biomedicines 14 00374 g007
Biomedicines 14 00374 g008
Figure 8. (A). The Western blot was used to detect the overexpression effect of RPS6KA1 in Lovo and HCT116 cells. (B). The qPCR was used to detect the overexpression effect of RPS6KA1 in Lovo and HCT116 cells. (C,D). Cell proliferation was detected by CCK8 assay. (E). Cell proliferation was detected by colony-forming assay. (F,G). Cell migration was detected by Scratch test. (H,I). Cell migration and invasion were detected by transwell test. (J). Images of subcutaneous tumor xenografts. (K). Tumor volume. (L). Tumor weight. (M). Immunohistochemical staining. * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 8. (A). The Western blot was used to detect the overexpression effect of RPS6KA1 in Lovo and HCT116 cells. (B). The qPCR was used to detect the overexpression effect of RPS6KA1 in Lovo and HCT116 cells. (C,D). Cell proliferation was detected by CCK8 assay. (E). Cell proliferation was detected by colony-forming assay. (F,G). Cell migration was detected by Scratch test. (H,I). Cell migration and invasion were detected by transwell test. (J). Images of subcutaneous tumor xenografts. (K). Tumor volume. (L). Tumor weight. (M). Immunohistochemical staining. * p < 0.05, ** p < 0.01, *** p < 0.001.
Biomedicines 14 00374 g008
Biomedicines 14 00374 g009
Figure 9. (A) Oil red O staining (scale bars 100 μm). (B,C) BODIPY 493/503 staining (scale bars 100 μm). (D) Measurement of free fatty acid levels. (E) Measurement of ATP levels. (F) Detection of mitochondrial membrane potential. ** p < 0.01, *** p < 0.001, **** p < 0.0001.
Figure 9. (A) Oil red O staining (scale bars 100 μm). (B,C) BODIPY 493/503 staining (scale bars 100 μm). (D) Measurement of free fatty acid levels. (E) Measurement of ATP levels. (F) Detection of mitochondrial membrane potential. ** p < 0.01, *** p < 0.001, **** p < 0.0001.
Biomedicines 14 00374 g009
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Liu, Q.; Peng, Z. RPS6KA1 Remodels Fatty Acid Metabolism and Suppresses Malignant Progression in Colorectal Cancer. Biomedicines 2026, 14, 374. https://doi.org/10.3390/biomedicines14020374

AMA Style

Liu Q, Peng Z. RPS6KA1 Remodels Fatty Acid Metabolism and Suppresses Malignant Progression in Colorectal Cancer. Biomedicines. 2026; 14(2):374. https://doi.org/10.3390/biomedicines14020374

Chicago/Turabian Style

Liu, Qixin, and Ziheng Peng. 2026. "RPS6KA1 Remodels Fatty Acid Metabolism and Suppresses Malignant Progression in Colorectal Cancer" Biomedicines 14, no. 2: 374. https://doi.org/10.3390/biomedicines14020374

APA Style

Liu, Q., & Peng, Z. (2026). RPS6KA1 Remodels Fatty Acid Metabolism and Suppresses Malignant Progression in Colorectal Cancer. Biomedicines, 14(2), 374. https://doi.org/10.3390/biomedicines14020374

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop