Sideroflexin family genes were dysregulated and associated with tumor progression in prostate cancers

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Recent studies showed that they were aberrantly expressed in certain human cancers. However, there is a paucity of information about their expression in prostate cancer. In this study, we took a comprehensive approach to investigate their expression profiles in benign prostate tissue, prostate-derived cell lines, and prostate cancer tissues using multiple transcriptome datasets. Our results showed that SFXN1/3/4 genes were predominantly expressed in prostate tissue and cell lines. SFXN2/4 genes were significantly upregulated while the SFXN3 gene was significantly downregulated in malignant tissues compared to benign tissues. SFXN4 expression was determined as a diagnostic biomarker and prognostic factor for unfavorite survival outcomes. In advanced prostate cancers, SFXN2/4 gene expressions were positively correlated with the androgen receptor signaling score but negatively correlated with the neuroendocrinal feature score. Further analysis discovered that SFXN2/4 gene expressions were modulated by the androgen receptor signaling pathway but not involved in neuroendocrinal progression. In conclusion, SFXN2/4 expression is a novel biomarker in prostate cancer diagnosis and prognosis modulated by the androgen receptor signal pathway. prostate cancer Sideroflexin family genes castration-resistance neuroendocrinal progression androgen receptor modulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction The sideroflexin (SFXN) family is a group of proteins that are found in the mitochondria of eukaryotes and are involved in cellular processes such as iron homeostasis, amino acid metabolism, and energy production [1]. SFXN proteins are multi-spanning transmembrane proteins with the N-terminus inside the cytoplasm and the C-terminus outside the cytoplasm [1]. SFXN proteins are highly conserved across eukaryotes and include five proteins in humans: SFXN1, SFXN2, SFXN3, SFXN4, and SFXN5 [2]. SFXN1, SFXN2, and SFXN3 are mitochondrial serine transporters that are important for one-carbon metabolism [1, 2]. Studies suggest that their role as a serine transporter and interaction with mitochondrial proteins could make it a target for treatments that address mitochondrial dysfunction common in neurodegenerative conditions [3]. SFXN4 is a complex I assembly factor that helps incorporate the ND6 subunit into complex I [4, 5]. The study of SFXN proteins is an emerging research field that could lead to discoveries about mitochondrial physiopathology [1, 5]. Mutations in SFXN4 can cause mitochondrial disease, including impaired mitochondrial respiration and hematopoietic abnormalities [1, 4]. SFXN4 deficiencies impact erythroid differentiation, as shown in studies on anemia and mitochondriopathies where SFXN4 disruption affects iron-sulfur (Fe-S) cluster biogenesis [4]. This links SFXN4 to broader metabolic and redox processes across various conditions, underscoring its therapeutic potential in diseases beyond cancer [6]. SFXN5, identified as a citrate transporter in vitro, is critical for neutrophil spreading, an initial step in adhesion and migration essential for immune response. In a recent study, SFXN5 deficiency in neutrophils (induced via siRNA or morpholino injection in mice and zebrafish models) led to impaired cell spreading, adhesion, chemotaxis, and reactive oxygen species (ROS) production [7]. This deficiency also reduced actin polymerization due to lower cytosolic citrate levels and downstream metabolites like acetyl-CoA and cholesterol, which are necessary for actin polymerization mediated by phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2). Supplementing with citrate or cholesterol partially restored these processes, highlighting SFXN5's pivotal role in cellular actin dynamics and neutrophil migration during inflammatory responses. Identifying novel prognostic biomarkers for prostate cancer is a dynamic and rapidly evolving field. Advances in genomics, proteomics, metabolomics, and imaging, combined with computational innovations, are driving the discovery of more accurate and reliable biomarkers [8, 9]. These novel markers hold the promise of improving prognostic accuracy, guiding personalized treatment, and ultimately enhancing survival and quality of life for prostate cancer patients [10]. Continued research, validation, and integration into clinical practice are essential to realize the full potential of these emerging biomarkers [11]. Currently, there is a paucity of information in the literature about the expression profiles and clinical significance of SFXN family genes in prostate cancers. In this study, we took a comprehensive approach of utilizing multiple transcriptome datasets and analyzed the expression profiles of SFXN family genes in prostate cancers, ranging from benign prostate tissue, and primary cancer to advanced castration-resistant stage. Our results revealed that SFXN2/SFXN4 genes were highly upregulated in prostate cancers. Their expressions were androgen responsive under the modulation of the androgen receptor (AR) signal pathway. High levels of their expression were associated with disease progression to the CRPC stage and worse overall survival outcomes in primary cancers. Materials and Methods Gene expression profiles in prostate tissue, cancer cell lines, and prostate cancer tissues Cell-specific analysis of gene expression was conducted using the NCBI GEO dataset #GDS1973 [12]. Four basic cell types were separated with magnetic cell sorting (MACS) based on cell-type specific cluster designation (CD) antigens, integrin beta4 for basal cells, dipeptidyl peptidase IV for luminal secretory cells, integrin alpha-1 for stromal fibromuscular cells, and PECAM-1 for endothelial cells, as described [12]. Gene expression of MACS-sorted cell populations was assessed with Affymetrix Human Genome U133 Plus 2.0 Array (HG-U133Plus2). The microarray data were downloaded from the NCBI GEO site, and the relative values of gene expression were calculated against the control group. Gene expression of prostate cell lines was analyzed using the Cancer Cell Line Encyclopedia transcriptome dataset [13]. The RNA-seq RPKM values (reads per kilobase per million mapped reads) of gene expression were downloaded on the cBioportal platform (www.cbioportal.org/). There was one benign prostate epithelial cell line (PRECLH) and seven prostate cancer cell lines. We used the Cancer Genome Atlas program (TCGA-PRAD) RNA-seq dataset to examine the expression profiles of the SFXN family genes in primary prostate cancer, as described in our recent publications [14-22]. Gene expression levels were compared using two approaches, case-matched pair comparison (52 cases) and group cohort comparison (500 patient cases) with 52 benign samples. Statistical analysis and data visualization were conducted on the XIANTAO online platform (https://www.xiantaozi.com/). A comparison of gene expression levels in subgroups stratified by molecular signatures (distinct gene fusion and common mutations) was conducted on the UALCAN platform (https://ualcan.path.uab.edu/). Patient survival outcome assessment We examined the association of gene expression with patient survival outcomes, including overall survival, disease-specific survival, and progression-free interval. Patients were stratified using the minimum p -value cut-off approach [23]. The Kaplan-Meier curve analysis and ROC-based prediction were performed on the XIANTAO platform. The results were visualized with the survminer package and ggplot2 package of the R package (version 4.2.1). Gene expression analysis in CRPC patients We used the SU2C/PCF RNA-seq dataset [24] for the analysis of SFXN gene expression at the mRNA levels in CRPC patients on the cBioportal platform. Patients were divided into different subgroups based on pathological subtype, androgen receptor (AR) signaling activity score, and neuroendocrinal feature of prostate cancer (NEPC) score for comparison. Correlation analysis between gene expression levels and AR score or NEPC score was conducted in categories of Spearman and Pearson coefficients on the cBioportal platform. Gene expression after castration or AR silencing in prostate cancer cell line and xenograft models We evaluated the effect of castration on gene expression in mouse prostate, human prostate cancer LuCaP35, and KUCaP-2 xenograft models. Gene expression analysis in mouse prostate tissues was conducted using the NCBI GEO dataset GDS#2562, as described previously [25]. Briefly, C57/B6 mice were sham-operated or castrated, and mouse prostate tissues were harvested at 3 or 14 days after surgery. One group of animals was implanted with testosterone (T) pellet at 15 mg/pellet/mouse at day 14 post-surgery for three days before tissue harvesting. Total RNA was purified with the RNeasy kit (Qiagen, Valencia, CA) for the Affymetrix MGU74A chip-based gene analysis. Human prostate cancer xenograft models KUCaP-2 with wild-type AR were established subcutaneously in nude mice [26]. Animals bearing KUCaP-2 xenograft tumors were castrated and xenograft tumor tissues were harvested at 3-5 months post-surgery. Total RNA was isolated and purified using the RNeasy Mini Kit (Qiagen) for cDNA microarray analysis with an Affymetrix Human Genome U133 Plus2.0, as described [26]. The data were downloaded from the NCBI GEO profile GDS#4107. Human prostate cancer LuCaP35 xenografts [27] were established in NOD/SCID mice [28]. After sham operation or castration, xenograft tumors were harvested for RNA extraction using the QIAGEN RNeasy Mini Kit (Valencia, CA), followed by GeneChip assays using the Affymetrix human genome U133 Plus 2.0 array. The results were downloaded from the NCBI GEO profile GDS#4120. To examine the AR involvement in modulating SFXN gene expression, AR gene expression was silenced in LNCaP cells with a small-hairpin RNA (shAR lentivirus or a nontargeting control shRNA [29]. Total cellular RNAs were extracted using the Qiagen RNeasy Mini kit (Valencia, CA), followed by gene chip assay with the Affymetrix human U133 Plus 2.0 microarrays. The results were downloaded from NCBI GEO profile GDS#4113. DepMap data analysis for gene knockout effect on cellular survival We utilized the gene effect scores derived from CRISPR knockout screens published by Broad's Achilles and Sanger's SCORE projects [30, 31]. Scores are normalized such that nonessential genes have a median score of 0 and independently identified common essential genes have a median score of -1. Gene effect scores were inferenced by Chronos [31]. Integration of the Broad and Sanger datasets was performed, as described [30]. Negative scores imply cell growth inhibition and/or death following gene knockout. The gene effect data were downloaded from the UALCAN platform. Statistical analysis Gene expression at the mRNA levels was used as Log 2 [TPM + 1]) value and presented as the MEAN ± the SEM (standard error of the mean). ANOVA analyses were conducted for multiple group comparisons. Student t -test was performed to determine the significance of the differences between the two groups. For data without normal distribution, the Wilcoxon rank sum test was utilized for the statistical analysis. The results were visualized using the ggplot2[3.3.6], stats [4.2.1], and car [3.1-0] from the R package (version 4.2.1). GraphPad software (version 9.1.0) was used for the graphic presentation. Results SFXN1/SFXN3/SFXN4 genes are highly expressed in benign prostate tissues and cancer cell lines To examine the expression profiles of SFXN family genes in human prostate tissue, we analyzed the single-cell RNA-seq data derived from NCBI GEO GDS1973 [12]. Prostatic basal, luminal secretory, stromal fibromuscular, and endothelial cells were separated with magnetic cell sorting (MACS) based on cell-type specific cluster designation (CD) antigens. Our results showed that SFXN1 and SFXN4 were the predominant genes expressed in prostate tissues followed by SFXN3, while SFXN2 and SFXN5 were expressed at a very low level (Fig 1A). Prostatic luminal cells mainly expressed SFXN1/SFXN4 genes (Fig 1B), but basal cells expressed only moderate levels of SFXN4/SFXN1 genes (Fig 1C). However, the SFXN3 gene was relatively enriched in basal cells. Prostatic endocrinal cells expressed higher levels of SFXN1 than other cell types. In human prostate epithelial and cancer cell lines, SFXN1/SFXN3/SFXN4 genes were expressed at higher levels than SFXN2/SFXN5 genes, of which SFXN5 gene was expressed at a very low level, indicating a less functional significance. In CRISPR/Cas9-based knockout screening experiments, the SFXN2 gene was identified as a survival-essential gene in all tested prostate benign and malignant cell lines (Fig 1E), indicating that the SFXN2 gene is critical for cellular survival [32, 33] while SFXN1/SFXN3/SFXN4/SFXN5 genes might be compensated by other isoforms. SFXN2/SFXN3/SFXN4 genes were aberrantly expressed in primary prostate cancers We examined the expression profiles of SFXN family genes in primary prostate cancers using the TCGA-PRAD RNA-seq dataset. We first compared gene expression levels in 52 case-matched pairs of benign and malignant tissues from patients who received radical prostatectomy. Our analysis revealed that SFXN2 and SFXN4 genes were significantly upregulated in malignant tissues (Fig 2A), especially the SFXN4 gene whose upregulation was constantly seen in all cases. In contrast, the SFXN3 gene was sharply downregulated in malignant tissues. We then conducted a group cohort comparison of 502 malignant tissues with benign tissues. The results showed a similar trend of dysregulation of these three genes in prostate cancers (Fig 2B). SFXN1 and SFXN5 genes did not show a significant alteration. An ROC analysis indicated that SFXN4 gene expression had the highest AUC value of 0.877 in distinguishing malignant from benign tissues, representing a potential diagnostic biomarker. We then analyzed the alterations of these genes in distinct molecular subtypes of prostate cancer stratified by unique genetic abnormalities [34]. Although SFXN1 gene expression did not show a significant alteration in group cohort comparison (Fig 2B), its expression was significantly upregulated in prostate cancers with genetic fusions of ERG, ETV1, and FLI1 genes (Fig 3A). Upregulations of SFXN2 and SFXN4 genes were observed in all subtypes except the IDH1 mutation subtype while SFXN3 downregulation was not observed in two subtypes of FLI fusion and IDH1 mutation (Fig 3B-3D). SFXN5 gene upregulation was observed only in the ERG fusion subtype (Fig 3E). The mechanisms for the potential regulation of ERG fusion on SFXN5 expression as well as IDH1 mutation on SFXN2/SFXN4 expression are worthy of further investigation. We also compared the expression levels of altered SFXN2/3/4 genes in different Gleason score groups. All subgroups with different Gleason scores consistently showed SFXN2/4 upregulation and SFXN3 downregulation (Fig 3F-3H), of which SFXN4 expression showed a gradually increasing trend along with increased Gleason scores (Fig 3H). These data suggest that SFXN4 expression is tightly correlated with disease progression. SFXN1/SFXN3/SFXN4 expressions were correlated with immune infiltration in prostate cancers We analyzed the correlations of SFXN gene expression with tumor immune infiltrations. The results showed that SFXN1 and SFXN2 shared very similar correlations with the top four (Tcm/T-helper/Th2/Eosinophil) and bottom four (Cytotoxic/NK CD56bright/NK/pDC) immune infiltrations (Fig 4A-4B), although SFXN1 showed a very strong correlation with top three infiltrations (Fig 4A). SFXN3 expression exerted a very broad and strong (coefficient r > 0.3) correlation with more than half of immune infiltrations that were proinflammatory lymphocytes (Fig 4C). In contrast, SFXN4 expression was negatively correlated with three pro-inflammatory immune infiltrations (Fig 4D), but SFXN5 expression only exhibited very mild correlations with immune infiltrations (Fig 4E). These data suggest that SFXN1 and SFXN2 shared a very similar correlation with immune infiltrations while SFXN3 was positively associated but SFXN4 was negatively associated with pro-inflammatory lymphocytes. SFXN4 expression was a worse prognostic factor in prostate cancer We conducted a prognosis analysis using the Kaplan-Meier survival curve approach with the TCGA-PRAD dataset. Our results showed that higher levels of SFXN4 expression were associated with worse overall survival outcomes in prostate cancer patients (Fig 5A). In addition, a borderline significance (p = 0.051) was observed in progression-free interval outcomes (Fig 5B). A ROC prediction model analysis indicated that SFXN4 expression had the best AUC value (0.770) as a predictor for a 10-year disease-specific survival prognosis. These data suggest that SFNX4 might serve as a moderate prognostic factor for prostate cancer outcome. SFXN2/4 expression was androgen receptor modulated and associated with CRPC progression Castration-resistant progression followed by neuroendocrinal trans-differentiation is the major clinical obstacle in prostate cancer management [35, 36]. We analyzed the expression levels of SFXN family genes in CRPC patient tissues using the SU2C/PCF RNA-seq dataset [24]. Our analysis determined that SFXN2/SFXN4 expressions were positive while SFXN3 expression was negatively correlated with AR signaling score with a very strong co-efficient (Fig 6A-6C). In addition, a strong and negative correlation was observed between SFXN2/SFXN4 expressions and NEPC scores (Fig 6D-6E). These data indicated that SFXN2/SFXN4 genes were possibly modulated by the AR signal pathway. We then examined the associations of SFXN gene expressions with NE features. Our results showed that SFXN1/SFXN5 expression levels were higher but SFXN4 expression was lower in NE feature-positive cases than those cases without NE features (Fig 7A-7E). Further in-depth analysis revealed that SFXN1/SFXN5 expressions were significantly higher and SFXN4 was significantly lower in small cell carcinoma (Fig 7F-7J), which is a neuroendocrinal type of prostate cancer [37]. Finally, we determined the androgen modulation on SFXN family genes. A microarray dataset derived from mice prostate tissues [25] was utilized to analyze SFXN2 gene expression, As shown in Fig 8A, castration of the animals significantly reduced SFXN2 expression at 3-14 days after surgery and testosterone replacement for 3 days largely restored SFXN2 expression. These data suggest that the SFXN2 gene was modulated by androgens. In addition, we analyzed the effect of castration on SFXN4 expression with two different human prostate cancer xenograft models, KUCaP-2 [26] and LuCaP35 [28]. Our results showed that castration of the animals significantly reduced SFXN4 gene expression in the xenograft tissues (Fig 8B-8C), indicating an androgen modulation of SFXN4 gene expression. Lastly, the AR involvement in modulating SFXN2/SFXN4 expression was verified in LNCaP cells, in which the AR gene expression was knocked down using the small hairpin RNA [29]. As shown in Fig 8D, knocking down AR gene expression significantly reduced both SFXN2 and SFXN4 gene expression, confirming the AR involvement in SFXN2/SFXN4 expression in prostate cancer cells. Discussion In this study, our comprehensive analysis determined that SFXN1/3/4 genes were predominantly expressed in prostate tissue, of which SFXN1/4 were mainly expressed in luminal cells while the SFXN3 gene was expressed in basal cells at a relatively higher level. In primary prostate cancers, SFXN2/SFXN4 genes were significantly upregulated but SFXN3 was downregulated compared to benign tissues. SFXN4 expression exerted as a strong diagnostic factor for prostate cancer and a prognostic factor for overall survival outcome. There were no molecular signature-specific dysregulations of the SFXN family genes. SFXN3 expression was broadly correlated with most pro-inflammatory immune infiltrations while SFXN4 expression was strongly correlated with three proinflammatory lymphocytes (Th1 cells, Neutrophils, and NK cells). In CRPC tumors, SFXN2/SFXN4 expressions were positively correlated with AR scores while negatively correlated with NEPC scores. Consistently, SFXN2/SFXN4 expressions were attenuated in castration animals and AR knocked down LNCaP cells, indicating an AR-dependent modulation. Although SFXN3 expression was negatively correlated with AR scores, its expression was not enhanced in castrated animals, indicating a non-AR modulated gene. Overexpression of SFXN2/SFXN4 genes has been observed in human cancers, where its levels correlate with different prognostic outcomes depending on the type of cancer [6, 32, 38, 39]. SFXN2 is particularly significant in breast cancer, where it is upregulated and linked to cancer cell growth [38]. This association makes it a potential biomarker for breast cancer prognosis and a possible target for therapeutic intervention. SFXN4 is gaining attention as a prognostic marker due to its role in oxidative phosphorylation and its correlation with immune infiltration in tumors. For example, higher SFXN4 expression in hepatocellular carcinoma (HCC) correlates with increased tumor aggressiveness and specific immune profiles, suggesting SFXN4 could be a therapeutic target for mitochondrial-based cancer treatments [39]. In our study, we also found SFXN2/SFXN4 overexpression in prostate cancers, of which only SFXN4 expression was associated with immune infiltration and disease progression. Particularly, we determined for the first time that SFXN2/SFXN4 genes were modulated by the AR signal pathway and further investigation is needed to understand their functional roles in CRPC progression. SFXN3 has revealed its role in different types of cancer, making it a protein of interest for therapeutic targeting [38, 40-44]. In head and neck squamous cell carcinoma (HNSC), SFXN3 expression correlates with poor prognosis, chemotherapy resistance, and an immunosuppressive tumor microenvironment [42]. This regulation is thought to involve a non-coding RNA pathway (LINC01270/hsa-miR-29c-3p/SFXN3), suggesting that SFXN3 might contribute to aggressive tumor behaviors by modulating immune responses and drug resistance mechanisms. In this study, we observed a broad and strong correlation of SFXN3 expression with pro-inflammatory immune infiltration in prostate cancers. Since SFXN3 expression was reduced in prostate cancer, suggesting an enhanced expression of the SFXN3 gene might be able to sensitize prostate cancer to immunotherapy. In hematological cancers, such as non-M3 acute myeloid leukemia (AML), high SFXN3 levels have been associated with unfavorable outcomes. Here, SFXN3 may increase sensitivity to hypomethylating therapies, like decitabine, offering potential treatment avenues in cases where other therapies have been ineffective. This finding opens new possibilities for using SFXN3 as a biomarker in treatment strategies tailored to AML patients with high SFXN3 expression [44]. SFXN1 has been identified as a potential biomarker in multiple human cancers [38, 45-47], including lung adenocarcinoma (LUAD) [45, 48-52]. Its high expression is correlated with factors like tumor size and metastasis, and it is associated with immune infiltration. In LUAD, SFXN1 expression may influence the tumor microenvironment by interacting with immune cells, such as T and B cells, and immune checkpoints [52]. This suggests that SFXN1 could be targeted for immune-based therapies in cancers like LUAD. In our study, SFXN1 expression was strongly correlated with pro-inflammatory immune infiltrations, although its expression levels were not significantly altered in malignant tissues. Recent research into SFXN5 has uncovered its role in neutrophil function and immune response, primarily through regulating actin polymerization [7]. SFXN5, identified as a citrate transporter in vitro, is critical for neutrophil spreading, an initial step in adhesion and migration essential for immune response [7]. SFXN5 deficiency in neutrophils (induced via siRNA or morpholino injection in mice and zebrafish models) led to impaired cell spreading, adhesion, chemotaxis, and reactive oxygen species (ROS) production. This deficiency also reduced actin polymerization due to lower cytosolic citrate levels and downstream metabolites like acetyl-CoA and cholesterol, which are necessary for actin polymerization mediated by phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2). Supplementing with citrate or cholesterol partially restored these processes, highlighting SFXN5's pivotal role in cellular actin dynamics and neutrophil migration during inflammatory responses. In this study, we found that SFXN5 was expressed at a very low level in prostate tissue, both benign and malignant. SFXN5 expression was not associated with disease progression and immune infiltration. These data suggest that SFXN5’s role in prostate cancer is potentially limited. In conclusion, our comprehensive analysis of SFXN family genes in prostate cancers revealed a diverse profile, of which SFXN2/SFXN4 genes were upregulated while SFXN3 was downregulated. SFXN2/SFXN4 expression was associated with prostate cancer progression and was modulated by the AR signal pathway. Most importantly, SFXN4 expression exerted as a strong biomarker for disease diagnosis and overall survival outcome. Declarations Acknowledgments The results shown in this study are in part based on data generated by the TCGA Research Network (https://www.cancer.gov/tcga). Ethical Approval: not applicable Funding This work was partially supported by the Hangzhou Zijingang Science & Technology City High-level Talent Plan Category B to Dr. Runzhi Zhu. Availability of data and materials: Not applicable Author contributions H Huang, W Liu, and B Li conducted the data analysis. H Lian, H Shao, R Zhu, and B Li drafted the manuscript. All authors approved the submission. Conflict of interest The authors declared no commercial or financial relationships construed as a potential conflict of interest. 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Chen Y, Qian J, Ding P, Wang W, Li X, Tang X, Tang C, Yang Y, Gu C: Elevated SFXN2 limits mitochondrial autophagy and increases iron-mediated energy production to promote multiple myeloma cell proliferation . Cell Death Dis 2022, 13 (9):822. Mon EE, Wei FY, Ahmad RNR, Yamamoto T, Moroishi T, Tomizawa K: Regulation of mitochondrial iron homeostasis by sideroflexin 2 . J Physiol Sci 2019, 69 (2):359-373. Cancer Genome Atlas Research N: The Molecular Taxonomy of Primary Prostate Cancer . Cell 2015, 163 (4):1011-1025. Gao D, Shen Y, Xu L, Sun Y, Hu H, Xu B, Wang Z, Xu H: Acetate utilization promotes hormone therapy resistance in prostate cancer through neuroendocrine differentiation . Drug Resist Updat 2024, 77 :101158. Ge R, Wang Z, Montironi R, Jiang Z, Cheng M, Santoni M, Huang K, Massari F, Lu X, Cimadamore A et al : Epigenetic modulations and lineage plasticity in advanced prostate cancer . Ann Oncol 2020, 31 (4):470-479. Chen CC, Tran W, Song K, Sugimoto T, Obusan MB, Wang L, Sheu KM, Cheng D, Ta L, Varuzhanyan G et al : Temporal evolution reveals bifurcated lineages in aggressive neuroendocrine small cell prostate cancer trans-differentiation . Cancer Cell 2023, 41 (12):2066-2082 e2069. Yuan D, Liu J, Sang W, Li Q: Comprehensive analysis of the role of SFXN family in breast cancer . Open Med (Wars) 2023, 18 (1):20230685. Du Z, Zhang Z, Han X, Xie H, Yan W, Tian D, Liu M, Rao C: Comprehensive Analysis of Sideroflexin 4 in Hepatocellular Carcinoma by Bioinformatics and Experiments . Int J Med Sci 2023, 20 (10):1300-1315. Dong Y, Jin F, Wang J, Li Q, Huang Z, Xia L, Yang M: SFXN3 is Associated with Poor Clinical Outcomes and Sensitivity to the Hypomethylating Therapy in Non-M3 Acute Myeloid Leukemia Patients . Curr Gene Ther 2023, 23 (5):410-418. Jin F, He L, Wang J, Zhang Y, Yang M: SFXN3 is a Prognostic Marker and Promotes the Growth of Acute Myeloid Leukemia . Cell Biochem Biophys 2024, 82 (3):2195-2204. Chen K, Gong S, Fang X, Li Q, Ye M, Li J, Huang S, Zhao Y, Liu N, Li Y et al : Non-coding RNA-mediated high expression of SFXN3 as a prognostic biomarker associated with paclitaxel resistance and immunosuppressive microenvironment in head and neck cancer . Front Immunol 2022, 13 :920136. Jin T, Ge L, Chen J, Wang W, Zhang L, Ge M: Identification of iron metabolism-related genes as prognostic indicators for papillary thyroid carcinoma: a retrospective study . PeerJ 2023, 11 :e15592. Zhou H, Wang F, Niu T: Prediction of prognosis and immunotherapy response of amino acid metabolism genes in acute myeloid leukemia . Front Nutr 2022, 9 :1056648. Li Y, Yang W, Liu C, Zhou S, Liu X, Zhang T, Wu L, Li X, Zhang J, Chang E: SFXN1-mediated immune cell infiltration and tumorigenesis in lung adenocarcinoma: A potential therapeutic target . Int Immunopharmacol 2024, 132 :111918. Andriani L, Ling YX, Yang SY, Zhao Q, Ma XY, Huang MY, Zhang YL, Zhang FL, Li DQ, Shao ZM: Sideroflexin-1 promotes progression and sensitivity to lapatinib in triple-negative breast cancer by inhibiting TOLLIP-mediated autophagic degradation of CIP2A . Cancer Lett 2024, 597 :217008. Yagi K, Shimada S, Akiyama Y, Hatano M, Asano D, Ishikawa Y, Ueda H, Watanabe S, Akahoshi K, Ono H et al : Loss of SFXN1 mitigates lipotoxicity and predicts poor outcome in non-viral hepatocellular carcinoma . Sci Rep 2023, 13 (1):9449. Zhang YH, Liu XS, Gao Y, Yuan LL, Huang ZM, Zhang Y, Liu ZY, Yang Y, Liu XY, Ke CB et al : SFXN1 as a potential diagnostic and prognostic biomarker of LUAD is associated with (18)F-FDG metabolic parameters . Lung Cancer 2024, 188 :107449. Liu W, Du Q, Mei T, Wang J, Huang D, Qin T: Comprehensive analysis the prognostic and immune characteristics of mitochondrial transport-related gene SFXN1 in lung adenocarcinoma . BMC Cancer 2024, 24 (1):94. Chen L, Kang Y, Jiang Y, You J, Huang C, Xu X, Chen F: Overexpression of SFXN1 indicates poor prognosis and promotes tumor progression in lung adenocarcinoma . Pathol Res Pract 2022, 237 :154031. Chen Q, Wang R, Zhang J, Zhou L: Sideroflexin1 as a novel tumor marker independently predicts survival in lung adenocarcinoma . Transl Cancer Res 2019, 8 (4):1170-1178. Dang HH, Ta HDK, Nguyen TTT, Anuraga G, Wang CY, Lee KH, Le NQK: Prospective role and immunotherapeutic targets of sideroflexin protein family in lung adenocarcinoma: evidence from bioinformatics validation . Funct Integr Genomics 2022, 22 (5):1057-1072. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Feb, 2025 Read the published version in Human Genomics → Version 1 posted Editorial decision: Revision requested 19 Nov, 2024 Reviews received at journal 18 Nov, 2024 Reviews received at journal 18 Nov, 2024 Reviewers agreed at journal 18 Nov, 2024 Reviewers agreed at journal 18 Nov, 2024 Reviewers invited by journal 17 Nov, 2024 Editor assigned by journal 13 Nov, 2024 Submission checks completed at journal 13 Nov, 2024 First submitted to journal 12 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5442270","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":379852687,"identity":"773d2a4c-218f-4cab-9d93-28c9941c9f5e","order_by":0,"name":"Hua Huang","email":"","orcid":"","institution":"Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Huang","suffix":""},{"id":379852688,"identity":"9de8ec3d-3e56-4209-8f85-d4b15a04e7ca","order_by":1,"name":"Huibo Lian","email":"","orcid":"","institution":"Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Huibo","middleName":"","lastName":"Lian","suffix":""},{"id":379852689,"identity":"48c30303-118e-47e2-a186-b52b812242b6","order_by":2,"name":"Runzhi Zhu","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Runzhi","middleName":"","lastName":"Zhu","suffix":""},{"id":379852690,"identity":"b700ef4d-dba3-457d-82bf-eb73b2da1e03","order_by":3,"name":"Haiyan Shao","email":"","orcid":"","institution":"Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Haiyan","middleName":"","lastName":"Shao","suffix":""},{"id":379852694,"identity":"3bc0ade8-8dd7-4920-b223-5d9c70aced79","order_by":4,"name":"Benyi Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAlElEQVRIiWNgGAWjYBACA3bGxocNUDaRWpgZmw1J1cLAJkmaFnNm5rbKmW3b5BnYm7dJEKXFspmx7ebGttuGDTzHyojTYnAYqOVh2+0EBokcM+K1FIK1yL8hQQvjRrAtPERqAfqlWXLGuduGbTxpxRZEaTFnb3/4safstjw/++GNN4jSAgdspCkfBaNgFIyCUYAXAAB7NCwh6WQDNQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Kansas Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Benyi","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-11-12 21:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5442270/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5442270/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40246-024-00705-6","type":"published","date":"2025-02-07T15:58:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70910294,"identity":"9d346dcc-f641-4e76-8aa0-d5ba45a85a41","added_by":"auto","created_at":"2024-12-09 07:10:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":70525,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of SFXN family genes in benign prostate tissue and prostate cancer cell lines. A-C \u003c/strong\u003eThe expression levels of SFXN family genes were downloaded from the cBioportal platform and RNA-seq RPKM values were utilized for the analysis. ANOVA test, * p \u0026lt; 0.05; **** p \u0026lt; 0.0001. \u003cstrong\u003eD\u003c/strong\u003e RNA-seq data was downloaded from the cBioportal platform. E DepMap data gene knockout effect was downloaded from the UALCAN platform.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5442270/v1/4561ab4f778a2846ec63240e.png"},{"id":70910292,"identity":"89cdeef3-37af-49a2-96ec-02dc7bdd9bfb","added_by":"auto","created_at":"2024-12-09 07:10:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":181573,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of SFXN family genes in primary prostate cancers. \u003c/strong\u003eCase-matched pairs (\u003cstrong\u003eA\u003c/strong\u003e) or group cohort (\u003cstrong\u003eB\u003c/strong\u003e) comparisons were conducted between benign and malignant tissues using the TCGA-PRAD dataset (log\u003csub\u003e2\u003c/sub\u003e [TPM + 1]). The asterisks indicate a significant difference compared to the control group.\u0026nbsp; * p \u0026lt; 0.05; ***, p \u0026lt; 0.001. \u003cstrong\u003eC\u003c/strong\u003e ROC analysis was conducted to determine the potential of prediction in distinguishing benign and malignant prostate tissues.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5442270/v1/b15b8db23ef7722cad71d9f1.png"},{"id":70910293,"identity":"77a40fcf-f000-498b-9186-fcbf4bd213aa","added_by":"auto","created_at":"2024-12-09 07:10:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":89988,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of SFXN\u003c/strong\u003e \u003cstrong\u003efamily genes in subgroups with specific molecular signatures or different Gleason scores. \u003c/strong\u003eGene expression data was downloaded from the TCGA-PRAD dataset on the UALCAN platform. The asterisks indicate a significant difference compared to the benign group. * p \u0026lt; 0.05; ** p \u0026lt; 0.01; ***, p \u0026lt; 0.001; ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5442270/v1/e0a4cd539c82a9a6af0d66ce.png"},{"id":70910296,"identity":"dff69032-5c8a-4811-83e9-945b9f92ff79","added_by":"auto","created_at":"2024-12-09 07:10:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":350370,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations of SFXN family genes with tumor immune infiltrations in prostate cancers. \u003c/strong\u003eThe TCGA-PRAD RNS-seq dataset was utilized for the correlation analysis with tumor immune infiltrations on the XIANTAO platform. The red line squares indicated special factors with SFXN gene expression.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-5442270/v1/b189014fd8b5d27f74e22059.png"},{"id":70910514,"identity":"94d249bb-97ef-4948-80f4-96b3fccd01d9","added_by":"auto","created_at":"2024-12-09 07:18:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":137925,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier survival analysis\u003c/strong\u003e. Gene expression data for the SFXN family genes were extracted from the TCGA-PRAD RNA-seq dataset. Kaplan-Meier survival curve (\u003cstrong\u003eA-B\u003c/strong\u003e) and ROC analysis (\u003cstrong\u003eC\u003c/strong\u003e) were conducted with a minimum \u003cem\u003ep\u003c/em\u003e-value approach on the XIANTAO platform.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-5442270/v1/ecacdbf3886cb4941ad3e50b.png"},{"id":70910297,"identity":"3e5d8e6d-bca6-46f4-9462-7ed6c950f3f9","added_by":"auto","created_at":"2024-12-09 07:10:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":245103,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of the SFXN family gene expression with the AR score (A-C) or NEPC score (D-E) in CRPC patients\u003c/strong\u003e. Gene expression at the mRNA levels (log\u003csub\u003e2\u003c/sub\u003e [TPM + 1]) was extracted from the SU2C/PCF dataset on the cBioportal platform. The asterisk indicates a significant difference between the two groups.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-5442270/v1/3352b116626e3f73da7b319a.png"},{"id":70910515,"identity":"4c9dbae1-d53b-4bf0-80f1-bcc8983b6540","added_by":"auto","created_at":"2024-12-09 07:18:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":239426,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of the SFXN family genes in CRPC patients\u003c/strong\u003e. Expression data at the mRNA levels (log\u003csub\u003e2\u003c/sub\u003e [TPM + 1]) were extracted from the SU2C/PCF dataset on the cBioportal platform. Student’s t-test, * p \u0026lt; 0.05, ** p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-5442270/v1/9f2d4adc0d0f95d6cb037911.png"},{"id":70910299,"identity":"da17e3ea-e513-4850-bbc1-706726fcd3b9","added_by":"auto","created_at":"2024-12-09 07:10:12","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":59960,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of SFXN2/4 genes in mouse prostate or human prostate xenografts in nude mice\u003c/strong\u003e \u003cstrong\u003eafter castration or testosterone replacement\u003c/strong\u003e. Microarray data were extracted from the NCBI GEO profiles of GDS2562 (\u003cstrong\u003eA\u003c/strong\u003e), GDS4107 (\u003cstrong\u003eB\u003c/strong\u003e), and GDS4120 (\u003cstrong\u003eC\u003c/strong\u003e). LNCaP cells were infected with control shRNA or AR shRNA for 3 days and gene expression data was extracted from GDS4113 (\u003cstrong\u003eD\u003c/strong\u003e). The relative values were calculated against the lowest value in the control group before statistical analysis. The asterisk indicates a significant difference. Student’s \u003cem\u003et\u003c/em\u003e-test, * p \u0026lt; 0.05, ** p \u0026lt; 0.01; *** p \u0026lt; 0.001; **** p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-5442270/v1/644d8b2fef25d9213810f63f.png"},{"id":75930565,"identity":"2ab84d00-8019-4208-a802-4a768b4e9884","added_by":"auto","created_at":"2025-02-10 16:13:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4183537,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5442270/v1/81f3dc73-d6ed-406a-8602-23135445488c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sideroflexin family genes were dysregulated and associated with tumor progression in prostate cancers","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe sideroflexin (SFXN) family is a group of proteins that are found in the mitochondria of eukaryotes and are involved in cellular processes such as iron homeostasis, amino acid metabolism, and energy production [1]. SFXN proteins are multi-spanning transmembrane proteins with the N-terminus inside the cytoplasm and the C-terminus outside the cytoplasm [1].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSFXN proteins are highly conserved across eukaryotes and include five proteins in humans: SFXN1, SFXN2, SFXN3, SFXN4, and SFXN5 [2]. SFXN1, SFXN2, and SFXN3 are mitochondrial serine transporters that are important for one-carbon metabolism [1, 2]. Studies suggest that their role as a serine transporter and interaction with mitochondrial proteins could make it a target for treatments that address mitochondrial dysfunction common in neurodegenerative conditions [3].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSFXN4 is a complex I assembly factor that helps incorporate the ND6 subunit into complex I [4, 5]. The study of SFXN proteins is an emerging research field that could lead to discoveries about mitochondrial physiopathology [1, 5]. Mutations in SFXN4 can cause mitochondrial disease, including impaired mitochondrial respiration and hematopoietic abnormalities [1, 4]. SFXN4 deficiencies impact erythroid differentiation, as shown in studies on anemia and mitochondriopathies where SFXN4 disruption affects iron-sulfur (Fe-S) cluster biogenesis [4]. This links SFXN4 to broader metabolic and redox processes across various conditions, underscoring its therapeutic potential in diseases beyond cancer [6].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSFXN5, identified as a citrate transporter in vitro, is critical for neutrophil spreading, an initial step in adhesion and migration essential for immune response. In a recent study, SFXN5 deficiency in neutrophils (induced via siRNA or morpholino injection in mice and zebrafish models) led to impaired cell spreading, adhesion, chemotaxis, and reactive oxygen species (ROS) production [7]. This deficiency also reduced actin polymerization due to lower cytosolic citrate levels and downstream metabolites like acetyl-CoA and cholesterol, which are necessary for actin polymerization mediated by phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2). Supplementing with citrate or cholesterol partially restored these processes, highlighting SFXN5's pivotal role in cellular actin dynamics and neutrophil migration during inflammatory responses.\u003c/p\u003e\n\u003cp\u003eIdentifying novel prognostic biomarkers for prostate cancer is a dynamic and rapidly evolving field. Advances in genomics, proteomics, metabolomics, and imaging, combined with computational innovations, are driving the discovery of more accurate and reliable biomarkers [8, 9]. These novel markers hold the promise of improving prognostic accuracy, guiding personalized treatment, and ultimately enhancing survival and quality of life for prostate cancer patients [10]. Continued research, validation, and integration into clinical practice are essential to realize the full potential of these emerging biomarkers [11].\u003c/p\u003e\n\u003cp\u003eCurrently, there is a paucity of information in the literature about the expression profiles and clinical significance of SFXN family genes in prostate cancers. In this study, we took a comprehensive approach of utilizing multiple transcriptome datasets and analyzed the expression profiles of SFXN family genes in prostate cancers, ranging from benign prostate tissue, and primary cancer to advanced castration-resistant stage. Our results revealed that SFXN2/SFXN4 genes were highly upregulated in prostate cancers. Their expressions were androgen responsive under the modulation of the androgen receptor (AR) signal pathway. High levels of their expression were associated with disease progression to the CRPC stage and worse overall survival outcomes in primary cancers.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cem\u003eGene expression profiles in prostate tissue, cancer cell lines, and prostate cancer tissues\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCell-specific analysis of gene expression was conducted using the NCBI GEO dataset #GDS1973 [12]. Four basic cell types were separated with magnetic cell sorting (MACS) based on cell-type specific cluster designation (CD) antigens, integrin beta4 for basal cells, dipeptidyl peptidase IV for luminal secretory cells, integrin alpha-1 for stromal fibromuscular cells, and PECAM-1 for endothelial cells, as described [12]. Gene expression of MACS-sorted cell populations was assessed with Affymetrix Human Genome U133 Plus 2.0 Array (HG-U133Plus2). The microarray data were downloaded from the NCBI GEO site, and the relative values of gene expression were calculated against the control group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGene expression of prostate cell lines was analyzed using the Cancer Cell Line Encyclopedia transcriptome dataset [13]. The RNA-seq RPKM values (reads per kilobase per million mapped reads) of gene expression were downloaded on the cBioportal platform (www.cbioportal.org/). There was one benign prostate epithelial cell line (PRECLH) and seven prostate cancer cell lines.\u003c/p\u003e\n\u003cp\u003eWe used the Cancer Genome Atlas program (TCGA-PRAD) RNA-seq dataset to examine the expression profiles of the SFXN family genes in primary prostate cancer, as described in our recent publications [14-22]. Gene expression levels were compared using two approaches, case-matched pair comparison (52 cases) and group cohort comparison (500 patient cases) with 52 benign samples. Statistical analysis and data visualization were conducted on the XIANTAO online platform (https://www.xiantaozi.com/). A comparison of gene expression levels in subgroups stratified by molecular signatures (distinct gene fusion and common mutations) was conducted on the UALCAN platform (https://ualcan.path.uab.edu/).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePatient survival outcome assessment\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe examined the association of gene expression with patient survival outcomes, including overall survival, disease-specific survival, and progression-free interval. Patients were stratified using the minimum \u003cem\u003ep\u003c/em\u003e-value cut-off approach [23]. The Kaplan-Meier curve analysis and ROC-based prediction were performed on the XIANTAO platform. The results were visualized with the survminer package and ggplot2 package of the R package (version 4.2.1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGene expression analysis in CRPC patients\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe used the SU2C/PCF RNA-seq dataset [24] for the analysis of SFXN gene expression at the mRNA levels in CRPC patients on the cBioportal platform. Patients were divided into different subgroups based on pathological subtype, androgen receptor (AR) signaling activity score, and neuroendocrinal feature of prostate cancer (NEPC) score for comparison. Correlation analysis between gene expression levels and AR score or NEPC score was conducted in categories of Spearman and Pearson coefficients on the cBioportal platform.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGene expression after castration or AR silencing in prostate cancer cell line and xenograft models\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe evaluated the effect of castration on gene expression in mouse prostate, human prostate cancer LuCaP35, and KUCaP-2 xenograft models. Gene expression analysis in mouse prostate tissues was conducted using the NCBI GEO dataset GDS#2562, as described previously [25]. Briefly, C57/B6 mice were sham-operated or castrated, and mouse prostate tissues were harvested at 3 or 14 days after surgery. One group of animals was implanted with testosterone (T) pellet at 15 mg/pellet/mouse at day 14 post-surgery for three days before tissue harvesting. Total RNA was purified with the RNeasy kit (Qiagen, Valencia, CA) for the Affymetrix MGU74A chip-based gene analysis.\u003c/p\u003e\n\u003cp\u003eHuman prostate cancer xenograft models KUCaP-2 with wild-type AR were established subcutaneously in nude mice [26]. Animals bearing KUCaP-2 xenograft tumors were castrated and xenograft tumor tissues were harvested at 3-5 months post-surgery. Total RNA was isolated and purified using the RNeasy Mini Kit (Qiagen) for cDNA microarray analysis with an Affymetrix Human Genome U133 Plus2.0, as described [26]. The data were downloaded from the NCBI GEO profile GDS#4107.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHuman prostate cancer LuCaP35 xenografts [27] were established in NOD/SCID mice [28]. After sham operation or castration, xenograft tumors were harvested for RNA extraction using the QIAGEN RNeasy Mini Kit (Valencia, CA), followed by GeneChip assays using the Affymetrix human genome U133 Plus 2.0 array. The results were downloaded from the NCBI GEO profile GDS#4120.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo examine the AR involvement in modulating SFXN gene expression, AR gene expression was silenced in LNCaP cells with a small-hairpin RNA (shAR lentivirus or a nontargeting control shRNA [29]. Total cellular RNAs were extracted using the Qiagen RNeasy Mini kit (Valencia, CA), followed by gene chip assay with the Affymetrix human U133 Plus 2.0 microarrays. The results were downloaded from NCBI GEO profile GDS#4113.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDepMap data analysis for gene knockout effect on cellular survival\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe utilized the gene effect scores derived from CRISPR knockout screens published by Broad\u0026apos;s Achilles and Sanger\u0026apos;s SCORE projects [30, 31]. Scores are normalized such that nonessential genes have a median score of 0 and independently identified common essential genes have a median score of -1. Gene effect scores were inferenced by Chronos [31]. Integration of the Broad and Sanger datasets was performed, as described [30]. Negative scores imply cell growth inhibition and/or death following gene knockout. The gene effect data were downloaded from the UALCAN platform.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; Gene expression at the mRNA levels was used as Log\u003csub\u003e2\u003c/sub\u003e [TPM + 1]) value and presented as the MEAN \u0026plusmn; the SEM (standard error of the mean). ANOVA analyses were conducted for multiple group comparisons. Student \u003cem\u003et\u003c/em\u003e-test was performed to determine the significance of the differences between the two groups. For data without normal distribution, the Wilcoxon rank sum test was utilized for the statistical analysis. The results were visualized using the ggplot2[3.3.6], stats [4.2.1], and car [3.1-0] from the R package (version 4.2.1). GraphPad software (version 9.1.0) was used for the graphic presentation.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eSFXN1/SFXN3/SFXN4 genes are highly expressed in benign prostate tissues and cancer cell lines\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo examine the expression profiles of SFXN family genes in human prostate tissue, we analyzed the single-cell RNA-seq data derived from NCBI GEO GDS1973 [12]. Prostatic basal, luminal secretory, stromal fibromuscular, and endothelial cells were separated with magnetic cell sorting (MACS) based on cell-type specific cluster designation (CD) antigens. Our results showed that SFXN1 and SFXN4 were the predominant genes expressed in prostate tissues followed by SFXN3, while SFXN2 and SFXN5 were expressed at a very low level (Fig 1A). Prostatic luminal cells mainly expressed SFXN1/SFXN4 genes (Fig 1B), but basal cells expressed only moderate levels of SFXN4/SFXN1 genes (Fig 1C). However, the SFXN3 gene was relatively enriched in basal cells. Prostatic endocrinal cells expressed higher levels of SFXN1 than other cell types.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn human prostate epithelial and cancer cell lines, SFXN1/SFXN3/SFXN4 genes were expressed at higher levels than SFXN2/SFXN5 genes, of which SFXN5 gene was expressed at a very low level, indicating a less functional significance. In CRISPR/Cas9-based knockout screening experiments, the SFXN2 gene was identified as a survival-essential gene in all tested prostate benign and malignant cell lines (Fig 1E), indicating that the SFXN2 gene is critical for cellular survival [32, 33] while SFXN1/SFXN3/SFXN4/SFXN5 genes might be compensated by other isoforms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSFXN2/SFXN3/SFXN4 genes were aberrantly expressed in primary prostate cancers\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; We examined the expression profiles of SFXN family genes in primary prostate cancers using the TCGA-PRAD RNA-seq dataset. We first compared gene expression levels in 52 case-matched pairs of benign and malignant tissues from patients who received radical prostatectomy. Our analysis revealed that SFXN2 and SFXN4 genes were significantly upregulated in malignant tissues (Fig 2A), especially the SFXN4 gene whose upregulation was constantly seen in all cases. In contrast, the SFXN3 gene was sharply downregulated in malignant tissues. We then conducted a group cohort comparison of 502 malignant tissues with benign tissues. The results showed a similar trend of dysregulation of these three genes in prostate cancers (Fig 2B). SFXN1 and SFXN5 genes did not show a significant alteration. An ROC analysis indicated that SFXN4 gene expression had the highest AUC value of 0.877 in distinguishing malignant from benign tissues, representing a potential diagnostic biomarker.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; We then analyzed the alterations of these genes in distinct molecular subtypes of prostate cancer stratified by unique genetic abnormalities [34]. Although SFXN1 gene expression did not show a significant alteration in group cohort comparison (Fig 2B), its expression was significantly upregulated in prostate cancers with genetic fusions of ERG, ETV1, and FLI1 genes (Fig 3A). Upregulations of SFXN2 and SFXN4 genes were observed in all subtypes except the IDH1 mutation subtype while SFXN3 downregulation was not observed in two subtypes of FLI fusion and IDH1 mutation (Fig 3B-3D). SFXN5 gene upregulation was observed only in the ERG fusion subtype (Fig 3E). The mechanisms for the potential regulation of ERG fusion on SFXN5 expression as well as IDH1 mutation on SFXN2/SFXN4 expression are worthy of further investigation.\u003c/p\u003e\n\u003cp\u003eWe also compared the expression levels of altered SFXN2/3/4 genes in different Gleason score groups. All subgroups with different Gleason scores consistently showed SFXN2/4 upregulation and SFXN3 downregulation (Fig 3F-3H), of which SFXN4 expression showed a gradually increasing trend along with increased Gleason scores (Fig 3H). These data suggest that SFXN4 expression is tightly correlated with disease progression.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSFXN1/SFXN3/SFXN4 expressions were correlated with immune infiltration in prostate cancers\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed the correlations of SFXN gene expression with tumor immune infiltrations. The results showed that SFXN1 and SFXN2 shared very similar correlations with the top four (Tcm/T-helper/Th2/Eosinophil) and bottom four (Cytotoxic/NK CD56bright/NK/pDC) immune infiltrations (Fig 4A-4B), although SFXN1 showed a very strong correlation with top three infiltrations (Fig 4A). SFXN3 expression exerted a very broad and strong (coefficient r \u0026gt; 0.3) correlation with more than half of immune infiltrations that were proinflammatory lymphocytes (Fig 4C). In contrast, SFXN4 expression was negatively correlated with three pro-inflammatory immune infiltrations (Fig 4D), but SFXN5 expression only exhibited very mild correlations with immune infiltrations (Fig 4E). These data suggest that SFXN1 and SFXN2 shared a very similar correlation with immune infiltrations while SFXN3 was positively associated but SFXN4 was negatively associated with pro-inflammatory lymphocytes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSFXN4 expression was a worse prognostic factor in prostate cancer\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a prognosis analysis using the Kaplan-Meier survival curve approach with the TCGA-PRAD dataset. Our results showed that higher levels of SFXN4 expression were associated with worse overall survival outcomes in prostate cancer patients (Fig 5A). In addition, a borderline significance (p = 0.051) was observed in progression-free interval outcomes (Fig 5B). A ROC prediction model analysis indicated that SFXN4 expression had the best AUC value (0.770) as a predictor for a 10-year disease-specific survival prognosis. These data suggest that SFNX4 might serve as a moderate prognostic factor for prostate cancer outcome.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSFXN2/4 expression was androgen receptor modulated and associated with CRPC progression\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCastration-resistant progression followed by neuroendocrinal trans-differentiation is the major clinical obstacle in prostate cancer management [35, 36]. We analyzed the expression levels of SFXN family genes in CRPC patient tissues using the SU2C/PCF RNA-seq dataset [24]. Our analysis determined that SFXN2/SFXN4 expressions were positive while SFXN3 expression was negatively correlated with AR signaling score with a very strong co-efficient (Fig 6A-6C). In addition, a strong and negative correlation was observed between SFXN2/SFXN4 expressions and NEPC scores (Fig 6D-6E). These data indicated that SFXN2/SFXN4 genes were possibly modulated by the AR signal pathway.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe then examined the associations of SFXN gene expressions with NE features. Our results showed that SFXN1/SFXN5 expression levels were higher but SFXN4 expression was lower in NE feature-positive cases than those cases without NE features (Fig 7A-7E). Further in-depth analysis revealed that SFXN1/SFXN5 expressions were significantly higher and SFXN4 was significantly lower in small cell carcinoma (Fig 7F-7J), which is a neuroendocrinal type of prostate cancer [37].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; Finally, we determined the androgen modulation on SFXN family genes. A microarray dataset derived from mice prostate tissues [25] was utilized to analyze SFXN2 gene expression, As shown in Fig 8A, castration of the animals significantly reduced SFXN2 expression at 3-14 days after surgery and testosterone replacement for 3 days largely restored SFXN2 expression. These data suggest that the SFXN2 gene was modulated by androgens. In addition, we analyzed the effect of castration on SFXN4 expression with two different human prostate cancer xenograft models, KUCaP-2 [26] and LuCaP35 [28]. Our results showed that castration of the animals significantly reduced SFXN4 gene expression in the xenograft tissues (Fig 8B-8C), indicating an androgen modulation of SFXN4 gene expression. Lastly, the AR involvement in modulating SFXN2/SFXN4 expression was verified in LNCaP cells, in which the AR gene expression was knocked down using the small hairpin RNA [29]. As shown in Fig 8D, knocking down AR gene expression significantly reduced both SFXN2 and SFXN4 gene expression, confirming the AR involvement in SFXN2/SFXN4 expression in prostate cancer cells.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, our comprehensive analysis determined that SFXN1/3/4 genes were predominantly expressed in prostate tissue, of which SFXN1/4 were mainly expressed in luminal cells while the SFXN3 gene was expressed in basal cells at a relatively higher level. In primary prostate cancers, SFXN2/SFXN4 genes were significantly upregulated but SFXN3 was downregulated compared to benign tissues. SFXN4 expression exerted as a strong diagnostic factor for prostate cancer and a prognostic factor for overall survival outcome. There were no molecular signature-specific dysregulations of the SFXN family genes. SFXN3 expression was broadly correlated with most pro-inflammatory immune infiltrations while SFXN4 expression was strongly correlated with three proinflammatory lymphocytes (Th1 cells, Neutrophils, and NK cells). In CRPC tumors, SFXN2/SFXN4 expressions were positively correlated with AR scores while negatively correlated with NEPC scores. Consistently, SFXN2/SFXN4 expressions were attenuated in castration animals and AR knocked down LNCaP cells, indicating an AR-dependent modulation. Although SFXN3 expression was negatively correlated with AR scores, its expression was not enhanced in castrated animals, indicating a non-AR modulated gene.\u003c/p\u003e\n\u003cp\u003eOverexpression of SFXN2/SFXN4 genes has been observed in human cancers, where its levels correlate with different prognostic outcomes depending on the type of cancer [6, 32, 38, 39]. SFXN2 is particularly significant in breast cancer, where it is upregulated and linked to cancer cell growth [38]. This association makes it a potential biomarker for breast cancer prognosis and a possible target for therapeutic intervention. SFXN4 is gaining attention as a prognostic marker due to its role in oxidative phosphorylation and its correlation with immune infiltration in tumors. For example, higher SFXN4 expression in hepatocellular carcinoma (HCC) correlates with increased tumor aggressiveness and specific immune profiles, suggesting SFXN4 could be a therapeutic target for mitochondrial-based cancer treatments [39]. In our study, we also found SFXN2/SFXN4 overexpression in prostate cancers, of which only SFXN4 expression was associated with immune infiltration and disease progression. Particularly, we determined for the first time that SFXN2/SFXN4 genes were modulated by the AR signal pathway and further investigation is needed to understand their functional roles in CRPC progression.\u003c/p\u003e\n\u003cp\u003eSFXN3 has revealed its role in different types of cancer, making it a protein of interest for therapeutic targeting [38, 40-44]. In head and neck squamous cell carcinoma (HNSC), SFXN3 expression correlates with poor prognosis, chemotherapy resistance, and an immunosuppressive tumor microenvironment [42]. This regulation is thought to involve a non-coding RNA pathway (LINC01270/hsa-miR-29c-3p/SFXN3), suggesting that SFXN3 might contribute to aggressive tumor behaviors by modulating immune responses and drug resistance mechanisms. In this study, we observed a broad and strong correlation of SFXN3 expression with pro-inflammatory immune infiltration in prostate cancers. Since SFXN3 expression was reduced in prostate cancer, suggesting an enhanced expression of the SFXN3 gene might be able to sensitize prostate cancer to immunotherapy. In hematological cancers, such as non-M3 acute myeloid leukemia (AML), high SFXN3 levels have been associated with unfavorable outcomes. Here, SFXN3 may increase sensitivity to hypomethylating therapies, like decitabine, offering potential treatment avenues in cases where other therapies have been ineffective. This finding opens new possibilities for using SFXN3 as a biomarker in treatment strategies tailored to AML patients with high SFXN3 expression [44].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; SFXN1 has been identified as a potential biomarker in multiple human cancers [38, 45-47], including lung adenocarcinoma (LUAD) [45, 48-52]. Its high expression is correlated with factors like tumor size and metastasis, and it is associated with immune infiltration. In LUAD, SFXN1 expression may influence the tumor microenvironment by interacting with immune cells, such as T and B cells, and immune checkpoints [52]. This suggests that SFXN1 could be targeted for immune-based therapies in cancers like LUAD. In our study, SFXN1 expression was strongly correlated with pro-inflammatory immune infiltrations, although its expression levels were not significantly altered in malignant tissues.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecent research into SFXN5 has uncovered its role in neutrophil function and immune response, primarily through regulating actin polymerization [7]. SFXN5, identified as a citrate transporter in vitro, is critical for neutrophil spreading, an initial step in adhesion and migration essential for immune response [7]. SFXN5 deficiency in neutrophils (induced \u003cem\u003evia\u003c/em\u003e siRNA or morpholino injection in mice and zebrafish models) led to impaired cell spreading, adhesion, chemotaxis, and reactive oxygen species (ROS) production. This deficiency also reduced actin polymerization due to lower cytosolic citrate levels and downstream metabolites like acetyl-CoA and cholesterol, which are necessary for actin polymerization mediated by phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2). Supplementing with citrate or cholesterol partially restored these processes, highlighting SFXN5\u0026apos;s pivotal role in cellular actin dynamics and neutrophil migration during inflammatory responses. In this study, we found that SFXN5 was expressed at a very low level in prostate tissue, both benign and malignant. SFXN5 expression was not associated with disease progression and immune infiltration. These data suggest that SFXN5\u0026rsquo;s role in prostate cancer is potentially limited.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our comprehensive analysis of SFXN family genes in prostate cancers revealed a diverse profile, of which SFXN2/SFXN4 genes were upregulated while SFXN3 was downregulated. SFXN2/SFXN4 expression was associated with prostate cancer progression and was modulated by the AR signal pathway. Most importantly, SFXN4 expression exerted as a strong biomarker for disease diagnosis and overall survival outcome.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results shown in this study are in part based on data generated by the TCGA Research Network (https://www.cancer.gov/tcga).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval:\u0026nbsp;\u003c/strong\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was partially supported by the Hangzhou Zijingang Science \u0026amp; Technology City High-level Talent Plan Category B to Dr. Runzhi Zhu.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; H Huang, W Liu, and B Li conducted the data analysis. H Lian, H Shao, R Zhu, and B Li drafted the manuscript. All authors approved the submission.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;The authors declared no commercial or financial relationships construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"Reference","content":"\u003col\u003e\n \u003cli\u003eTifoun N, De Las Heras JM, Guillaume A, Bouleau S, Mignotte B, Le Floch N: \u003cstrong\u003eInsights into the Roles of the Sideroflexins/SLC56 Family in Iron Homeostasis and Iron-Sulfur Biogenesis\u003c/strong\u003e. \u003cem\u003eBiomedicines\u0026nbsp;\u003c/em\u003e2021, \u003cstrong\u003e9\u003c/strong\u003e(2).\u003c/li\u003e\n \u003cli\u003eAttwood MM, Schioth HB: \u003cstrong\u003eCharacterization of Five Transmembrane Proteins: With Focus on the Tweety, Sideroflexin, and YIP1 Domain Families\u003c/strong\u003e. \u003cem\u003eFront Cell Dev Biol\u0026nbsp;\u003c/em\u003e2021, 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\u003cstrong\u003eProspective role and immunotherapeutic targets of sideroflexin protein family in lung adenocarcinoma: evidence from bioinformatics validation\u003c/strong\u003e. \u003cem\u003eFunct Integr Genomics\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e22\u003c/strong\u003e(5):1057-1072.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"human-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hugm","sideBox":"Learn more about [Human Genomics](http://humgenomics.biomedcentral.com/)","snPcode":"40246","submissionUrl":"https://submission.nature.com/new-submission/40246/3","title":"Human Genomics","twitterHandle":"@OAgenetics","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"prostate cancer, Sideroflexin family genes, castration-resistance, neuroendocrinal progression, androgen receptor modulation","lastPublishedDoi":"10.21203/rs.3.rs-5442270/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5442270/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Sideroflexin (SFXN) family genes encode for a group of mitochondrial proteins involved in cellular processes such as iron homeostasis, amino acid metabolism, and energy production. Recent studies showed that they were aberrantly expressed in certain human cancers. However, there is a paucity of information about their expression in prostate cancer. In this study, we took a comprehensive approach to investigate their expression profiles in benign prostate tissue, prostate-derived cell lines, and prostate cancer tissues using multiple transcriptome datasets. Our results showed that SFXN1/3/4 genes were predominantly expressed in prostate tissue and cell lines. SFXN2/4 genes were significantly upregulated while the SFXN3 gene was significantly downregulated in malignant tissues compared to benign tissues. SFXN4 expression was determined as a diagnostic biomarker and prognostic factor for unfavorite survival outcomes. In advanced prostate cancers, SFXN2/4 gene expressions were positively correlated with the androgen receptor signaling score but negatively correlated with the neuroendocrinal feature score. Further analysis discovered that SFXN2/4 gene expressions were modulated by the androgen receptor signaling pathway but not involved in neuroendocrinal progression. In conclusion, SFXN2/4 expression is a novel biomarker in prostate cancer diagnosis and prognosis modulated by the androgen receptor signal pathway.","manuscriptTitle":"Sideroflexin family genes were dysregulated and associated with tumor progression in prostate cancers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-09 07:10:07","doi":"10.21203/rs.3.rs-5442270/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-19T07:41:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-18T23:48:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-18T15:02:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36925323135725923878794415255898820094","date":"2024-11-18T13:50:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"138713026298281156798666409633790050837","date":"2024-11-18T13:36:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-17T17:42:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-13T07:27:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-13T07:24:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Human Genomics","date":"2024-11-12T21:29:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"human-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hugm","sideBox":"Learn more about [Human Genomics](http://humgenomics.biomedcentral.com/)","snPcode":"40246","submissionUrl":"https://submission.nature.com/new-submission/40246/3","title":"Human Genomics","twitterHandle":"@OAgenetics","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"13c3735b-a83d-4d80-a7cc-871799221a83","owner":[],"postedDate":"December 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-10T16:06:40+00:00","versionOfRecord":{"articleIdentity":"rs-5442270","link":"https://doi.org/10.1186/s40246-024-00705-6","journal":{"identity":"human-genomics","isVorOnly":false,"title":"Human Genomics"},"publishedOn":"2025-02-07 15:58:26","publishedOnDateReadable":"February 7th, 2025"},"versionCreatedAt":"2024-12-09 07:10:07","video":"","vorDoi":"10.1186/s40246-024-00705-6","vorDoiUrl":"https://doi.org/10.1186/s40246-024-00705-6","workflowStages":[]},"version":"v1","identity":"rs-5442270","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5442270","identity":"rs-5442270","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}