ALKBH5 promotes Liver hepatocellular carcinoma cell proliferation, migration and invasion by regulating TTI1 expression

ALKBH5 promotes Liver hepatocellular carcinoma cell proliferation, migration and invasion by regulating TTI1 expression | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article ALKBH5 promotes Liver hepatocellular carcinoma cell proliferation, migration and invasion by regulating TTI1 expression Qimeng Chang, Xiang Zhou, Huarong Mao, Jinfeng Feng, Xubo Wu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3898749/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose The objective of this research was to investigate the potential mechanisms of ALKBH5 in Liver Hepatocellular Carcinoma (LIHC). Methods We examined the expression of ALKBH5 in pan-cancer and its correlation with clinical factors of LIHC. In vitro experiments were conducted to verify ALKBH5 expression in LIHC and its effect on LIHC cell proficiency. Differentially expressed genes (DEGs) were screened from LIHC patients associated with ALKBH5, and downstream genes associated with ALKBH5 were identified by bioinformatics analysis. We further examined the expression of the downstream genes and constructed a prognostic nomogram. Lastly, we analyzed the exact functions of ALKBH5 and TTI1 in LIHC cells. Results We found that ALKBH5 is significantly overexpressed in most pan-cancer types. In vitro experiments confirmed ALKBH5 as an oncogene in LIHC, with its knockdown suppressing the proliferation, migration, and invasion of LIHC cells. Bioinformatics analyses revealed that TTI1 is significantly positively correlated with ALKBH5. TTI1 was highly expressed in LIHC cells and has good prognostic ability for LIHC patients. Further experimental evidence confirmed that the suppression of TTI1 impeded cell proliferation, migration, and invasion, an impact partially offset by the overexpression of ALKBH5. In contrast, the promotion of these cellular progressions was observed with TTI1 overexpression but was tempered by a decrease in ALKBH5 expression. Conclusion In conclusion, our findings indicate that ALKBH5 may influence the proliferation, migration and invasion of LIHC by modulating TTI1 expression, providing a new direction for the treatment of LIHC. Liver hepatocellular carcinoma ALKBH5 TTI1 Proliferation Migration Invasion Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Liver carcinoma, characterized as a malignant neoplasm, has been extensively linked with risk factors such as excessive alcohol consumption, viral hepatitis, consumption of mold-contaminated food, and genetic predispositions ( 1 ). This disease can be histopathologically subdivided into two principal categories: Liver hepatocellular carcinoma (LIHC) and intrahepatic cholangiocarcinoma (iCCA)( 2 ). LIHC, accounting for 90% of primary liver cancer cases, represents the predominant histological subtype of this malignancy. Key contributory factors implicated in LIHC include aflatoxin exposure, obesity, epigenetic alterations, heredity, infection with the hepatitis C virus, tobacco smoking, chronic hepatitis B, and diabetes( 3 ). Currently, among methods for LIHC treatment, transplantation is the most effective( 4 ). Interventional therapy, ablation therapy, chemoradiotherapy, and targeted therapy can be applied to patients with unresectable liver carcinoma( 5 , 6 ). Regrettably, despite these interventions, the prognosis for LIHC remains dismal due to high rates of metastasis and recurrence( 7 ). This highlights the necessity of uncovering the molecular processes driving LIHC progression and pinpointing new therapeutic targets. The AlkB homolog 5, RNA demethylase (ALKBH5), also known as ABH5, OFOXD, or OFOXD1, is implicated in the biological cascades of various neoplasms( 8 ). Notably, Guo et al. showed that ALKBH5 could curb pancreatic carcinoma progression through PER1 activation in an m6A-YTHDF2-mediated pathway. This finding reveals that ALKBH5 inhibits pancreatic cancer by regulating the post-transcriptional activation of PER1 through modulation of m6A modifications( 9 ). Another literature by Chen et al. established that ALKBH5 selectively augments the incidence of acute myeloid leukemia (AML) and the self-renewal of carcinoma stem cells( 10 ). Moreover, ALKBH5 has been reported to function as a tumor promoter in AML by post-transcriptionally modulating pivotal targets, such as TACC3( 11 ), an oncogene related to prognosis in a broad spectrum of carcinomas( 12 – 15 ). Together, these observations underscore the central function of ALKBH5 in the pathogenesis of leukemia and the self-renewal of leukemia stem cells/leukemia-initiating cells (LSC/LIC), thereby highlighting the role of ALKBH5/N6-methyladenine (m6A) axis for therapeutic potential. These revelations pave the way for further exploration of the roles played by ALKBH5 in the pathogenesis of diverse human carcinomas. Our research attempted to illuminate the role of ALKBH5 in LIHC, utilizing an integrative approach of bioinformatics analysis and cellular experiments. In parallel, we worked to uncover the precise mechanism by which ALKBH5 and LIHC are interconnected. It is anticipated that our findings may provide new insights and pave the way for advances in therapeutic intervention, diagnosis, and prognostic assessment of LIHC patients. Materials and methods Evaluation of ALKBH5 expression levels across pan-cancers To assess the expression levels of ALKBH5 across a range of cancer types, we utilized data procured from The Cancer Genome Atlas (TCGA; https://tcga-data.nci.nih.gov/tcga ) and the Genotype-Tissue Expression (GTEx; https://www.gtexportal.org/home/ ) database. The TCGA includes large multidimensional map of key genomic changes in various cancers. On the other hand, the GTEx project provides a valuable resource that facilitates the study of human gene expression and regulation in different tissue types. By integrating these resources, we conducted a pan-cancer study exploring ALKBH5 expression across 33 cancer types, in comparison to normal tissues. The analysis was executed using R software, offering a comprehensive visual depiction of ALKBH5 expression distribution. Correlation analysis between ALKBH5 expression and LIHC clinical parameters LIHC samples were obtained from the TCGA for comprehensive surveys. We used the Kruskal-Wallis method to analyze the differential expression of ALKBH5 in the context of multiple clinical parameters of LIHC, including nodal status (Node), metastatic status (Metatasis), pT stage, pTNM stage, grade, HBV and HCV. This nonparametric statistical test allowed us to compare expression levels across numerous independent groups. Subsequently, to graphically illustrate the mutual relationships and transitions among these clinical parameters, ALKBH5 expression, and patient status, we employed the "ggplot2" package in R to generate an informative and visual Sankey diagram. This diagram serves to provide an intuitive understanding of the interplay among these key aspects. Analysis of functional pathways in differentially expressed genes (DEGs) According to the expression of ALKBH5 in LIHC, we divided the TCGA-LIHC patient data into ALKBH5-high and ALKBH5-low cohorts for screening DEGs. Subsequently, we screened for up-regulated DEGs ( P 1.5) and down-regulated DEGs ( P < 0.05 and FC < 0.67) using the Limma package in R software. Next, to further elucidate the biological roles of the DEGs, we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using the Enrichr tool ( https://maayanlab.cloud/Enrichr/ ). Findings with a P -value below 0.05 were deemed to be of statistical relevance. Analysis of genes associated with prognosis of LIHC We performed a progression-free survival (PFS) analysis of 100 up-regulated and 100 down-regulated DEGs. Statistical tests for differences in PFS between low and high expression groups were performed using the log-rank test in Kaplan-Meier (KM) survival analysis, and hazard ratios (HR), 95% confidence intervals (CI), and P -values were subsequently generated. From this survival analysis, we focused on genes with P-values less than 0.05, indicating statistical significance. To gain a comprehensive understanding of the protein-protein interaction (PPI) associated with these genes, we exploited the potential of the proteins encoded by statistically significant genes. We used the Search Tool for the Retrieval of Interacting Genes (STRING) STRING database ( https://string-db.org/ ) as the primary platform for interactive data. Additionally, network visualization and analysis were performed using Cytoscape software. Subsequently, for the genes that showed statistical significance in the survival analysis, we performed a gene correlation analysis, the results of which were visualized by the R package "heatmap". Screening of candidate genes in the prognostic model We utilized Least Absolute Shrinkage and Selection Operator (LASSO) regression, implemented through the "glmnet" package in R, to construct a polygenic signature for prognostic prediction of LIHC using genes that displayed significant P -values. To ensure the reliability and objectivity of the analysis, ten-fold cross-validation was conducted to select the optimal lambda (λ) value that corresponds to the smallest error fraction. Subsequently, LIHC patients were divided into high-risk and low-risk groups, and their survival time, risk score, as well as survival status were obtained from the selected dataset. The z-scores of 6 gene expression (TTI1, ACIN1, ADNP, CFHR3, SPP2, HGFAC) in these patients were displayed by heat map. Ultimately, the prognostic implications of the high-risk and low-risk groups were substantiated through KM survival analysis. To identify significant variations in PFS probability between the two groups, a log-rank test was carried out. Additionally, time-dependent receiver operating characteristic (ROC) analysis for 1-year, 3-year, and 5-year survival forecasts was used to assess the prognostic performance of the risk model. The area under the curve (AUC) method was used to determine the prediction accuracy of the model. Correlation analysis between ALKBH5 and candidate target genes As a newly developed interactive web server, Gene Expression Profiling Interactive Analysis (GEPIA) offers features that are customized, which are inclusive of patient survival analysis, similar gene detection, etc. In this study, we employed GEPIA to investigate the relationship between ALKBH5 and potential target genes. Through the computation of correlation coefficients, we identified a key gene displaying the strongest correlation with ALKBH5 and LIHC. When the P < 0.05, the results were deemed statistically significant. Construction and verification of the predictive nomogram with TTI1 Initially, we evaluated the expression level of TTI1 in LIHC samples utilizing the Wilcoxon test. Subsequently, the prognostic significance of TTI1 was compared with clinical parameters, such as pM stage, pT stage, pTNM stage, age, and grade, through a univariate Cox proportional hazards regression analysis. In order to ascertain if TTI1 could function as an independent prognostic factor for risk stratification in LIHC patients, a multivariate Cox proportional hazards regression analysis was executed. This analysis incorporated additional clinical parameters that exhibited statistical significance ( P < 0.05) in the univariate Cox regression model. Based on the independent prognostic factors identified from the preceding analyses, a composite nomogram was constructed via the "rms" package in R. This predictive tool was designed to forecast 1-year, 3-year, and 5-year survival probabilities, and its performance was subsequently assessed through a calibration curve. Cell culture and transfection We procured LO2 (normal liver cell), MHCC-97L, MHCC-97H and SNU387 (LIHC cell lines) from American Type Culture Collection (ATCC, Manassas, USA). LO2 cells were maintained in IMDM-RMPI supplemented with 1% penicillin-streptomycin and 10% fetal bovine serum (FBS), while MHCC-97L, MHCC-97H, and SNU387 cells were cultured in DMEM, fortified with the same supplements.. All cell lines were incubated at 37°C in an environment containing 5% CO 2 . For cell transfection, we acquired si-ALKBH5 #1, si-ALKBH5 #2, si-ALKBH5 #3, over-ALKBH5, si-TTI1 #1, si-TTI1 #2, si-TTI1 #3, over-TTI1, along with their negative controls (over-NC and si-NC) from the Shanghai Biotech Company, Gemma Gene. Transfection was carried out using Lipofectamine 3000 (Invitrogen), and the efficiency of this process was evaluated under a fluorescence microscope. RNA extraction and Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) analysis TRIzol (Invitrogen) was used to extract the total RNA from the cells. The RevertAid First Strand cDNA Synthesis Kit from Invitrogen was used to create complementary DNA (cDNA). qRT-PCR was carried out using an Applied Biosystems 7900 Real-time PCR System and the SYBR Green PCR Master Mix. The 2 −ΔΔCt technique was used to compare the relative expression levels of ALKBH5 and TTI1, with GAPDH acting as an internal control. The following primers were used in this experiment: ALKBH5 (forward: 5'- CGGCGAAGGCTACACTTACG-3'; reverse: 5'-CCACCAGCTTTTGGATCACCA-3'), TTI1 (forward: 5'-CCACAGCTGAAGACATCGAA-3'; reverse: 5 '- ACATCTGGACGGGTGTCATT-3') and GAPDH (forward: 5'- CAAGGTCATCCATGACAACTTTG − 3'; reverse: 5'- GGGCCATCCACAGTCTTCT − 3'). Western Blotting (WB) assay ALKBH5 and TTI1 protein expression in LIHC cell lines were evaluated using a WB assay. Cells underwent lysis and protein extraction with RIPA buffer containing a protease inhibitor cocktail. Protein concentrations were determined via a BCA assay kit. Proteins were then subjected to SDS-PAGE and transferred to PVDF membranes. Post-transfer, membranes were blocked using 5% non-fat milk for non-specific binding prevention. They were then probed overnight at 4°C with primary antibodies for ALKBH5 (1:1000) and TTI1 (1:500). GAPDH (1:1000) served as the control. After washing, membranes were exposed to HRP-linked secondary antibodies (1:5000) for an hour. Protein bands were detected with an enhanced chemiluminescence (ECL) system and quantified by densitometry. Assay for cell proliferation CCK-8 kit (Shiga Doto Molecular Technology, Japan) was applied to measure cell proliferation. First, we plated 2×10 4 of the transfected SNU387 cells in a 96-well plate in triplicate. Then, 10ul of CCK-8 solution per well was added into cells for the indicated time. After 24h, 48h, 72h, 96h, and 120h, we finished measuring the optical density (OD) value of the cells at 450 nm via iMark Microplate Reader (Bio-Rad) for plotting the cell proliferation curve. Transwell assay The invasive and migratory abilities of cells were determined utilizing a Transwell chamber assay (Corning). The upper chamber was covered with 100 l of Matrigel (Corning) and allowed to set up for an hour at 37°C in preparation for the invasion assay. Subsequently, a 0.5µl cell suspension was added to the Matrigel-coated chamber, which was filled with DMEM medium devoid of serum. After the invasion and migration phases are complete, cells are stained with DAPI to reveal nuclei. High-resolution images were captured after a 20-minute staining period, utilizing an Olympus BX53 upright microscope equipped with a digital camera. The migration assay was performed in a similar manner, except the Matrigel coating step was omitted. Each of these experimental steps was independently replicated three times to ensure the accuracy and reproducibility of the findings. Statistical analysis R software was used for all statistical analyses. The Kruskal-Wallis or Wilcoxon test was used to analyze differences in expression among groups. The log-rank test was used to examine KM survival curves. Correlation coefficients were used to examine the relationship between ALKBH5 and clinical factors or prospective target genes. The prognostic model was built using LASSO regression with ten-fold cross-validation. To investigate prognostic value, univariate and multivariate Cox proportional hazards regression models were used. The in vitro experiments were conducted three times, and the results are reported as mean ± standard deviation. Statistical significance was defined as P < 0.05. Results Significantly high expression of ALKBH5 in the majority of pan-cancers We have analyzed the expression levels of ALKBH5 across 33 different human malignancies, utilizing data from both the TCGA and GTEx databases. As illustrated in Fig. 1 , ALKBH5 exhibits significant overexpression in a majority of tumor tissues when compared to their neighboring normal tissues, including Breast invasive carcinoma (BRCA), Adrenocortical carcinoma (ACC), Cholangiocarcinoma (CHOL), and LIHC, etc. This pattern of differential expression implies a potential role of ALKBH5 in the oncogenesis and progression of these tumor types, warranting further investigation into its mechanistic contributions. Effect of differential expression of ALKBH5 in LIHC on staging, HBV infection and patient survival To elucidate the role of ALKBH5 in LIHC, we scrutinized its expression profile in relation to various clinicopathological parameters (Figs. 2 A- 2 G). Notably, ALKBH5 expression showed significant variations between T2 and T3 stages, as well as between patients with and HBV + and HBV- infection. In contrast, no substantial differences in ALKBH5 expression were found when examined across other clinicopathological parameters. Furthermore, we observed a noteworthy interconnection between ALKBH5 expression, clinical characteristics, and patient survival across different stages of LIHC. A visual representation of these associations was provided in the form of a Sankey diagram (Fig. 2 H). The observed patterns suggest a potential differential role of ALKBH5 in specific stages of tumor progression and in the context of HBV infection. The association of ALKBH5 expression with patient survival further implies its potential as a prognostic marker in LIHC. Knockdown of ALKBH5 inhibits growth of LIHC cells in vitro We initially examined the expression of ALKBH5 in LIHC cell lines and normal cells. The results demonstrated an upregulation of ALKBH5 in LIHC cell lines, particularly in the SNU387 and MHCC-97H cells, prompting us to select these cell lines for subsequent investigations (Fig. 3 A). Next, we subjected the SNU387 and MHCC-97H cells to knockdown procedures, finding that si-ALKBH5 #1 demonstrated the highest efficiency and was consequently selected for further experimentation (Figs. 3 B and 3 C). Data from the CCK-8 assay revealed that the knockdown of ALKBH5 resulted in suppressed proliferation of SNU387 and MHCC-97H cells (Figs. 3 D and 3 E). Furthermore, results from the Transwell assay indicated that compared with the si-NC transfected cells, SNU387 and MHCC-97H cells transfected with si-ALKBH5#1 exhibited significantly reduced invasion and migration capabilities (Figs. 3 F- 3 I). These results suggest a critical function of ALKBH5 in regulating the proliferative and metastatic potential of LIHC cells. KEGG pathway enrichment analysis on DEGs From the two groups of samples with differential expression of ALKBH5, we screened 3105 up-regulated DEGs and 156 down-regulated DEGs (Fig. 4 A). DEGs that were up-regulated in the KEGG pathway were primarily enriched in Shigellosis, Focal adhesion, Regulation of actin cytoskeleton, Proteoglycans in cancer, etc. (Fig. 4 B). DEGs that were down-regulated in the KEGG pathway were primarily enriched in Retinol metabolism, Complement and coagulation cascades, Chemical carcinogenesis-DNA adducts, etc. (Fig. 4 C). 36 key genes associated with LIHC prognosis In our analysis, we selected the top 100 up-regulated and 100 down-regulated DEGs for PFS rate analysis. This led to the identification of 36 genes showing a significant association with PFS ( P < 0.05) (Fig. 5 A). Interestingly, higher expression of genes such as CHFR1, AZGP1, APOC3, and ITIH1 was associated with a favorable prognosis, while the overexpression of genes like TBCCD1, ZNF362, ZNF318, ZMYM3, UBE3B, and TTI1 correlated with a poorer prognosis. Further investigation into these 36 genes using the STRING database generated a PPI network consisting of 36 nodes and 45 edges (Fig. 5 B). The correlation analysis revealed significant positive or negative interactions among these 36 genes (Fig. 5 C). These findings underscore the critical role of these genes in LIHC progression and potentially highlight novel prognostic markers for LIHC. Identification of 6 candidate genes with prognostic value associated with LIHC We utilized the glmnet package in R to construct a LASSO Cox regression model for the 36 genes with significant P values. With 10-fold cross-validation, we chose 0.0521 as the minimum standard for λ (Figs. 6 A and 6 B). Based on the non-zero coefficients of the genes, we computed the risk score for each patient as follows: (0.1124)*TTI1+(0.06)*ACIN1+(0.0402)*ADNP+(-0.0422)*CFHR3+(-0.0236)*SPP2+(-0.0094)* HGFAC. We used the median cutoff point obtained from the "survminer" R package to segregate the patients into high-risk (n = 185) and low-risk (n = 185) groups. As depicted in Fig. 6 C, patients in the high-risk group demonstrated reduced survival time compared to the low-risk group. The distribution of the six candidate prognostic genes also varied between the two groups, with the expression of ADNP, ACIN1, and TTI1 increasing as the risk score escalated. Further, the KM survival curves indicated that the low-risk group had improved PFS compared to the high-risk group (Fig. 6 D). Lastly, the risk model exhibited a substantial AUC value of 0.704 in 1-year survival from the ROC analysis (Fig. 6 E). These findings suggest that 6 candidate prognostic genes may be effective targets for predicting one-year survival of LIHC patients. Establishing TTI1 as a key downstream gene of ALKBH5 We compared the association of six candidate genes with ALKBH5 using the GEPIA database. At a statistical significance threshold of P < 0.05, a significant positive connection was observed between ALKBH5 and three genes (Figs. 7 A- 7 C). Among them, TTI1 has the highest correlation with ALKBH5 (r = 0.46), followed by ADNP (r = 0.39) and ACIN1 (r = 0.36), therefore, we identified TTI1 as the key downstream gene of ALKBH5. TTI1 expression was shown to be considerably greater in LIHC tumors than in normal tissues (Fig. 7 D). After ALKBH5 was overexpressed in SNU387 and MHCC-97H cells, qRT-PCR detected a significant overexpression efficiency (Fig. 7 E). The results of CCK-8 showed that overexpressed ALKBH5 significantly promoted the proliferation of SNU387 and MHCC-97H cells (Figs. 7 F and 7 G). Furthermore, we found that the expression of TTI1 was decreased when ALKBH5 was knocked down, and conversely, the expression of TTI1 was increased when ALKBH5 was overexpressed (Figs. 7 H and 7 I). TTI1 was discovered as an independent predictive predictor for overall survival (OS) in LIHC patients in univariate and multivariate Cox proportional hazards regression studies (Fig. 7 J). In clinical practice, we developed a TTI1 nomogram to estimate 1-, 3-, and 5-year survival in LIHC patients (Fig. 7 K). The calibration plot indicated its predictions closely aligned with actual outcomes (Fig. 7 L). ALKBH5 combined with TTI1 affects the proliferation, migration and invasion of LIHC cells Our study investigated the impact of TTI1 knockdown in SNU387 and MHCC-97H cells. Through qRT-PCR, we found si-TTI1 #2 demonstrated the most significant knockdown efficiency (Figs. 8 A and 8 B). To elucidate the functional mechanism of TTI1 and its upstream gene ALKBH5 in LIHC, we performed a CCK-8 assay. The results showed a decrease in cellular proliferation following the knockdown of TTI1. Interestingly, overexpression of ALKBH5 could partially mitigate the suppressive effect of si-TTI1#2 on cell proliferation (Figs. 8 C and 8 D). We also induced overexpression of TTI1 in SNU387 and MHCC-97H cells (Fig. 8 E), which resulted in enhanced cellular proliferation, an effect which was diminished by the low expression of ALKBH5 (Figs. 8 F and 8 G). Further substantiating these observations, migration and invasion assays mirrored the trends witnessed in proliferation studies, indicating a tangible influence of ALKBH5 and TTI1 expression levels on the migratory and invasive potentials of LIHC cells (Figs. 8 H- 8 O). Therefore, we posit that ALKBH5, through its regulatory action on TTI1 expression, serves as a pivotal determinant in either promoting or inhibiting specific malignancy-associated cellular behaviors in LIHC, underlining a sophisticated network of genetic interactions pivotal to cancer cell dynamics. Discussion We investigated the function of ALKBH5 in the progression and prognosis of LIHC in this study. Consistent with the known risk factors for liver carcinoma, such as excessive alcohol consumption, viral hepatitis, and genetic predispositions( 16 , 17 ), our findings further confirm the complex and multifactorial nature of the etiology of LIHC. Notably, we found an association between high expression of ALKBH5 and LIHC. This adds a novel dimension to the already complex landscape of LIHC molecular mechanisms and provides fresh insights into potential molecular targets for therapy. Our analysis underscored the prevalence of ALKBH5 overexpression in numerous tumor types, including LIHC, thus augmenting its potential role as a pan-cancer molecular marker. Furthermore, ALKBH5 expression levels were consistently greater in LIHC clinical stage tissues compared to their normal counterparts, emphasizing its likely significance in LIHC pathological development. Intriguingly, we noted minimal variation in ALKBH5 expression across groups with differing clinical factors, suggesting that its upregulation might be a universal event in LIHC, independent of individual patient characteristics. Our research serves as a seminal contribution towards comprehending the molecular underpinnings of LIHC and provides robust evidence implicating ALKBH5 as a potential therapeutic target. The findings in vitro point to ALKBH5 being a critical regulator of LIHC cellular behavior, influencing proliferation, migration, and invasion. We observed that ALKBH5 was significantly overexpressed in LIHC cell lines, especially in SNU387 cells. Knockdown of ALKBH5 resulted in decreased cell proliferation and impaired invasion and migration capabilities, further confirming the critical role of ALKBH5 in regulating the malignancy of LIHC cells. Corroborating previous studies, ALKBH5, recognized as a prominent m6A demethylase( 18 ), has been identified as a key player in a diverse array of cancers, such as breast carcinoma, stomach carcinoma, and colorectal carcinoma( 19 , 20 ). The versatile roles of ALKBH5 in various cancer types entail the modulation of numerous biological processes encompassing proliferation, metastasis, migration, invasion, metastasis, as well as tumor growth. Interestingly, the influence of ALKBH5 appears to be context-dependent, with its expression level acting either as an oncogenic promoter or a tumor suppressor, depending on the type of carcinoma( 21 , 22 ). Further supporting its multifaceted role, recent evidence also points towards an intriguing interaction between ALKBH5 and NEAT1 in colorectal carcinoma, proposing the ALKBH5-NEAT1 axis as a potential therapeutic target( 23 ). Taken together, our findings underscore ALKBH5 as an influential factor in the pathogenesis of LIHC. More thorough and in-depth research are needed, however, to elucidate the specific processes by which ALKBH5 promotes LIHC development and to prove its efficacy in clinical settings. In the subsequent phase of our study, we performed a differential gene expression screen on LIHC patients, based on ALKBH5 expression levels. In the KEGG pathway analysis, the up-regulated DEGs-enriched KEGG pathways include Wnt signaling pathway, Renal cell carcinoma, and the Hippo signaling pathway. Shuai He et al. postulated that the WNT/β-catenin signaling pathway, a highly conserved and tightly controlled molecular mechanism, governs cellular differentiation, proliferation, and embryonic development( 24 ). Notably, there is increasing evidence that abnormalities in WNT/β-catenin signaling contribute to the progression and development of liver carcinoma, which contained LIHC and cholangiocarcinoma( 25 , 26 ). In addition, when Takebumi Usui et al. studied cases of renal cell carcinoma liver metastases, they found that many patients with renal cell carcinoma after surgical resection would develop in the direction of liver carcinoma( 27 ). The Hippo pathway was found to be a critical regulator of liver size, metabolism, development, regeneration, and homeostasis in genetic studies on murine livers conducted by Jordan H. Driskill and Duojia Pan. Abnormalities in this pathway may contribute to common liver diseases like liver carcinoma and fatty liver disease( 28 ). Besides, the down-regulated DEGs are abundant in Tryptophan metabolism, Retinol metabolism, Pyruvate metabolism, Histidine and Glutathione metabolism. This underlines the intricate interplay between liver carcinoma and functional molecular metabolism within the human body. For instance, research by Qunhua Han et al. delineates an age-related metabolic imbalance in the liver involving glycerophospholipids, arachidonic acid, histidine, and linoleic acid( 29 ). In summary, our exploration of the roles of various metabolic and signaling pathways provides valuable insights into the molecular landscape of LIHC, highlighting the potential for targeting these specific pathways for therapeutic intervention. Through PFS survival, PPI, correlation, LASSO, Cox and other prognostic value analyses, we identified 6 key genes (HGFAC, SPP2, CFHR3, ADNP, ACIN1, TTI1) associated with LIHC prognosis. Following that, we used the GEPIA database to determine the connection between ALKBH5 and these genes, finally identifying TTI1 as the most significant prognostic gene. TTI1, or TELO2 Interacting Protein 1, plays a vital role in various biological processes, yet remains relatively understudied. Existing literature suggests that TTI1 is involved in multiple metabolic pathways and in the activation of mTORC1 signaling, which promotes cell growth( 30 , 31 ). For instance, TTI1 has been shown to facilitate survival in multiple myeloma via the mTORC1 pathway( 32 ). Rao et al. revealed the role of TTI1 in binding ATM and DNA-PKcs, triggering the activation of p-53 and S-15 phosphorylation pathways to initiate cancer cell death programs( 33 ). Furthermore, research on colorectal cancer by Peng Xu et al. indicated higher TTI1 expression in tumor tissue relative to adjacent normal tissue, demonstrating its critical role in colorectal cancer proliferation( 34 ). Nevertheless, the influence of TTI1 on the development of liver carcinoma is still not clear. We conducted a thorough study to investigate the function of TTI1 in LIHC, and the findings underscored the importance of both TTI1 and ALKBH5 in the development of LIHC. TTI1 was discovered to be considerably overexpressed in LIHC tumors as compared to normal tissues. Notably, ALKBH5 overexpression was seen to significantly enhance SNU387 cell proliferation, an effect inversely mirrored by TTI1 under-expression. TTI1 also emerged as an independent prognostic indicator for overall survival in LIHC patients, prompting us to construct a predictive TTI1 nomogram with high consistency between predicted and actual survival rates. The interaction between TTI1 and ALKBH5 revealed their influence on LIHC cell growth. Downregulation of TTI1 suppressed cell proliferation, migration and invasion, a result that was partially counteracted by ALKBH5 overexpression. TTI1 overexpression, on the other hand, enhanced cell proliferation, migration, and invasion but was inhibited by reduced ALKBH5 expression. Altogether, these findings underscore a potential regulatory role of ALKBH5 in LIHC progression via modulation of TTI1 expression, illuminating novel avenues for potential therapeutic strategies. To sum up, our findings confirm the characterization of ALKBH5 and TTI1 as oncogenes in LIHC, emphasizing their potential as novel markers in LIHC. Through bioinformatics analysis and cellular experiments, we elucidated that the interaction between ALKBH5 and TTI1 significantly affects the proliferation, migration and invasion of LIHC cells, suggesting that ALKBH5 may exert a key regulatory influence on LIHC progression by regulating TTI1 expression. These findings greatly advance the current understanding of LIHC and pave the way for innovative directions for future research. Declarations Acknowledgements None. Author Contributions Conception and design of the research: Zhiqiu Hu and Ziping Zhang. Acquisition of data: Xubo Wu and Jinfeng Feng and Huarong Mao. Analysis and interpretation of data: Xiang Zhou and Qimeng Chang. Statistical analysis: Qimeng Chang and Zhiqiu Hu. Drafting the manuscript: Zhiqiu Hu and Xiang Zhou. Revision of manuscript for important intellectual content: Ziping Zhang and Qimeng Chang. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Funding This work is supported by Commission of Science Technology of Minhang District (2019MHZ079 to ZQH). Conflict of interest None. References Hatta MNA, Mohamad Hanif EA, Chin SF, Neoh HM (2021) Pathogens and Carcinogenesis: A Review. Biology (Basel). ;10(6) Kiani A, Uyumazturk B, Rajpurkar P, Wang A, Gao R, Jones E et al (2020) Impact of a deep learning assistant on the histopathologic classification of liver cancer. NPJ Digit Med 3:23 Montella M, Crispo A, Giudice A (2011) HCC, diet and metabolic factors: Diet and HCC. 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Mol Cancer 19(1):91 Shen C, Sheng Y, Zhu AC, Robinson S, Chen J (2020) RNA Demethylase ALKBH5 Selectively Promotes Tumorigenesis and Cancer Stem Cell Self-Renewal in Acute Myeloid Leukemia. Cell Stem Cell. ;27(1) Shen C, Sheng Y, Zhu AC, Robinson S, Jiang X, Dong L et al (2020) RNA Demethylase ALKBH5 Selectively Promotes Tumorigenesis and Cancer Stem Cell Self-Renewal in Acute Myeloid Leukemia. Cell Stem Cell 27(1):64–80e9 Akanda MR, Park JS, Noh MG, Ha GH, Park YS, Lee JH et al (2021) TACC3 Promotes Gastric Carcinogenesis by Promoting Epithelial-mesenchymal Transition Through the ERK/Akt/cyclin D1 Signaling Pathway. Anticancer Res 41(7):3349–3361 Carneiro BA, Elvin JA, Kamath SD, Ali SM, Paintal AS, Restrepo A et al (2015) FGFR3-TACC3: A novel gene fusion in cervical cancer. Gynecol Oncol Rep 13:53–56 Fan X, Liu B, Wang Z, He D (2021) TACC3 is a prognostic biomarker for kidney renal clear cell carcinoma and correlates with immune cell infiltration and T cell exhaustion. Aging 13(6):8541–8562 Ha GH, Park JS, Breuer EK (2013) TACC3 promotes epithelial-mesenchymal transition (EMT) through the activation of PI3K/Akt and ERK signaling pathways. Cancer Lett 332(1):63–73 Toh MR, Wong EYT, Wong SH, Ng AWT, Loo L-H, Chow PK-H et al (2023) Global epidemiology and genetics of hepatocellular carcinoma. Gastroenterology 164(5):766–782 Valenti L, Pedica F, Colombo M (2022) Distinctive features of hepatocellular carcinoma in non-alcoholic fatty liver disease. Dig Liver Disease 54(2):154–163 Wang J, Wang J, Gu Q, Ma Y, Yang Y, Zhu J et al (2020) The biological function of m6A demethylase ALKBH5 and its role in human disease. Cancer Cell Int 20(1):1–7 Guo T, Liu D-F, Peng S-H, Xu A-M (2020) ALKBH5 promotes colon cancer progression by decreasing methylation of the lncRNA NEAT1. Am J Translational Res 12(8):4542 Qu J, Yan H, Hou Y, Cao W, Liu Y, Zhang E et al (2022) RNA demethylase ALKBH5 in cancer: from mechanisms to therapeutic potential. J Hematol Oncol 15(1):1–24 Dong S, Wu Y, Liu Y, Weng H, Huang H (2021) N6-methyladenosine Steers RNA Metabolism and Regulation in Cancer. Cancer Commun 41(7):538–559 Xue J, Xiao P, Yu X, Zhang X (2021) A positive feedback loop between AlkB homolog 5 and miR-193a-3p promotes growth and metastasis in esophageal squamous cell carcinoma. Hum Cell 34(2):502–514 Guo T, Liu DF, Peng SH, Xu AM (2020) ALKBH5 promotes colon cancer progression by decreasing methylation of the lncRNA NEAT1. Am J Transl Res 12(8):4542–4549 He S, Tang S (2020) WNT/beta-catenin signaling in the development of liver cancers. Biomed Pharmacother 132:110851 Wei S, Dai M, Zhang C, Teng K, Wang F, Li H et al (2021) KIF2C: a novel link between Wnt/β-catenin and mTORC1 signaling in the pathogenesis of hepatocellular carcinoma. Protein Cell 12(10):788–809 He S, Tang S (2020) WNT/β-catenin signaling in the development of liver cancers. Biomed Pharmacother 132:110851 Usui T, Kuhara K, Nakayasu Y, Tsuchiya A, Shimojima Y, Kono T et al (2021) [Four Cases of Liver Resection for Liver Metastases from Renal Cell Carcinoma]. Gan To Kagaku Ryoho 48(13):1700–1702 Driskill JH, Pan D (2021) The Hippo Pathway in Liver Homeostasis and Pathophysiology. Annu Rev Pathol 16:299–322 Han Q, Li H, Jia M, Wang L, Zhao Y, Zhang M et al (2021) Age-related changes in metabolites in young donor livers and old recipient sera after liver transplantation from young to old rats. Aging Cell 20(7):e13425 Zhang LX, Yang X, Wu ZB, Liao ZM, Wang DG, Chen SW et al (2023) TTI1 promotes non-small‐cell lung cancer progression by regulating the mTOR signaling pathway. Cancer Sci 114(3):855 Bakan I, Laplante M (2012) Connecting mTORC1 signaling to SREBP-1 activation. Curr Opin Lipidol 23(3):226–234 Li J, Zhu J, Cao B, Mao X (2014) The mTOR signaling pathway is an emerging therapeutic target in multiple myeloma. Curr Pharm Des 20(1):125–135 Rao F, Cha J, Xu J, Xu R, Vandiver MS, Tyagi R et al (2020) Inositol Pyrophosphates Mediate the DNA-PK/ATM-p53 Cell Death Pathway by Regulating CK2 Phosphorylation of Tti1/Tel2. Mol Cell 79(4):702 Xu P, Du G, Guan H, Xiao W, Sun L, Yang H (2021) A role of TTI1 in the colorectal cancer by promoting proliferation. Transl Cancer Res 10(3):1378–1388 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-3898749","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269726945,"identity":"7765a06d-f5d2-4603-854e-d59a8465ae4c","order_by":0,"name":"Qimeng Chang","email":"","orcid":"","institution":"Minhang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qimeng","middleName":"","lastName":"Chang","suffix":""},{"id":269726946,"identity":"d91cc3ac-64e4-4c79-b802-d1fbf3aa0bce","order_by":1,"name":"Xiang Zhou","email":"","orcid":"","institution":"Minhang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Zhou","suffix":""},{"id":269726947,"identity":"1a6ded0f-7384-4265-9b93-086893d566b9","order_by":2,"name":"Huarong Mao","email":"","orcid":"","institution":"Minhang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huarong","middleName":"","lastName":"Mao","suffix":""},{"id":269726948,"identity":"5e356b2d-f3f2-4e48-8f56-6d451489b00f","order_by":3,"name":"Jinfeng Feng","email":"","orcid":"","institution":"Minhang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jinfeng","middleName":"","lastName":"Feng","suffix":""},{"id":269726949,"identity":"f23eff05-741a-44b2-b793-cdcfe1d27557","order_by":4,"name":"Xubo Wu","email":"","orcid":"","institution":"Minhang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xubo","middleName":"","lastName":"Wu","suffix":""},{"id":269726950,"identity":"d7426bd6-7cda-404f-8b21-06920c9e9fc2","order_by":5,"name":"Ziping Zhang","email":"","orcid":"","institution":"Minhang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ziping","middleName":"","lastName":"Zhang","suffix":""},{"id":269726951,"identity":"ce79f67c-0f2e-410b-ac18-5f3e7cf3540a","order_by":6,"name":"Zhiqiu Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBACAxDxwYaBsQHE4CFWC+OMNFK1MPOQpMVcIv3hbZuEw7L9sxsYH7xtY5A3J6TFckZCsnVOwmHjGXcOMBvObWMw3NlAyGE3Eo5J5/44nNhwI4FNmreNIcHgAEEtiW3SFgmHE+ffSGD/TaSWZDZpBqCWDUBbmInTcuYZs2VPQrrxxhuJzZJzzkkYbiCo5Xj6wxs/Eqxl591IPvjhTZmNPEFbQEACQoGjRoII9cQrGwWjYBSMghELAJ1YQqhzm1AlAAAAAElFTkSuQmCC","orcid":"","institution":"Minhang Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zhiqiu","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2024-01-26 02:29:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3898749/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3898749/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50391209,"identity":"dd8cc7f6-2f78-45d2-9077-137e5f511afd","added_by":"auto","created_at":"2024-01-30 18:47:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":475569,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression verification of ALKBH5 in 33 tumors.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe abscissa represents the tumor and normal samples in the TCGA and GTEx databases, the ordinate represents the expression distribution of ALKBH5, and different colors represent different groups. **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3898749/v1/44ee2fb0cb22fec909730762.png"},{"id":50391211,"identity":"d49862cf-c476-48d7-bb8f-e60f1f7b4c48","added_by":"auto","created_at":"2024-01-30 18:47:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":995569,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eALKBH5 expressions in LIHC patients with different clinical factors.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-G) Kruskal-Wallis test ALKBH5 expression in lymph node metastasis status, distant metastasis, pT stage, Gage, hepatitis B virus infection, hepatitis C virus infection and pTNM stage. **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001, ****\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001, ns means not statistically significant.\u003c/p\u003e\n\u003cp\u003e(H) Sankey diagram. Every column stands for a characteristic variable, different colors stand for different types or stages, and the lines stand for the distribution of the same sample in different characteristic variables.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3898749/v1/4879b5b1ff45bc14a17cc28b.png"},{"id":50390076,"identity":"4dc81114-e701-458a-aa6a-ace172eae32b","added_by":"auto","created_at":"2024-01-30 18:39:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":738333,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression and functional analysis of ALKBH5 in normal and LIHC cell lines.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) qRT-PCR and WB detection of ALKBH5 expression levels in normal cells and LIHC cell lines.\u003c/p\u003e\n\u003cp\u003e(B) qRT-PCR and WB detection of knockdown efficiency of ALKBH5 in SNU387 cells.\u003c/p\u003e\n\u003cp\u003e(C) qRT-PCR and WB detection of knockdown efficiency of ALKBH5 in MHCC-97H cells.\u003c/p\u003e\n\u003cp\u003e(D and E) CCK-8 detects the regulation of si-ALKBH5#1 on the proliferation of SNU387 and MHCC-97H cells.\u003c/p\u003e\n\u003cp\u003e(F-I) Transwell detection of the regulation of si-ALKBH5#1 on the invasion and migration of SNU387 and MHCC-97H cells. The left panel shows magnified field views, while the right panel represents the quantified bar graphs.\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3898749/v1/70141e70cc96e552913a5e91.png"},{"id":50391210,"identity":"88004cdd-70ec-4390-bb9a-b8efc95754da","added_by":"auto","created_at":"2024-01-30 18:47:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1023119,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution and pathway enrichment analysis of DEGs in LIHC samples based on ALKBH5 expression levels.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Heat map of cluster distribution of up-regulated DEGs and down-regulated DEGs in LIHC samples with high and low expression of ALKBH5.\u003c/p\u003e\n\u003cp\u003e(B and C) Bubble plots of KEGG pathway enrichment analysis for up-regulated and down-regulated DEGs. Each bubble in the plot represents a different KEGG pathway, the size of the bubble corresponds to the number of DEGs associated with a particular pathway, and the color of the bubble represents the significance of the enrichment.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3898749/v1/8acce73279e45e6eedc40573.png"},{"id":50390079,"identity":"a9b54e90-daa7-47b2-be49-2998d8b29e81","added_by":"auto","created_at":"2024-01-30 18:39:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":15066626,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBioinformatics analysis of 36 genes with significant prognostic value in LIHC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) KM survival curves for 36 genes with significant \u003cem\u003eP\u003c/em\u003e values, the horizontal axis represents the survival time, and the vertical axis represents the survival probability.\u003c/p\u003e\n\u003cp\u003e(B) PPI network of 36 genes, nodes represent genes and edges represent interconnections between genes.\u003c/p\u003e\n\u003cp\u003e(C) Correlation heat map of 36 genes, the abscissa and ordinate represent genes, red represents positive correlation, blue represents negative correlation.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3898749/v1/83db6ce8ec7e9a288cfff6af.png"},{"id":50390073,"identity":"d8c25074-4881-44af-b169-ecd06e9079e6","added_by":"auto","created_at":"2024-01-30 18:39:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":789833,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe identification of key gene with prognostic value related to ALKBH5 and LIHC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) LASSO coefficient profile of 36 genes, different colored lines represent different genes.\u003c/p\u003e\n\u003cp\u003e(B) LASSO regression with ten-fold cross-validation obtained 6 prognostic genes using the minimum λ value.\u003c/p\u003e\n\u003cp\u003e(C) The upper panel shows the risk score distribution of LIHC patients, the middle panel represents the survival status of patients, and the lower panel is a heatmap of the expression profiles of the six prognostic genes.\u003c/p\u003e\n\u003cp\u003e(D) KM survival curves showing the difference in PFS between high-risk and low-risk samples, with a median time of 1 and 3 years for the two groups of samples.\u003c/p\u003e\n\u003cp\u003e(E) ROC analysis of the risk model, curves showing the true positive rate (sensitivity) versus the false positive rate (1-specificity) for different cut-off points of the risk score.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3898749/v1/bbc3969bba102ce275e4a595.png"},{"id":50390078,"identity":"1c42e05a-14c5-4911-888d-cf76f62d5750","added_by":"auto","created_at":"2024-01-30 18:39:45","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":355143,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe construction of predicative nomogram for LIHC prognosis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-C) Scatterplots of correlation analysis of ALKBH5 with TTI1, ADNP, and ACIN1 in the GEPIA database. Statistically significant \u003cem\u003eP\u003c/em\u003e-values and correlation coefficient r values are shown in the upper left corner of each graph.\u003c/p\u003e\n\u003cp\u003e(D) Boxplot of TTI1 expression levels in normal samples and LIHC samples.\u003c/p\u003e\n\u003cp\u003e(E) qRT-PCR and WB detection of overexpression efficiency of ALKBH5 in SNU387 as well as MHCC-97H cells.\u003c/p\u003e\n\u003cp\u003e(F and G) Regulation of over-ALKBH5 on the proliferation of SNU387 and MHCC-97H cells in CCK-8 assay.\u003c/p\u003e\n\u003cp\u003e(H and I) qRT-PCR and WB detected the regulation of TTI1 expression level by knockdown or overexpression of ALKBH5 in SNU387 cells.\u003c/p\u003e\n\u003cp\u003e(J) Univariate and multivariate Cox analysis of TTI1 and clinical characteristics (pT stage, pM stage, pTNM stage, Grade).\u003c/p\u003e\n\u003cp\u003e(K) Nomogram predicting the effect of TTI1 on the 1-, 3-, and 5-year survival of LIHC patients.\u003c/p\u003e\n\u003cp\u003e(L) Calibration curve of the overall survival nomogram model in TTI1, the diagonal dashed line stands for the ideal nomogram and the red, yellow and grey lines stand for the observed 1-, 3- and 5-year prognosis.\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, ****\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3898749/v1/4e346a210fa75595e23db661.png"},{"id":50391212,"identity":"4bb3523d-485c-4a7c-9634-c34b7b1fe90b","added_by":"auto","created_at":"2024-01-30 18:47:45","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1221307,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInfluence of TTI1 and ALKBH5 on cell proliferation, migration, and invasion in LIHC cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A and B) The relative expression of TTI1 mRNA in SNU387 and MHCC-97H cells transfected with si-TTI1#1, #2 and #3 was detected by qRT-PCR.\u003c/p\u003e\n\u003cp\u003e(C and D) CCK-8 test for the regulation of cell proliferation by si-TTI1#2 combined with over-ALKBH5.\u003c/p\u003e\n\u003cp\u003e(E) qRT-PCR detection of the overexpression efficiency of TTI1 in SNU387 and MHCC-97H cells.\u003c/p\u003e\n\u003cp\u003e(F and G) CCK-8 test for the regulation of over-TTI1 combined with si-ALKBH5#1 on cell proliferation.\u003c/p\u003e\n\u003cp\u003e(H-K) Transwell assay analysis of the regulation of si-TTI1#2 and over-ALKBH5 on cell migration and invasion.\u003c/p\u003e\n\u003cp\u003e(L-O) Transwell assay analysis of the regulation of over-TTI1 combined with si-ALKBH5#1 on cell proliferation.\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs. si-NC or over-NC, #\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs si-TTI1 #2 or over-TTI1.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-3898749/v1/09c1b367f10d447a3d548d6f.png"},{"id":50575960,"identity":"b638bd02-89e6-43ce-a8c7-0c5024a6a3de","added_by":"auto","created_at":"2024-02-02 17:36:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4069867,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3898749/v1/f3f5b6c4-ace2-4db7-abdf-a62a9320a802.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ALKBH5 promotes Liver hepatocellular carcinoma cell proliferation, migration and invasion by regulating TTI1 expression","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLiver carcinoma, characterized as a malignant neoplasm, has been extensively linked with risk factors such as excessive alcohol consumption, viral hepatitis, consumption of mold-contaminated food, and genetic predispositions (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This disease can be histopathologically subdivided into two principal categories: Liver hepatocellular carcinoma (LIHC) and intrahepatic cholangiocarcinoma (iCCA)(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). LIHC, accounting for 90% of primary liver cancer cases, represents the predominant histological subtype of this malignancy. Key contributory factors implicated in LIHC include aflatoxin exposure, obesity, epigenetic alterations, heredity, infection with the hepatitis C virus, tobacco smoking, chronic hepatitis B, and diabetes(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Currently, among methods for LIHC treatment, transplantation is the most effective(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Interventional therapy, ablation therapy, chemoradiotherapy, and targeted therapy can be applied to patients with unresectable liver carcinoma(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Regrettably, despite these interventions, the prognosis for LIHC remains dismal due to high rates of metastasis and recurrence(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This highlights the necessity of uncovering the molecular processes driving LIHC progression and pinpointing new therapeutic targets.\u003c/p\u003e \u003cp\u003eThe AlkB homolog 5, RNA demethylase (ALKBH5), also known as ABH5, OFOXD, or OFOXD1, is implicated in the biological cascades of various neoplasms(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Notably, Guo et al. showed that ALKBH5 could curb pancreatic carcinoma progression through PER1 activation in an m6A-YTHDF2-mediated pathway. This finding reveals that ALKBH5 inhibits pancreatic cancer by regulating the post-transcriptional activation of PER1 through modulation of m6A modifications(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Another literature by Chen et al. established that ALKBH5 selectively augments the incidence of acute myeloid leukemia (AML) and the self-renewal of carcinoma stem cells(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Moreover, ALKBH5 has been reported to function as a tumor promoter in AML by post-transcriptionally modulating pivotal targets, such as TACC3(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), an oncogene related to prognosis in a broad spectrum of carcinomas(\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Together, these observations underscore the central function of ALKBH5 in the pathogenesis of leukemia and the self-renewal of leukemia stem cells/leukemia-initiating cells (LSC/LIC), thereby highlighting the role of ALKBH5/N6-methyladenine (m6A) axis for therapeutic potential. These revelations pave the way for further exploration of the roles played by ALKBH5 in the pathogenesis of diverse human carcinomas.\u003c/p\u003e \u003cp\u003eOur research attempted to illuminate the role of ALKBH5 in LIHC, utilizing an integrative approach of bioinformatics analysis and cellular experiments. In parallel, we worked to uncover the precise mechanism by which ALKBH5 and LIHC are interconnected. It is anticipated that our findings may provide new insights and pave the way for advances in therapeutic intervention, diagnosis, and prognostic assessment of LIHC patients.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of ALKBH5 expression levels across pan-cancers\u003c/h2\u003e \u003cp\u003eTo assess the expression levels of ALKBH5 across a range of cancer types, we utilized data procured from The Cancer Genome Atlas (TCGA; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcga-data.nci.nih.gov/tcga\u003c/span\u003e\u003cspan address=\"https://tcga-data.nci.nih.gov/tcga\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the Genotype-Tissue Expression (GTEx; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gtexportal.org/home/\u003c/span\u003e\u003cspan address=\"https://www.gtexportal.org/home/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database. The TCGA includes large multidimensional map of key genomic changes in various cancers. On the other hand, the GTEx project provides a valuable resource that facilitates the study of human gene expression and regulation in different tissue types. By integrating these resources, we conducted a pan-cancer study exploring ALKBH5 expression across 33 cancer types, in comparison to normal tissues. The analysis was executed using R software, offering a comprehensive visual depiction of ALKBH5 expression distribution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis between ALKBH5 expression and LIHC clinical parameters\u003c/h2\u003e \u003cp\u003eLIHC samples were obtained from the TCGA for comprehensive surveys. We used the Kruskal-Wallis method to analyze the differential expression of ALKBH5 in the context of multiple clinical parameters of LIHC, including nodal status (Node), metastatic status (Metatasis), pT stage, pTNM stage, grade, HBV and HCV. This nonparametric statistical test allowed us to compare expression levels across numerous independent groups. Subsequently, to graphically illustrate the mutual relationships and transitions among these clinical parameters, ALKBH5 expression, and patient status, we employed the \"ggplot2\" package in R to generate an informative and visual Sankey diagram. This diagram serves to provide an intuitive understanding of the interplay among these key aspects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of functional pathways in differentially expressed genes (DEGs)\u003c/h2\u003e \u003cp\u003eAccording to the expression of ALKBH5 in LIHC, we divided the TCGA-LIHC patient data into ALKBH5-high and ALKBH5-low cohorts for screening DEGs. Subsequently, we screened for up-regulated DEGs (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;1.5) and down-regulated DEGs (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FC\u0026thinsp;\u0026lt;\u0026thinsp;0.67) using the Limma package in R software. Next, to further elucidate the biological roles of the DEGs, we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using the Enrichr tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/Enrichr/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/Enrichr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Findings with a \u003cem\u003eP\u003c/em\u003e-value below 0.05 were deemed to be of statistical relevance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of genes associated with prognosis of LIHC\u003c/h2\u003e \u003cp\u003eWe performed a progression-free survival (PFS) analysis of 100 up-regulated and 100 down-regulated DEGs. Statistical tests for differences in PFS between low and high expression groups were performed using the log-rank test in Kaplan-Meier (KM) survival analysis, and hazard ratios (HR), 95% confidence intervals (CI), and \u003cem\u003eP\u003c/em\u003e-values were subsequently generated. From this survival analysis, we focused on genes with P-values less than 0.05, indicating statistical significance. To gain a comprehensive understanding of the protein-protein interaction (PPI) associated with these genes, we exploited the potential of the proteins encoded by statistically significant genes. We used the Search Tool for the Retrieval of Interacting Genes (STRING) STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as the primary platform for interactive data. Additionally, network visualization and analysis were performed using Cytoscape software. Subsequently, for the genes that showed statistical significance in the survival analysis, we performed a gene correlation analysis, the results of which were visualized by the R package \"heatmap\".\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eScreening of candidate genes in the prognostic model\u003c/h2\u003e \u003cp\u003eWe utilized Least Absolute Shrinkage and Selection Operator (LASSO) regression, implemented through the \"glmnet\" package in R, to construct a polygenic signature for prognostic prediction of LIHC using genes that displayed significant \u003cem\u003eP\u003c/em\u003e-values. To ensure the reliability and objectivity of the analysis, ten-fold cross-validation was conducted to select the optimal lambda (λ) value that corresponds to the smallest error fraction. Subsequently, LIHC patients were divided into high-risk and low-risk groups, and their survival time, risk score, as well as survival status were obtained from the selected dataset. The z-scores of 6 gene expression (TTI1, ACIN1, ADNP, CFHR3, SPP2, HGFAC) in these patients were displayed by heat map. Ultimately, the prognostic implications of the high-risk and low-risk groups were substantiated through KM survival analysis. To identify significant variations in PFS probability between the two groups, a log-rank test was carried out. Additionally, time-dependent receiver operating characteristic (ROC) analysis for 1-year, 3-year, and 5-year survival forecasts was used to assess the prognostic performance of the risk model. The area under the curve (AUC) method was used to determine the prediction accuracy of the model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis between ALKBH5 and candidate target genes\u003c/h2\u003e \u003cp\u003eAs a newly developed interactive web server, Gene Expression Profiling Interactive Analysis (GEPIA) offers features that are customized, which are inclusive of patient survival analysis, similar gene detection, etc. In this study, we employed GEPIA to investigate the relationship between ALKBH5 and potential target genes. Through the computation of correlation coefficients, we identified a key gene displaying the strongest correlation with ALKBH5 and LIHC. When the \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, the results were deemed statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and verification of the predictive nomogram with TTI1\u003c/h2\u003e \u003cp\u003eInitially, we evaluated the expression level of TTI1 in LIHC samples utilizing the Wilcoxon test. Subsequently, the prognostic significance of TTI1 was compared with clinical parameters, such as pM stage, pT stage, pTNM stage, age, and grade, through a univariate Cox proportional hazards regression analysis. In order to ascertain if TTI1 could function as an independent prognostic factor for risk stratification in LIHC patients, a multivariate Cox proportional hazards regression analysis was executed. This analysis incorporated additional clinical parameters that exhibited statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the univariate Cox regression model. Based on the independent prognostic factors identified from the preceding analyses, a composite nomogram was constructed via the \"rms\" package in R. This predictive tool was designed to forecast 1-year, 3-year, and 5-year survival probabilities, and its performance was subsequently assessed through a calibration curve.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCell culture and transfection\u003c/h2\u003e \u003cp\u003eWe procured LO2 (normal liver cell), MHCC-97L, MHCC-97H and SNU387 (LIHC cell lines) from American Type Culture Collection (ATCC, Manassas, USA). LO2 cells were maintained in IMDM-RMPI supplemented with 1% penicillin-streptomycin and 10% fetal bovine serum (FBS), while MHCC-97L, MHCC-97H, and SNU387 cells were cultured in DMEM, fortified with the same supplements.. All cell lines were incubated at 37\u0026deg;C in an environment containing 5% CO\u003csub\u003e2\u003c/sub\u003e. For cell transfection, we acquired si-ALKBH5 #1, si-ALKBH5 #2, si-ALKBH5 #3, over-ALKBH5, si-TTI1 #1, si-TTI1 #2, si-TTI1 #3, over-TTI1, along with their negative controls (over-NC and si-NC) from the Shanghai Biotech Company, Gemma Gene. Transfection was carried out using Lipofectamine 3000 (Invitrogen), and the efficiency of this process was evaluated under a fluorescence microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRNA extraction and Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) analysis\u003c/h2\u003e \u003cp\u003eTRIzol (Invitrogen) was used to extract the total RNA from the cells. The RevertAid First Strand cDNA Synthesis Kit from Invitrogen was used to create complementary DNA (cDNA). qRT-PCR was carried out using an Applied Biosystems 7900 Real-time PCR System and the SYBR Green PCR Master Mix. The 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e technique was used to compare the relative expression levels of ALKBH5 and TTI1, with GAPDH acting as an internal control. The following primers were used in this experiment: ALKBH5 (forward: 5'- CGGCGAAGGCTACACTTACG-3'; reverse: 5'-CCACCAGCTTTTGGATCACCA-3'), TTI1 (forward: 5'-CCACAGCTGAAGACATCGAA-3'; reverse: 5 '- ACATCTGGACGGGTGTCATT-3') and GAPDH (forward: 5'- CAAGGTCATCCATGACAACTTTG \u0026minus;\u0026thinsp;3'; reverse: 5'- GGGCCATCCACAGTCTTCT \u0026minus;\u0026thinsp;3').\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWestern Blotting (WB) assay\u003c/h2\u003e \u003cp\u003eALKBH5 and TTI1 protein expression in LIHC cell lines were evaluated using a WB assay. Cells underwent lysis and protein extraction with RIPA buffer containing a protease inhibitor cocktail. Protein concentrations were determined via a BCA assay kit. Proteins were then subjected to SDS-PAGE and transferred to PVDF membranes. Post-transfer, membranes were blocked using 5% non-fat milk for non-specific binding prevention. They were then probed overnight at 4\u0026deg;C with primary antibodies for ALKBH5 (1:1000) and TTI1 (1:500). GAPDH (1:1000) served as the control. After washing, membranes were exposed to HRP-linked secondary antibodies (1:5000) for an hour. Protein bands were detected with an enhanced chemiluminescence (ECL) system and quantified by densitometry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssay for cell proliferation\u003c/h2\u003e \u003cp\u003eCCK-8 kit (Shiga Doto Molecular Technology, Japan) was applied to measure cell proliferation. First, we plated 2\u0026times;10\u003csup\u003e4\u003c/sup\u003e of the transfected SNU387 cells in a 96-well plate in triplicate. Then, 10ul of CCK-8 solution per well was added into cells for the indicated time. After 24h, 48h, 72h, 96h, and 120h, we finished measuring the optical density (OD) value of the cells at 450 nm via iMark Microplate Reader (Bio-Rad) for plotting the cell proliferation curve.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTranswell assay\u003c/h2\u003e \u003cp\u003eThe invasive and migratory abilities of cells were determined utilizing a Transwell chamber assay (Corning). The upper chamber was covered with 100 l of Matrigel (Corning) and allowed to set up for an hour at 37\u0026deg;C in preparation for the invasion assay. Subsequently, a 0.5\u0026micro;l cell suspension was added to the Matrigel-coated chamber, which was filled with DMEM medium devoid of serum. After the invasion and migration phases are complete, cells are stained with DAPI to reveal nuclei. High-resolution images were captured after a 20-minute staining period, utilizing an Olympus BX53 upright microscope equipped with a digital camera. The migration assay was performed in a similar manner, except the Matrigel coating step was omitted. Each of these experimental steps was independently replicated three times to ensure the accuracy and reproducibility of the findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eR software was used for all statistical analyses. The Kruskal-Wallis or Wilcoxon test was used to analyze differences in expression among groups. The log-rank test was used to examine KM survival curves. Correlation coefficients were used to examine the relationship between ALKBH5 and clinical factors or prospective target genes. The prognostic model was built using LASSO regression with ten-fold cross-validation. To investigate prognostic value, univariate and multivariate Cox proportional hazards regression models were used. The \u003cem\u003ein vitro\u003c/em\u003e experiments were conducted three times, and the results are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Statistical significance was defined as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSignificantly high expression of ALKBH5 in the majority of pan-cancers\u003c/h2\u003e \u003cp\u003eWe have analyzed the expression levels of ALKBH5 across 33 different human malignancies, utilizing data from both the TCGA and GTEx databases. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, ALKBH5 exhibits significant overexpression in a majority of tumor tissues when compared to their neighboring normal tissues, including Breast invasive carcinoma (BRCA), Adrenocortical carcinoma (ACC), Cholangiocarcinoma (CHOL), and LIHC, etc. This pattern of differential expression implies a potential role of ALKBH5 in the oncogenesis and progression of these tumor types, warranting further investigation into its mechanistic contributions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEffect of differential expression of ALKBH5 in LIHC on staging, HBV infection and patient survival\u003c/h2\u003e \u003cp\u003eTo elucidate the role of ALKBH5 in LIHC, we scrutinized its expression profile in relation to various clinicopathological parameters (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). Notably, ALKBH5 expression showed significant variations between T2 and T3 stages, as well as between patients with and HBV\u0026thinsp;+\u0026thinsp;and HBV- infection. In contrast, no substantial differences in ALKBH5 expression were found when examined across other clinicopathological parameters. Furthermore, we observed a noteworthy interconnection between ALKBH5 expression, clinical characteristics, and patient survival across different stages of LIHC. A visual representation of these associations was provided in the form of a Sankey diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). The observed patterns suggest a potential differential role of ALKBH5 in specific stages of tumor progression and in the context of HBV infection. The association of ALKBH5 expression with patient survival further implies its potential as a prognostic marker in LIHC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eKnockdown of ALKBH5 inhibits growth of LIHC cells\u003c/b\u003e \u003cb\u003ein vitro\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe initially examined the expression of ALKBH5 in LIHC cell lines and normal cells. The results demonstrated an upregulation of ALKBH5 in LIHC cell lines, particularly in the SNU387 and MHCC-97H cells, prompting us to select these cell lines for subsequent investigations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Next, we subjected the SNU387 and MHCC-97H cells to knockdown procedures, finding that si-ALKBH5 #1 demonstrated the highest efficiency and was consequently selected for further experimentation (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Data from the CCK-8 assay revealed that the knockdown of ALKBH5 resulted in suppressed proliferation of SNU387 and MHCC-97H cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Furthermore, results from the Transwell assay indicated that compared with the si-NC transfected cells, SNU387 and MHCC-97H cells transfected with si-ALKBH5#1 exhibited significantly reduced invasion and migration capabilities (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI). These results suggest a critical function of ALKBH5 in regulating the proliferative and metastatic potential of LIHC cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eKEGG pathway enrichment analysis on DEGs\u003c/h2\u003e \u003cp\u003eFrom the two groups of samples with differential expression of ALKBH5, we screened 3105 up-regulated DEGs and 156 down-regulated DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). DEGs that were up-regulated in the KEGG pathway were primarily enriched in Shigellosis, Focal adhesion, Regulation of actin cytoskeleton, Proteoglycans in cancer, etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). DEGs that were down-regulated in the KEGG pathway were primarily enriched in Retinol metabolism, Complement and coagulation cascades, Chemical carcinogenesis-DNA adducts, etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e36 key genes associated with LIHC prognosis\u003c/h2\u003e \u003cp\u003eIn our analysis, we selected the top 100 up-regulated and 100 down-regulated DEGs for PFS rate analysis. This led to the identification of 36 genes showing a significant association with PFS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Interestingly, higher expression of genes such as CHFR1, AZGP1, APOC3, and ITIH1 was associated with a favorable prognosis, while the overexpression of genes like TBCCD1, ZNF362, ZNF318, ZMYM3, UBE3B, and TTI1 correlated with a poorer prognosis. Further investigation into these 36 genes using the STRING database generated a PPI network consisting of 36 nodes and 45 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The correlation analysis revealed significant positive or negative interactions among these 36 genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). These findings underscore the critical role of these genes in LIHC progression and potentially highlight novel prognostic markers for LIHC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of 6 candidate genes with prognostic value associated with LIHC\u003c/h2\u003e \u003cp\u003eWe utilized the glmnet package in R to construct a LASSO Cox regression model for the 36 genes with significant \u003cem\u003eP\u003c/em\u003e values. With 10-fold cross-validation, we chose 0.0521 as the minimum standard for λ (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Based on the non-zero coefficients of the genes, we computed the risk score for each patient as follows: (0.1124)*TTI1+(0.06)*ACIN1+(0.0402)*ADNP+(-0.0422)*CFHR3+(-0.0236)*SPP2+(-0.0094)* HGFAC. We used the median cutoff point obtained from the \"survminer\" R package to segregate the patients into high-risk (n\u0026thinsp;=\u0026thinsp;185) and low-risk (n\u0026thinsp;=\u0026thinsp;185) groups. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, patients in the high-risk group demonstrated reduced survival time compared to the low-risk group. The distribution of the six candidate prognostic genes also varied between the two groups, with the expression of ADNP, ACIN1, and TTI1 increasing as the risk score escalated. Further, the KM survival curves indicated that the low-risk group had improved PFS compared to the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Lastly, the risk model exhibited a substantial AUC value of 0.704 in 1-year survival from the ROC analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). These findings suggest that 6 candidate prognostic genes may be effective targets for predicting one-year survival of LIHC patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eEstablishing TTI1 as a key downstream gene of ALKBH5\u003c/h2\u003e \u003cp\u003eWe compared the association of six candidate genes with ALKBH5 using the GEPIA database. At a statistical significance threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, a significant positive connection was observed between ALKBH5 and three genes (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Among them, TTI1 has the highest correlation with ALKBH5 (r\u0026thinsp;=\u0026thinsp;0.46), followed by ADNP (r\u0026thinsp;=\u0026thinsp;0.39) and ACIN1 (r\u0026thinsp;=\u0026thinsp;0.36), therefore, we identified TTI1 as the key downstream gene of ALKBH5. TTI1 expression was shown to be considerably greater in LIHC tumors than in normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). After ALKBH5 was overexpressed in SNU387 and MHCC-97H cells, qRT-PCR detected a significant overexpression efficiency (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). The results of CCK-8 showed that overexpressed ALKBH5 significantly promoted the proliferation of SNU387 and MHCC-97H cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG). Furthermore, we found that the expression of TTI1 was decreased when ALKBH5 was knocked down, and conversely, the expression of TTI1 was increased when ALKBH5 was overexpressed (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI). TTI1 was discovered as an independent predictive predictor for overall survival (OS) in LIHC patients in univariate and multivariate Cox proportional hazards regression studies (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eJ). In clinical practice, we developed a TTI1 nomogram to estimate 1-, 3-, and 5-year survival in LIHC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eK). The calibration plot indicated its predictions closely aligned with actual outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eL).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eALKBH5 combined with TTI1 affects the proliferation, migration and invasion of LIHC cells\u003c/h2\u003e \u003cp\u003eOur study investigated the impact of TTI1 knockdown in SNU387 and MHCC-97H cells. Through qRT-PCR, we found si-TTI1 #2 demonstrated the most significant knockdown efficiency (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). To elucidate the functional mechanism of TTI1 and its upstream gene ALKBH5 in LIHC, we performed a CCK-8 assay. The results showed a decrease in cellular proliferation following the knockdown of TTI1. Interestingly, overexpression of ALKBH5 could partially mitigate the suppressive effect of si-TTI1#2 on cell proliferation (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). We also induced overexpression of TTI1 in SNU387 and MHCC-97H cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE), which resulted in enhanced cellular proliferation, an effect which was diminished by the low expression of ALKBH5 (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eG). Further substantiating these observations, migration and invasion assays mirrored the trends witnessed in proliferation studies, indicating a tangible influence of ALKBH5 and TTI1 expression levels on the migratory and invasive potentials of LIHC cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eH-\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eO). Therefore, we posit that ALKBH5, through its regulatory action on TTI1 expression, serves as a pivotal determinant in either promoting or inhibiting specific malignancy-associated cellular behaviors in LIHC, underlining a sophisticated network of genetic interactions pivotal to cancer cell dynamics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe investigated the function of ALKBH5 in the progression and prognosis of LIHC in this study. Consistent with the known risk factors for liver carcinoma, such as excessive alcohol consumption, viral hepatitis, and genetic predispositions(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), our findings further confirm the complex and multifactorial nature of the etiology of LIHC. Notably, we found an association between high expression of ALKBH5 and LIHC. This adds a novel dimension to the already complex landscape of LIHC molecular mechanisms and provides fresh insights into potential molecular targets for therapy. Our analysis underscored the prevalence of ALKBH5 overexpression in numerous tumor types, including LIHC, thus augmenting its potential role as a pan-cancer molecular marker. Furthermore, ALKBH5 expression levels were consistently greater in LIHC clinical stage tissues compared to their normal counterparts, emphasizing its likely significance in LIHC pathological development. Intriguingly, we noted minimal variation in ALKBH5 expression across groups with differing clinical factors, suggesting that its upregulation might be a universal event in LIHC, independent of individual patient characteristics. Our research serves as a seminal contribution towards comprehending the molecular underpinnings of LIHC and provides robust evidence implicating ALKBH5 as a potential therapeutic target.\u003c/p\u003e \u003cp\u003eThe findings \u003cem\u003ein vitro\u003c/em\u003e point to ALKBH5 being a critical regulator of LIHC cellular behavior, influencing proliferation, migration, and invasion. We observed that ALKBH5 was significantly overexpressed in LIHC cell lines, especially in SNU387 cells. Knockdown of ALKBH5 resulted in decreased cell proliferation and impaired invasion and migration capabilities, further confirming the critical role of ALKBH5 in regulating the malignancy of LIHC cells. Corroborating previous studies, ALKBH5, recognized as a prominent m6A demethylase(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), has been identified as a key player in a diverse array of cancers, such as breast carcinoma, stomach carcinoma, and colorectal carcinoma(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The versatile roles of ALKBH5 in various cancer types entail the modulation of numerous biological processes encompassing proliferation, metastasis, migration, invasion, metastasis, as well as tumor growth. Interestingly, the influence of ALKBH5 appears to be context-dependent, with its expression level acting either as an oncogenic promoter or a tumor suppressor, depending on the type of carcinoma(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Further supporting its multifaceted role, recent evidence also points towards an intriguing interaction between ALKBH5 and NEAT1 in colorectal carcinoma, proposing the ALKBH5-NEAT1 axis as a potential therapeutic target(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Taken together, our findings underscore ALKBH5 as an influential factor in the pathogenesis of LIHC. More thorough and in-depth research are needed, however, to elucidate the specific processes by which ALKBH5 promotes LIHC development and to prove its efficacy in clinical settings.\u003c/p\u003e \u003cp\u003eIn the subsequent phase of our study, we performed a differential gene expression screen on LIHC patients, based on ALKBH5 expression levels. In the KEGG pathway analysis, the up-regulated DEGs-enriched KEGG pathways include Wnt signaling pathway, Renal cell carcinoma, and the Hippo signaling pathway. Shuai He et al. postulated that the WNT/β-catenin signaling pathway, a highly conserved and tightly controlled molecular mechanism, governs cellular differentiation, proliferation, and embryonic development(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Notably, there is increasing evidence that abnormalities in WNT/β-catenin signaling contribute to the progression and development of liver carcinoma, which contained LIHC and cholangiocarcinoma(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In addition, when Takebumi Usui et al. studied cases of renal cell carcinoma liver metastases, they found that many patients with renal cell carcinoma after surgical resection would develop in the direction of liver carcinoma(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The Hippo pathway was found to be a critical regulator of liver size, metabolism, development, regeneration, and homeostasis in genetic studies on murine livers conducted by Jordan H. Driskill and Duojia Pan. Abnormalities in this pathway may contribute to common liver diseases like liver carcinoma and fatty liver disease(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Besides, the down-regulated DEGs are abundant in Tryptophan metabolism, Retinol metabolism, Pyruvate metabolism, Histidine and Glutathione metabolism. This underlines the intricate interplay between liver carcinoma and functional molecular metabolism within the human body. For instance, research by Qunhua Han et al. delineates an age-related metabolic imbalance in the liver involving glycerophospholipids, arachidonic acid, histidine, and linoleic acid(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). In summary, our exploration of the roles of various metabolic and signaling pathways provides valuable insights into the molecular landscape of LIHC, highlighting the potential for targeting these specific pathways for therapeutic intervention.\u003c/p\u003e \u003cp\u003eThrough PFS survival, PPI, correlation, LASSO, Cox and other prognostic value analyses, we identified 6 key genes (HGFAC, SPP2, CFHR3, ADNP, ACIN1, TTI1) associated with LIHC prognosis. Following that, we used the GEPIA database to determine the connection between ALKBH5 and these genes, finally identifying TTI1 as the most significant prognostic gene. TTI1, or TELO2 Interacting Protein 1, plays a vital role in various biological processes, yet remains relatively understudied. Existing literature suggests that TTI1 is involved in multiple metabolic pathways and in the activation of mTORC1 signaling, which promotes cell growth(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). For instance, TTI1 has been shown to facilitate survival in multiple myeloma via the mTORC1 pathway(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Rao et al. revealed the role of TTI1 in binding ATM and DNA-PKcs, triggering the activation of p-53 and S-15 phosphorylation pathways to initiate cancer cell death programs(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Furthermore, research on colorectal cancer by Peng Xu et al. indicated higher TTI1 expression in tumor tissue relative to adjacent normal tissue, demonstrating its critical role in colorectal cancer proliferation(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Nevertheless, the influence of TTI1 on the development of liver carcinoma is still not clear.\u003c/p\u003e \u003cp\u003eWe conducted a thorough study to investigate the function of TTI1 in LIHC, and the findings underscored the importance of both TTI1 and ALKBH5 in the development of LIHC. TTI1 was discovered to be considerably overexpressed in LIHC tumors as compared to normal tissues. Notably, ALKBH5 overexpression was seen to significantly enhance SNU387 cell proliferation, an effect inversely mirrored by TTI1 under-expression. TTI1 also emerged as an independent prognostic indicator for overall survival in LIHC patients, prompting us to construct a predictive TTI1 nomogram with high consistency between predicted and actual survival rates. The interaction between TTI1 and ALKBH5 revealed their influence on LIHC cell growth. Downregulation of TTI1 suppressed cell proliferation, migration and invasion, a result that was partially counteracted by ALKBH5 overexpression. TTI1 overexpression, on the other hand, enhanced cell proliferation, migration, and invasion but was inhibited by reduced ALKBH5 expression. Altogether, these findings underscore a potential regulatory role of ALKBH5 in LIHC progression via modulation of TTI1 expression, illuminating novel avenues for potential therapeutic strategies.\u003c/p\u003e \u003cp\u003eTo sum up, our findings confirm the characterization of ALKBH5 and TTI1 as oncogenes in LIHC, emphasizing their potential as novel markers in LIHC. Through bioinformatics analysis and cellular experiments, we elucidated that the interaction between ALKBH5 and TTI1 significantly affects the proliferation, migration and invasion of LIHC cells, suggesting that ALKBH5 may exert a key regulatory influence on LIHC progression by regulating TTI1 expression. These findings greatly advance the current understanding of LIHC and pave the way for innovative directions for future research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design of the research: Zhiqiu Hu and Ziping Zhang.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcquisition of data: Xubo Wu and Jinfeng Feng and Huarong Mao.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnalysis and interpretation of data: Xiang Zhou and Qimeng Chang.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical analysis: Qimeng Chang and Zhiqiu Hu.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDrafting the manuscript: Zhiqiu Hu and Xiang Zhou.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRevision of manuscript for important intellectual content: Ziping Zhang and Qimeng Chang. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is supported by Commission of Science Technology of Minhang District (2019MHZ079 to ZQH).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHatta MNA, Mohamad Hanif EA, Chin SF, Neoh HM (2021) Pathogens and Carcinogenesis: A Review. 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Curr Pharm Des 20(1):125\u0026ndash;135\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRao F, Cha J, Xu J, Xu R, Vandiver MS, Tyagi R et al (2020) Inositol Pyrophosphates Mediate the DNA-PK/ATM-p53 Cell Death Pathway by Regulating CK2 Phosphorylation of Tti1/Tel2. Mol Cell 79(4):702\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu P, Du G, Guan H, Xiao W, Sun L, Yang H (2021) A role of TTI1 in the colorectal cancer by promoting proliferation. Transl Cancer Res 10(3):1378\u0026ndash;1388\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Liver hepatocellular carcinoma, ALKBH5, TTI1, Proliferation, Migration, Invasion","lastPublishedDoi":"10.21203/rs.3.rs-3898749/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3898749/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThe objective of this research was to investigate the potential mechanisms of ALKBH5 in Liver Hepatocellular Carcinoma (LIHC).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe examined the expression of ALKBH5 in pan-cancer and its correlation with clinical factors of LIHC. \u003cem\u003eIn vitro\u003c/em\u003e experiments were conducted to verify ALKBH5 expression in LIHC and its effect on LIHC cell proficiency. Differentially expressed genes (DEGs) were screened from LIHC patients associated with ALKBH5, and downstream genes associated with ALKBH5 were identified by bioinformatics analysis. We further examined the expression of the downstream genes and constructed a prognostic nomogram. Lastly, we analyzed the exact functions of ALKBH5 and TTI1 in LIHC cells.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe found that ALKBH5 is significantly overexpressed in most pan-cancer types. \u003cem\u003eIn vitro\u003c/em\u003e experiments confirmed ALKBH5 as an oncogene in LIHC, with its knockdown suppressing the proliferation, migration, and invasion of LIHC cells. Bioinformatics analyses revealed that TTI1 is significantly positively correlated with ALKBH5. TTI1 was highly expressed in LIHC cells and has good prognostic ability for LIHC patients. Further experimental evidence confirmed that the suppression of TTI1 impeded cell proliferation, migration, and invasion, an impact partially offset by the overexpression of ALKBH5. In contrast, the promotion of these cellular progressions was observed with TTI1 overexpression but was tempered by a decrease in ALKBH5 expression.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn conclusion, our findings indicate that ALKBH5 may influence the proliferation, migration and invasion of LIHC by modulating TTI1 expression, providing a new direction for the treatment of LIHC.\u003c/p\u003e","manuscriptTitle":"ALKBH5 promotes Liver hepatocellular carcinoma cell proliferation, migration and invasion by regulating TTI1 expression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-30 18:39:40","doi":"10.21203/rs.3.rs-3898749/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"feedefba-e0a9-4d2f-b09f-0c704d288e79","owner":[],"postedDate":"January 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-18T07:50:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-30 18:39:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3898749","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3898749","identity":"rs-3898749","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}