A pan-cancer analysis of the oncogenic role of KIF13A in human tumors

A pan-cancer analysis of the oncogenic role of KIF13A in human tumors | 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 A pan-cancer analysis of the oncogenic role of KIF13A in human tumors Shangke Huang, Jizhang Chen, Yongxia Cui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7237847/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Despite the increasing evidence supporting the association between KIF13A and cancer, pan-cancer analysis is currently limited. Therefore, we aimed to investigate the potential for KIF13A to contribute to oncogenesis in thirty-three different tumors using publicly accessible databases. Our research findings indicate that KIF13A has lower RNA tissue specificity and exhibits lower levels of expression compared to healthy tissue. However, we discovered distinct associations between KIF13A expression and the outcome of diverse tumor types. Genetic variation analysis revealed that cases of UCEC with genetic alterations in KIF13A exhibited a better prognosis compared to cases without genetic alterations in KIF13A. Analysis of immune infiltration revealed an inverse association between KIF13A expression and CD8 + T-cell infiltration levels in HNSC, HNSC-HPV-, HNSC-HPV+, and LUSC, ‌but correlated positively with the abundance of cancer-associated fibroblasts in LUAD, PAAD, and STAD. Furthermore, we observed differences in KIF13A (NP_017396.4) phosphorylation levels between normal tissues and primary tumor tissues at different phosphorylation sites across various tumor cases. Specifically, we noted an increased phosphorylation level of KIF13A at the S1698 site in HNSC and HCC, correlating with the early differentiation of human embryonic stem cells. In conclusion, this pioneering pan-cancer study offers thorough comprehension of the role of KIF13A in various cancers. Pan-cancer KIF13A Survival prognosis Immune infiltration Genetic variation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The development of tumors is intricate, and understanding the relationship among the expression patterns, prognosis, and underlying molecular mechanisms of crucial genes across various cancer types is crucial. With the increasing improvement of tumor genome data in public databases including TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus), pan-cancer research has become available. Although studies have confirmed the association between KIF13A and tumor development, there are few studies on its pan-cancer implications. KIF13A belongs to the kinesin-3 family and has a distinctive function in organelle dynamics. It was first detected in mouse through cDNA analysis[ 1 ] and has been found to be evolutionarily maintained across the evolutionary spectrum from Caenorhabditis elegans to mammals[ 2 , 3 ]. KIF13A contains seven distinct domains, including the Motor_domain (cl00286) (11–352), FHA (cl00062) (471–534), KIF1B (pfam12423) (748–792), DUF3694 (pfam12473) (1003–1083), DUF3585 (pfam12130) (1171–1284), PEPcase (cl14656), and CAP_GLY (pfam01302) [ 4 – 6 ]. A growing number of studies have demonstrated that KIF13A serves as a key mechanical motor on the endosomal network, facilitating cargo delivery from recycling endosomes (REs) and the Trans-Golgi Network (TGN) to the plasma membrane (PM) [ 7 ]. Additionally, studies have revealed that the transport process from endosomes, especially tubular/vesicular endosomes (a subset of total REs), to enlarging melanosomes is contingent upon KIF13A ‌[8]. Moreover, studies have suggested that KIF13A is involved in various cellular processes such as pathogenesis, pathogen survival[ 9 , 10], cell migration[ 11 ], and cell division [12]. In addition to the information provided above, a growing number of studies suggest that KIF13A is linked to various diseases. KIF13A was initially discovered at a locus that is suspected to be linked to schizophrenia[ 3 ]. Dysfunctions in KIF13A have been associated with numerous cancers[ 13 , 14 ], and increased amplification of this gene has been observed in certain cancers[ 15 ], such as lung adenocarcinoma[ 13 , 16 ], liver cancer[ 17 ], melanoma, glioma[ 4 ], cervical carcinoma[ 18 ], and retinoblastoma[ 19 ]. However, research on the overall impact of KIF13A on various types of cancer using comprehensive omics and clinical data is currently limited. Here, We present a pan-cancer analysis of KIF13A, utilizing datasets from both the TCGA and GEO databases. This analysis focuses on examining the link between KIF13A expression and factors such as prognosis, DNA methylation, protein phosphorylation, immune infiltration, and related signal pathways. The goal is to uncover potential molecular mechanisms that are involved in the oncogenic functions of KIF13A in 33 different tumor types. 2. Material and Methods 2.1 The investigation of KIF13A expression We logged into TIMER2 ( http://timer.cistrome.org/ ) and entered KIF13A into the "Gene_DE" module. After submitting the data, we obtained box plots that showed the expression of KIF13A across different cancers, their subcategories, and normal tissues. In cases where normal tissues were scarce for GBM (Glioblastoma multiforme) and LAML (Acute myeloid leukemia), We employed the "Expression analysis-Box Plots" function of the GEPIA2 (Gene expression profiling interactive analysis, version 2) platform ( http://gepia2.cancer-pku.cn/#analysis ) to assess the expression of KIF13A in tumor tissues versus corresponding normal tissues sourced from the GTEx (Genotype-tissue expression) database. We implemented the criteria with a p-value cutoff at 0.01 and log2FC cutoff at 1, ensuring the "Match TCGA normal and GTEX data" option was checked. Additionally, we used the GEPIA2 Pathological Stage Plot tool to generate violin plots that displayed the KIF13A expression patterns across different pathological stages of all TCGA tumors. To enhance precision in box and violin plots, the expression data were converted to log2 [TPM (Transcripts per million) + 1] . Finally, we employed Ualcan ( http://ualcan.path.uab.edu/analysis-prot.html ), an online platform designed for cancer Omics data analysis, to investigate protein expression pattern using data from the CPTAC (Clinical Proteomic Tumor Analysis Consortium). By submitting "KIF13A" for analysis, ‌We examined the expression profiles of both total and phosphorylated protein variants of KIF13A (NP_071396.4) in tumor versus normal tissues. 2.2 Survival Outcome analysis We accessed GEPIA2 Expression Survival Map tool[ 20 ]to retrieve the Overall Survival (OS) and Disease-Free Survival (DFS) significance heatmap for KIF13A across all tumors included in the TCGA. ‌We employed a 50% expression threshold to categorize patients into high and low-expression cohorts. ‌In order to test our hypothesis, we applied the log-rank test. Subsequently, We navigated to the GEPIA2 Expression module to perform Survival Analysis and generate survival plots. 2.3 Genetic variation analysis We visited the cBioPortal ( https://www.cbioportal.org/ )[ 21 , 22 ] and utilized the 'Quick select' feature to select the 'TCGA Pan Cancer Atlas Studies'. By inputting "KIF13A", we were able to investigate the genetic alteration features of this gene. The results concerning alteration frequency, mutation classifications, and Copy Number Alteration (CNA) in all TCGA tumors are presented in the "Cancer Types Summary". Furthermore, the mutated residues of KIF13A can be visualized in the schematic representation of the protein structure or the 3D (Three-dimensional) structure within the "Mutations" tool. We employed the "Comparison/Survival" module to gather statistics on Overall Survival, Disease-Free Survival, Progression-Free Survival, and Disease-Specific Survival for TCGA cancer instances, both with and without KIF13A genetic variations. Additionally, Log-rank P-values were calculated and Kaplan-Meier plots were also produced. 2.4 Profiling immune cell infiltration We utilized the TIMER2 Immune-Gene tool ( http://timer.cistrome.org/ ) to examine the relationship between KIF13A expression and immune infiltrates in various TCGA Cancer. Various algorithms were employed to estimate immune infiltration level of CD8 + T-cells and cancer-associated fibroblasts. The purity-adjusted Spearman's rank correlation test was used to calculate P-values and partial correlation values, which were then visualized with a heatmap and scatter plot. 2.5 KIF13A-related gene enrichment analysis We utilized the STRING platform ( https://string-db.org/ ) to gather information on the KIF13A protein of Homo sapiens. We then configured the parameters: requiring a minimum interaction score of 0.150 (regarded as low confidence), using network edges to represent evidence, limiting the display to 50 interactors in the primary shell, and only considering experimental sources for interactions. As a result, we identified 23 proteins that were experimentally identified and found to bind to KIF13A, referred to as group A. ‌ Then, We access the GEPIA2 Similar Gene Detection platform ( http://gepia2.cancer-pku.cn/#similar ) to retrieve the top 100 target genes that exhibit expression correlation with KIF13A (designated as group B), based on comprehensive TCGA datasets encompassing both tumor and normal tissues. Additionally, pairwise gene Pearson correlation analysis between KIF13A and pre-selected genes (GTF2H1, CLIP1, MIB1, CDYL, KDM1B, ZFP91) will be ‌carried out‌ via the GEPIA2 correlation analysis tool. Scatter plots‌ will be generated using Log2 TPM values to ‌visually represent‌ correlations between KIF13A and target genes, ‌annotated with‌ p-values and Pearson’s R coefficients. Furthermore, we accessed TIMER2.0_Gene_Corr module ( http://timer.cistrome.org/ ) and entered KIF13A, along with the genes we selected (GTF2H1, CLIP1, MIB1, CDYL, KDM1B, ZFP91), into the "Interested Gene" and "Gene Expression" fields. This procedure was implemented‌ to produce heatmap encompassing partial correlation coefficients (cor) and p-values derived from purity-corrected Spearman’s rank correlation analyses for the selected genes. In the end, we merged the two groups into a single list for performing KEGG and GO analysis. The enriched pathways were then visualized using R packages such as ggpubr, clusterProfiler, dplyr, tidyr, and ggplot2. The analysis was carried out using R version 4.4.1. A p-value below 0.05 in a two-tailed test was considered significant. 3. Results 3.1 Gene expression analysis Here, this study focused on investigating‌ the tumor-promoting function of human KIF13A (mRNA: NM_022113.6; protein: NP_071396.4), ‌as illustrated in‌ Figure S1 a. The protein structure of KIF13A, as illustrated in Figure S1 b, is conserved among different species and is primarily composed of seven domains, including Motor_domain (cl00286), FHA (cl00062), KIF1B (pfam12423), DUF3694 (pfam12473), DUF3585 (pfam12130), GAP_GLY3694 (pfam01302), and PEPcase (cl4656) domain[4]. The phylogenetic relationships‌ of KIF13A protein across species ‌are presented in Figure S2 ‌, illustrating evolutionary divergence patterns. To investigate the tissue specificity of KIF13A expression, we accessed the HPA (Human Protein Atlas) website ( https://www.proteinatlas.org/humanproteome/pathology ) to analyze its expression levels in non-tumor tissues using the Consensus dataset of HPA / GTEx / FANTOM5. The findings revealed that KIF13A exhibited lower RNA tissue specificity, suggesting that it has a ubiquitous expression profile. The highest expression levels were observed in skeletal muscle, followed by the tongue, heart muscle, and urinary bladder (Fig. S3 a). Additionally, a low level of RNA-based discrimination among blood cell types does not indicate specificity when examining KIF13A expression in various blood cells from the HPA/Monaco/Schmiedel datasets (Fig. S3 b), with Neutrophils displaying the highest levels of KIF13A expression. However, our analysis using mass spectrometry and immunoassays in Peptide Atlas did not detect KIF13A in plasma, indicating a lack of physiological outward leakage of the intracellular KIF13A protein (Fig. S3 c and Fig. S3 d). Next, we accessed the TIMER2_Gene_DE module to examine KIF13A expression across different cancer types in TCGA database. As illustrated in Fig. 1 a, KIF13A expression is generally suppressed in tumor tissues compared to their normal counterparts. This trend is observed in various cancers such as BLCA, BRCA, KICH, KIRP, LUAD (Lung Adenocarcinoma), READ, THCA, UCEC, COAD, KICR, and LUSC (Wilcoxon test, p < 0.01). In contrast, the expression pattern of KIF13A is higher than the tumor tissues in CHOL, HNSC, and LIHC compared to normal tissue (Wilcoxon test, p < 0.01) (Fig. 1 a). The research findings indicate that KIF13A ‌acts as a tumor suppressor in most of tumors, while functioning as a tumor promoter in others. As the TIMER2 database does not contain normal tissue data for all tumors, we utilized the GEPIA2_boxplot tool, which includes normal tissue samples from the GTEx dataset serving as controls, to reanalyze the expression status of KIF13A across different cancer types. The findings indicate that the expression status of KIF13A is generally lower in most tumors, such as ACC, BLCA, UCEC, and UCS, compared to normal tissue. Nevertheless, the expression status of KIF13A is elevated in tumor tissues of CHOL, LGG, and PAAD (Fig. 1 b, Wilcoxon test, p < 0.05). For the remaining tumors, the difference in expression level between normal and tumor tissue is not significant (Figure S4 a). To further validate the research results, we logged into the GENT2 database ( http://gent2.appex.kr/gent2/ ) to re-analyze the expression status of KIF13A. The research results also indicated lower expression of KIF13A in the majority of cancer tissues (two-sample t-test, p < 0.05) (Figure S5 ). Additionally, the Ualcan_CPTAC dataset also showed decreased levels of total KIF13A protein in cancer tissues of ccRCC., UCEC, OV, COAD, LIHC, LUAD, and LUSC (Fig. 1 c, Wilcoxon test). In BRCA, there was a decrease in the expression of total KIF13A protein in the cancer tissues compared to normal tissues, although it was not statistically significant. On the other hand, the Ualcan_CPTAC dataset revealed elevated levels of total KIF13A protein in cancer tissues of HNSC, PAAD, and GBM (Fig. 1 c, Wilcoxon test). The research results from the four databases mentioned above are generally consistent. Specifically, the expression pattern of KIF13A in most tumor tissues is lower than in normal tissues. This finding also aligns with previous research results[ 13 ]. Ultimately, we also utilized the "Pathological Stage Plot" tool of GEPIA2 to examine the association between KIF13A expression and the pathological stages of various cancers, including OV, COAD, SKCM, and KIRC.The significant difference among the pathological stages of tumor is evident (Fig. 1 d, Wilcoxon test, P < 0.001), but it is not significant for the remaining cancers (Figure S4 b). 3.2 Survival analysis We stratified cancer patients into high and low KIF13A expression groups and analyzed the link between KIF13A expression pattern and patient outcomes across multiple tumor types in the TCGA dataset. As shown in Fig. 2 a, high levels of KIF13A expression were associated with poor overall survival (OS) for KICH (p = 0.047) and LIHC (p = 0.0017), while they were linked to good OS for KIRC (p = 0.00019), LGG (p = 3.5e-06), and READ (p = 0.031). Additionally, high levels of KIF13A expression were associated with poor Disease-free survival (DFS) for COAD (p = 0.028) and LGG (p = 0.00045), whereas they were linked to good Disease-free survival (DFS) for KICH (p = 0.071) (Fig. 2 b, Table S8 ). The correlation was not significant for the rest of the cancers . To further investigate the association between KIF13A expression pattern and prognosis, we accessed the gene chip data from the Kaplan-Meier Plotter website ( http://kmplot.com/analysis/index.php?p=background ) [ 23 , 24 ]. The results revealed that high levels of KIF13A expression are correlated with favorable overall survival (OS) (p = 0.00018 and p = 8.9e-05), distant metastasis-free survival (DMFS) (p = 0.00126 and p = 5e-04), relapse-free survival (RFS) (p = 1.8e-09 and p = 4.8e-11), and post-progression survival (PPS) (p = 0.041 and p = 0.031) in patients with BRCA and BRCA HER2-negative (Figure S6 a and S6c). In the case of lung cancer, high KIF13A expression is correlated with better OS (p = 4.4E-14), progression-free survival (FP) (p = 3.1e-06), and PPS (p = 1.7e-05) (Figure S6 g). Similarly, in gastric cancer, high expression of KIF13A is linked to improved OS (p = 0.0025) and FP (p = 0.0062) (Figure S6 h). Notably, in ovarian cancer, high KIF13A expression is associated with worse outcomes in terms of OS (p = 0.005), PFS (p = 0.00028), and PPS (p = 0.026) (Figure S6 e). Importantly, no substantial association was detected between KIF13A levels and survival outcomes across other cancer types. Additionally, subgroup analyses stratified by clinical factors in breast, ovarian, liver, lung, and gastric cancers yielded context-specific associations (Table S1 - S5 ). The findings suggest that the impact of KIF13A expression on prognosis differs across different subtypes of tumors. Ultimately, we accessed the RNA-seq_Pan-cancer database of Kaplan-Meier tool to assess the association between OS/RFS and KIF13A expression. The findings revealed that high levels of KIF13A expression are linked to favorable OS and RFS in BRCA (OS, p = 0.0022; RFS, p = 0.0022), EAC (OS, p = 0.042; RFS, p = 0.045), and OV (OS, P = 0.0057; RFS, P = 0.0039). On the other hand, increased levels of KIF13A expression are associated with unfavorable OS and RFS in KIRP (OS, p = 0.0093; RFS, p = 0.016), LIHC (OS, p = 0.0063; RFS, P = 0.04) (Figure S7 a and S7b, Table S7 ). Furthermore, heightened KIF13A expression is only related to positive OS in READ (p = 0.0035), KIRC (p = 5.1e-7), and STAD (p = 0.022), while it is connected to adverse OS in BLCA (p = 0.039) and THYM (p = 0.02). Increased levels of KIF13A expression are linked to favorable RFS in LUAD (p = 0.0032) and TGCT ( p = 0.024), but associated with poor RFS in ESSC (p = 0.028) (Fig S7 a and S7b, Table S7 ). Combined, the research results suggested that a high expression pattern of KIF13A is associated with a positive prognosis for the majority of cancers. 3.3 Genetic alteration analysis We examined the mutational landscape of KIF13A across diverse TCGA tumor datasets. As illustrated in Fig. 3 a, SKCM displayed the most frequent KIF13A alterations (> 9.23%), where mutations predominated, followed by UCEC (7.75%). CNA was the most common type of amplification in OV cases, with an alteration frequency of approximately 7.53% (Fig. 3 a), followed by BLCA at 5.84%. As depicted in Fig. 3 a, deep deletions constituted the predominant genetic alteration in MESO (1.15%), with STAD ranking second at 1.14%. Additionally, the spectrum of KIF13A-associated genetic variants is comprehensively mapped in Fig. 3 b. It was found that missense mutations predominated among genetic alterations. Frameshift mutations in KIF13A were predominantly triggered by the V1116Sfs*23/4 alteration, particularly in STAD (n = 6) compared to EAC (n = 1) and COAD (n = 2) cohorts (Fig. 3 b). This results in the translation from Valine (V) to Serine (S) at the 1116 site of the KIF13A, ultimately leading to truncation of the KIF13A protein. Additionally, we examined the possible link between genetic changes in KIF13A and the survival outcomes of various cancer types. The findings showed that cases of UCEC with altered KIF13A had a more favorable prognosis (OS, p = 0.0278; DSS, p = 0.0231; DFS, p = 0.0129; PFS, p = 2.363e-3) compared to UCEC cases without KIF13A alterations (Fig. 3 d). However, this correlation was not statistically significant for other type of cancers. Next, we analyzed the correlation between KIF13A mRNA abundance and tumor mutational landscape parameters (TMB/MSI) across 33 TCGA cancer types. The results revealed a positive correlation between KIF13A expression and TMB for COAD (P = 0.026), but a negative correlated with TMB for LUAD (P = 0.0026) and THCA (P = 0.019) (Figure S8 ). In the meantime, a positive relationship exists between KIF13A expression and MSI in LUSC (P = 0.0015), OV (P = 0.011), COAD (P = 6.2e-05), READ (P = 8.4e-10), LMAL (Acute Myeloid Leukemia, P = 0.046), and STAD (P = 0.0015), but negatively correlated with MSI in PRAD (P = 4.9e-08) and HNSC (P = 0.0042) (Figure S9 ). 3.4 KIF13A methylation analysis DNMIVD was employed to ‌evaluate whether‌ KIF13A methylation contributes to pathogenesis in diverse tumors. Analysis of differentially methylated genes (DMG) of KIF13A in pan-cancer studies revealed significant differences in methylation levels between adjacent and tumor tissues in the promoter region of KIF13A for BRCA (P = 0.00107), HNSC (P = 0.029), KIRP (P = 0.00037), and PAAD (P = 0.0004) (Figure S10 ). Additionally, A significant inverse correlation‌ was observed between KIF13A methylation and its expression in BRCA (P = 0.00048824), HNSC (P = 0.00132566), KIRP (P = 0.000220667), LIHC (P = 0.0167125), SARC (Sarcoma, P = 0.0182044), SKCM (P = 0.0000014301), and STAD (P = 0.0072986), but a positive correlation in COAD (P = 0.0452013) and THCA (P = 0.0300103) (Figure S11 ). 3.5 Protein phosphorylation analysis ‌Utilizing the CPTAC database, we assessed differential phosphorylation status of KIF13A (NP_017396.4) in primary tumor versus matched normal tissues spanning diverse malignancies.‌ As illustrated in Fig. 4 g, the phosphoproteomic analysis revealed significant changes in KIF13A phosphorylation at specific residues. In ccRCC, Primary tumors exhibit attenuated phosphorylation at the S1529 (p = 1.02E-06) residue compared to matched normal renal tissues (Fig. 4 a). In LUAD, primary tumors exhibit ‌divergent phosphorylation patterns‌: elevated at S1529 ((p = 2.58E-08) yet reduced at S363 (p = 4.47E-04) and S1283 (p = 5.71E-29) versus normal tissues. (Fig. 4 b). For LUSC, primary tumors manifest ‌significant hyperphosphorylation at S1529‌ (p = 3.38E-07) relative to matched normal bronchial epithelia (Fig. 4 c). In HNSC, Primary tumors ‌exhibit significantly elevated phosphorylation‌ at S636 relative to normal counterparts (p = 1.78E-19), T1285 (p = 2.32E-03), T1447 (p = 2.45E-06), S1682 (p = 2.67E-04), S1698 (p = 6.64E-05), and S1700 (p = 2.32E-03) (Fig. 4 d). In HCC (hepatocellular carcinoma), primary tumors exhibit ‌hyperphosphorylation‌ at both site S1529 (p = 9.22E-07) and S1698 (p = 3.06E-16) compared to adjacent normal liver tissues (Fig. 4 e). For PAAD, primary tumors demonstrate ‌significant hypophosphorylation‌ at S1529 (p = 1.28E-02) compared to normal tissues (Fig. 4 f). Overall, KIF13A (NP_017396.4) exhibits ‌tumor-specific phosphorylation heterogeneity‌ across clinical specimens, with divergent patterns between normal and malignant tissues. Leveraging the PhosphoNET knowledgebase, we performed ‌functional annotation‌ of CPTAC-identified phosphosites on KIF13A (NP_017396.4). This revealed S1698 as a ‌conserved regulatory hub‌ coordinating early pluripotency exit in human embryonic stem cells (hESCs) [25, 26]. This stem cell-related phosphomotif warrants ‌functional validation in carcinogenic contexts‌, particularly assessing its ‌crosstalk with oncogenic kinases‌ (e.g., AKT/PKM2) during EMT progression. 3.6 Immune infiltration analysis As critical constituents of the tumor microenvironment (TME), tumor-infiltrating immune cells mechanistically contribute to carcinogenesis, malignant progression, and metastatic dissemination. Here, employing seven algorithms (TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, XCELL, MCPCOUNTER, EPIC), this investigation systematically profiled tumor-infiltrating immune cell subsets and evaluated their associations with KIF13A expression across TCGA malignancies. We identified a significant inverse association between CD8 + T-lymphocyte infiltration density and KIF13A transcript abundance across HNSC, HNSC subtypes (HNSC-HPV-, HNSC-HPV+) and LUSC malignancies (Figure S12 ). Conversely, Cancer-associated fibroblasts (CAFs) infiltration density demonstrates significant positive covariation with KIF13A transcriptional activity in LUAD, PAAD, and STAD tumor ecosystems (Fig. 5 ). The scatterplot data for these tumors created using the XCELL algorithm demonstrates concordant positive correlations between KIF13A expression and cancer-associated fibroblast (CAF) infiltration densities in STAD, as mechanistically illustrated in Figs. 5 A (Fig. 5 , Rho = 0.162, p = 1.53e-03). These findings demonstrate context-dependent regulatory dynamics between KIF13A transcriptional activity and heterogeneous tumor-immune microenvironmental constituents. 3.7 Enrichment analysis of KIF13A-related partners To delineate KIF13A's oncogenic mechanisms, we employed STRING (v12.0) and GEPIA2 databases to define: (i) Group A: 23 high-confidence KIF13A interactors(Fig. 6a), and (ii) Group B: 100 top co-expressed genes. .Subsequent multi-layered functional enrichment analyses were performed. The scatter plot demonstrates positive regulation between KIF13A and CLIP1 (R = 0.45), GTF2H1 (R = 0.47), MIB1 (R = 0.48), KDM1B (R = 0.53), ZFP91 (R = 0.52) and CDYL (R = 0.52), with all R values above 0.45 and p values below 0.001. The corresponding heatmap also shows a positive correlation between KIF13A and aforementioned six genes across different cancer types (Fig. 6c). Subsequently, we combined the data from both groups to conduct functional enrichment analysis. KEGG results suggest that the "Lysosome" pathway may be participate in the effect of KIF13A on tumor pathogenesis (Fig. 6d). Furthermore, the GO enrichment analysis data indicates that most of these genes are associated with vesicle organization (BP, Biological Process), Golgi apparatus subcompartment (CC, Cellular Component) (Fig. 6e). 4. Discussion Evidence from multiple model organisms establishes KIF13A as a pleiotropic molecular motor involved in cell division[ 12 ], tubular endosome formation[ 27 ], chemokine activity, and influencing tumor occurrence and development through the mTORC1-KIF13A-M6PR pathway[ 28 ]. Although KIF13A dysregulation correlates with tumorigenesis across multiple cancers[ 13 , 17 ], elucidation of whether conserved molecular pathways mediate its pro-oncogenic functions requires systematic investigation. To address this gap, we conducted a comprehensive analysis of KIF13A gene expression, genetic alterations, DNA methylation, and protein phosphorylation in different tumors of TCGA and GEO databases. Utilizing the GEPIA2 platform, we assessed the prognostic relevance of KIF13A expression levels across human malignancies. The results showed that elevated KIF13A expression portends worse clinical outcomes in hepatocellular carcinoma (LIHC) patients (OS, p = 0.0017; RFS, p = 0.31) (Fig. 2 a). Subsequently, Kaplan-Meier plotter's pan-cancer module was leveraged to conduct focused survival analysis evaluating KIF13A-associated prognostic outcomes in LIHC cohorts. The results also showed elevated KIF13A levels portended significantly poorer survival outcomes (OS, p = 0.0006; RFS, p = 0.0405) (Figure S7 , Table S7 ). However, when analyzing the correlation between KIF13A expression and survival outcome in LIHC using the RNA-seq module of the Kaplan-Meier plotter, the results showed that the correlation was not significant. Therefore, Current clinical evidence fails to substantiate KIF13A as a prognostic biomarker in specified malignancies., such as LIHC. This discrepancy could be due to differences in data processing methods or the lack of access to updated survival information. For BRCA, GEPIA2 pan-cancer analytics demonstrated no statistically significant prognostic association for KIF13A expression (OS, p = 0.89; RFS, p = 0.062) (Table S8 ). Contrasting with GEPIA2 findings, Kaplan-Meier plotter's RNA-seq pan-cancer analysis demonstrated KIF13A expression is significantly correlated with prognosis (RFS, p = 0.0022) (Fig S7 , Table S7 ). Furthermore, the Kaplan-Meier plotter_RNA-chip_breast cancer module analysis yielded the same result (OS, p = 0.00018; RFS, p = 1.8e-9; DMFS, p = 0.013; PPS, p = 0.041). Additionally, KIF13A expression in HER2-negative BRCA displayed a more significant correlation with prognosis (OS, p = 8.9e-5; RFS, p = 4.8e-11; DMFS, p = 0.0005; PPS, p = 0.0314) (Figure S6 c). Therefore, current clinical evidence successfully establishes that KIF13A can serve as a prognostic biomarker in specific malignancies (e.g., breast cancer). Collectively, this study delineates KIF13A's oncogenic properties through multi-dimensional validation of its tumorigenic linkages, and provide a mechanistic scaffold for deconvoluting KIF13A-driven oncogenic cascades, delineating its pathophysiological hierarchy in malignant progression. Declarations Acknowledgements This work was supported by the Doctoral Scientific Research Startup Fund of the Affiliated Hospital of Southwest Medical University(ID: 16226), Southwest Medical University Research Grant (ID: 2017-ZRQN-015) and Undergraduate Innovation Training Program Project of Southwest Medical University (ID: 202410632042 ). Authorship contribution statement Jizhang Chen and Shangke Huang performed the data analyses and wrote the original draft, which was then carefully revised by Yongxia Cui. Competing interest The authors affirm that they do not have any competing financial interests or personal relationships that could have influenced the work reported in this paper. clinical trial number: Not applicable. Consent to Participate Not applicable. Ethics Statement This study did not require ethics committee review, as it utilized publicly available genomic datasets without direct involvement of human subjects or animal experimentation. Accordingly, no informed consent procedures were necessary for this secondary data analysis. Not applicable. Consent for publication Not applicable. Data availability The datasets presented in this study are available in online repositories or can be obtained from the corresponding author upon request. The names of the repositories and their links are provided in the article and Supplementary materials and methods. References TERUNAGA NAKAGAWA YT, EIJI MATSUOKA, SATORU KONDO, YASUSHI OKADA, YASUKO NODA, YOSHIMITSU KANAI. AND NOBUTAKA HIROKAWA, Identification and classification of 16 new kinesin superfamily (KIF) proteins in mouse genome, Proc. Natl. Acad. Sci. USA, 94 (1997) 9654–9659. Terunaga Nakagawa MS, Seog D-H, Ogasawara K, Dohmae N, Takio K, Hirokawa N, Novel Motor A. KIF13A, Transports Mannose-6- Phosphate Receptor to Plasma Membrane through Direct Interaction with AP-1 Complex, cell, 103 (2000) 569–581. Jamain S, Quach H, Fellous M, Bourgeron T. Identification of the human KIF13A gene homologous to Drosophila kinesin-73 and candidate for schizophrenia. Genomics. 2001;74:36–44. Pan D, Fang X, Li J. Identification of a Novel Gene Signature Based on Kinesin Family Members to Predict Prognosis in Glioma. Medicina; 2023. p. 59. Siddiqui SA. The Kinesin-3 Family: Long-Distance Transporters. In: Friel CT, editor. The Kinesin Superfamily Handbook: Transporter, Creator, Destroyer. Abingdon (UK): CRC; 2020. Thankachan JM, Setty SRG. KIF13A-A Key Regulator of Recycling Endosome Dynamics. Front Cell Dev Biol. 2022;10:877532. Patel NM, Siva MSA, Kumari R, Shewale DJ, Rai A, Ritt M et al. KIF13A Motors Are Regulated by Rab22A to Function as Weak Dimers inside the Cell. Sci Adv, 7 (2021). Shakya S, Sharma P, Bhatt AM, Jani RA, Delevoye C, Setty SR. Rab22A recruits BLOC-1 and BLOC-2 to promote the biogenesis of recycling endosomes. EMBO Rep, 19 (2018). 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Identification of a novel KIF13A-RET fusion in lung adenocarcinoma by next-generation sequencing. Lung Cancer. 2018;118:27–9. Banerjee P, Xiao GY, Tan X, Zheng VJ, Shi L, Rabassedas MNB, Guo HF, Liu X, Yu J, Diao L, Wang J, Russell WK, Roszik J, Creighton CJ, Kurie JM. The EMT activator ZEB1 accelerates endosomal trafficking to establish a polarity axis in lung adenocarcinoma cells. Nat Commun. 2021;12:6354. Chandrasekaran G, Tatrai P, Gergely F. Hitting the brakes: targeting microtubule motors in cancer. Br J Cancer. 2015;113:693–8. Park HY, Park JH, Shin MG, Han SJ, Ji YS, Oh HJ, Kim YC, Lee T, Choi YD, Oh IJ. Case Report: A case of ultra-late recurrence of KIF13A-RET fusion non-small cell lung cancer response to selpercatinib. Front Oncol. 2023;13:1178762. Zhang L, Zhang C, Xing Z, Lou C, Fang J, Wang Z, Li M, He H, Bai H. Fibronectin 1 derived from tumor-associated macrophages and fibroblasts promotes metastasis through the JUN pathway in hepatocellular carcinoma. Int Immunopharmacol. 2022;113:109420. Lee JY, Yoon JK, Kim B, Kim S, Kim MA, Lim H, Bang D, Song YS. Tumor evolution and intratumor heterogeneity of an epithelial ovarian cancer investigated using next-generation sequencing. BMC Cancer. 2015;15:85. Orlic M, Spencer CE, Wang L, Gallie BL. Expression analysis of 6p22 genomic gain in retinoblastoma. Genes Chromosomes Cancer. 2006;45:72–82. Tang Z, Kang B, Li C, Chen T, Zhang Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019;47:W556–60. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, Cerami E, Sander C, Schultz N. Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal. Sci Signal, 6 (2013). Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, Antipin Y, Reva B, Goldberg AP, Sander C. Schultz, The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data. Cancer Discov. 2012;2:401–4. Gyorffy B. Discovery and ranking of the most robust prognostic biomarkers in serous ovarian cancer, Geroscience, (2023). Lanczky A, Gyorffy B. Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation. J Med Internet Res. 2021;23:e27633. Jesper V, Olsen LJ Jensen,2 Florian Gnad,1 Jürgen Cox,1, Jensen,7 TS, Erich SB. A. Nigg, 2,7 Matthias Mann1,2†, Quantitative Phosphoproteomics Reveals Widespread Full Phosphorylation Site Occupancy During Mitosis, cell, 3 (2010). Van Hoof D, Munoz J, Braam SR, Pinkse MW, Linding R, Heck AJ, Mummery CL, Krijgsveld J. Phosphorylation dynamics during early differentiation of human embryonic stem cells. Cell Stem Cell. 2009;5:214–26. Etoh K, Fukuda M. Rab10 regulates tubular endosome formation through KIF13A and KIF13B motors. J Cell Sci, 132 (2019). Ahmed KA, Xiang J. mTORC1 regulates mannose-6-phosphate receptor transport and T-cell vulnerability to regulatory T cells by controlling kinesin KIF13A. Cell Discov. 2017;3:17011. Additional Declarations No competing interests reported. Supplementary Files plainSupplementary.docx TableS1TableS8.docx FigS1.pdf FigS2.pdf FigS8TMB.pdf FigS9MSI.pdf FigS10.pdf FigS11.pdf FigS7.pdf FigS6.pdf FigS5.pdf FigS12.pdf FigS3.pdf FigS4.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Oct, 2025 Reviews received at journal 20 Sep, 2025 Reviews received at journal 31 Aug, 2025 Reviews received at journal 21 Aug, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers invited by journal 19 Aug, 2025 Editor assigned by journal 19 Aug, 2025 Editor invited by journal 19 Aug, 2025 Submission checks completed at journal 14 Aug, 2025 First submitted to journal 14 Aug, 2025 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. 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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-7237847","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504846612,"identity":"b1abd035-9b06-47c4-baf0-d010c881fd05","order_by":0,"name":"Shangke Huang","email":"","orcid":"","institution":"Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shangke","middleName":"","lastName":"Huang","suffix":""},{"id":504846614,"identity":"6a21bf6b-54d7-45fb-bfdf-2f825845f47a","order_by":1,"name":"Jizhang Chen","email":"","orcid":"","institution":"Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jizhang","middleName":"","lastName":"Chen","suffix":""},{"id":504846615,"identity":"06c6c0d7-3422-4fad-818c-28f7b408865c","order_by":2,"name":"Yongxia Cui","email":"data:image/png;base64,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","orcid":"","institution":"Affiliated Hospital of Southwest Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yongxia","middleName":"","lastName":"Cui","suffix":""}],"badges":[],"createdAt":"2025-07-29 01:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7237847/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7237847/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90299306,"identity":"c7d5e559-77f9-4da7-baf0-775b391b940d","added_by":"auto","created_at":"2025-09-01 08:50:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":670165,"visible":true,"origin":"","legend":"\u003cp\u003eillustrates the expression levels of the KIF13A gene in various tumors and pathological stages.(a) The expression status of KIF13A in different cancers or specific cancer subtypes was analyzed using TIMER2. Statistical significance is indicated by * P \u0026lt; .05; ** P \u0026lt; .01; *** P \u0026lt; .001.(b) For the types CHOL, LGG, PAAD, ACC, BLCA, UCEC, and UCS in the TCGA project, corresponding normal tissues from the GTEx database were utilized as controls. Box plot data is provided with significance denoted by ** P \u0026lt; 0.01, * P \u0026lt; 0.05.(c) Utilizing the CPTAC dataset, the protein expression levels of KIF13A were analyzed between normal tissue (N) and primary tissue (P) in various cancers including Ovarian Cancer, Colon Cancer, Clear cell RCC, Hepatocellular carcinoma, UCEC, Glioblastoma multiforme, Pancreatic adenocarcinoma, Head and neck squamous carcinoma, Lung squamous cell carcinoma, and LUAD.(d) Analysis of the TCGA data revealed the expression levels of the KIF13A gene across different pathological stages (stage I, stage II, stage III, and stage IV) in OV, COAD, KIRC, and SKCM. Log2 (TPM + 1) was used for log-scale representation.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7237847/v1/aca239fbc35bff3fa37b0c47.jpg"},{"id":90299309,"identity":"ec0e1f78-f0c4-43a5-827d-46a7f9918610","added_by":"auto","created_at":"2025-09-01 08:50:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":651498,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between KIF13A expression and the survival prognosis of cancers in TCGA. We utilized the GEPIA2 tool to conduct overall survival (a) and disease-free survival (b) analyses of various tumors in TCGA based on KIF13A expression levels. The survival map and Kaplan-Meier curves with significant results are provided.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7237847/v1/34196aa7a36d69acdd073aae.jpg"},{"id":90299328,"identity":"a693f2d0-38eb-45a6-be0f-a7ad60c774f8","added_by":"auto","created_at":"2025-09-01 08:50:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":808521,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the mutation feature of KIF13A in various tumors from the TCGA was conducted. By utilizing the cBioPortal tool, we assessed the mutation characteristics of KIF13A across the TCGA tumors. The alteration frequency, along with mutation type (a) and mutation site (b), were examined. The mutation site with the highest alteration frequency (V1116Sfs*23/4) was visualized in the 3D structure of KIF13A (c). Additionally, we explored the potential correlation between mutation status and overall, disease-specific, disease-free, and progression-free survival of UCEC (d) through the cBioPortal tool.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7237847/v1/58103f1e8bd865d21fb68915.jpg"},{"id":90301327,"identity":"d4a38224-9ecd-4da0-9ce9-2ce4ce999df7","added_by":"auto","created_at":"2025-09-01 08:58:30","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":855035,"visible":true,"origin":"","legend":"\u003cp\u003ePhosphorylation analysis of the KIF13A protein in various tumors was conducted. Using the CPTAC dataset, we compared the expression levels of the KIF13A phosphoprotein (NP_017396.4) in normal tissue and primary tissue of different selected tumors through UALCAN. Box plots are provided for different cancers, including ccRCC (a), LUAD (b), LUSC (c), HNSC (d), HCC (e), and PAAD (f). Significantly altered phosphoprotein sites are highlighted in the schematic diagram of the KIF13A protein (g). In the diagram, a red arrow pointing upwards indicates an increase in KIF13A expression in tumor tissue at that phosphorylation site compared to normal tissue, while a red arrow pointing downwards indicates a decrease in KIF13A expression in the tumor tissue at that phosphorylation site.\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7237847/v1/4510001d990d805381a03573.jpg"},{"id":90301331,"identity":"fdb3166c-d5cd-49cc-9603-fc524561eabf","added_by":"auto","created_at":"2025-09-01 08:58:30","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1438780,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis was conducted to examine the relationship between KIF13A expression and immune infiltration of cancer-associated fibroblasts.a. Various algorithms were employed to investigate the potential correlation between KIF13A expression levels and cancer-associated fibroblast infiltration levels in all types of cancer in TCGA.b. The correlation between KIF13A expression levels and cancer-associated fibroblast infiltration levels in LUAD, PAAD, and STAD was analyzed by adjusting for purity.\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7237847/v1/4ddf5fd4a7eeec1595f73241.jpg"},{"id":90299314,"identity":"c538ea6c-f7dc-49b8-881c-30183ca87f83","added_by":"auto","created_at":"2025-09-01 08:50:30","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1115739,"visible":true,"origin":"","legend":"\u003cp\u003eKIF13A-related gene enrichment analysis.(a) We obtained a list of experimentally determined KIF13A-binding proteins using the STRING tool.(b) Additionally, we used the GEPIA2 approach to identify the top 100 KIF13A-correlated genes in TCGA projects and analyzed the expression correlation between KIF13A and selected target genes, including CDYL, CLIP1, GTF2H1, KDF1B, ZFP91, and MIB1.(c) The corresponding heatmap data for detailed cancer types is displayed.(d) KEGG pathway analysis was performed based on the KIF13A binding and interacted genes.(e) GO analysis was also conducted based on the KIF13A binding and interacted genes.\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7237847/v1/71794b8dbda216fe952a7d94.jpg"},{"id":90304754,"identity":"3a9f98fb-2b7a-4477-bc82-dc3ab48c0aed","added_by":"auto","created_at":"2025-09-01 09:22:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6183922,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7237847/v1/9044c765-5b56-4694-8104-f9fb72deac0f.pdf"},{"id":90299307,"identity":"e05e31d6-2206-4993-8913-9ef0e3f00b6a","added_by":"auto","created_at":"2025-09-01 08:50:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22935,"visible":true,"origin":"","legend":"","description":"","filename":"plainSupplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-7237847/v1/dd4104a9f759e25522329943.docx"},{"id":90301322,"identity":"6b55d94e-0acc-45f8-97e9-07f9b6c98ae0","added_by":"auto","created_at":"2025-09-01 08:58:29","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":79284,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1TableS8.docx","url":"https://assets-eu.researchsquare.com/files/rs-7237847/v1/eb6b1d9f84e51c81534b347c.docx"},{"id":90304343,"identity":"98439b90-cdc4-4051-82e1-5be7aa370c9c","added_by":"auto","created_at":"2025-09-01 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08:58:31","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":435217,"visible":true,"origin":"","legend":"","description":"","filename":"FigS8TMB.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7237847/v1/b2610068a9139160e65f8af3.pdf"},{"id":90302172,"identity":"1824f103-7569-4759-b943-12cbd8064592","added_by":"auto","created_at":"2025-09-01 09:06:30","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":432376,"visible":true,"origin":"","legend":"","description":"","filename":"FigS9MSI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7237847/v1/2d15641d514d6e06ea1c90f2.pdf"},{"id":90299331,"identity":"30796a86-183a-4161-bbab-4dbb0ed2fbdf","added_by":"auto","created_at":"2025-09-01 08:50:30","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":394542,"visible":true,"origin":"","legend":"","description":"","filename":"FigS10.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7237847/v1/f96ce9f20dcf5efc50a537a6.pdf"},{"id":90299320,"identity":"8167d74d-204b-4f9f-bb0d-51a1221ab23b","added_by":"auto","created_at":"2025-09-01 08:50:30","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":381437,"visible":true,"origin":"","legend":"","description":"","filename":"FigS11.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7237847/v1/6143d55199f6c78aed20f988.pdf"},{"id":90301335,"identity":"8c56b284-6d7c-49d0-8552-6e80e7602d8a","added_by":"auto","created_at":"2025-09-01 08:58:30","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":868449,"visible":true,"origin":"","legend":"","description":"","filename":"FigS7.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7237847/v1/cf8944f5b9f8d34f0e8d96ca.pdf"},{"id":90299318,"identity":"be0d19b8-7fbd-42e2-a967-491ae2a23a1c","added_by":"auto","created_at":"2025-09-01 08:50:30","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":1132645,"visible":true,"origin":"","legend":"","description":"","filename":"FigS6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7237847/v1/5c004033205aa5669600d83d.pdf"},{"id":90299362,"identity":"c906b407-d4a4-4075-8b2c-308448a5934a","added_by":"auto","created_at":"2025-09-01 08:50:31","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":1137101,"visible":true,"origin":"","legend":"","description":"","filename":"FigS5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7237847/v1/5d86d9ed2edf6f1b8ba56a93.pdf"},{"id":90299360,"identity":"edf8a5c9-b40b-4019-909f-0ffdfd2f1dc2","added_by":"auto","created_at":"2025-09-01 08:50:31","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":3806550,"visible":true,"origin":"","legend":"","description":"","filename":"FigS12.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7237847/v1/08010b2243c7d296e5d67d01.pdf"},{"id":90299337,"identity":"c039d586-0169-4488-a86a-19c5b7ec2799","added_by":"auto","created_at":"2025-09-01 08:50:30","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":3702264,"visible":true,"origin":"","legend":"","description":"","filename":"FigS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7237847/v1/25fb2c415343d819f0403a32.pdf"},{"id":90299364,"identity":"029854ac-8cc6-4bca-b368-fc281d0932c8","added_by":"auto","created_at":"2025-09-01 08:50:31","extension":"pdf","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":13151901,"visible":true,"origin":"","legend":"","description":"","filename":"FigS4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7237847/v1/c82f4c743906293af99ed9b8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A pan-cancer analysis of the oncogenic role of KIF13A in human tumors","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe development of tumors is intricate, and understanding the relationship among the expression patterns, prognosis, and underlying molecular mechanisms of crucial genes across various cancer types is crucial. With the increasing improvement of tumor genome data in public databases including TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus), pan-cancer research has become available. Although studies have confirmed the association between KIF13A and tumor development, there are few studies on its pan-cancer implications.\u003c/p\u003e\u003cp\u003eKIF13A belongs to the kinesin-3 family and has a distinctive function in organelle dynamics. It was first detected in mouse through cDNA analysis[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and has been found to be evolutionarily maintained across the evolutionary spectrum from Caenorhabditis elegans to mammals[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. KIF13A contains seven distinct domains, including the Motor_domain (cl00286) (11\u0026ndash;352), FHA (cl00062) (471\u0026ndash;534), KIF1B (pfam12423) (748\u0026ndash;792), DUF3694 (pfam12473) (1003\u0026ndash;1083), DUF3585 (pfam12130) (1171\u0026ndash;1284), PEPcase (cl14656), and CAP_GLY (pfam01302) [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA growing number of studies have demonstrated that KIF13A serves as a key mechanical motor on the endosomal network, facilitating cargo delivery from recycling endosomes (REs) and the Trans-Golgi Network (TGN) to the plasma membrane (PM) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Additionally, studies have revealed that the transport process from endosomes, especially tubular/vesicular endosomes (a subset of total REs), to enlarging melanosomes is contingent upon KIF13A \u0026zwnj;[8]. Moreover, studies have suggested that KIF13A is involved in various cellular processes such as pathogenesis, pathogen survival[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, 10], cell migration[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and cell division [12].\u003c/p\u003e\u003cp\u003eIn addition to the information provided above, a growing number of studies suggest that KIF13A is linked to various diseases. KIF13A was initially discovered at a locus that is suspected to be linked to schizophrenia[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Dysfunctions in KIF13A have been associated with numerous cancers[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and increased amplification of this gene has been observed in certain cancers[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], such as lung adenocarcinoma[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], liver cancer[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], melanoma, glioma[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], cervical carcinoma[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and retinoblastoma[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, research on the overall impact of KIF13A on various types of cancer using comprehensive omics and clinical data is currently limited.\u003c/p\u003e\u003cp\u003eHere, We present a pan-cancer analysis of KIF13A, utilizing datasets from both the TCGA and GEO databases. This analysis focuses on examining the link between KIF13A expression and factors such as prognosis, DNA methylation, protein phosphorylation, immune infiltration, and related signal pathways. The goal is to uncover potential molecular mechanisms that are involved in the oncogenic functions of KIF13A in 33 different tumor types.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 The investigation of KIF13A expression\u003c/h2\u003e\u003cp\u003eWe logged into TIMER2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and entered KIF13A into the \"Gene_DE\" module. After submitting the data, we obtained box plots that showed the expression of KIF13A across different cancers, their subcategories, and normal tissues. In cases where normal tissues were scarce for GBM (Glioblastoma multiforme) and LAML (Acute myeloid leukemia), We employed the \"Expression analysis-Box Plots\" function of the GEPIA2 (Gene expression profiling interactive analysis, version 2) platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn/#analysis\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn/#analysis\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to assess the expression of KIF13A in tumor tissues versus corresponding normal tissues sourced from the GTEx (Genotype-tissue expression) database. We implemented the criteria with a p-value cutoff at 0.01 and log2FC cutoff at 1, ensuring the \"Match TCGA normal and GTEX data\" option was checked. Additionally, we used the GEPIA2 Pathological Stage Plot tool to generate violin plots that displayed the KIF13A expression patterns across different pathological stages of all TCGA tumors. To enhance precision in box and violin plots, the expression data were converted to log2 \u003csup\u003e[TPM (Transcripts per million) + 1]\u003c/sup\u003e. Finally, we employed Ualcan (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ualcan.path.uab.edu/analysis-prot.html\u003c/span\u003e\u003cspan address=\"http://ualcan.path.uab.edu/analysis-prot.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), an online platform designed for cancer Omics data analysis, to investigate protein expression pattern using data from the CPTAC (Clinical Proteomic Tumor Analysis Consortium). By submitting \"KIF13A\" for analysis, \u0026zwnj;We examined the expression profiles of both total and phosphorylated protein variants of KIF13A (NP_071396.4) in tumor versus normal tissues.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Survival Outcome analysis\u003c/h2\u003e\u003cp\u003eWe accessed GEPIA2 Expression Survival Map tool[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]to retrieve the Overall Survival (OS) and Disease-Free Survival (DFS) significance heatmap for KIF13A across all tumors included in the TCGA. \u0026zwnj;We employed a 50% expression threshold to categorize patients into high and low-expression cohorts. \u0026zwnj;In order to test our hypothesis, we applied the log-rank test. Subsequently, We navigated to the GEPIA2 Expression module to perform Survival Analysis and generate survival plots.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Genetic variation analysis\u003c/h2\u003e\u003cp\u003eWe visited the cBioPortal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and utilized the 'Quick select' feature to select the 'TCGA Pan Cancer Atlas Studies'. By inputting \"KIF13A\", we were able to investigate the genetic alteration features of this gene. The results concerning alteration frequency, mutation classifications, and Copy Number Alteration (CNA) in all TCGA tumors are presented in the \"Cancer Types Summary\". Furthermore, the mutated residues of KIF13A can be visualized in the schematic representation of the protein structure or the 3D (Three-dimensional) structure within the \"Mutations\" tool. We employed the \"Comparison/Survival\" module to gather statistics on Overall Survival, Disease-Free Survival, Progression-Free Survival, and Disease-Specific Survival for TCGA cancer instances, both with and without KIF13A genetic variations. Additionally, Log-rank P-values were calculated and Kaplan-Meier plots were also produced.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e2.4 Profiling immune cell infiltration\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eWe utilized the TIMER2 Immune-Gene tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to examine the relationship between KIF13A expression and immune infiltrates in various TCGA Cancer. Various algorithms were employed to estimate immune infiltration level of CD8\u0026thinsp;+\u0026thinsp;T-cells and cancer-associated fibroblasts. The purity-adjusted Spearman's rank correlation test was used to calculate P-values and partial correlation values, which were then visualized with a heatmap and scatter plot.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 KIF13A-related gene enrichment analysis\u003c/h2\u003e\u003cp\u003eWe utilized the STRING platform (\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) to gather information on the KIF13A protein of Homo sapiens. We then configured the parameters: requiring a minimum interaction score of 0.150 (regarded as low confidence), using network edges to represent evidence, limiting the display to 50 interactors in the primary shell, and only considering experimental sources for interactions. As a result, we identified 23 proteins that were experimentally identified and found to bind to KIF13A, referred to as group A.\u003c/p\u003e\u003cp\u003e\u0026zwnj; Then, We access the GEPIA2 Similar Gene Detection platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn/#similar\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn/#similar\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to retrieve the top 100 target genes that exhibit expression correlation with KIF13A (designated as group B), based on comprehensive TCGA datasets encompassing both tumor and normal tissues.\u003c/p\u003e\u003cp\u003eAdditionally, pairwise gene Pearson correlation analysis between KIF13A and pre-selected genes (GTF2H1, CLIP1, MIB1, CDYL, KDM1B, ZFP91) will be \u0026zwnj;carried out\u0026zwnj; via the GEPIA2 correlation analysis tool. Scatter plots\u0026zwnj; will be generated using Log2 TPM values to \u0026zwnj;visually represent\u0026zwnj; correlations between KIF13A and target genes, \u0026zwnj;annotated with\u0026zwnj; p-values and Pearson\u0026rsquo;s R coefficients.\u003c/p\u003e\u003cp\u003eFurthermore, we accessed TIMER2.0_Gene_Corr module (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and entered KIF13A, along with the genes we selected (GTF2H1, CLIP1, MIB1, CDYL, KDM1B, ZFP91), into the \"Interested Gene\" and \"Gene Expression\" fields. This procedure was implemented\u0026zwnj; to produce heatmap encompassing partial correlation coefficients (cor) and p-values derived from purity-corrected Spearman\u0026rsquo;s rank correlation analyses for the selected genes.\u003c/p\u003e\u003cp\u003eIn the end, we merged the two groups into a single list for performing KEGG and GO analysis. The enriched pathways were then visualized using R packages such as ggpubr, clusterProfiler, dplyr, tidyr, and ggplot2. The analysis was carried out using R version 4.4.1. A p-value below 0.05 in a two-tailed test was considered significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Gene expression analysis\u003c/h2\u003e\u003cp\u003eHere, this study focused on investigating\u0026zwnj; the tumor-promoting function of human KIF13A (mRNA: NM_022113.6; protein: NP_071396.4), \u0026zwnj;as illustrated in\u0026zwnj; Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea. The protein structure of KIF13A, as illustrated in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb, is conserved among different species and is primarily composed of seven domains, including Motor_domain (cl00286), FHA (cl00062), KIF1B (pfam12423), DUF3694 (pfam12473), DUF3585 (pfam12130), GAP_GLY3694 (pfam01302), and PEPcase (cl4656) domain[4]. The phylogenetic relationships\u0026zwnj; of KIF13A protein across species \u0026zwnj;are presented in Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u0026zwnj;, illustrating evolutionary divergence patterns.\u003c/p\u003e\u003cp\u003eTo investigate the tissue specificity of KIF13A expression, we accessed the HPA (Human Protein Atlas) website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/humanproteome/pathology\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/humanproteome/pathology\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to analyze its expression levels in non-tumor tissues using the Consensus dataset of HPA / GTEx / FANTOM5. The findings revealed that KIF13A exhibited lower RNA tissue specificity, suggesting that it has a ubiquitous expression profile. The highest expression levels were observed in skeletal muscle, followed by the tongue, heart muscle, and urinary bladder (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003ea). Additionally, a low level of RNA-based discrimination among blood cell types does not indicate specificity when examining KIF13A expression in various blood cells from the HPA/Monaco/Schmiedel datasets (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eb), with Neutrophils displaying the highest levels of KIF13A expression. However, our analysis using mass spectrometry and immunoassays in Peptide Atlas did not detect KIF13A in plasma, indicating a lack of physiological outward leakage of the intracellular KIF13A protein (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003ec and Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003ed).\u003c/p\u003e\u003cp\u003eNext, we accessed the TIMER2_Gene_DE module to examine KIF13A expression across different cancer types in TCGA database. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, KIF13A expression is generally suppressed in tumor tissues compared to their normal counterparts. This trend is observed in various cancers such as BLCA, BRCA, KICH, KIRP, LUAD (Lung Adenocarcinoma), READ, THCA, UCEC, COAD, KICR, and LUSC (Wilcoxon test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In contrast, the expression pattern of KIF13A is higher than the tumor tissues in CHOL, HNSC, and LIHC compared to normal tissue (Wilcoxon test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The research findings indicate that KIF13A \u0026zwnj;acts as a tumor suppressor in most of tumors, while functioning as a tumor promoter in others.\u003c/p\u003e\u003cp\u003eAs the TIMER2 database does not contain normal tissue data for all tumors, we utilized the GEPIA2_boxplot tool, which includes normal tissue samples from the GTEx dataset serving as controls, to reanalyze the expression status of KIF13A across different cancer types. The findings indicate that the expression status of KIF13A is generally lower in most tumors, such as ACC, BLCA, UCEC, and UCS, compared to normal tissue. Nevertheless, the expression status of KIF13A is elevated in tumor tissues of CHOL, LGG, and PAAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, Wilcoxon test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For the remaining tumors, the difference in expression level between normal and tumor tissue is not significant (Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003eTo further validate the research results, we logged into the GENT2 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gent2.appex.kr/gent2/\u003c/span\u003e\u003cspan address=\"http://gent2.appex.kr/gent2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to re-analyze the expression status of KIF13A. The research results also indicated lower expression of KIF13A in the majority of cancer tissues (two-sample t-test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, the Ualcan_CPTAC dataset also showed decreased levels of total KIF13A protein in cancer tissues of ccRCC., UCEC, OV, COAD, LIHC, LUAD, and LUSC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, Wilcoxon test). In BRCA, there was a decrease in the expression of total KIF13A protein in the cancer tissues compared to normal tissues, although it was not statistically significant. On the other hand, the Ualcan_CPTAC dataset revealed elevated levels of total KIF13A protein in cancer tissues of HNSC, PAAD, and GBM (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, Wilcoxon test).\u003c/p\u003e\u003cp\u003eThe research results from the four databases mentioned above are generally consistent. Specifically, the expression pattern of KIF13A in most tumor tissues is lower than in normal tissues. This finding also aligns with previous research results[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUltimately, we also utilized the \"Pathological Stage Plot\" tool of GEPIA2 to examine the association between KIF13A expression and the pathological stages of various cancers, including OV, COAD, SKCM, and KIRC.The significant difference among the pathological stages of tumor is evident (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, Wilcoxon test, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but it is not significant for the remaining cancers (Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eb).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Survival analysis\u003c/h2\u003e\u003cp\u003eWe stratified cancer patients into high and low KIF13A expression groups and analyzed the link between KIF13A expression pattern and patient outcomes across multiple tumor types in the TCGA dataset. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, high levels of KIF13A expression were associated with poor overall survival (OS) for KICH (p\u0026thinsp;=\u0026thinsp;0.047) and LIHC (p\u0026thinsp;=\u0026thinsp;0.0017), while they were linked to good OS for KIRC (p\u0026thinsp;=\u0026thinsp;0.00019), LGG (p\u0026thinsp;=\u0026thinsp;3.5e-06), and READ (p\u0026thinsp;=\u0026thinsp;0.031). Additionally, high levels of KIF13A expression were associated with poor Disease-free survival (DFS) for COAD (p\u0026thinsp;=\u0026thinsp;0.028) and LGG (p\u0026thinsp;=\u0026thinsp;0.00045), whereas they were linked to good Disease-free survival (DFS) for KICH (p\u0026thinsp;=\u0026thinsp;0.071) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Table \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e). The correlation was not significant for the rest of the cancers .\u003c/p\u003e\u003cp\u003eTo further investigate the association between KIF13A expression pattern and prognosis, we accessed the gene chip data from the Kaplan-Meier Plotter website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://kmplot.com/analysis/index.php?p=background\u003c/span\u003e\u003cspan address=\"http://kmplot.com/analysis/index.php?p=background\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The results revealed that high levels of KIF13A expression are correlated with favorable overall survival (OS) (p\u0026thinsp;=\u0026thinsp;0.00018 and p\u0026thinsp;=\u0026thinsp;8.9e-05), distant metastasis-free survival (DMFS) (p\u0026thinsp;=\u0026thinsp;0.00126 and p\u0026thinsp;=\u0026thinsp;5e-04), relapse-free survival (RFS) (p\u0026thinsp;=\u0026thinsp;1.8e-09 and p\u0026thinsp;=\u0026thinsp;4.8e-11), and post-progression survival (PPS) (p\u0026thinsp;=\u0026thinsp;0.041 and p\u0026thinsp;=\u0026thinsp;0.031) in patients with BRCA and BRCA HER2-negative (Figure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003ea and S6c). In the case of lung cancer, high KIF13A expression is correlated with better OS (p\u0026thinsp;=\u0026thinsp;4.4E-14), progression-free survival (FP) (p\u0026thinsp;=\u0026thinsp;3.1e-06), and PPS (p\u0026thinsp;=\u0026thinsp;1.7e-05) (Figure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eg). Similarly, in gastric cancer, high expression of KIF13A is linked to improved OS (p\u0026thinsp;=\u0026thinsp;0.0025) and FP (p\u0026thinsp;=\u0026thinsp;0.0062) (Figure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eh). Notably, in ovarian cancer, high KIF13A expression is associated with worse outcomes in terms of OS (p\u0026thinsp;=\u0026thinsp;0.005), PFS (p\u0026thinsp;=\u0026thinsp;0.00028), and PPS (p\u0026thinsp;=\u0026thinsp;0.026) (Figure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003ee). Importantly, no substantial association was detected between KIF13A levels and survival outcomes across other cancer types. Additionally, subgroup analyses stratified by clinical factors in breast, ovarian, liver, lung, and gastric cancers yielded context-specific associations (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-\u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). The findings suggest that the impact of KIF13A expression on prognosis differs across different subtypes of tumors.\u003c/p\u003e\u003cp\u003eUltimately, we accessed the RNA-seq_Pan-cancer database of Kaplan-Meier tool to assess the association between OS/RFS and KIF13A expression. The findings revealed that high levels of KIF13A expression are linked to favorable OS and RFS in BRCA (OS, p\u0026thinsp;=\u0026thinsp;0.0022; RFS, p\u0026thinsp;=\u0026thinsp;0.0022), EAC (OS, p\u0026thinsp;=\u0026thinsp;0.042; RFS, p\u0026thinsp;=\u0026thinsp;0.045), and OV (OS, P\u0026thinsp;=\u0026thinsp;0.0057; RFS, P\u0026thinsp;=\u0026thinsp;0.0039). On the other hand, increased levels of KIF13A expression are associated with unfavorable OS and RFS in KIRP (OS, p\u0026thinsp;=\u0026thinsp;0.0093; RFS, p\u0026thinsp;=\u0026thinsp;0.016), LIHC (OS, p\u0026thinsp;=\u0026thinsp;0.0063; RFS, P\u0026thinsp;=\u0026thinsp;0.04) (Figure \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003ea and S7b, Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e). Furthermore, heightened KIF13A expression is only related to positive OS in READ (p\u0026thinsp;=\u0026thinsp;0.0035), KIRC (p\u0026thinsp;=\u0026thinsp;5.1e-7), and STAD (p\u0026thinsp;=\u0026thinsp;0.022), while it is connected to adverse OS in BLCA (p\u0026thinsp;=\u0026thinsp;0.039) and THYM (p\u0026thinsp;=\u0026thinsp;0.02). Increased levels of KIF13A expression are linked to favorable RFS in LUAD (p\u0026thinsp;=\u0026thinsp;0.0032) and TGCT ( p\u0026thinsp;=\u0026thinsp;0.024), but associated with poor RFS in ESSC (p\u0026thinsp;=\u0026thinsp;0.028) (Fig \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003ea and S7b, Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCombined, the research results suggested that a high expression pattern of KIF13A is associated with a positive prognosis for the majority of cancers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Genetic alteration analysis\u003c/h2\u003e\u003cp\u003eWe examined the mutational landscape of KIF13A across diverse TCGA tumor datasets. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, SKCM displayed the most frequent KIF13A alterations (\u0026gt;\u0026thinsp;9.23%), where mutations predominated, followed by UCEC (7.75%). CNA was the most common type of amplification in OV cases, with an alteration frequency of approximately 7.53% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), followed by BLCA at 5.84%. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, deep deletions constituted the predominant genetic alteration in MESO (1.15%), with STAD ranking second at 1.14%. Additionally, the spectrum of KIF13A-associated genetic variants is comprehensively mapped in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb. It was found that missense mutations predominated among genetic alterations. Frameshift mutations in KIF13A were predominantly triggered by the V1116Sfs*23/4 alteration, particularly in STAD (n\u0026thinsp;=\u0026thinsp;6) compared to EAC (n\u0026thinsp;=\u0026thinsp;1) and COAD (n\u0026thinsp;=\u0026thinsp;2) cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). This results in the translation from Valine (V) to Serine (S) at the 1116 site of the KIF13A, ultimately leading to truncation of the KIF13A protein.\u003c/p\u003e\u003cp\u003eAdditionally, we examined the possible link between genetic changes in KIF13A and the survival outcomes of various cancer types. The findings showed that cases of UCEC with altered KIF13A had a more favorable prognosis (OS, p\u0026thinsp;=\u0026thinsp;0.0278; DSS, p\u0026thinsp;=\u0026thinsp;0.0231; DFS, p\u0026thinsp;=\u0026thinsp;0.0129; PFS, p\u0026thinsp;=\u0026thinsp;2.363e-3) compared to UCEC cases without KIF13A alterations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). However, this correlation was not statistically significant for other type of cancers.\u003c/p\u003e\u003cp\u003eNext, we analyzed the correlation between KIF13A mRNA abundance and tumor mutational landscape parameters (TMB/MSI) across 33 TCGA cancer types. The results revealed a positive correlation between KIF13A expression and TMB for COAD (P\u0026thinsp;=\u0026thinsp;0.026), but a negative correlated with TMB for LUAD (P\u0026thinsp;=\u0026thinsp;0.0026) and THCA (P\u0026thinsp;=\u0026thinsp;0.019) (Figure \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e). In the meantime, a positive relationship exists between KIF13A expression and MSI in LUSC (P\u0026thinsp;=\u0026thinsp;0.0015), OV (P\u0026thinsp;=\u0026thinsp;0.011), COAD (P\u0026thinsp;=\u0026thinsp;6.2e-05), READ (P\u0026thinsp;=\u0026thinsp;8.4e-10), LMAL (Acute Myeloid Leukemia, P\u0026thinsp;=\u0026thinsp;0.046), and STAD (P\u0026thinsp;=\u0026thinsp;0.0015), but negatively correlated with MSI in PRAD (P\u0026thinsp;=\u0026thinsp;4.9e-08) and HNSC (P\u0026thinsp;=\u0026thinsp;0.0042) (Figure \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 KIF13A methylation analysis\u003c/h2\u003e\u003cp\u003eDNMIVD was employed to \u0026zwnj;evaluate whether\u0026zwnj; KIF13A methylation contributes to pathogenesis in diverse tumors. Analysis of differentially methylated genes (DMG) of KIF13A in pan-cancer studies revealed significant differences in methylation levels between adjacent and tumor tissues in the promoter region of KIF13A for BRCA (P\u0026thinsp;=\u0026thinsp;0.00107), HNSC (P\u0026thinsp;=\u0026thinsp;0.029), KIRP (P\u0026thinsp;=\u0026thinsp;0.00037), and PAAD (P\u0026thinsp;=\u0026thinsp;0.0004) (Figure \u003cspan refid=\"MOESM10\" class=\"InternalRef\"\u003eS10\u003c/span\u003e). Additionally, A significant inverse correlation\u0026zwnj; was observed between KIF13A methylation and its expression in BRCA (P\u0026thinsp;=\u0026thinsp;0.00048824), HNSC (P\u0026thinsp;=\u0026thinsp;0.00132566), KIRP (P\u0026thinsp;=\u0026thinsp;0.000220667), LIHC (P\u0026thinsp;=\u0026thinsp;0.0167125), SARC (Sarcoma, P\u0026thinsp;=\u0026thinsp;0.0182044), SKCM (P\u0026thinsp;=\u0026thinsp;0.0000014301), and STAD (P\u0026thinsp;=\u0026thinsp;0.0072986), but a positive correlation in COAD (P\u0026thinsp;=\u0026thinsp;0.0452013) and THCA (P\u0026thinsp;=\u0026thinsp;0.0300103) (Figure \u003cspan refid=\"MOESM11\" class=\"InternalRef\"\u003eS11\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Protein phosphorylation analysis\u003c/h2\u003e\u003cp\u003e\u0026zwnj;Utilizing the CPTAC database, we assessed differential phosphorylation status of KIF13A (NP_017396.4) in primary tumor versus matched normal tissues spanning diverse malignancies.\u0026zwnj; As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg, the phosphoproteomic analysis revealed significant changes in KIF13A phosphorylation at specific residues. In ccRCC, Primary tumors exhibit attenuated phosphorylation at the S1529 (p\u0026thinsp;=\u0026thinsp;1.02E-06) residue compared to matched normal renal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). In LUAD, primary tumors exhibit \u0026zwnj;divergent phosphorylation patterns\u0026zwnj;: elevated at S1529 ((p\u0026thinsp;=\u0026thinsp;2.58E-08) yet reduced at S363 (p\u0026thinsp;=\u0026thinsp;4.47E-04) and S1283 (p\u0026thinsp;=\u0026thinsp;5.71E-29) versus normal tissues. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). For LUSC, primary tumors manifest \u0026zwnj;significant hyperphosphorylation at S1529\u0026zwnj; (p\u0026thinsp;=\u0026thinsp;3.38E-07) relative to matched normal bronchial epithelia (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). In HNSC, Primary tumors \u0026zwnj;exhibit significantly elevated phosphorylation\u0026zwnj; at S636 relative to normal counterparts (p\u0026thinsp;=\u0026thinsp;1.78E-19), T1285 (p\u0026thinsp;=\u0026thinsp;2.32E-03), T1447 (p\u0026thinsp;=\u0026thinsp;2.45E-06), S1682 (p\u0026thinsp;=\u0026thinsp;2.67E-04), S1698 (p\u0026thinsp;=\u0026thinsp;6.64E-05), and S1700 (p\u0026thinsp;=\u0026thinsp;2.32E-03) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). In HCC (hepatocellular carcinoma), primary tumors exhibit \u0026zwnj;hyperphosphorylation\u0026zwnj; at both site S1529 (p\u0026thinsp;=\u0026thinsp;9.22E-07) and S1698 (p\u0026thinsp;=\u0026thinsp;3.06E-16) compared to adjacent normal liver tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). For PAAD, primary tumors demonstrate \u0026zwnj;significant hypophosphorylation\u0026zwnj; at S1529 (p\u0026thinsp;=\u0026thinsp;1.28E-02) compared to normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef). Overall, KIF13A (NP_017396.4) exhibits \u0026zwnj;tumor-specific phosphorylation heterogeneity\u0026zwnj; across clinical specimens, with divergent patterns between normal and malignant tissues.\u003c/p\u003e\u003cp\u003eLeveraging the PhosphoNET knowledgebase, we performed \u0026zwnj;functional annotation\u0026zwnj; of CPTAC-identified phosphosites on KIF13A (NP_017396.4). This revealed S1698 as a \u0026zwnj;conserved regulatory hub\u0026zwnj; coordinating early pluripotency exit in human embryonic stem cells (hESCs) [25, 26]. This stem cell-related phosphomotif warrants \u0026zwnj;functional validation in carcinogenic contexts\u0026zwnj;, particularly assessing its \u0026zwnj;crosstalk with oncogenic kinases\u0026zwnj; (e.g., AKT/PKM2) during EMT progression.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Immune infiltration analysis\u003c/h2\u003e\u003cp\u003eAs critical constituents of the tumor microenvironment (TME), tumor-infiltrating immune cells mechanistically contribute to carcinogenesis, malignant progression, and metastatic dissemination. Here, employing seven algorithms (TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, XCELL, MCPCOUNTER, EPIC), this investigation systematically profiled tumor-infiltrating immune cell subsets and evaluated their associations with KIF13A expression across TCGA malignancies. We identified a significant inverse association between CD8\u0026thinsp;+\u0026thinsp;T-lymphocyte infiltration density and KIF13A transcript abundance across HNSC, HNSC subtypes (HNSC-HPV-, HNSC-HPV+) and LUSC malignancies (Figure \u003cspan refid=\"MOESM12\" class=\"InternalRef\"\u003eS12\u003c/span\u003e). Conversely, Cancer-associated fibroblasts (CAFs) infiltration density demonstrates significant positive covariation with KIF13A transcriptional activity in LUAD, PAAD, and STAD tumor ecosystems (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The scatterplot data for these tumors created using the XCELL algorithm demonstrates concordant positive correlations between KIF13A expression and cancer-associated fibroblast (CAF) infiltration densities in STAD, as mechanistically illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Rho\u0026thinsp;=\u0026thinsp;0.162, p\u0026thinsp;=\u0026thinsp;1.53e-03). These findings demonstrate context-dependent regulatory dynamics between KIF13A transcriptional activity and heterogeneous tumor-immune microenvironmental constituents.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Enrichment analysis of KIF13A-related partners\u003c/h2\u003e\u003cp\u003eTo delineate KIF13A's oncogenic mechanisms, we employed STRING (v12.0) and GEPIA2 databases to define: (i) Group A: 23 high-confidence KIF13A interactors(Fig.\u0026nbsp;6a), and (ii) Group B: 100 top co-expressed genes. .Subsequent multi-layered functional enrichment analyses were performed. The scatter plot demonstrates positive regulation between KIF13A and CLIP1 (R\u0026thinsp;=\u0026thinsp;0.45), GTF2H1 (R\u0026thinsp;=\u0026thinsp;0.47), MIB1 (R\u0026thinsp;=\u0026thinsp;0.48), KDM1B (R\u0026thinsp;=\u0026thinsp;0.53), ZFP91 (R\u0026thinsp;=\u0026thinsp;0.52) and CDYL (R\u0026thinsp;=\u0026thinsp;0.52), with all R values above 0.45 and p values below 0.001. The corresponding heatmap also shows a positive correlation between KIF13A and aforementioned six genes across different cancer types (Fig.\u0026nbsp;6c). Subsequently, we combined the data from both groups to conduct functional enrichment analysis. KEGG results suggest that the \"Lysosome\" pathway may be participate in the effect of KIF13A on tumor pathogenesis (Fig.\u0026nbsp;6d). Furthermore, the GO enrichment analysis data indicates that most of these genes are associated with vesicle organization (BP, Biological Process), Golgi apparatus subcompartment (CC, Cellular Component) (Fig.\u0026nbsp;6e).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eEvidence from multiple model organisms establishes KIF13A as a pleiotropic molecular motor involved in cell division[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], tubular endosome formation[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], chemokine activity, and influencing tumor occurrence and development through the mTORC1-KIF13A-M6PR pathway[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough KIF13A dysregulation correlates with tumorigenesis across multiple cancers[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], elucidation of whether conserved molecular pathways mediate its pro-oncogenic functions requires systematic investigation. To address this gap, we conducted a comprehensive analysis of KIF13A gene expression, genetic alterations, DNA methylation, and protein phosphorylation in different tumors of TCGA and GEO databases.\u003c/p\u003e\u003cp\u003eUtilizing the GEPIA2 platform, we assessed the prognostic relevance of KIF13A expression levels across human malignancies. The results showed that elevated KIF13A expression portends worse clinical outcomes in hepatocellular carcinoma (LIHC) patients (OS, p\u0026thinsp;=\u0026thinsp;0.0017; RFS, p\u0026thinsp;=\u0026thinsp;0.31) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Subsequently, Kaplan-Meier plotter's pan-cancer module was leveraged to conduct focused survival analysis evaluating KIF13A-associated prognostic outcomes in LIHC cohorts. The results also showed elevated KIF13A levels portended significantly poorer survival outcomes (OS, p\u0026thinsp;=\u0026thinsp;0.0006; RFS, p\u0026thinsp;=\u0026thinsp;0.0405) (Figure \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e, Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e). However, when analyzing the correlation between KIF13A expression and survival outcome in LIHC using the RNA-seq module of the Kaplan-Meier plotter, the results showed that the correlation was not significant. Therefore, Current clinical evidence fails to substantiate KIF13A as a prognostic biomarker in specified malignancies., such as LIHC. This discrepancy could be due to differences in data processing methods or the lack of access to updated survival information.\u003c/p\u003e\u003cp\u003eFor BRCA, GEPIA2 pan-cancer analytics demonstrated no statistically significant prognostic association for KIF13A expression (OS, p\u0026thinsp;=\u0026thinsp;0.89; RFS, p\u0026thinsp;=\u0026thinsp;0.062) (Table \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e). Contrasting with GEPIA2 findings, Kaplan-Meier plotter's RNA-seq pan-cancer analysis demonstrated KIF13A expression is significantly correlated with prognosis (RFS, p\u0026thinsp;=\u0026thinsp;0.0022) (Fig \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e, Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e). Furthermore, the Kaplan-Meier plotter_RNA-chip_breast cancer module analysis yielded the same result (OS, p\u0026thinsp;=\u0026thinsp;0.00018; RFS, p\u0026thinsp;=\u0026thinsp;1.8e-9; DMFS, p\u0026thinsp;=\u0026thinsp;0.013; PPS, p\u0026thinsp;=\u0026thinsp;0.041). Additionally, KIF13A expression in HER2-negative BRCA displayed a more significant correlation with prognosis (OS, p\u0026thinsp;=\u0026thinsp;8.9e-5; RFS, p\u0026thinsp;=\u0026thinsp;4.8e-11; DMFS, p\u0026thinsp;=\u0026thinsp;0.0005; PPS, p\u0026thinsp;=\u0026thinsp;0.0314) (Figure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003ec). Therefore, current clinical evidence successfully establishes that KIF13A can serve as a prognostic biomarker in specific malignancies (e.g., breast cancer).\u003c/p\u003e\u003cp\u003eCollectively, this study delineates KIF13A's oncogenic properties through multi-dimensional validation of its tumorigenic linkages, and provide a mechanistic scaffold for deconvoluting KIF13A-driven oncogenic cascades, delineating its pathophysiological hierarchy in malignant progression.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Doctoral Scientific Research Startup Fund of the Affiliated Hospital of Southwest Medical University(ID: 16226), Southwest Medical University Research Grant (ID: 2017-ZRQN-015) and Undergraduate Innovation Training Program Project of Southwest Medical University (ID: 202410632042 ).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJizhang Chen and Shangke Huang performed the data analyses and wrote the original draft, which was then carefully revised by Yongxia Cui.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors affirm that they do not have any competing financial interests or personal relationships that could have influenced the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eclinical trial number:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not require ethics committee review, as it utilized publicly available genomic datasets without direct involvement of human subjects or animal experimentation. Accordingly, no informed consent procedures were necessary for this secondary data analysis. Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets presented in this study are available in online repositories or can be obtained from the corresponding author upon request. The names of the repositories and their links are provided in the article and Supplementary materials and methods.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTERUNAGA NAKAGAWA YT, EIJI MATSUOKA, SATORU KONDO, YASUSHI OKADA, YASUKO NODA, YOSHIMITSU KANAI. AND NOBUTAKA HIROKAWA, Identification and classification of 16 new kinesin superfamily (KIF) proteins in mouse genome, Proc. Natl. Acad. Sci. USA, 94 (1997) 9654\u0026ndash;9659.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTerunaga Nakagawa MS, Seog D-H, Ogasawara K, Dohmae N, Takio K, Hirokawa N, Novel Motor A. KIF13A, Transports Mannose-6- Phosphate Receptor to Plasma Membrane through Direct Interaction with AP-1 Complex, cell, 103 (2000) 569\u0026ndash;581.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJamain S, Quach H, Fellous M, Bourgeron T. Identification of the human KIF13A gene homologous to Drosophila kinesin-73 and candidate for schizophrenia. Genomics. 2001;74:36\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePan D, Fang X, Li J. Identification of a Novel Gene Signature Based on Kinesin Family Members to Predict Prognosis in Glioma. Medicina; 2023. p. 59.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSiddiqui SA. The Kinesin-3 Family: Long-Distance Transporters. In: Friel CT, editor. The Kinesin Superfamily Handbook: Transporter, Creator, Destroyer. Abingdon (UK): CRC; 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThankachan JM, Setty SRG. KIF13A-A Key Regulator of Recycling Endosome Dynamics. Front Cell Dev Biol. 2022;10:877532.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePatel NM, Siva MSA, Kumari R, Shewale DJ, Rai A, Ritt M et al. KIF13A Motors Are Regulated by Rab22A to Function as Weak Dimers inside the Cell. Sci Adv, 7 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShakya S, Sharma P, Bhatt AM, Jani RA, Delevoye C, Setty SR. Rab22A recruits BLOC-1 and BLOC-2 to promote the biogenesis of recycling endosomes. EMBO Rep, 19 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRamos-Nascimento A, Kellen B, Ferreira F, Alenquer M, Vale-Costa S, Raposo G, Delevoye C, Amorim MJ. KIF13A mediates trafficking of influenza A virus ribonucleoproteins. J Cell Sci. 2017;130:4038\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFehling SK, Noda T, Maisner A, Lamp B, Conzelmann KK, Kawaoka Y, Klenk HD, Garten W, Strecker T. The microtubule motor protein KIF13A is involved in intracellular trafficking of the Lassa virus matrix protein Z. Cell Microbiol. 2013;15:315\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGong X, Didan Y, Lock JG, Stromblad S. KIF13A-regulated RhoB plasma membrane localization governs membrane blebbing and blebby amoeboid cell migration. EMBO J, 37 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSagona AP, Nezis IP, Pedersen NM, Liestol K, Poulton J, Rusten TE, Skotheim RI, Raiborg C, Stenmark H. PtdIns(3)P controls cytokinesis through KIF13A-mediated recruitment of FYVE-CENT to the midbody. 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Cell Discov. 2017;3:17011.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pan-cancer, KIF13A, Survival prognosis, Immune infiltration, Genetic variation","lastPublishedDoi":"10.21203/rs.3.rs-7237847/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7237847/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite the increasing evidence supporting the association between KIF13A and cancer, pan-cancer analysis is currently limited. Therefore, we aimed to investigate the potential for KIF13A to contribute to oncogenesis in thirty-three different tumors using publicly accessible databases. Our research findings indicate that KIF13A has lower RNA tissue specificity and exhibits lower levels of expression compared to healthy tissue. However, we discovered distinct associations between KIF13A expression and the outcome of diverse tumor types. Genetic variation analysis revealed that cases of UCEC with genetic alterations in KIF13A exhibited a better prognosis compared to cases without genetic alterations in KIF13A. Analysis of immune infiltration revealed an inverse association between KIF13A expression and CD8\u0026thinsp;+\u0026thinsp;T-cell infiltration levels in HNSC, HNSC-HPV-, HNSC-HPV+, and LUSC, \u0026zwnj;but correlated positively with the abundance of cancer-associated fibroblasts in LUAD, PAAD, and STAD. Furthermore, we observed differences in KIF13A (NP_017396.4) phosphorylation levels between normal tissues and primary tumor tissues at different phosphorylation sites across various tumor cases. Specifically, we noted an increased phosphorylation level of KIF13A at the S1698 site in HNSC and HCC, correlating with the early differentiation of human embryonic stem cells. In conclusion, this pioneering pan-cancer study offers thorough comprehension of the role of KIF13A in various cancers.\u003c/p\u003e","manuscriptTitle":"A pan-cancer analysis of the oncogenic role of KIF13A in human tumors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 08:50:24","doi":"10.21203/rs.3.rs-7237847/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-07T10:53:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-20T09:58:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-31T05:03:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-21T21:19:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108322304353785293750557238112196582965","date":"2025-08-21T13:03:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53967920953419536370668947485363381323","date":"2025-08-19T12:43:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11280525211355336339591076644681225236","date":"2025-08-19T11:44:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-19T09:15:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-19T08:36:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-19T07:59:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-15T03:08:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-08-15T03:04:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"28f5b949-acf5-408a-aacd-797fac905b2c","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-17T17:24:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 08:50:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7237847","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7237847","identity":"rs-7237847","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}