TIMM9 expression is significantly elevated in cancerous tissuesWe used TIMER 2.0 to investigate the differential RNA expression of TIMM9 between cancer tissues and normal tissues. Our analysis revealed significant overexpression of TIMM9 in 13 different types of cancers (Fig. 1A), including CHOL, COAD, ESCA, GBM, HNSC, KICH, KIRC, LIHC, LUAD, LUSC, PRAD, READ and STAD. However, TIMM9 was significantly down-regulated in BRCA, KICH, THCA, and UCEC. The box plot generated by TNMplot illustrates TIMM9 expression levels in various normal and tumor tissues across multiple cancer types. TIMM9 expression is consistently upregulated in various tumors compared to corresponding normal tissues, as analyzed by the TNMplot database (Fig. S1). Experimental data from RT-qPCR trials further demonstrated that TIMM9 expression in tumor cell lines was higher than in corresponding normal epithelial cells in lung cancer and liver cancer (Fig. 1B), indicating that the overexpression of TIMM9 is a common phenomenon in tumorigenesis. Using the GSCA database, we found that TIMM9 was upregulated in specific pathological stages of cancer, such as between Stage I and Stage IV in HNSC, Stage I and Stage II in LIHC, Stage I and Stage III in LUSC, and Stage II and Stage III in LUSC (Fig. 1C). Compared to primary tumors, TNMplot database analysis revealed that TIMM9 was overexpressed in metastatic states of colon cancer, liver cancer, lung cancer and skin cancer (Fig. 1D).Fig. 1TIMM9 is overexpressed in various types of cancer. (A) Differential RNA expressions of TIMM9 in TCGA, analyzed by TIMER 2.0 database. (B) RT-qPCR assays detected differential expressions of TIMM9 between tumor cell lines (lung: PC9, NCI-H460, NCI-H1299; liver: HepG2) and normal epithelial cells (lung: BEAS-2B, liver: LO2). (C) RNA expression levels of TIMM9 in pathological stages. (D) Differential RNA expressions of TIMM9 among normal, tumor and metastatic groups. (E) Differential expressions of TIMM9 in proteomics levels. *, **, ***, **** correspond to p < 0.05, p < 0.01, p < 0.001, and p < 0.0001 respectively.The DepMap Portal analysis elucidated a significant positive correlation between TIMM9 gene expression and protein levels (p = 3.08E−8, Fig. S13B). TIMM9 was found to be upregulated at the protein level in colon cancer, ovarian cancer, UCEC, lung adenocarcinoma, lung squamous cell carcinoma, and hepatocellular carcinoma, while it was downregulated in breast cancer, clear cell renal cell carcinoma, glioblastoma multiforme, head and neck squamous carcinoma, and pancreatic adenocarcinoma (Fig. 1E and Fig. S2). Immunohistochemical analysis from the HPA database revealed that TIMM9 protein levels are significantly upregulated in various cancers, including lung cancer, liver cancer, colorectal cancer, ovarian cancer, prostate cancer, and endometrial cancer (Figs. S3, S4).TIMM9 is overexpressed in cancer cells within tumor tissuesDiagrams were obtained from the TISIDB database to visualize the expression localization of TIMM9 (Fig. 2A). TIMM9 showed a higher expression level in malignant cell clusters compared to other adjacent cell clusters in CESC, KIRC, CHOL, LIHC, ESCA, NSCLC, LSCC and PRAD. Additionally, cell differentiation trajectories in the CellTracer database demonstrated that TIMM9 maintained a high expression level in pseudo-times of malignant cells (Fig. 2B), Indicating that the overexpression of TIMM9 may influence tumor differentiation and contribute to the formation of tumor heterogeneity across various cancer types.Fig. 2Differential expressions of TIMM9 at single-cell transcriptome levels. (A) The expression levels of TIMM9 among multiple types of cells in TME. (B) The expression levels of TIMM9 in pseudo-time trajectories of tumor cells differentiations. TME tumor microenvironment.High expression of TIMM9 indicates poor cancer prognosisWe used the GEPIA 2 database to investigate the correlation between TIMM9 expression and clinical outcomes by plotting Kaplan–Meier plots. Our analysis revealed a significant association between high expression of TIMM9 and poor overall survival (OS) in ACC, BLCA, HNSC, KICH, LIHC and LUAD and disease-free survival (PFS) in ACC, BLCA, KICH, KIRP, LIHC and LUAD (Fig. 3A,B). Interestingly, high TIMM9 expression was unexpectedly associated with a more favorable prognosis in the OS of KIRC. This trend, although not statistically significant, was similarly observed in the GSCA database (Table S1), indicating that the relationship between TIMM9 expression and prognosis is more complex and may vary depending on the cancer type. We then used the OncoLnc database to confirm these findings by generating Kaplan–Meier plots (Fig. 3C), which showed a relationship between high expression of TIMM9 and poor OS prognosis in BLCA, CHOL, EAC, HNSCC and LIHC. Altogether, in most cases, high expression of TIMM9 is associated with poor prognosis outcomes.Fig. 3Correlation analyses between expression of TIMM9 and prognosis of tumors. (A,B) Overall survival (A) and disease-free survival (B) were analyzed by GEPIA 2 database. (C) Overall survival of various cancers was analyzed by LOGpc database.
TIMM9 exhibits genomic instabilities in tumorsThe cBioPortal database analysis revealed that TIMM9 has genomic alterations in the majority of types of cancers (Fig. 4A). We analyzed the mutation landscape (Fig. 4C) and collected data on missense mutations (Table S2). The structure of the TIMM9–TIMM10 complex was obtained from the RCSB PDB database (pdb code: 7cgp, chain D: TIMM9, chain H: TIMM10). We performed in silico site-directed mutagenesis simulations using PyMOL 2.4.0 software (Fig. S5A) to obtain the structures after missense mutations. We calculated the binding energies of TIMM9 and TIMM10 using the PDBePISA platform and found that the binding energy of S49L was largely decreased (Table S2), indicating an improvement in binding capability (interface of TIMM9 with SER-49: − 20.0 kcal/mol; interface of TIMM9 with LEU-49: − 21.1 kcal/mol). S49L, a missense mutation, improves binding capability between TIMM9 and TIMM10. The 3D (Fig. 4B) and 2D (Fig. 4D) structures of WT and S49L were visualized. SER-49 only formed a van der Waals interaction with MET-65 of TIMM10, whereas LEU-49 formed one with ILE-61 and MET-65 of TIMM10. Independent Gradient Modeling (IGM) also confirmed that LEU-49 can form a stronger interaction with TIMM10 (Fig. 4E), indicating S49L may positively affects TIMM9-associated functions by forming a hydrophobic nucleus.Fig. 4TIMM9 exhibited genomic instabilities in tumors. (A) The visualization of genomic alterations in TIMM9. Red, blue, and green represent amplification, deep deletion, and mutation, respectively. (B) The site-directed mutagenesis simulation and structural exhibition of S49L missense mutation, in TIMM9. The original structure was obtained from PDB database (PDB code: 7cgp, green: TIMM9, cyan: TIMM10). (C) Landscape of concrete mutation sites in TIMM9 (green dots: missense mutations, brown dots: splice mutations). (D) 2-D visualization of molecular micro-environment of S49L. (E) Weak interactions around 49th residue of TIMM9 were analyzed using IGM method, the strength of interaction increases from green to blue. (F) CNV landscape of TIMM9 (Hete heterozygous, Homo homozygous, Amp amplification, Del deletion). (G) Correlation between CNV and expression of TIMM9 and TIMM10 in tumors. (H) Association between differentiation in CNV level and prognosis. (I) Differential methylation of TIMM9. **, ***, correspond to p < 0.01, p < 0.001, respectively. (J) Correlation between methylation and expression of TIMM9 and TIMM10 in tumors. (K) Association between methylation and prognosis. CNV copy number variation.A global exhibition of copy number variations (CNV) of TIMM9 was visualized by a pie chart (Fig. 4F). High CNV levels of TIMM9 and TIMM10 are associated with their high expression (Fig. 4G). Kaplan–Meier plots revealed that high CNV (amplification or deletion) of TIMM9 is significantly associated with poor prognosis in OS of KIRC, KIRP, LGG, MESO, PCPG, UCEC, and UCS (Fig. 4H), PFS of KIRC, KIRP, LGG, LIHC, MESO, READ, and UCEC (Fig. S5D), and DSS of KIRC, KIRP, LGG, MESO, and UCEC (Fig. S5E).The methylation of TIMM9 was also investigated and the methylation level of TIMM9 is significantly decreased in KIRP, LIHC and PRAD (Fig. 4I). Coherently, the SMART database analysis showed that TIMM9 methylation (CpG site: cg16020706) is significantly lower in tumor tissues compared to normal tissues in BLCA, BRCA, ESCA, HNSC, KIRC, KIRP, LIHC, LUSC, PAAD, PRAD, SARC, and UCEC (Fig. S6). Methylation levels of TIMM9 and TIMM10 are negatively correlated with their expression (Fig. 4J). We also found that low TIMM9 methylations is associated with poor prognosis in OS of BLCA, GBM, LAML, and LUAD (Fig. 4K), PFS of GBM, KIRP, PRAD and UCS (Fig. S5B), and DSS of BLCA and GBM (Fig. S5C).To summarize, the genomic instabilities of TIMM9, including missense mutations, CNV, and methylation, could serve as biomarkers for oncogenesis and tumor prognosis.
TIMM9 is strongly related with cancer-associated biological functionsGSEA enrichments were used to gain insight into biological functions associated with TIMM9. Critical signaling pathways, including “Cell cycle”, “DNA replication”, “Oxidative Phosphorylation”, “Nucleotide excision repair” and “PI3K-Akt signaling pathway” were enriched in cancers (Fig. 5A). TIMM9 expression was positively associated with “Cell Cycle”, “Cell Division”,and “DNA repair”, but was negatively associated with “Innate Immune Response” and “T Cell Mediated Immunity” across multiple cancer types in ssGSEA analysis (Fig. 5B). The heat maps generated by the CellTracer database showed that TIMM9 expression levels were strongly correlated with “Cell Cycle”, “DNA Damage”, “DNA Repair” and “invasion”, all along pseudo-times in single-cell sequencing (Fig. S7A). Scatter plots generated by the CellTracer database provided consistent results, showing that TIMM9 expression levels were positively correlated with “Cell Cycle”, “DNA Repair” and “Proliferation” phenotypes (Fig. S7B–D). DepMap Portal analysis demonstrated that a consistent negative correlation exists between TIMM9 knockout and cell survival across various cell lines, including melanoma, non-small cell lung cancer, hepatocellular carcinoma, and ovarian epithelial tumors (Table S3). The negative gene effect scores indicate that the absence of TIMM9 significantly impairs cell viability, suggesting its critical role in cancer cell survival. Similarly, mTOR knockout also impairs cell survival, indicating its essential role in supporting cancer cell viability (Fig. S13A). More detailed GSEA visualizations correlated TIMM9 with “Cell cycle”, “DNA replication”, “Ribosome”, “Oxidative phosphorylation”, “Respiratory electron transport”, “The citric acid cycle”, and “Metabolism of xenobiotics by cytochrome P450” (Figs. S8, S9), indicating that TIMM9 is positively associated with cell proliferation, ribosomes, and mitochondria. ssGSEA analyses clarified that TIMM9 mediates variations in glycolysis, oxidative phosphorylation, the TCA cycle and respiratory chain complex (Fig. S10). Enrichment analysis of TIMM9 expression-related genes from the DepMap Portal reveals significant involvement in various biological processes. Key processes include cell migration, cell–cell junction assembly, cell adhesion mediated by cadherin, DNA damage response, DNA repair, cell motility, angiogenesis, the G1/S transition of the mitotic cell cycle, and positive regulation of the G2/M transition of the mitotic cell cycle (Fig. S14, Table S4).Fig. 5Associations between phenotypes and mechanisms of cancers. (A) GSEA enrichments indicate association between TIMM9 and signaling pathways. (B) Associations between differential expression of TIMM9 and cancer-associated phenotypes. (C) RT-qPCR analysis of TIMM9 differential expression with or without 10 µM of OSI-027 (mTOR inhibitor). *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.The ssGSEA analysis indicates that TIMM9 expression levels are significantly upregulated in various cancers when the mTOR signaling pathway is elevated (Fig. S11). Specifically, higher TIMM9 expression was observed in BRCA, CRC, KICH, KIRC, LGG, LUAD, LUSC, NSCLC, and SKCM cohorts with elevated mTOR pathway activity. This suggests a strong correlation between TIMM9 expression and mTOR pathway activation across multiple cancer types. The UALCAN analysis reveals an interesting trend: TIMM9 expression levels in the mTOR Pathway-altered group consistently show greater deviation from the Normal group compared to the Others group (Fig. S12). This suggests that changes in TIMM9 expression are more pronounced with alterations in the mTOR pathway. The DepMap Portal analysis demonstrated a significant positive correlation between TIMM9 and mTOR in cell activity function. Our RT-qPCR assays demonstrated that OSI-027, an mTOR inhibitor, regulates TIMM9 expression in NCI-H1299 and NCI-H460 cell lines, suggesting that TIMM9 may support the findings from the above research (Fig. 5C).Overall, TIMM9 can be considered a biomarker for cancer-related biological functions and signaling pathway alterations.
TIMM9 is associated with tumor microenvironment (TME) variationTIMM9 was associated with variations in immune cell clusters (Fig. 6A), including BRCA, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD and UCEC (p < 0.001). Cells with a lymphocyte-depleted phenotype had higher expression of TIMM9 in BRCA, KIRC, KIRP, LIHC, PRAD and UCEC (Fig. 6B), suggesting a possible relationship between TIMM9 and lymphocytes exhaustion. TIMM9 expression was negatively correlated with infiltration levels of CD4+ effector memory cells (Fig. S15), but positively correlated with Macrophage M2 cells and Myeloid-derived suppressor cells (MDSC) (Fig. S16). Additionally, TIMM9 expression showed extensively negative correlations with immunostimulators at the expression level (Fig. 6C). TIMM9 expression was positively associated with immune-related genomic scores, such as MANTIS, Tumor Mutation Burden (TMB) and neoantigen loads (Fig. 6D–F).Fig. 6Analysis of TIMM9 participating in immune process. (A) Associations between TIMM9 expression and immune subtypes across various cancers. (B) Association between expression variation of TIMM9 and subtypes of cancers (C1 = wound healing; C2 = IFN-gamma dominant; C3 = inflammatory; C4 = lymphocyte depleted; C5 = immunologically quiet; C6 = TGF-b dominant). (C) Association between expression of TIMM9 and immunostimulators. (D–F) Relation between TMB (D), MANTIS (E) and Neoantigen Loads (F). *, **, *** represent p < 0.05, p < 0.01, and p < 0.001, respectively.TIMM9 expression in KIRC shows a distinctive correlation with immune cells. It is positively associated with immune-activating cells like NK, NKT, and MAIT, and negatively associated with immunosuppressive cells such as iTreg, nTreg, and Tr1 (Fig. S17). This pattern contrasts with other cancers, where TIMM9 does not show a similar correlation with immune activation and suppression. This unique interaction may explain the better prognosis seen in KIRC with high TIMM9 expression, differing from its role in other cancers like LUAD.Despite this, TIMM9 could also serve as a biomarker for good prognosis in overall survival (OS) and progression-free survival (PFS) of immunotherapies (Fig. S18A). TIMM9 expression levels were upregulated after the treatment with IFNγ, IFNβ, or TNFα in multiple types of mouse cancer cell lines (Fig. S18B), and upregulations of TIMM9 was associated with good responses to immunotherapies (Fig. S18C). The DRMref database analysis demonstrated that TIMM9 is upregulated after immunotherapy in Acute Lymphoblastic Leukemia (Table S5). Similarly, the ICBatlas database analysis revealed that TIMM9 expression is notably upregulated following anti-PD1 treatment in various cancers, particularly melanoma (Table S6). Additionally, there is a consistent trend of increased TIMM9 expression in several cancer types, although not all changes reached statistical significance. Furthermore, the TISIDB database analysis showed that TIMM9 expression is significantly higher in responders compared to non-responders in melanoma (Table S7).Altogether, TIMM9 is associated with variations in the TME and oncogenesis, and may could involve in associated biofunctions. TIMM9 could be seem as a biomarker in TME and immunotherapies.
TIMM9 affects drug sensitivities of medical treatmentsCorrelations between TIMM9 and drug sensitivities of chemotherapies or targeted therapies were calculated using the RNAactDrug platform. We found that high CNV of TIMM9 was positively correlated with drug sensitivities to Lestaurtinib (FDR = 5.93E−07), NG-25 (FDR = 4.90E−09), Nutlin-3a (−) (FDR = 1.08E−07), NVP-BHG712 (FDR = 6.29E−08), and TL-1-85 (FDR = 5.68E−09) (Fig. 7A), while the expression level of TIMM9 was negatively correlated with drug sensitivities to 5-Fluorouracil (5-FU) (FDR = 1.12E−07), and TAK-715 (FDR = 6.81E−09) (Fig. 7B). The methylation level of TIMM9 was positively correlated with drug sensitivities to Dabrafenib (FDR = 5.35E−09), PLX-4720 (FDR = 4.57E−12) and SB590885 (FDR = 3.79E−09) (Fig. 7C). Prognosis analyses showed positive associations between high expression of TIMM9 and poor outcomes in Gastric cancer treatments with 5-Fluorouracil, but with good outcomes in Ovarian cancer treatments with Platin (Fig. 7D). ROC diagrams showed that TIMM9 is related to pathological response, and boxplots exhibited an association between high TIMM9 expression and non-response to treatments (Fig. 7E). Additionally, GSCA platform analysis showed that both TIMM9 and TIMM10 were negatively correlated with drug sensitivities in most therapies (Fig. 7F). Our CCK8 assay also demonstrated that NCI-H1299 (highly expressed TIMM9) was insensitive to 5-FU compared to other cell lines with low TIMM9 expression (NCI-H460 and PC9) (Fig. 7G).Fig. 7Association between TIMM9 expression and therapeutic responses. (A–C) Association between between drug sensitivity and CNV (A), expression (B) and methylation (C). (D) Association between TIMM9 expression level and patients’ survival after chemotherapies. (E) ROC curves and boxplots indicating the expression of TIMM9 is associated with responses of chemotherapies. (F) Correlation between CTRP drug sensitivity and the mRNA expression variation of TIMM9 as well as TIMM10. (G) CCK8 experiment of cell activity after adding 5-FU (*** means the cell viability of NCI-H1299 is significantly higher than PC9 (p < 0.001), ### means the cell viability of NCI-H1299 is significantly higher than NCI-H460 (p < 0.001)). CNV copy number variations, ROC receiver operating characteristic.Lestaurtinib, NG-25, Nutlin-3a (−), NVP-BHG712, and TL-1-85 were selected as candidates to target the TIMM9–TIMM10 complex since their drug sensitivity was associated with high CNV in TIMM9. The druggability of the TIMM9–TIMM10 complex was evaluated using CavityPlus software, which identified two significant druggable pockets at the binding interface between TIMM9 and TIMM10 (Fig. S19, Table S8). The pocket with the highest rank of druggability was used for molecular simulations. Molecular docking was performed using Autodock Vina 1.1.2 and NVP-BHG712 showed the best docking affinity of − 11.2 kcal/mol (Fig. S20A, Table S9). The complex of TIMM9–TIMM10 binding with NVP-BHG712 was subjected to 50 ns molecular dynamics simulation using Gromacs 2020.4 software. The RMSD curve demonstrated the stability of the complex (Fig. S20C, D). The conformations of the ligand showed only minor differences (Fig. S20B). B-factor analysis showed that the binding site of the protein was stable (Fig. S20G). A 2-D diagram and NCI analysis of the last frame (50 ns) clarified the crucial role of van der Waals interactions (Fig. S20 F, H), but not hydrogen bonds (2.017982E−01 ± 1.438853E−02 on average, Fig. S20E).Analysis using the CREAMMIST database identified several drugs with significant Spearman correlations between TIMM9 expression and IC50 values across different cancer cell lines (Table S10). Notably, Z-LLNle-CHO exhibited the highest correlation (r = 0.20009, p = 8.59E−05) across 380 cell lines, indicating a potential association with TIMM9 activity. Other drugs such as alpha-cyano-4-hydroxycinnamic acid and oxythiamine also showed significant correlations. These findings suggest that higher TIMM9 expression may be associated with reduced therapeutic response to these drugs, as indicated by the increased IC50 values.
TIMM9 mediates cancer-associated gene networksGeneMANIA and BioGRID databases were used to define the gene networks mediated by TIMM9 (Fig. 8A, Table S11). The Metascape database identified two gene networks associated with oxidative phosphorylation and the regulation of T cell differentiation (Fig. 8B). GO enrichments of the two gene sets indicated a strong relationship between the gene sets and mitochondria-associated biological functions (Fig. 8C,D). Multiple enrichments by databases comprehensively demonstrated that the gene set obtained from the BioGRID database is correlated with chemical carcinogenesis, oxidative phosphorylation, ATP synthesis, respiratory electron transport and the TCA cycle (Fig. 8E). All these results revealed that the gene sets could admirably explain the biological function of TIMM9.Fig. 8Comprehensive insight into the signaling pathways and networks mediated by TIMM9. (A) Analysis of gene networks mediated by GeneMANIA and BioGRID (yellow lines: physical edges, green lines: genetic edges, purple lines: physical/genetic edges) databases. (B) Metascape enrichment of genes in networks. (C,D) GO enrichment of genes in networks of GeneMANIA (C) and BioGRID (BP biological process, CC cellular component, MF molecular function) (D). (E) KEGG, INTERPRO, and REACTOME enrichment analyses of genes in BioGRID.The Metascape database was utilized to identify hub genes in the gene set (BioGRID database), which physically interacted with TIMM9 (Fig. 9A). Protein–protein docking by the ZDOCK Server was utilized to evaluate binding affinities between these proteins and TIMM9 (Fig. S21A), and the TIMM9-ITFG1 complex achieved the lowest binding energy (Table S12). A 2-D diagram was plotted to visualize the specific residues in the interface (Fig. 9C). 35 ns molecular dynamics by Gromacs 2020.4 software were utilized to further expound the details of the interaction between TIMM9 and ITFG1. The RMSD (Fig. S21B) and Rg (Radius of gyration, Fig. S21C) consistently indicated that the trajectory of the simulation undoubtedly converged from 25 to 35 ns, and the secondary structure remained stable (Fig. S21D). Surprisingly, the average number of total weak interactions within 0.35 nm was up to 38.22517 pairs, and the average number of hydrogen bonds was 8.91578 (Fig. 9B). The Gibbs Energy Landscape was utilized to further hunt for the most stable conformation of the TIMM9-ITFG1 complex (Fig. S21E). The result of IGM further demonstrated that weak interactions broadly formed between TIMM9 and ITFG1 in the conformation with the lowest Gibbs Energy (Fig. 9D), indicating promising structural matching. PCA analysis was used to identify functional movements, and allosteric changes were found in the first and second principal components (PC1, PC2) (Fig. 9E). We preliminary found an “open-close” synergistical movement of ITFG1 in residues near the N-terminal of TIMM9 (Fig. 9F).Fig. 9Protein–protein interaction networks mediated by TIMM9. (A) Hub genes selected by Metascape from gene set obtained from BioGRID. (B) Hydrogen Bonds curve plot after MD simulation of TIMM9-ITFG1 complex. (C) The 2-D intermolecular interactions analysis of last frame structure (35 ns) in MD trajectory (Chain A: TIMM9, Chain B: ITFG1, green lines: hydrogen bonds; residues except for which forming hydrogen bonds formed van der Waals interactions). (D) IGM analysis to determine the strength of interactions (the strength becomes stronger from green to blue, green usually represents van der Waals interactions and pi-pi stacking, blue usually represents hydrogen bonds, red represents steric hindrances). (E) PCA analysis of TIMM9-ITFG1 complex. (F) Structural analysis of movement in first principal component (PC1). RMSD root mean square deviation, Rg radius of gyration, MD molecular dynamics, IGM independent gradient model.Comprehensive analysis of TIMM9 expression and its implications in lung adenocarcinoma (LUAD)The LUAD expression matrix from the TCGA dataset was obtained since LUAD has been shown to correlate with differential expression (Fig. 1), poor prognosis (Fig. 3), TIMM9-related phenotypes (Fig. 5), and expression data (Fig. 6A,B). Twenty tumor samples with the highest or lowest expression levels of TIMM9 were obtained separately, totaling 40 samples. The R package DESeq2 was used to obtain differential expression genes between the “low expression of TIMM9 group” and the “high expression of TIMM9 group”. These differential expression genes were utilized to perform WGCNA analysis. We set the soft threshold to 3 with R2 > 0.85 and high average connectivity (Fig. 10A). Genes were separated into nine modules (Fig. 10B). TIMM9 was positively correlated with the “MEblue” (r = 0.902925, p = 1.64E−15), “MEyellow” (r = 0.755647, p = 1.74E−08), and “MEgreen” (r = 0.680308665, p = 1.37E−06) modules; negatively correlated with the “MEturquoise” (r = − 0.587936468, p = 6.63E−05), “MEpink” (r = − 0.533037917, p = 0.000398256), “MEred” (r = − 0.5248887, p = 0.000506638), “MEbrown” (r = − 0.485311155, p = 0.001502772), and “MEblack” (r = − 0.47096988, p = 0.002160406) modules; and not correlated with the “MEmagenta” (r = − 0.119299994, p = 0.463425268) and “MEgrey” (r = 0.111817245, p = 0.49212794) modules. Visualizations of the relationships among each module (Fig. 10C) and each gene (Fig. 10D) were performed, and the correlation between the expression of TIMM9 and each gene module was visualized as a heatmap (Fig. 10E). Modules positively associated with TIMM9 expression (“MEblue”, “MEyellow”, “MEgreen”) were correlated with different biological processes, such as “cell adhesion”, “ribosome”, and “cell cycle” (Fig. 10F–H). These results may explain the phenotypes mediated by TIMM9.Fig. 10WGCNA gene co-expression network. (A) Relationship of soft threshold and scale-free topology model fit and mean connectivity. (B) Gene classification into multiple colors indicating different modules in the clustering tree. (C) Heat map of module feature genes. Red color means a high correlation, and blue color indicates a low correlation. (D) Clustering dendrogram of module feature genes. Light-colored areas show a strong correlation. (E) Correlations between each module and expression variation of TIMM9. The left triangle presents the correlation between TIMM9 and different modules. Red shows a high correlation. The right triangle indicates the p value, *p < 0.05, **p < 0.01, ***p < 0.001. (F–H) GO enrichment of each module, “MEblue” (F), “MEyellow” (G), and “MEgreen” (H), separately.PCA analysis of TIMM9-related genes significantly positively correlated with TIMM9 expression in LUAD (from GEPIA, Table S13) reveals distinct clustering of tumor and normal samples (Fig. 11A,B). GSVA scores are higher in tumor tissues compared to normal tissues (Fig. 11C). Higher GSVA scores are associated with poorer overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS) in LUAD patients (Fig. 11D–F). Additionally, GSVA scores show a positive correlation with cell cycle pathway activity (Fig. 11G) and EMT pathway activity (Fig. 11H).Fig. 11GSCA database analysis of TIMM9-related gene set from the GEPIA database. (A) PCA analysis results executed by GEPIA, showing the variance explained by each principal component. (B) The first two principal components from the PCA dimensionality reduction, distinguishing between tumor (red) and normal (yellow) samples in LUAD. (C) Visualization of GSVA score differences between tumor and normal groups in LUAD. (D) Overall survival (OS) analysis comparing patients with higher and lower GSVA scores in LUAD. (E) Progression-free survival (PFS) analysis for patients with different GSVA scores in LUAD. (F) Disease-specific survival (DSS) analysis for patients with different GSVA scores in LUAD. (G) Spearman correlation between GSVA scores and cell cycle pathway activity in LUAD, showing a positive correlation. (H) Spearman correlation between GSVA scores and EMT pathway activity in LUAD, showing a positive correlation. The gene set used in these analyses was obtained from the GEPIA database and includes genes significantly positively correlated with TIMM9 expression in LUAD.The GSCA database analysis of the TIMM9-related gene set from the BioGRID database reveals significant findings in LUAD. The GSVA scores, calculated for the gene set significantly positively correlated with TIMM9 expression, are higher in tumor tissues compared to normal tissues (Fig. S22A). Patients with higher GSVA scores exhibit poorer overall survival (OS) and disease-specific survival (DSS) compared to those with lower scores (Fig. S22B,C). Additionally, a positive correlation is observed between GSVA scores and cell cycle pathway activity (Fig. S22D), indicating a potential role of TIMM9 in cell cycle regulation and cancer progression.In summary, TIMM9 is significantly associated with various phenotypes and survival outcomes in LUAD, highlighting its potential role in cancer biology and as a therapeutic target.
TIMM9 as a prognostic biomarker in multiple cancers and its associated biological processes
