LncRNA TMEM105 Promotes Malignancy via the MYC-Ribosome Biogenesis Axis: A Novel Prognostic Biomarker and Therapeutic Target in Colorectal Cancer

LncRNA TMEM105 Promotes Malignancy via the MYC-Ribosome Biogenesis Axis: A Novel Prognostic Biomarker and Therapeutic Target in Colorectal Cancer | 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 LncRNA TMEM105 Promotes Malignancy via the MYC-Ribosome Biogenesis Axis: A Novel Prognostic Biomarker and Therapeutic Target in Colorectal Cancer Ahmad Rezaei Nasab, Seyed Jalal Zargar, Maryam Peymani, Kamran Ghaedi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7789906/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jan, 2026 Read the published version in Cancer Cell International → Version 1 posted 9 You are reading this latest preprint version Abstract Transmembrane protein 105 (TMEM105) has recently emerged as a potential oncogenic factor in various malignancies, yet its role in colorectal cancer (CRC) remains unclear. In this study, we comprehensively investigated the expression and function of TMEM105 in CRC through an integrated in silico, ex vivo, and in vitro approach. Publicly available datasets (TCGA, GSE41328, and GSE25070) were analyzed to assess TMEM105 expression and its association with clinicopathological features, followed by weighted gene co-expression network analysis to identify relevant biological pathways. The expression levels were further validated via RT‒qPCR in 25 paired CRC and adjacent non-tumorous tissues. Functional assays were performed after siRNA-mediated silencing of TMEM105 in CRC cell lines to evaluate its impact on cell viability, clonogenicity, migration, apoptosis, and pathway-specific gene expression. TMEM105 was significantly upregulated in CRC tissues, and elevated TMEM105 expression was correlated with advanced stage (stage IV) and metastasis. Co-expression analysis revealed ribosome biogenesis and MYC signaling as pathways strongly associated with TMEM105. The functional inhibition of TMEM105 reduced cell viability, impaired colony formation, suppressed migration, and promoted apoptosis, accompanied by the downregulation of the ribosomal genes RPL7 and RPS2 and a marked decrease in global protein synthesis. Collectively, these findings establish TMEM105 as a putative oncogenic driver that promotes CRC progression by modulating ribosome biogenesis, potentially in concert with MYC signaling. TMEM105 may therefore serve as a promising prognostic biomarker and novel therapeutic target for advanced colorectal cancer. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Colorectal cancer (CRC) is the third most commonly diagnosed cancer and the second leading cause of cancer-related deaths globally [ 1 ]. Its development is a multistep process characterized by the accumulation of genetic and epigenetic alterations, including mutations in tumor suppressors and oncogenes such as APC, MLH1, and KRAS, along with lifestyle-associated risk factors such as red meat consumption, obesity, and smoking [ 2 , 3 ]. Inherited syndromes such as Lynch syndrome and familial adenomatous polyposis (FAP) further increase the risk of CRC [ 4 ]. Among the molecular changes contributing to CRC progression, dysregulation of gene expression plays a critical role in modulating tumor progression, immune evasion, and the response to therapy, highlighting its relevance in early detection and targeted treatment strategies [ 5 , 6 ]. Long non-coding RNAs (lncRNAs), a heterogeneous class of transcripts longer than 200 nucleotides without protein-coding capacity, have garnered considerable attention as key regulators of cancer biology. Owing to their unique nucleotide sequences and secondary structures, lncRNAs engage in intricate interactions with DNA, RNA, and proteins, modulating gene expression through chromatin remodeling, transcriptional control, RNA splicing, and regulation [ 7 , 8 ]. In CRC, dysregulated lncRNAs have been implicated in a range of biological processes, including epithelial‒mesenchymal transition (EMT), apoptosis, proliferation, metabolism, and therapeutic resistance [ 9 ]. Prominent examples include MIR17HG and FTX, which promote aerobic glycolysis and metastasis through ceRNA networks involving the miR-138-5p/HK1 and miR-215-3p/YAP1 axes, respectively [ 10 , 11 ]. Transmembrane protein 105 (TMEM105), recently identified as an oncogenic lncRNA, is located at chromosome 17q25.3 and is expressed in multiple tissues, including intestinal epithelial cells. While TMEM105 has been associated with cancer progression in other tumor types, its role in CRC remains unexplored. In breast cancer, elevated TMEM105 expression is correlated with poor prognosis and enhanced liver metastasis [ 12 ]. Mechanistically, TMEM105 acts as a competitive endogenous RNA (ceRNA) by sponging miR-1208, thereby increasing LDHA expression and promoting glycolysis. This, in turn, creates a positive feedback loop via the SHH–MAZ signaling pathway that further amplifies TMEM105 expression [ 12 ]. Similarly, in pancreatic ductal adenocarcinoma (PDAC), TMEM105 has been identified as a disulfidptosis-related lncRNA that stabilizes β-catenin, enhances c-MYC and GLUT1 expression, and drives tumor proliferation and metabolic reprogramming [ 13 ]. Despite compelling evidence of the oncogenic potential of TMEM105 in other malignancies, its expression profile and functional role in CRC have not been characterized. In this study, we aimed to elucidate the expression dynamics and oncogenic functions of TMEM105 in CRC. We first conducted in silico analyses to evaluate TMEM105 expression and its association with cancer-related signaling pathways via publicly available transcriptomic datasets. We subsequently validated these observations through comparative expression analysis of paired CRC tumor and adjacent non-tumorous tissues. Finally, we performed in vitro experiments to assess the impact of TMEM105 on CRC cell survival, migration, apoptosis, and clonogenic potential. Overall, this work sought to uncover the functional relevance of TMEM105 in CRC carcinogenesis and evaluate its utility as a novel biomarker and therapeutic target. 2. Materials and methods 2.1 Study Design and Overview This study was designed as a multiphase investigation aimed at elucidating the role of TMEM105 in cancer progression, integrating computational, experimental, and clinical approaches. The methodological framework was structured to ensure both analytical rigor and translational relevance, enabling the systematic exploration of TMEM105 expression patterns, functional roles, and mechanistic pathways across multiple experimental tiers. The research framework encompassed sequential in silico analyses, ex vivo validation, in vitro functional assays, mechanistic exploration, and in vivo confirmation. The overall workflow of the study, which integrates these complementary approaches, is illustrated in Fig. 1 , providing a stepwise overview from hypothesis formulation to clinical interpretation. This schematic serves as a visual guide to the logical progression of the research and the interconnection between its analytical and experimental components. The sample size for the clinical cohort was determined on the basis of precedent from similar studies and the availability of patient samples. For in vitro assays, the number of experimental replicates was selected to provide adequate statistical power to detect significant differences between groups. 2.2 Data Sources and Preprocessing To investigate the differential expression of TMEM105 and its potential association with colorectal cancer (CRC) malignancy, RNA-sequencing data from The Cancer Genome Atlas (TCGA) were employed. Raw count data in STAR-counts format were retrieved via the TCGAbiolinks R package [ 14 ]. Data preprocessing included TMM normalization and removal of genes with negligible expression (defined as CPM < 10 in more than 70% of samples) to minimize low-expression noise and enhance statistical robustness, followed by log2 transformation [ 15 ]. The resulting expression matrix served as the basis for all subsequent analyses. The TCGA cohort comprised 483 tumor samples and 41 non-tumor tissues spanning multiple clinical stages. Updated clinical metadata were incorporated to evaluate the relationships between TMEM105 expression and relevant clinical parameters. In addition, two independent GEO datasets (GSE41328 and GSE25070), consisting of paired tumor and adjacent normal tissues, were analyzed. The raw microarray data were processed the limma package, which included background correction, RMA normalization, and log2 transformation [ 16 ]. 2.3 Co-expression Network and Clustering Analysis To identify genes co-expressed with TMEM105, Pearson correlation coefficients were computed between TMEM105 and all other genes in the normalized TCGA expression matrix. Genes with correlation coefficients > 0.5 and p-values < 0.01 were retained for downstream analysis, including pathway enrichment. The mentioned criterion serves as an empirical measure that can identify genes with a putative functional association with TMEM105. Co-expression networks were visualized in Cytoscape. CRC tumor samples were then clustered based on expression profiles of TMEM105-associated genes. The optimal number of clusters was determined using the within-cluster sum of squares (WSS) method. K-means clustering was subsequently applied to divide the samples into two clusters: Cluster C1 (low TMEM105 expression) and Cluster C2 (high TMEM105 expression). The algorithm process was repeated 100 times with 1,000 centroid repositionings to ensure robust classification. 2.4 Differential expression and pathway enrichment analysis In the TCGA dataset, samples were grouped into tumor and non-tumor tissue categories based on clinical annotations, and paired tumor/normal tissues were applied to the GSE41328 and GSE25070 datasets. Differential gene expression analysis was performed using a linear modeling approach via the limma package [ 16 ]. A false discovery rate (FDR) of less than 0.01 was used to determine statistical significance. Differentially expressed genes between Clusters C2 and C1 were also identified to facilitate pathway analysis. Enrichment analysis of cluster-specific differentially expressed genes (DEGs) was performed via Enrichr ( https://maayanlab.cloud/Enrichr/ ), with annotations from the KEGG and MSigDB databases. Enrichment analysis was also conducted for the TMEM105 co-expressed gene network via the same databases. 2.5 Immune cell infiltration analysis To evaluate the relationship between TMEM105 expression and immune cell infiltration in CRC, the TIMER 2.0 web server ( https://timer.cistrome.org/ ) was used. Outputs from the EPIC and TIMER algorithms were considered. Only statistically significant associations between TMEM105 expression and immune cell populations have been reported. 2.6 Tissue collection and ex vivo samples A total of 25 CRC tumors and matched adjacent healthy tissues were collected from the Iran Tumor Bank at Imam Khomeini Hospital, Tehran. The diagnosis of cancer and healthy tissue was performed by a certified pathologist. The samples were immediately frozen in liquid nitrogen and stored until testing. All patients provided written informed consent. The study protocol was approved by the Research Ethics Committee of the University of Tehran in compliance with guidelines from the Iranian Ministry of Health. The study cohort included 25 patients with a histologically confirmed diagnosis of colorectal cancer, comprising 15 males and 10 females. Regarding age, 19 patients were over 50 years old, and 6 were under 50 years old. All collected samples met the predefined inclusion criteria and were used for subsequent analysis, with no sample attrition during the study. The clinical characteristics of the patients are detailed in Table 1 . Table 1 Clinical information for CRC samples characteristic Number (N = 25) Age 50 6 19 Gender Male Female 15 10 TNM stage I II III IV 3 9 8 5 Tumor size 5cm 14 11 2.7 Cell Culture HT-29 and HCT-116 human CRC cell lines were purchased from the Pasteur Institute of Iran. The cells were cultured in DMEM supplemented with 10% fetal bovine serum (FBS), 1% penicillin‒streptomycin (100 U/mL penicillin and 100 µg/mL streptomycin), and GlutaMAX™. Cultures were maintained at 37°C in a humidified 5% CO₂ incubator. The cell lines were obtained from the Pasteur Institute of Iran, where their authenticity was certified by the supplier, and they were confirmed to be free of mycoplasma contamination. The cells were subcultured at 80–90% confluence with 0.25% trypsin-EDTA and cryopreserved with a mixture of 90% FBS and 10% DMSO. 2.8 siRNA Design and Cell Transfection Target-specific siRNAs for TMEM105 were designed via the siDirect online tool ( https://sidirect2.rnai.jp/ ) and are listed in Table 2 . Transfections of HT-29 and HCT-116 cells were carried out via Lipofectamine™ (Invitrogen, USA). Initial optimization of the transfection conditions was performed by varying the Lipofectamine volume (0.2–2 µL/100 µL culture medium) and siRNA concentration (5–80 nM), with cytotoxicity assessed via the MTT assay. Table 2 The sequence list and information of the primers and primers used in this study are summarized in the table below. Gene Names Forward primer (5’->3’) Reverse primer (5’->3’) Tm NCBI accession number CCND1 GCTCACGCTTACCTCAACCA ATCCAGGACTTGTGCCCTTG 60 NM_053056.3 CCNE1 ATACTTGCTGCTTCGGCCTT TCAGTTTTGAGCTCCCCGTC 58 NM_001238.4 NM_001322259.2 NM_001322261.2 NM_001322262.2 NM_001440305.1 NM_001440306.1 NM_001440307.1 BCL2 GAACTGGGGGAGGATTGTGG GCCGGTTCAGGTACTCAGTC 60 NM_000633.3 NM_000657.3 NM_001438935.1 BAX CCCCGAGAGGTCTTTTTCCG GCACAGGGCCTTGAGCAC 60 NM_001291428.2 NM_001291429.2 NM_001291430.2 NM_001291431.2 NM_004324.4 NM_138761.4 NM_138763.4 NM_138764.5 GAPDH GGGAGCCAAAAGGGTCATCA GTGCTAAGCAGTTGGTGGTG 60 NM_001101.5 TMEM105 TCTCATCTCCCCACAGGAATC TTTGCTTCTTAGCCCCCAACC NR_165247.1 siRNA (scramble) UUCUCCGAACGUGUCACGU ACGUGACACGUUCGGAGAA - - siRNA-1 CCCAUAGCUGACACUUCUA UAGAAGUGUCAGCUAUGGG - NR_165247.1 siRNA-2 GGCAAGCUCUGAUCUUACA UGUAAGAUCAGAGCUUGCC - NR_165247.1 The cells were seeded according to the manufacturer’s protocol such that they reached 60–70% confluence within 24 hours at the time of transfection. To minimize potential bias from positional effects, the assignment of wells to the various treatment groups (e.g., Control, Scramble, siRNA-1, and siRNA-2) was randomized for each independent experiment. siRNAs and Lipofectamine were separately diluted in serum- and antibiotic-free media, incubated at room temperature for 5 minutes, combined, and incubated for an additional 15–20 minutes to form siRNA‒lipid complexes. The complexes were added to the cells and incubated for 8 hours, after which the medium was replaced with complete culture medium (culture medium containing 10% FBS and antibiotics). The knockdown efficiency was measured via qRT‒PCR 24 to 48 hours after transfection. 2.9 MTT Assay for Cell Viability Cells were seeded in 96-well plates and allowed to adhere for 24 hours, followed by siRNA treatment for 48 hours. Then, 10 µL of MTT reagent was added to each well and incubated for 2 hours. After the formazan crystals formed, they were solubilized with 100 µL of DMSO, and the absorbance was measured at 570 nm via an ELISA plate reader. Cell viability was calculated relative to that of the untreated controls. 2.10 Wound Healing Assay (Cell Migration) Migration was evaluated via a wound healing assay. HT-29 and HCT-116 cells were cultured to 70–80% confluence in 6-well plates. A uniform scratch was made using a 100-µL pipette tip, and the cells were washed with culture medium to remove debris. The cells were incubated in low-serum medium (1% FBS) and treated with siRNA. Initial scratch images (0 h) were captured, and the plates were incubated at 37°C and 5% CO₂. Migration was assessed by imaging the same fields after 24 and 48 hours. Wound closure was quantified via ImageJ, and migration percentages were calculated. 2.11 Colony Formation Assay HT-29 and HCT-116 cells were seeded in 6-well plates at 500 cells/well and treated with siRNA for 24 hours. Colonies were allowed to form over 14 days, and the media was changed every 48 hours. At the end of the incubation period, the medium was removed, and the cells were washed with PBS, fixed with methanol for 20 minutes, and stained with 0.5% crystal violet for 15 minutes. The plates were washed and air-dried, and the colonies were counted via ImageJ software. 2.12 Annexin V/PI Apoptosis Assay Apoptosis analysis was performed with an Annexin V-FITC/PI apoptosis detection kit according to the manufacturer's instructions. Following 48 hours of siRNA treatment, HT-29 and HCT-116 cells were harvested, washed with PBS, and detached with trypsin without EDTA. The cells were subsequently centrifuged at 1,000 rpm for 5 minutes, after which the pellet was resuspended in 100 µL of binding buffer. Then, 5 µL of Annexin V-FITC and 5 µL of PI were added to each sample and incubated in the dark at room temperature for 15 minutes. Afterward, 400 µL of binding buffer was added, and the samples were analyzed by flow cytometry (FACSCalibur, USA). The fluorescence from Annexin V and PI was detected via the FL1 and FL3 channels, respectively. The data were analyzed via quadrant gating to quantify the percentages of viable (Annexin V⁻/PI⁻), early apoptotic (Annexin V⁺/PI⁻), late apoptotic (Annexin V⁺/PI⁺), and necrotic (Annexin V⁻/PI⁺) cells. 2.13 RNA Extraction, cDNA Synthesis, and RT‒qPCR Total RNA was extracted via TRIzol reagent following the manufacturer’s protocol. RNA purity and concentration were assessed via optical density at 260/280 nm, and equal amounts of RNA were used for cDNA synthesis. The samples were treated with DNase I to eliminate genomic DNA contamination. cDNA synthesis was performed via a commercial kit (Yekta Tajhiz), oligo (dT), random hexamer primers, and reverse transcriptase enzyme. Specific primers were designed via Primer-BLAST and Oligo7 software; all the gene sequences are summarized in Table 2 . Gene expression was quantified by RT‒qPCR using SYBR Green dye and gene-specific primers. GAPDH was used as the internal control. The expression levels were calculated via the 2 ⁻ΔCt method. 2.14 Protein quantification via a BCA assay Total protein concentration was determined via a BCA protein assay kit (Pars Tous, Iran) following the manufacturer's protocol. A standard curve was generated via the use of serial dilutions of bovine serum albumin (BSA) in the appropriate lysis buffer, ranging from 0 to 2000 µg/mL. After treatment, equal numbers of cells were seeded for each group to reduce variability. The cells were washed three times with PBS to remove extracellular proteins, harvested, and lysed. The lysates were diluted to ensure that they fell within the linear range of the standard curve. In a 96-well plate, 25 µL of each sample or standard was added per well, followed by the addition of 200 µL of working reagent. After gentle mixing, the plates were incubated at 37°C for 30 minutes, and the absorbance was measured at 562 nm via a microplate reader. 2.15 Statistics and Software Preprocessing and analysis of TCGA and GEO data were conducted via the R programming language (v. 4.8). The false discovery rate was employed to assess the significance levels within the in silico data. The Pearson correlation test was applied for the co-expression network analysis. One-way ANOVA was utilized to evaluate the significance of differences among various experimental groups. To prevent observer bias during data acquisition and analysis, key quantitative steps, such as the analysis of wound healing assays, colony counting, and flow cytometry data, were performed by an investigator blinded to the treatment conditions. GraphPad Prism (v. 8.4) was used to generate graphical representations. 3. Results 3.1 TMEM105 is upregulated in CRC and is correlated with clinical features In silico analysis of TCGA data revealed significant upregulation of TMEM105 in CRC tumor samples relative to healthy tissues, with a > 2-fold increase in expression (Fig. 2 A; logFC = 1.2, FDR < 0.0001). Consistently, paired analysis of tumor and adjacent non-tumorous samples (GSE41328 and GSE25070) revealed significantly elevated TMEM105 expression in tumor tissues (Fig. 2 B and 2 C; FDR < 0.0001). Analysis of TCGA clinical annotations revealed that TMEM105 expression was significantly higher in advanced-stage (III–IV) colorectal tumors than in early-stage (I–II) tumors (Fig. 2 D; FDR < 0.01). Patients with metastatic disease (M1) also presented significantly elevated TMEM105 levels compared with nonmetastatic patients (M0) (Fig. 2 E; FDR < 0.01). In addition, the invasive molecular subtype presented higher TMEM105 expression than the other subgroups did (Fig. 2 F; FDR < 0.05). In contrast, TMEM105 expression did not differ significantly with respect to tumor anatomical location and TNM-M stage (Fig. 2 G, 1 H). These findings indicate that TMEM105 is markedly upregulated in CRC and may be associated with features of tumor aggressiveness and malignancy-related features. 3.2 TMEM105 expression is correlated with genes involved in ribosome biogenesis and MYC targets Co-expression network analysis of TCGA data identified 106 genes that were positively co-expressed with TMEM105 (r > 0.5, P < 0.01) (Fig. 3 A). Enrichment analysis showed significant involvement of these genes in ribosome biogenesis and MYC target pathways (Fig. 3 B; FDR < 0.01), highlighting a potential regulatory link. Given that MYC is a master regulator of ribosome biogenesis, this finding supports a mechanistic association between TMEM105 and MYC-driven oncogenic programs (Fig. 3 B; FDR < 0.01). Notably, MYC is a well-established master regulator of ribosomal biogenesis, and genes involved in ribosome biogenesis are highlighted in red for visualization in Fig. 3 A. Using the expression profiles of these ribosomal genes, tumor samples were stratified into two clusters by K-means clustering: Cluster C1 (n = 138), characterized by low expression, and Cluster C2 (n = 345), characterized by high expression (Fig. 3 C). In support of the validity of these clusters, TMEM105 expression was significantly elevated in Cluster C2 compared with C1 (Fig. 3 D; log2FC = 0.7, FDR 0.7, FDR < 0.01) and 615 downregulated genes (logFC < -0.7, FDR < 0.01) in Cluster C2 (Fig. 3 E). The functional enrichment of the genes upregulated in C2 was significantly associated with oxidative phosphorylation, ribosome biogenesis, MYC target pathways, mTORC1 signaling, and E2F target pathways (Fig. 3 F; FDR < 0.01). In contrast, the genes downregulated in C2 were predominantly associated with immune-related pathways (Fig. 3 G; FDR < 0.01). These findings suggest that TMEM105 may contribute to CRC pathogenesis by modulating ribosomal biogenesis and MYC-driven oncogenic programs. 3.3 High TMEM105 expression is associated with reduced immune cell infiltration The relationship between TMEM105 expression and immune cell infiltration in CRC was subsequently evaluated via TIMER 2.0. The immune cell composition was estimated in the TCGA dataset via computational deconvolution. High TMEM105 expression was negatively correlated with reduced infiltration of several key immune cell populations, including CD8 + T cells, neutrophils, natural killer (NK) cells, dendritic cells (DCs), and macrophages (Fig. 4 ; P < 0.01). These results suggest that the overexpression of TMEM105 may contribute to immune evasion in CRC by suppressing the recruitment of these immune cells to the TME. However, as these associations are based on in silico analysis, further experimental validation is needed to confirm the role of TMEM105 in tumor-immune interactions. 3.4 TMEM105 is upregulated in tumor tissues, and its silencing reduces the viability of CRC cell lines RT‒qPCR was used to assess TMEM105 expression in 25 paired CRC and adjacent healthy tissues to validate our in silico findings. TMEM105 was significantly upregulated in tumor samples compared with adjacent non-tumorous tissues (Fig. 5 A; P = 0.01). Among the CRC cell lines, the HT-29 line presented higher TMEM105 expression than the HCT-116 line did (Fig. 5 B; P = 0.01). For functional studies, HT-29 and HCT-116 cells were transfected with TMEM105-targeting siRNAs via Lipofectamine. Toxicity assays indicated that Lipofectamine volumes exceeding 1 µL per 100 µL of culture medium were cytotoxic, so 1 µL/100 µL was used in all the transfection experiments (Fig. 5 C, 5 D). Serial dilution experiments revealed that a 20 nM siRNA concentration provided optimal knockdown efficiency (Fig. 5 E, 5 F). RT‒qPCR confirmed that co-transfection with two siRNAs (siRNA-1 and siRNA-2) resulted in a > 50% reduction in TMEM105 expression at 48 hours in both cell lines (Fig. 5 G– 4 J), and these conditions were used for downstream functional assays. TMEM105 knockdown significantly reduced the viability of CRC cells (Fig. 6 A; P < 0.02). Moreover, the expression of key proliferation markers, Cyclin E1 (CCNE1) and Cyclin D1 (CCND1), was significantly downregulated following TMEM105 silencing (Fig. 6 B- 5 E). Collectively, these findings indicate that TMEM105 is crucial for CRC cell growth and survival and support its role as a candidate oncogene and a potential therapeutic target in CRC. 3.5 Silencing TMEM105 Impairs Migration and Colony Formation and Promotes the Apoptosis of CRC Cells To comprehensively investigate the impact of TMEM105 knockdown on the malignant phenotypes of CRC, we initially conducted wound healing assays to assess the migratory capacity of the cells. These assays demonstrated a significant decrease in the migration ability of both the HT-29 and HCT-116 cell lines following siRNA-mediated silencing of TMEM105. Quantitative analysis confirmed that this reduction was statistically significant, with P < 0.05 (Fig. 7 A–D). These findings suggest that TMEM105 plays a critical role in promoting CRC cell motility. In the colony formation assays, compared with control cells, TMEM105-deficient cells presented a significantly lower clonogenic capacity (Fig. 7 E–F; P < 0.05), suggesting impaired long-term proliferative potential. The apoptotic response following TMEM105 knockdown was quantified via Annexin V-FITC/PI staining. Compared with those in the control and scrambled siRNA groups, the apoptotic population in the HT-29 cells increased by more than 20% (Fig. 8 A–B; P = 0.001). This effect was accompanied by upregulation of the pro-apoptotic gene BAX and downregulation of the anti-apoptotic gene BCL-2 (Fig. 8 C–D; P < 0.05). Similar trends were observed in HCT-116 cells, where apoptosis increased by nearly 30% following TMEM105 silencing, along with a shift in BAX/BCL-2 expression, which was consistent with the activation of the intrinsic apoptotic pathway (Fig. 8 E–H; P < 0.05). These findings demonstrate that TMEM105 promotes key oncogenic behaviors in CRC cells, including migration, clonogenicity, and survival, while its silencing induces apoptotic cell death, reinforcing its functional role in CRC tumor progression. 3.6 TMEM105 Knockdown Reduces Ribosome Biogenesis-Related Gene Expression and Total Protein Content Given the strong co-expression between TMEM105 and ribosomal genes, the effect of TMEM105 silencing on the representative ribosome biogenesis genes RPS2 and RPL7 was examined (Fig. 3 , red nodes). RT‒qPCR analysis revealed significant downregulation of RPS2 and RPL7 in both HT-29 and HCT-116 cells following TMEM105 knockdown (Fig. 8 A– 8 D; P < 0.05). Moreover, the total protein concentration was significantly reduced in TMEM105-depleted cells (Fig. 8 E and 8 F; P < 0.05), suggesting that TMEM105 may modulate global protein synthesis. Collectively, these data suggest that TMEM105 promotes CRC progression by enhancing ribosome formation and global protein production, processes critical for tumor cell growth and metabolism. 4. Discussion Long non-coding RNAs (lncRNAs) are increasingly recognized as crucial regulators of cellular processes and have emerged as promising diagnostic biomarkers and therapeutic targets in a variety of diseases, including cancer [ 17 ]. With recent advances in RNA-based therapeutics—particularly antisense oligonucleotide drugs now progressing into phase II clinical trials—the landscape for targeting lncRNAs is becoming increasingly viable and clinically relevant [ 18 ]. In the present study, we investigated the functional role of TMEM105, an understudied lncRNA, in the progression and malignancy of CRC. Our integrated approach, which combines in silico analyses with ex vivo experiments on CRC tissues and cell lines, consistently revealed significant upregulation of TMEM105 in tumor samples compared with adjacent healthy tissues. Notably, elevated TMEM105 expression was significantly associated with advanced clinicopathological parameters, including stage III/IV tumors and metastatic status (TNM.M1). These observations are consistent with a growing body of evidence documenting the aberrant overexpression of TMEM105 across a spectrum of human malignancies, thereby substantiating its role as a putative pan-cancer oncogenic lncRNA. For example, in breast cancer, increased TMEM105 expression has been linked to poor prognosis and increased metastatic potential [ 12 ]. Similarly, in, high TMEM105 expression has been correlated with reduced overall survival [ 13 ]. Evidence also implicates TMEM105 in the tumorigenesis of head and neck cancers, suggesting a broader oncogenic function [ 19 ]. In thyroid cancer, TMEM105 appears to regulate cell cycle progression, with its overexpression correlating with unfavorable clinical outcomes [ 20 ]. Moreover, in gastric cancer, in silico data have demonstrated its upregulation and association with poor patient prognosis [ 21 ]. Collectively, these findings support a conserved oncogenic function of TMEM105 across multiple cancer types and are consistent with its role in driving CRC aggressiveness, as observed in our study. Functionally, our data demonstrated that silencing TMEM105 significantly inhibited the viability, migration, and colony-forming capacity of CRC cell lines while simultaneously increasing their apoptotic activity. These results highlight TMEM105 as a potential driver of tumor growth and cellular aggressiveness. Our findings are in line with earlier studies in breast cancer, which reported that suppressing TMEM105 expression impairs cell migration and colony formation [ 12 ]. Additionally, research on pancreatic cancer has shown that depletion of TMEM105 inhibits colony formation and migration, reinforcing its pro-tumorigenic role across malignancies [ 13 ]. Furthermore, to elucidate the molecular mechanisms underlying the protumorigenic functions of TMEM105, we performed our transcriptomic and functional enrichment analyses, which focused on pathways integral to ribosome biogenesis and protein synthesis. Specifically, co-expression network analysis revealed a strong positive correlation between TMEM105 and several ribosome-related genes, including RPS2 and RPL7, whose expression levels were markedly reduced following TMEM105 knockdown. Notably, genes associated with MYC signaling pathways were also enriched among TMEM105-associated targets, suggesting that TMEM105 may exert its oncogenic influence, at least in part, via the modulation of MYC-driven transcriptional programs. Given the central role of MYC in orchestrating ribosomal RNA transcription and ribosome assembly [ 22 , 23 ], our findings provide mechanistic insight into how TMEM105 may contribute to CRC pathogenesis through the enhancement of translational capacity and cellular proliferation. Interestingly, a similar axis has been reported in pancreatic cancer, suggesting that TMEM105 may act as a conserved modulator of MYC-dependent oncogenic signaling across different tumor types [ 13 ]. Despite these compelling findings, several limitations should be acknowledged. First, the study relies heavily on in silico analyses and in vitro experiments using only two CRC cell lines, which may not fully capture the heterogeneity of CRC tumors. Second, the absence of in vivo validation restricts our ability to draw definitive conclusions regarding the role of TMEM105 in tumor progression and metastasis within a physiological context. 5. Conclusion Collectively, our findings establish TMEM105 as a novel oncogenic lncRNA implicated in the progression of colorectal cancer (CRC). Its upregulation is significantly associated with adverse clinical features, whereas its silencing attenuates cardinal features of malignancy, including cell proliferation, migration, and survival. Mechanistically, our data reveal a compelling link between TMEM105, ribosome biogenesis, and MYC-driven transcriptional programs, providing a plausible axis for its oncogenic activity. Taken together, these results highlight TMEM105 as a compelling candidate for further investigation, with potential as both a prognostic biomarker and a viable therapeutic target in CRC. Subsequent investigations utilizing in vivo models and comprehensive clinical cohorts are warranted to fully validate its clinical utility and explore the translational feasibility of TMEM105-targeted interventions. Abbreviations TMEM105 Transmembrane protein 105 LncRNA Long non coding RNA CRC Colorectal cancer TCGA The Cancer Genome Atlas RT‒qPCR reverse transcription quantitative polymerase chain reaction SiRNA Small interfering RNA MYC MYC proto-oncogene TNM tumor, node, metastasis (cancer staging system) NK cells Natural killer cells DC cells Dendritic cells FDR False discovery rate LogFC Log Fold Change Declarations Competing interests: The authors declare that they have no competing financial interests. Specifically, none of the authors has received funding, honoraria, consulting fees, stock ownership, patent rights, or any other financial or personal relationships that could have appeared to influence the work reported in this manuscript. Funding: No funding to report. Author Contribution A.R.N. wrote the main manuscript, conducted the experiments, performed investigation, visualization, and experimental data analysis, developed the project, designed the study, performed data interpretation, and provided reagents. S.J.Z. and M.P. developed the project and designed the study. S.J.Z. supervised the entire project. M.P. provided technical assistance and contributed clinical insights and technical support. K.G. contributed to data interpretation and manuscript review. All authors reviewed and approved the final version of the manuscript. Data availability: No datasets were generated or analysed during the current study. References Xi Y, Xu P. Global colorectal cancer burden in 2020 and projections to 2040. Translational Oncol. 2021;14(10):101174. Marino P, et al. Healthy lifestyle and cancer risk: modifiable risk factors to prevent cancer. Nutrients. 2024;16(6):800. Seppälä TT, Burkhart RA, Katona BW. 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Guerrieri AN, et al. The Interplay Between the MYC Oncogene and Ribosomal Proteins in Osteosarcoma Onset and Progression: Potential Mechanisms and Indication of Candidate Therapeutic Targets. Int J Mol Sci. 2024;25(22):12031. Schlosser I, et al. A role for c-Myc in the regulation of ribosomal RNA processing. Nucleic Acids Res. 2003;31(21):6148–56. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Jan, 2026 Read the published version in Cancer Cell International → Version 1 posted Editorial decision: Revision requested 22 Nov, 2025 Reviews received at journal 09 Nov, 2025 Reviews received at journal 06 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers agreed at journal 02 Nov, 2025 Reviewers invited by journal 13 Oct, 2025 Editor assigned by journal 10 Oct, 2025 Submission checks completed at journal 10 Oct, 2025 First submitted to journal 06 Oct, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":1656320,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic representation of the integrated research workflow investigating the role of TMEM105 in cancer progression. \u003c/strong\u003eThe diagram illustrates a comprehensive multi-phase approach, beginning with the formulation of the research question and hypothesis, followed by in silico analyses, including differential expression profiling (TCGA and GEO datasets), correlation with clinicopathological parameters, and co-expression network with pathway enrichment analysis. Target genes identified computationally were validated ex vivo via RT-qPCR in patient-derived tissues, and in vitro functional assays were used to assess cell viability, proliferation, migration, colony formation, and apoptosis, alongside marker analysis (BAX, BCL-2, Cyclin E1, and Cyclin D1). In vivo validation via murine xenograft models was performed to evaluate tumor growth and metastatic potential. Mechanistic insights were explored through ribosome biogenesis, MYC signaling, and immune infiltration analyses. The expanded clinical data analysis incorporated survival analysis and ROC curve assessment. Data from all experimental tiers were statistically integrated to derive final conclusions, underscoring the potential of TMEM105 as a prognostic biomarker and therapeutic target.\u003c/p\u003e","description":"","filename":"FIG1.png","url":"https://assets-eu.researchsquare.com/files/rs-7789906/v1/78540c29f70e3eb8307e9cff.png"},{"id":94451661,"identity":"71bab00f-c745-4bf1-8962-34d674e0dcdc","added_by":"auto","created_at":"2025-10-27 14:40:16","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1082832,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUpregulation of TMEM105 expression in colorectal cancer (CRC).\u003c/strong\u003e (A–C) Comparative analysis of TMEM105 expression between tumor and normal tissues in the TCGA, GSE41328, and GSE25070 datasets. The expression values were normalized and log2-transformed for visualization. (D–H) Correlations between TMEM105 expression levels and clinicopathological features were examined via TCGA data. TMEM105 expression was significantly associated with tumor stage, TNM-M classification, and distinct CRC subgroups. (logFC: log fold change; FDR: false discovery rate)\u003c/p\u003e","description":"","filename":"FIG2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7789906/v1/c4640262422b4648df254225.jpg"},{"id":94451920,"identity":"c492a7e0-03a0-4d93-8a61-236b7d6468fd","added_by":"auto","created_at":"2025-10-27 14:40:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2609944,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCoexpressionanalysis revealed that the upregulation of TMEM105 is associated with ribosome biogenesis and MYC-driven pathways in colorectal cancer (CRC). \u003c/strong\u003e(A) Co-expression network of genes significantly correlated with TMEM105 expression in the TCGA dataset. (B) Pathway enrichment analysis of the TMEM105-associated co-expression network, highlighting genes involved in ribosome biogenesis. (C) K-means clustering of TCGA tumor samples on the basis of the expression of ribosome biogenesis–related genes, stratifying samples into two groups: C1 (low expression of candidate genes) and C2 (high expression of candidate genes). (D) TMEM105 expression across the defined clusters, with significantly higher expression in cluster C2. (E) Volcano plot displaying genes that were differentially expressed between clusters C2 and C1. (F–G) Pathway enrichment analysis of upregulated (F) and downregulated (G) genes in cluster C2 compared with those in cluster C1. (logFC: log fold change; FDR: false discovery rate)\u003c/p\u003e","description":"","filename":"FIG3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7789906/v1/bc8ae2185bd88c8c8c6f9121.jpg"},{"id":94452146,"identity":"36f42579-6813-4a5f-a311-2f8f3929dc70","added_by":"auto","created_at":"2025-10-27 14:40:51","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1310587,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between TMEM105 expression and immune cell infiltration in colorectal cancer (CRC). \u003c/strong\u003eImmune cell infiltration levels were analyzed in relation to TMEM105 expression via the TIMER 2.0 platform on the basis of TCGA-COAD data. (Rho: Spearman’s correlation coefficient; p: p-value)\u003c/p\u003e","description":"","filename":"FIG4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7789906/v1/6381f9c3ca08070c8a020381.jpg"},{"id":94451510,"identity":"8d36e262-3265-473c-8576-13973e0ccf4e","added_by":"auto","created_at":"2025-10-27 14:40:12","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1538963,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eElevated expression of TMEM105 in colorectal cancer (CRC) tissues and optimization of siRNA-mediated knockdown in CRC cell lines. \u003c/strong\u003e(A) Relative TMEM105 mRNA expression in patient-derived CRC tissues compared with that in matched adjacent normal tissues (n = 9 per group), as quantified via real-time PCR. (B) Comparative analysis of TMEM105 expression across CRC cell lines revealed significantly elevated levels in HT‑29 cells. (C, D) Evaluation of lipofectamine-induced cytotoxicity in HT‑29 (C) and HCT‑116 (D) cells at various reagent concentrations (μL/100 μL) over 24 h and 48 h, expressed as percentage cell viability. (E, F) Dose-dependent suppression of TMEM105 expression in HT‑29 (E) and HCT‑116 (F) cells following siRNA treatment at the indicated concentrations, as measured by real-time PCR. (G, H) Comparison of the efficacy of three distinct siRNA sequences in reducing TMEM105 expression in HCT‑116 (G) and HT‑29 (H) cells, expressed as the fold change relative to the control. (I, J) Time-course analysis of TMEM105 knockdown efficiency via siRNA‑2 in HT‑29 (I) and HCT‑116 (J) cells at 24 h, 48 h, and 72 h post-transfection.\u003c/p\u003e","description":"","filename":"FIG5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7789906/v1/c279c2f60852b4fcd9d18421.jpg"},{"id":94451490,"identity":"554594a8-1414-43c1-830a-7f37d06347f3","added_by":"auto","created_at":"2025-10-27 14:40:11","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":749074,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional consequences of TMEM105 silencing on cell viability and the expression of proliferation-associated genes in CRC cell lines. \u003c/strong\u003e(A) Cell viability of HT‑29 and HCT‑116 cells at 48 h post-transfection with control, scramble, or TMEM105-targeting siRNA‑1/siRNA‑2, as determined by the MTT assay, revealing a significant reduction in viability in HCT‑116 cells treated with either siRNA. (B, C) Relative mRNA expression of CCNE1 (B) and CCND1 (C) in HCT‑116 cells at 48 h following TMEM105 knockdown, normalized to that of GAPDH. (D, E) Relative mRNA expression of CCND1 (D) and CCNE1 (E) in HT‑29 cells under the same experimental conditions, showing statistically significant downregulation.\u003c/p\u003e","description":"","filename":"FIG6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7789906/v1/d2a4645c80ce88495dfd5f11.jpg"},{"id":94452118,"identity":"312c58dd-14d3-436c-b78b-7d4cd45eebe7","added_by":"auto","created_at":"2025-10-27 14:40:46","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3185311,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpaired migration and colony formation following TMEM105 knockdown. (\u003c/strong\u003eA–D) Wound-healing assays in HT-29 and HCT-116 cells showing both qualitative and quantitative reductions in migratory capacity upon TMEM105 silencing. (E, F) Colony formation assays comparing siRNA-treated cells with control cells revealed a significant decrease in colony number after TMEM105 knockdown. The error bars represent the standard deviations.\u003c/p\u003e","description":"","filename":"FIG7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7789906/v1/85f0a45857c1ac5578f4b87a.jpg"},{"id":94452133,"identity":"c92559d7-5769-49b0-907d-011190047083","added_by":"auto","created_at":"2025-10-27 14:40:48","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1536153,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnhanced apoptosis induced by TMEM105 knockdown in CRC cells. \u003c/strong\u003e(A, B) Apoptosis analysis of HT-29 cells after TMEM105 silencing, which revealed a significant increase in the number of apoptotic cells. (C, D) Relative expression of apoptosis-associated markers (BAX and BCL2) in the siRNA-treated HT-29 cells compared with the control cells. (E, F) Annexin V/PI staining of HCT-116 cells, which revealed increased apoptosis following TMEM105 knockdown. (G, H) Expression profiles of apoptosis-related genes (BAX and BCL2) in HCT 116 cells before and after TMEM105 silencing.\u003c/p\u003e","description":"","filename":"FIG8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7789906/v1/fd1348af41a11dae78dff2f0.jpg"},{"id":94451891,"identity":"22e18b8a-6bcf-42d5-82c5-c93fd721210b","added_by":"auto","created_at":"2025-10-27 14:40:27","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":831098,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKnockdown of TMEM105 downregulates the expression of ribosomal protein genes and attenuates global protein synthesis in colorectal cancer cells. \u003c/strong\u003e(A–D) Relative mRNA expression of ribosomal protein genes (RPL7 and RPS2) was quantified via RT‒qPCR in HT-29 and HCT-116 cells 48 hours post-transfection with a non-targeting scramble siRNA or two independent siRNAs targeting TMEM105 (siRNA-1 and siRNA-2). Significant downregulation of both genes was observed following TMEM105 knockdown. (E, F) Consistent with these findings, quantification of the total cellular protein concentration revealed a significant reduction in both HT-29 (E) and HCT-116 (F) cells upon TMEM105 silencing. The data are presented as the means ± SDs from three independent experiments. p-valus for the indicated comparisons are shown.\u003c/p\u003e","description":"","filename":"FIG9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7789906/v1/231520c63c014df463a564eb.jpg"},{"id":100069247,"identity":"3bea5925-7036-437a-9487-b5097763b69e","added_by":"auto","created_at":"2026-01-12 16:11:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15393005,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7789906/v1/4c621558-342e-4c9b-88e4-a16f54a9aa30.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"LncRNA TMEM105 Promotes Malignancy via the MYC-Ribosome Biogenesis Axis: A Novel Prognostic Biomarker and Therapeutic Target in Colorectal Cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is the third most commonly diagnosed cancer and the second leading cause of cancer-related deaths globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Its development is a multistep process characterized by the accumulation of genetic and epigenetic alterations, including mutations in tumor suppressors and oncogenes such as APC, MLH1, and KRAS, along with lifestyle-associated risk factors such as red meat consumption, obesity, and smoking [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Inherited syndromes such as Lynch syndrome and familial adenomatous polyposis (FAP) further increase the risk of CRC [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Among the molecular changes contributing to CRC progression, dysregulation of gene expression plays a critical role in modulating tumor progression, immune evasion, and the response to therapy, highlighting its relevance in early detection and targeted treatment strategies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLong non-coding RNAs (lncRNAs), a heterogeneous class of transcripts longer than 200 nucleotides without protein-coding capacity, have garnered considerable attention as key regulators of cancer biology. Owing to their unique nucleotide sequences and secondary structures, lncRNAs engage in intricate interactions with DNA, RNA, and proteins, modulating gene expression through chromatin remodeling, transcriptional control, RNA splicing, and regulation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In CRC, dysregulated lncRNAs have been implicated in a range of biological processes, including epithelial‒mesenchymal transition (EMT), apoptosis, proliferation, metabolism, and therapeutic resistance [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Prominent examples include MIR17HG and FTX, which promote aerobic glycolysis and metastasis through ceRNA networks involving the miR-138-5p/HK1 and miR-215-3p/YAP1 axes, respectively [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTransmembrane protein 105 (TMEM105), recently identified as an oncogenic lncRNA, is located at chromosome 17q25.3 and is expressed in multiple tissues, including intestinal epithelial cells. While TMEM105 has been associated with cancer progression in other tumor types, its role in CRC remains unexplored. In breast cancer, elevated TMEM105 expression is correlated with poor prognosis and enhanced liver metastasis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Mechanistically, TMEM105 acts as a competitive endogenous RNA (ceRNA) by sponging miR-1208, thereby increasing LDHA expression and promoting glycolysis. This, in turn, creates a positive feedback loop via the SHH\u0026ndash;MAZ signaling pathway that further amplifies TMEM105 expression [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Similarly, in pancreatic ductal adenocarcinoma (PDAC), TMEM105 has been identified as a disulfidptosis-related lncRNA that stabilizes β-catenin, enhances c-MYC and GLUT1 expression, and drives tumor proliferation and metabolic reprogramming [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite compelling evidence of the oncogenic potential of TMEM105 in other malignancies, its expression profile and functional role in CRC have not been characterized. In this study, we aimed to elucidate the expression dynamics and oncogenic functions of TMEM105 in CRC. We first conducted in silico analyses to evaluate TMEM105 expression and its association with cancer-related signaling pathways via publicly available transcriptomic datasets. We subsequently validated these observations through comparative expression analysis of paired CRC tumor and adjacent non-tumorous tissues. Finally, we performed in vitro experiments to assess the impact of TMEM105 on CRC cell survival, migration, apoptosis, and clonogenic potential. Overall, this work sought to uncover the functional relevance of TMEM105 in CRC carcinogenesis and evaluate its utility as a novel biomarker and therapeutic target.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Study Design and Overview\u003c/h2\u003e\n\u003cp\u003eThis study was designed as a multiphase investigation aimed at elucidating the role of TMEM105 in cancer progression, integrating computational, experimental, and clinical approaches. The methodological framework was structured to ensure both analytical rigor and translational relevance, enabling the systematic exploration of TMEM105 expression patterns, functional roles, and mechanistic pathways across multiple experimental tiers. The research framework encompassed sequential in silico analyses, ex vivo validation, in vitro functional assays, mechanistic exploration, and in vivo confirmation. The overall workflow of the study, which integrates these complementary approaches, is illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, providing a stepwise overview from hypothesis formulation to clinical interpretation. This schematic serves as a visual guide to the logical progression of the research and the interconnection between its analytical and experimental components. The sample size for the clinical cohort was determined on the basis of precedent from similar studies and the availability of patient samples. For in vitro assays, the number of experimental replicates was selected to provide adequate statistical power to detect significant differences between groups.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 Data Sources and Preprocessing\u003c/h2\u003e\n\u003cp\u003eTo investigate the differential expression of TMEM105 and its potential association with colorectal cancer (CRC) malignancy, RNA-sequencing data from The Cancer Genome Atlas (TCGA) were employed. Raw count data in STAR-counts format were retrieved via the TCGAbiolinks R package [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. Data preprocessing included TMM normalization and removal of genes with negligible expression (defined as CPM\u0026thinsp;\u0026lt;\u0026thinsp;10 in more than 70% of samples) to minimize low-expression noise and enhance statistical robustness, followed by log2 transformation [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. The resulting expression matrix served as the basis for all subsequent analyses. The TCGA cohort comprised 483 tumor samples and 41 non-tumor tissues spanning multiple clinical stages. Updated clinical metadata were incorporated to evaluate the relationships between TMEM105 expression and relevant clinical parameters. In addition, two independent GEO datasets (GSE41328 and GSE25070), consisting of paired tumor and adjacent normal tissues, were analyzed. The raw microarray data were processed the limma package, which included background correction, RMA normalization, and log2 transformation [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3 Co-expression Network and Clustering Analysis\u003c/h2\u003e\n\u003cp\u003eTo identify genes co-expressed with TMEM105, Pearson correlation coefficients were computed between TMEM105 and all other genes in the normalized TCGA expression matrix. Genes with correlation coefficients\u0026thinsp;\u0026gt;\u0026thinsp;0.5 and p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.01 were retained for downstream analysis, including pathway enrichment. The mentioned criterion serves as an empirical measure that can identify genes with a putative functional association with TMEM105. Co-expression networks were visualized in Cytoscape. CRC tumor samples were then clustered based on expression profiles of TMEM105-associated genes. The optimal number of clusters was determined using the within-cluster sum of squares (WSS) method. K-means clustering was subsequently applied to divide the samples into two clusters: Cluster C1 (low TMEM105 expression) and Cluster C2 (high TMEM105 expression). The algorithm process was repeated 100 times with 1,000 centroid repositionings to ensure robust classification.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4 Differential expression and pathway enrichment analysis\u003c/h2\u003e\n\u003cp\u003eIn the TCGA dataset, samples were grouped into tumor and non-tumor tissue categories based on clinical annotations, and paired tumor/normal tissues were applied to the GSE41328 and GSE25070 datasets. Differential gene expression analysis was performed using a linear modeling approach via the limma package [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. A false discovery rate (FDR) of less than 0.01 was used to determine statistical significance. Differentially expressed genes between Clusters C2 and C1 were also identified to facilitate pathway analysis. Enrichment analysis of cluster-specific differentially expressed genes (DEGs) was performed via Enrichr (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/Enrichr/\u003c/span\u003e\u003c/span\u003e), with annotations from the KEGG and MSigDB databases. Enrichment analysis was also conducted for the TMEM105 co-expressed gene network via the same databases.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e2.5 Immune cell infiltration analysis\u003c/h2\u003e\n\u003cp\u003eTo evaluate the relationship between TMEM105 expression and immune cell infiltration in CRC, the TIMER 2.0 web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://timer.cistrome.org/\u003c/span\u003e\u003c/span\u003e) was used. Outputs from the EPIC and TIMER algorithms were considered. Only statistically significant associations between TMEM105 expression and immune cell populations have been reported.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e2.6 Tissue collection and ex vivo samples\u003c/h2\u003e\n\u003cp\u003eA total of 25 CRC tumors and matched adjacent healthy tissues were collected from the Iran Tumor Bank at Imam Khomeini Hospital, Tehran. The diagnosis of cancer and healthy tissue was performed by a certified pathologist. The samples were immediately frozen in liquid nitrogen and stored until testing. All patients provided written informed consent. The study protocol was approved by the Research Ethics Committee of the University of Tehran in compliance with guidelines from the Iranian Ministry of Health. The study cohort included 25 patients with a histologically confirmed diagnosis of colorectal cancer, comprising 15 males and 10 females. Regarding age, 19 patients were over 50 years old, and 6 were under 50 years old. All collected samples met the predefined inclusion criteria and were used for subsequent analysis, with no sample attrition during the study. The clinical characteristics of the patients are detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eClinical information for CRC samples\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003echaracteristic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNumber (N\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTNM stage\u003c/p\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003cp\u003eIV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTumor size\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;5cm\u003c/p\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;5cm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e2.7 Cell Culture\u003c/h2\u003e\n\u003cp\u003eHT-29 and HCT-116 human CRC cell lines were purchased from the Pasteur Institute of Iran. The cells were cultured in DMEM supplemented with 10% fetal bovine serum (FBS), 1% penicillin‒streptomycin (100 U/mL penicillin and 100 \u0026micro;g/mL streptomycin), and GlutaMAX\u0026trade;. Cultures were maintained at 37\u0026deg;C in a humidified 5% CO₂ incubator. The cell lines were obtained from the Pasteur Institute of Iran, where their authenticity was certified by the supplier, and they were confirmed to be free of mycoplasma contamination. The cells were subcultured at 80\u0026ndash;90% confluence with 0.25% trypsin-EDTA and cryopreserved with a mixture of 90% FBS and 10% DMSO.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e2.8 siRNA Design and Cell Transfection\u003c/h2\u003e\n\u003cp\u003eTarget-specific siRNAs for TMEM105 were designed via the siDirect online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sidirect2.rnai.jp/\u003c/span\u003e\u003c/span\u003e) and are listed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Transfections of HT-29 and HCT-116 cells were carried out via Lipofectamine\u0026trade; (Invitrogen, USA). Initial optimization of the transfection conditions was performed by varying the Lipofectamine volume (0.2\u0026ndash;2 \u0026micro;L/100 \u0026micro;L culture medium) and siRNA concentration (5\u0026ndash;80 nM), with cytotoxicity assessed via the MTT assay.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe sequence list and information of the primers and primers used in this study are summarized in the table below.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGene Names\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eForward primer (5\u0026rsquo;-\u0026gt;3\u0026rsquo;)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eReverse primer (5\u0026rsquo;-\u0026gt;3\u0026rsquo;)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTm\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNCBI accession number\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCND1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGCTCACGCTTACCTCAACCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eATCCAGGACTTGTGCCCTTG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNM_053056.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCNE1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eATACTTGCTGCTTCGGCCTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTCAGTTTTGAGCTCCCCGTC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNM_001238.4\u003c/p\u003e\n\u003cp\u003eNM_001322259.2\u003c/p\u003e\n\u003cp\u003eNM_001322261.2\u003c/p\u003e\n\u003cp\u003eNM_001322262.2\u003c/p\u003e\n\u003cp\u003eNM_001440305.1\u003c/p\u003e\n\u003cp\u003eNM_001440306.1\u003c/p\u003e\n\u003cp\u003eNM_001440307.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBCL2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGAACTGGGGGAGGATTGTGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGCCGGTTCAGGTACTCAGTC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNM_000633.3\u003c/p\u003e\n\u003cp\u003eNM_000657.3\u003c/p\u003e\n\u003cp\u003eNM_001438935.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBAX\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCCCGAGAGGTCTTTTTCCG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGCACAGGGCCTTGAGCAC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNM_001291428.2\u003c/p\u003e\n\u003cp\u003eNM_001291429.2\u003c/p\u003e\n\u003cp\u003eNM_001291430.2\u003c/p\u003e\n\u003cp\u003eNM_001291431.2\u003c/p\u003e\n\u003cp\u003eNM_004324.4\u003c/p\u003e\n\u003cp\u003eNM_138761.4\u003c/p\u003e\n\u003cp\u003eNM_138763.4\u003c/p\u003e\n\u003cp\u003eNM_138764.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGAPDH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGGGAGCCAAAAGGGTCATCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGTGCTAAGCAGTTGGTGGTG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNM_001101.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTMEM105\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTCTCATCTCCCCACAGGAATC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTTTGCTTCTTAGCCCCCAACC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNR_165247.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003esiRNA (scramble)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUUCUCCGAACGUGUCACGU\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eACGUGACACGUUCGGAGAA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003esiRNA-1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCCAUAGCUGACACUUCUA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUAGAAGUGUCAGCUAUGGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNR_165247.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003esiRNA-2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGGCAAGCUCUGAUCUUACA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUGUAAGAUCAGAGCUUGCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNR_165247.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe cells were seeded according to the manufacturer\u0026rsquo;s protocol such that they reached 60\u0026ndash;70% confluence within 24 hours at the time of transfection. To minimize potential bias from positional effects, the assignment of wells to the various treatment groups (e.g., Control, Scramble, siRNA-1, and siRNA-2) was randomized for each independent experiment. siRNAs and Lipofectamine were separately diluted in serum- and antibiotic-free media, incubated at room temperature for 5 minutes, combined, and incubated for an additional 15\u0026ndash;20 minutes to form siRNA‒lipid complexes. The complexes were added to the cells and incubated for 8 hours, after which the medium was replaced with complete culture medium (culture medium containing 10% FBS and antibiotics). The knockdown efficiency was measured via qRT‒PCR 24 to 48 hours after transfection.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e2.9 MTT Assay for Cell Viability\u003c/h2\u003e\n\u003cp\u003eCells were seeded in 96-well plates and allowed to adhere for 24 hours, followed by siRNA treatment for 48 hours. Then, 10 \u0026micro;L of MTT reagent was added to each well and incubated for 2 hours. After the formazan crystals formed, they were solubilized with 100 \u0026micro;L of DMSO, and the absorbance was measured at 570 nm via an ELISA plate reader. Cell viability was calculated relative to that of the untreated controls.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e2.10 Wound Healing Assay (Cell Migration)\u003c/h2\u003e\n\u003cp\u003eMigration was evaluated via a wound healing assay. HT-29 and HCT-116 cells were cultured to 70\u0026ndash;80% confluence in 6-well plates. A uniform scratch was made using a 100-\u0026micro;L pipette tip, and the cells were washed with culture medium to remove debris. The cells were incubated in low-serum medium (1% FBS) and treated with siRNA. Initial scratch images (0 h) were captured, and the plates were incubated at 37\u0026deg;C and 5% CO₂. Migration was assessed by imaging the same fields after 24 and 48 hours. Wound closure was quantified via ImageJ, and migration percentages were calculated.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e2.11 Colony Formation Assay\u003c/h2\u003e\n\u003cp\u003eHT-29 and HCT-116 cells were seeded in 6-well plates at 500 cells/well and treated with siRNA for 24 hours. Colonies were allowed to form over 14 days, and the media was changed every 48 hours. At the end of the incubation period, the medium was removed, and the cells were washed with PBS, fixed with methanol for 20 minutes, and stained with 0.5% crystal violet for 15 minutes. The plates were washed and air-dried, and the colonies were counted via ImageJ software.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003e2.12 Annexin V/PI Apoptosis Assay\u003c/h2\u003e\n\u003cp\u003eApoptosis analysis was performed with an Annexin V-FITC/PI apoptosis detection kit according to the manufacturer's instructions. Following 48 hours of siRNA treatment, HT-29 and HCT-116 cells were harvested, washed with PBS, and detached with trypsin without EDTA. The cells were subsequently centrifuged at 1,000 rpm for 5 minutes, after which the pellet was resuspended in 100 \u0026micro;L of binding buffer. Then, 5 \u0026micro;L of Annexin V-FITC and 5 \u0026micro;L of PI were added to each sample and incubated in the dark at room temperature for 15 minutes. Afterward, 400 \u0026micro;L of binding buffer was added, and the samples were analyzed by flow cytometry (FACSCalibur, USA). The fluorescence from Annexin V and PI was detected via the FL1 and FL3 channels, respectively. The data were analyzed via quadrant gating to quantify the percentages of viable (Annexin V⁻/PI⁻), early apoptotic (Annexin V⁺/PI⁻), late apoptotic (Annexin V⁺/PI⁺), and necrotic (Annexin V⁻/PI⁺) cells.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003e2.13 RNA Extraction, cDNA Synthesis, and RT‒qPCR\u003c/h2\u003e\n\u003cp\u003eTotal RNA was extracted via TRIzol reagent following the manufacturer\u0026rsquo;s protocol. RNA purity and concentration were assessed via optical density at 260/280 nm, and equal amounts of RNA were used for cDNA synthesis. The samples were treated with DNase I to eliminate genomic DNA contamination. cDNA synthesis was performed via a commercial kit (Yekta Tajhiz), oligo (dT), random hexamer primers, and reverse transcriptase enzyme. Specific primers were designed via Primer-BLAST and Oligo7 software; all the gene sequences are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Gene expression was quantified by RT‒qPCR using SYBR Green dye and gene-specific primers. GAPDH was used as the internal control. The expression levels were calculated via the 2\u003csup\u003e⁻\u0026Delta;Ct\u003c/sup\u003e method.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003e2.14 Protein quantification via a BCA assay\u003c/h2\u003e\n\u003cp\u003eTotal protein concentration was determined via a BCA protein assay kit (Pars Tous, Iran) following the manufacturer's protocol. A standard curve was generated via the use of serial dilutions of bovine serum albumin (BSA) in the appropriate lysis buffer, ranging from 0 to 2000 \u0026micro;g/mL. After treatment, equal numbers of cells were seeded for each group to reduce variability. The cells were washed three times with PBS to remove extracellular proteins, harvested, and lysed. The lysates were diluted to ensure that they fell within the linear range of the standard curve. In a 96-well plate, 25 \u0026micro;L of each sample or standard was added per well, followed by the addition of 200 \u0026micro;L of working reagent. After gentle mixing, the plates were incubated at 37\u0026deg;C for 30 minutes, and the absorbance was measured at 562 nm via a microplate reader.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003e2.15 Statistics and Software\u003c/h2\u003e\n\u003cp\u003ePreprocessing and analysis of TCGA and GEO data were conducted via the R programming language (v. 4.8). The false discovery rate was employed to assess the significance levels within the in silico data. The Pearson correlation test was applied for the co-expression network analysis. One-way ANOVA was utilized to evaluate the significance of differences among various experimental groups. To prevent observer bias during data acquisition and analysis, key quantitative steps, such as the analysis of wound healing assays, colony counting, and flow cytometry data, were performed by an investigator blinded to the treatment conditions. GraphPad Prism (v. 8.4) was used to generate graphical representations.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.1 TMEM105 is upregulated in CRC and is correlated with clinical features\u003c/h2\u003e\u003cp\u003eIn silico analysis of TCGA data revealed significant upregulation of TMEM105 in CRC tumor samples relative to healthy tissues, with a\u0026thinsp;\u0026gt;\u0026thinsp;2-fold increase in expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA; logFC\u0026thinsp;=\u0026thinsp;1.2, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Consistently, paired analysis of tumor and adjacent non-tumorous samples (GSE41328 and GSE25070) revealed significantly elevated TMEM105 expression in tumor tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC; FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAnalysis of TCGA clinical annotations revealed that TMEM105 expression was significantly higher in advanced-stage (III\u0026ndash;IV) colorectal tumors than in early-stage (I\u0026ndash;II) tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD; FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Patients with metastatic disease (M1) also presented significantly elevated TMEM105 levels compared with nonmetastatic patients (M0) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE; FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In addition, the invasive molecular subtype presented higher TMEM105 expression than the other subgroups did (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF; FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, TMEM105 expression did not differ significantly with respect to tumor anatomical location and TNM-M stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). These findings indicate that TMEM105 is markedly upregulated in CRC and may be associated with features of tumor aggressiveness and malignancy-related features.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.2 TMEM105 expression is correlated with genes involved in ribosome biogenesis and MYC targets\u003c/h2\u003e\u003cp\u003eCo-expression network analysis of TCGA data identified 106 genes that were positively co-expressed with TMEM105 (r\u0026thinsp;\u0026gt;\u0026thinsp;0.5, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Enrichment analysis showed significant involvement of these genes in ribosome biogenesis and MYC target pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB; FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01), highlighting a potential regulatory link. Given that MYC is a master regulator of ribosome biogenesis, this finding supports a mechanistic association between TMEM105 and MYC-driven oncogenic programs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB; FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Notably, MYC is a well-established master regulator of ribosomal biogenesis, and genes involved in ribosome biogenesis are highlighted in red for visualization in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUsing the expression profiles of these ribosomal genes, tumor samples were stratified into two clusters by K-means clustering: Cluster C1 (n\u0026thinsp;=\u0026thinsp;138), characterized by low expression, and Cluster C2 (n\u0026thinsp;=\u0026thinsp;345), characterized by high expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In support of the validity of these clusters, TMEM105 expression was significantly elevated in Cluster C2 compared with C1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD; log2FC\u0026thinsp;=\u0026thinsp;0.7, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001), further supporting the biological coherence of the stratification. Differential expression analysis between these clusters revealed 639 upregulated genes (logFC\u0026thinsp;\u0026gt;\u0026thinsp;0.7, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and 615 downregulated genes (logFC \u0026lt; -0.7, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in Cluster C2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). The functional enrichment of the genes upregulated in C2 was significantly associated with oxidative phosphorylation, ribosome biogenesis, MYC target pathways, mTORC1 signaling, and E2F target pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF; FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In contrast, the genes downregulated in C2 were predominantly associated with immune-related pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG; FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These findings suggest that TMEM105 may contribute to CRC pathogenesis by modulating ribosomal biogenesis and MYC-driven oncogenic programs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.3 High TMEM105 expression is associated with reduced immune cell infiltration\u003c/h2\u003e\u003cp\u003eThe relationship between TMEM105 expression and immune cell infiltration in CRC was subsequently evaluated via TIMER 2.0. The immune cell composition was estimated in the TCGA dataset via computational deconvolution. High TMEM105 expression was negatively correlated with reduced infiltration of several key immune cell populations, including CD8\u0026thinsp;+\u0026thinsp;T cells, neutrophils, natural killer (NK) cells, dendritic cells (DCs), and macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These results suggest that the overexpression of TMEM105 may contribute to immune evasion in CRC by suppressing the recruitment of these immune cells to the TME. However, as these associations are based on in silico analysis, further experimental validation is needed to confirm the role of TMEM105 in tumor-immune interactions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.4 TMEM105 is upregulated in tumor tissues, and its silencing reduces the viability of CRC cell lines\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRT‒qPCR was used to assess TMEM105 expression in 25 paired CRC and adjacent healthy tissues to validate our in silico findings. TMEM105 was significantly upregulated in tumor samples compared with adjacent non-tumorous tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA; P\u0026thinsp;=\u0026thinsp;0.01). Among the CRC cell lines, the HT-29 line presented higher TMEM105 expression than the HCT-116 line did (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB; P\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor functional studies, HT-29 and HCT-116 cells were transfected with TMEM105-targeting siRNAs via Lipofectamine. Toxicity assays indicated that Lipofectamine volumes exceeding 1 \u0026micro;L per 100 \u0026micro;L of culture medium were cytotoxic, so 1 \u0026micro;L/100 \u0026micro;L was used in all the transfection experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Serial dilution experiments revealed that a 20 nM siRNA concentration provided optimal knockdown efficiency (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). RT‒qPCR confirmed that co-transfection with two siRNAs (siRNA-1 and siRNA-2) resulted in a\u0026thinsp;\u0026gt;\u0026thinsp;50% reduction in TMEM105 expression at 48 hours in both cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ), and these conditions were used for downstream functional assays.\u003c/p\u003e\u003cp\u003eTMEM105 knockdown significantly reduced the viability of CRC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA; P\u0026thinsp;\u0026lt;\u0026thinsp;0.02). Moreover, the expression of key proliferation markers, Cyclin E1 (CCNE1) and Cyclin D1 (CCND1), was significantly downregulated following TMEM105 silencing (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Collectively, these findings indicate that TMEM105 is crucial for CRC cell growth and survival and support its role as a candidate oncogene and a potential therapeutic target in CRC.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Silencing TMEM105 Impairs Migration and Colony Formation and Promotes the Apoptosis of CRC Cells\u003c/h2\u003e\u003cp\u003eTo comprehensively investigate the impact of TMEM105 knockdown on the malignant phenotypes of CRC, we initially conducted wound healing assays to assess the migratory capacity of the cells. These assays demonstrated a significant decrease in the migration ability of both the HT-29 and HCT-116 cell lines following siRNA-mediated silencing of TMEM105. Quantitative analysis confirmed that this reduction was statistically significant, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA\u0026ndash;D). These findings suggest that TMEM105 plays a critical role in promoting CRC cell motility.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the colony formation assays, compared with control cells, TMEM105-deficient cells presented a significantly lower clonogenic capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE\u0026ndash;F; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting impaired long-term proliferative potential.\u003c/p\u003e\u003cp\u003eThe apoptotic response following TMEM105 knockdown was quantified via Annexin V-FITC/PI staining. Compared with those in the control and scrambled siRNA groups, the apoptotic population in the HT-29 cells increased by more than 20% (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA\u0026ndash;B; P\u0026thinsp;=\u0026thinsp;0.001). This effect was accompanied by upregulation of the pro-apoptotic gene BAX and downregulation of the anti-apoptotic gene BCL-2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC\u0026ndash;D; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similar trends were observed in HCT-116 cells, where apoptosis increased by nearly 30% following TMEM105 silencing, along with a shift in BAX/BCL-2 expression, which was consistent with the activation of the intrinsic apoptotic pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE\u0026ndash;H; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese findings demonstrate that TMEM105 promotes key oncogenic behaviors in CRC cells, including migration, clonogenicity, and survival, while its silencing induces apoptotic cell death, reinforcing its functional role in CRC tumor progression.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.6 TMEM105 Knockdown Reduces Ribosome Biogenesis-Related Gene Expression and Total Protein Content\u003c/h2\u003e\u003cp\u003eGiven the strong co-expression between TMEM105 and ribosomal genes, the effect of TMEM105 silencing on the representative ribosome biogenesis genes RPS2 and RPL7 was examined (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, red nodes). RT‒qPCR analysis revealed significant downregulation of RPS2 and RPL7 in both HT-29 and HCT-116 cells following TMEM105 knockdown (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA\u0026ndash;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, the total protein concentration was significantly reduced in TMEM105-depleted cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that TMEM105 may modulate global protein synthesis. Collectively, these data suggest that TMEM105 promotes CRC progression by enhancing ribosome formation and global protein production, processes critical for tumor cell growth and metabolism.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eLong non-coding RNAs (lncRNAs) are increasingly recognized as crucial regulators of cellular processes and have emerged as promising diagnostic biomarkers and therapeutic targets in a variety of diseases, including cancer [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. With recent advances in RNA-based therapeutics\u0026mdash;particularly antisense oligonucleotide drugs now progressing into phase II clinical trials\u0026mdash;the landscape for targeting lncRNAs is becoming increasingly viable and clinically relevant [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In the present study, we investigated the functional role of TMEM105, an understudied lncRNA, in the progression and malignancy of CRC.\u003c/p\u003e\u003cp\u003eOur integrated approach, which combines in silico analyses with ex vivo experiments on CRC tissues and cell lines, consistently revealed significant upregulation of TMEM105 in tumor samples compared with adjacent healthy tissues. Notably, elevated TMEM105 expression was significantly associated with advanced clinicopathological parameters, including stage III/IV tumors and metastatic status (TNM.M1). These observations are consistent with a growing body of evidence documenting the aberrant overexpression of TMEM105 across a spectrum of human malignancies, thereby substantiating its role as a putative pan-cancer oncogenic lncRNA. For example, in breast cancer, increased TMEM105 expression has been linked to poor prognosis and increased metastatic potential [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Similarly, in, high TMEM105 expression has been correlated with reduced overall survival [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Evidence also implicates TMEM105 in the tumorigenesis of head and neck cancers, suggesting a broader oncogenic function [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In thyroid cancer, TMEM105 appears to regulate cell cycle progression, with its overexpression correlating with unfavorable clinical outcomes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Moreover, in gastric cancer, in silico data have demonstrated its upregulation and association with poor patient prognosis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Collectively, these findings support a conserved oncogenic function of TMEM105 across multiple cancer types and are consistent with its role in driving CRC aggressiveness, as observed in our study.\u003c/p\u003e\u003cp\u003eFunctionally, our data demonstrated that silencing TMEM105 significantly inhibited the viability, migration, and colony-forming capacity of CRC cell lines while simultaneously increasing their apoptotic activity. These results highlight TMEM105 as a potential driver of tumor growth and cellular aggressiveness. Our findings are in line with earlier studies in breast cancer, which reported that suppressing TMEM105 expression impairs cell migration and colony formation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, research on pancreatic cancer has shown that depletion of TMEM105 inhibits colony formation and migration, reinforcing its pro-tumorigenic role across malignancies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurthermore, to elucidate the molecular mechanisms underlying the protumorigenic functions of TMEM105, we performed our transcriptomic and functional enrichment analyses, which focused on pathways integral to ribosome biogenesis and protein synthesis. Specifically, co-expression network analysis revealed a strong positive correlation between TMEM105 and several ribosome-related genes, including RPS2 and RPL7, whose expression levels were markedly reduced following TMEM105 knockdown. Notably, genes associated with MYC signaling pathways were also enriched among TMEM105-associated targets, suggesting that TMEM105 may exert its oncogenic influence, at least in part, via the modulation of MYC-driven transcriptional programs. Given the central role of MYC in orchestrating ribosomal RNA transcription and ribosome assembly [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], our findings provide mechanistic insight into how TMEM105 may contribute to CRC pathogenesis through the enhancement of translational capacity and cellular proliferation. Interestingly, a similar axis has been reported in pancreatic cancer, suggesting that TMEM105 may act as a conserved modulator of MYC-dependent oncogenic signaling across different tumor types [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite these compelling findings, several limitations should be acknowledged. First, the study relies heavily on in silico analyses and in vitro experiments using only two CRC cell lines, which may not fully capture the heterogeneity of CRC tumors. Second, the absence of in vivo validation restricts our ability to draw definitive conclusions regarding the role of TMEM105 in tumor progression and metastasis within a physiological context.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eCollectively, our findings establish TMEM105 as a novel oncogenic lncRNA implicated in the progression of colorectal cancer (CRC). Its upregulation is significantly associated with adverse clinical features, whereas its silencing attenuates cardinal features of malignancy, including cell proliferation, migration, and survival. Mechanistically, our data reveal a compelling link between TMEM105, ribosome biogenesis, and MYC-driven transcriptional programs, providing a plausible axis for its oncogenic activity. Taken together, these results highlight TMEM105 as a compelling candidate for further investigation, with potential as both a prognostic biomarker and a viable therapeutic target in CRC. Subsequent investigations utilizing in vivo models and comprehensive clinical cohorts are warranted to fully validate its clinical utility and explore the translational feasibility of TMEM105-targeted interventions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTMEM105\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTransmembrane protein 105\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLncRNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLong non coding RNA\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCRC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eColorectal cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTCGA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRT‒qPCR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ereverse transcription quantitative polymerase chain reaction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSiRNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSmall interfering RNA\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMYC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMYC proto-oncogene\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTNM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etumor, node, metastasis (cancer staging system)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNK cells\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNatural killer cells\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDC cells\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDendritic cells\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFalse discovery rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLogFC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLog Fold Change\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests:\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing financial interests. Specifically, none of the authors has received funding, honoraria, consulting fees, stock ownership, patent rights, or any other financial or personal relationships that could have appeared to influence the work reported in this manuscript.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eNo funding to report.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.R.N. wrote the main manuscript, conducted the experiments, performed investigation, visualization, and experimental data analysis, developed the project, designed the study, performed data interpretation, and provided reagents. S.J.Z. and M.P. developed the project and designed the study. S.J.Z. supervised the entire project. M.P. provided technical assistance and contributed clinical insights and technical support. K.G. contributed to data interpretation and manuscript review. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData availability:\u003c/h2\u003e\u003cp\u003eNo datasets were generated or analysed during the current study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eXi Y, Xu P. Global colorectal cancer burden in 2020 and projections to 2040. Translational Oncol. 2021;14(10):101174.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarino P, et al. Healthy lifestyle and cancer risk: modifiable risk factors to prevent cancer. Nutrients. 2024;16(6):800.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSepp\u0026auml;l\u0026auml; TT, Burkhart RA, Katona BW. Hereditary colorectal, gastric, and pancreatic cancer: comprehensive review. BJS open. 2023;7(3):zrad023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen L, Ye L, Hu B. Hereditary colorectal cancer syndromes: molecular genetics and precision medicine. 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Front Endocrinol. 2023;14:1110987.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen X, et al. The ferroptosis-related noncoding RNA signature as a novel prognostic biomarker in the tumor microenvironment, immunotherapy, and drug screening of gastric adenocarcinoma. Front Oncol. 2021;11:778557.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuerrieri AN, et al. The Interplay Between the MYC Oncogene and Ribosomal Proteins in Osteosarcoma Onset and Progression: Potential Mechanisms and Indication of Candidate Therapeutic Targets. Int J Mol Sci. 2024;25(22):12031.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchlosser I, et al. A role for c-Myc in the regulation of ribosomal RNA processing. Nucleic Acids Res. 2003;31(21):6148\u0026ndash;56.\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":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7789906/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7789906/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTransmembrane protein 105 (TMEM105) has recently emerged as a potential oncogenic factor in various malignancies, yet its role in colorectal cancer (CRC) remains unclear. In this study, we comprehensively investigated the expression and function of TMEM105 in CRC through an integrated in silico, ex vivo, and in vitro approach. Publicly available datasets (TCGA, GSE41328, and GSE25070) were analyzed to assess TMEM105 expression and its association with clinicopathological features, followed by weighted gene co-expression network analysis to identify relevant biological pathways. The expression levels were further validated via RT‒qPCR in 25 paired CRC and adjacent non-tumorous tissues. Functional assays were performed after siRNA-mediated silencing of TMEM105 in CRC cell lines to evaluate its impact on cell viability, clonogenicity, migration, apoptosis, and pathway-specific gene expression. TMEM105 was significantly upregulated in CRC tissues, and elevated TMEM105 expression was correlated with advanced stage (stage IV) and metastasis. Co-expression analysis revealed ribosome biogenesis and MYC signaling as pathways strongly associated with TMEM105. The functional inhibition of TMEM105 reduced cell viability, impaired colony formation, suppressed migration, and promoted apoptosis, accompanied by the downregulation of the ribosomal genes RPL7 and RPS2 and a marked decrease in global protein synthesis. Collectively, these findings establish TMEM105 as a putative oncogenic driver that promotes CRC progression by modulating ribosome biogenesis, potentially in concert with MYC signaling. TMEM105 may therefore serve as a promising prognostic biomarker and novel therapeutic target for advanced colorectal cancer.\u003c/p\u003e","manuscriptTitle":"LncRNA TMEM105 Promotes Malignancy via the MYC-Ribosome Biogenesis Axis: A Novel Prognostic Biomarker and Therapeutic Target in Colorectal Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 11:33:04","doi":"10.21203/rs.3.rs-7789906/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-23T01:06:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-09T08:04:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-06T18:43:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336251710325147467946886836668202989901","date":"2025-11-03T17:24:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169432028636960388001503932849722708572","date":"2025-11-03T04:14:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-13T05:40:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-10T09:03:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-10T09:03:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Cell International","date":"2025-10-06T09:19:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"12869c36-a432-4329-bb68-510e3d97009d","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:03:05+00:00","versionOfRecord":{"articleIdentity":"rs-7789906","link":"https://doi.org/10.1186/s12935-025-04156-4","journal":{"identity":"cancer-cell-international","isVorOnly":false,"title":"Cancer Cell International"},"publishedOn":"2026-01-09 15:57:15","publishedOnDateReadable":"January 9th, 2026"},"versionCreatedAt":"2025-10-27 11:33:04","video":"","vorDoi":"10.1186/s12935-025-04156-4","vorDoiUrl":"https://doi.org/10.1186/s12935-025-04156-4","workflowStages":[]},"version":"v1","identity":"rs-7789906","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7789906","identity":"rs-7789906","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}