Silencing TMEM105 suppresses gastric cancer cell growth and migration via proliferation-associated pathways

Abstract

Background

Numerous studies in the field of cancer indicate that long non-coding RNAs (lncRNAs) play a significant role in the development and malignancy of various cancers. In this study, the expression changes of TMEM105 in gastric cancer (GC) and its association with malignancy were examined.

Methods

In the in silico section, TMEM105 expression in GC and its correlation with patient prognosis were analyzed using publicly available datasets. Using WGCNA analysis on TCGA data, modules and potential pathways associated with TMEM105 were identified. Expression changes of TMEM105 in GC tissues compared with adjacent healthy tissues were analyzed by RT-qPCR. The expression level of TMEM105 in GC cell lines, including AGS and MKN-45, was modulated using siRNA. The relationship between TMEM105 expression and malignant characteristics, including cell proliferation, migration, and colony formation, was examined in these cell lines.

Results

TMEM105 was significantly upregulated in public datasets, including TCGA, GSE19826, and GSE54129. TMEM105 expression was associated with advanced disease stage and poor prognosis. WGCNA analysis revealed that TMEM105 expression clustered with genes related to cell proliferation, such as E2F target genes, within the same module. Silencing TMEM105 reduced the viability of GC cell lines and decreased mRNA expression of proliferation-related genes, including E2F1, Cyclin D1, and Ki-67. Silencing TMEM105 markedly reduced migration rates in GC cells. Moreover, TMEM105 expression affected the colony-forming ability of these cell lines.

Conclusion

High TMEM105 expression in GC is associated with poorer patient outcomes. It appears to influence cellular proliferation and contribute to the development and malignancy of gastric carcinoma. Consequently, TMEM105 could be a valuable therapeutic and prognostic target.

Introduction

Despite remarkable advances in health care and medical science, cancer remains a major global health challenge. Gastric cancer (GC) is one of the most common malignancies of the gastrointestinal tract, affecting nearly one million people each year, with a five-year survival rate of around 30% [1]. Early diagnosis can significantly improve treatment outcomes and patient survival [2, 3]. However, due to the similarity of its clinical manifestations with other gastrointestinal diseases, most patients are diagnosed at an advanced stage [4]. Current treatment options include surgery, chemotherapy, radiotherapy, immunotherapy, and targeted therapy [5]. Targeted therapy requires the identification of key molecular mechanisms involved in disease progression; therefore, recognizing genes that contribute to the development and malignancy of GC is of great importance [6]. The identification of such genes can also aid early diagnosis and prognostication of patient survival, underscoring the importance of investigating genes associated with GC.

The regulation of intracellular signaling pathways involves complex interactions among diverse molecules and genes. Non-coding RNAs, especially long non-coding RNAs (lncRNAs), can regulate numerous cellular processes and play vital roles in signaling pathways like those that control cell proliferation [7–9]. One of the fundamental characteristics of cancer cells is uncontrolled proliferation, which drives tumor growth and malignancy. To date, many studies have shown that lncRNAs are involved in drug resistance, immune responses, metabolism, cell proliferation, angiogenesis, and apoptosis in GC [9–11]. Numerous other examples further indicate that lncRNAs can regulate proliferation and invasion processes in GC cells [12]. Two crucial pathways associated with metastasis in GC, namely epithelial-mesenchymal transition (EMT) and transforming growth factor (TGF-β) signaling, can be regulated by various lncRNAs [9, 13–15]. These findings underscore the significant role of lncRNAs in the progression and malignancy of GC, as well as their potential as targets for therapeutic intervention and diagnosis. Therefore, it is essential to elucidate the molecular mechanisms by which lncRNAs function in cancer development and progression.

TMEM105, an lncRNA located at 17q25.3, remains poorly characterized with respect to its molecular mechanisms and its association with cancer. To the best of our knowledge, only limited information is available regarding its biological role. However, in pancreatic cancer, TMEM105 has been reported to be upregulated and correlated with poor prognosis. It has also been demonstrated that TMEM105 promotes pancreatic cancer cell growth via the β-catenin pathway [16]. In breast cancer, evidence suggests an oncogenic role for TMEM105 and its association with unfavorable patient outcomes [17]. Furthermore, increased TMEM105 expression and its link to cell division have been observed in head and neck cancers [18]. In GC, TMEM105 overexpression and its association with poor patient prognosis have also been reported [19]. However, the molecular mechanisms underlying TMEM105 and the relationship between its expression level and key characteristics of GC cells remain poorly elucidated.

The objective of this study was to elucidate potential molecular mechanisms of TMEM105 in GC. To this end, we initially examined the differential expression of TMEM105 between GC tissues and normal tissues utilizing publicly available datasets. Additionally, the co-expression patterns of TMEM105 with other genes and related pathways were analyzed. The results were subsequently validated through ex vivo and in vitro experimental procedures.

Materials and methods

Databases

To examine alterations in TMEM105 expression in GC and its correlation with malignancy, data from The Cancer Genome Atlas (TCGA) were utilized. The TCGA-STAD dataset comprised 412 tumor samples from various stages and 36 non-tumor samples. The most recent version of the TCGA clinical data was obtained and analyzed to evaluate the association between TMEM105 expression and clinical characteristics, as well as patient survival. For additional validation, the datasets GSE19826 and GSE54129 from Gene Expression Omnibus (GEO), which included both tumor and healthy samples, were examined.

Analysis of public datasets

The raw TCGA-STAD RNA-seq data (STAR-Counts format) were downloaded using the TCGAbiolinks package [20]. The initial preprocessing step involved filtering out low-expression genes (CPM < 1 in 70% of samples) using the edgeR package [21]. Data were then normalized using the TMM method and log2-transformed [22]. The processed expression matrix was used for all analyses, including differential expression, correlation of TMEM105 with survival, and co-expression network analysis. Differential expression of TMEM105 in TCGA data was assessed using a linear model implemented in the limma package [23].

The GSE19826 and GSE54129 datasets were downloaded in raw format from the GEO database and processed using the Affy package [24]. Background correction, normalization (based on the RMA method), and log2 transformation were applied. Each sample was annotated as tumor or non-tumor, and differential expression of all probes was calculated using a linear model. Statistical significance in all TCGA and GEO analyses was determined using the False Discovery Rate (FDR), with an adjusted p-value threshold of < 0.05.

Survival analysis

Expression data for TMEM105 obtained from TCGA were integrated with the corresponding clinical data derived from tumor samples. Samples with overall survival durations of 0, 1, or missing values (NA) were excluded from the analysis [25]. A Cox regression analysis was conducted to examine the association between TMEM105 expression and clinical outcomes. Further evaluation was carried out using Kaplan–Meier survival curves. For the Kaplan–Meier analysis, TCGA tumor samples were divided into high- and low-expression groups based on the median TMEM105 expression level. The log-rank test was employed to determine statistical significance in all survival analyses.

Identification of TMEM105-related pathways and weighted gene co-expression network analysis (WGCNA)

To identify genes co-expressed with TMEM105 and potential pathways associated with it, a WGCNA was performed. The normalized TCGA expression matrix was used for this analysis. Initially, the variance of all genes across samples was calculated, and genes with variance < 0.3 were removed to reduce network noise. Samples were clustered using Euclidean distance, and outlier samples that could introduce noise were removed. A series of soft-threshold powers (β = 1–20) was tested to determine the optimal power based on Scale-Free Topology Fit and Mean Connectivity. Modules were then constructed using the optimized β value. Gene clustering and network topology were simulated using the Topological Overlap Matrix (TOM). The TOM matrix was generated using the TOMsimilarityFromExpr function, and gene dendrograms were produced via hierarchical clustering (hclust). Modules were identified using the blockwiseModules function. Module eigengenes (MEs) were computed to represent each module’s overall expression profile, and a hierarchical clustering tree (MEtree) was built to evaluate module relationships. Modules with highly correlated eigengenes (r ≥ 0.75) were merged using a cut height of 0.25. The module containing TMEM105 was identified, and all genes within it were extracted for pathway enrichment analysis. Candidate genes from this module were analyzed for functional pathways using the Enrichr tool (https://maayanlab.cloud/Enrichr/). All analyses were performed using the WGCNA package [26].

Sample collection

For ex vivo validation, paired GC tissues and adjacent normal tissues were collected. A total of 30 GC samples and 30 matched adjacent healthy tissues were obtained from the Iran Tumor Bank. Samples were collected from Imam Khomeini Hospital (Tehran, Iran), and a pathologist confirmed their tumor or healthy status. Samples were stored in liquid nitrogen until use. Written informed consent was obtained from all patients. All ethical procedures complied with the regulations of the Ministry of Health and Medical Education of Iran. Ethical approval was granted by the Internal Ethics Committee of the Genius Gene Research Center (IR-GG-1402-018). This study involved only laboratory analysis of anonymized human samples and did not include any direct human intervention. Clinical information of all samples used in this study is summarized in Table 1.

Table 1.

Clinical information for GC samples

characteristic	Number (N = 30)
Age
< 50	13
> 50	17
Gender
Male	18
Female	12
TNM stage
I	2
II	11
III	9
IV	8
TNM.M
M0	19
M1	11

Cell culture

Human GC cell lines AGS and MKN-45 were cultured in DMEM (Genex, Iran) medium supplemented with 10% fetal bovine serum (FBS; Genex, Iran), 1% penicillin-streptomycin (final concentration: 100 U/mL penicillin and 100 µg/mL streptomycin), and L-glutamine (DENAzist, Iran). The cells were maintained at 37 °C in a humidified incubator containing 5% CO₂. When the cells reached 70–80% confluency, they were passaged using 0.25% trypsin-EDTA (DENAzist, Iran) and subsequently used for experiments. For long-term storage, cells were frozen in a 90% FBS/10% DMSO (Genex, Iran) mixture.

SiRNA design and cell transfection

Two siRNAs targeting TMEM105 were designed using the siDirect online database (https://sidirect2.rnai.jp/), and their sequences are listed in Table 2. For TMEM105 knockdown, AGS and MKN-45 cells were transfected with the designed siRNAs using Lipofectamine (Invitrogen, USA). A serial dilution of Lipofectamine was prepared to assess cytotoxicity using the MTT assay (ViraGene, Iran), and 1 µL was added to 100 µL of culture medium for transfection. siRNA dilutions at 5, 10, 20, 40, and 80 nM were tested, and the optimal concentrations were determined to be 20 nM for AGS and 40 nM for MKN-45. Cells were seeded to reach 70–80% confluency before transfection. The siRNA-Lipofectamine complexes were prepared in antibiotic- and serum-free medium and then added to the cells. After 6 h of incubation, the medium was replaced with complete medium containing FBS. Gene silencing efficiency was evaluated 24–48 h post-transfection by qRT-PCR, with the 48-hour time point yielding the best results. All subsequent assays, including gene expression, cell viability, and migration analyses, were performed 48 h post-transfection using the mentioned concentrations. siRNA-negative (scramble) was used as a negative control.

Table 2.

The primer and SiRNA sequences and information 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
E2F1	CCGTGGACTCTTCGGAGAAC	ATCCCACCTACGGTCTCCTC	60	NM_005225.3
MKI67	CGTCCCAGTGGAAGAGTTGT	CGACCCCGCTCCTTTTGATA	60	NM_001145966.2 NM_002417.5
B2M	GAGTATGCCTGCCGTGTGAA	TGCGGCATCTTCAAACCTCC	60	NM_004048.4
TMEM105	TCTCATCTCCCCACAGGAATC	TTTGCTTCTTAGCCCCCAACC		NR_165247.1
siRNA (scramble)	UUCUCCGAACGUGUCACGU	ACGUGACACGUUCGGAGAA	-	-
siRNA-1	CCCAUAGCUGACACUUCUA	UAGAAGUGUCAGCUAUGGG	-	NR_165247.1
siRNA-2	GGCAAGCUCUGAUCUUACA	UGUAAGAUCAGAGCUUGCC	-	NR_165247.1

Cell viability assay (CCK-8 assay)

Cells in the exponential growth phase were seeded into 96-well plates at a final volume of 100 µL per well. After 24 h and complete attachment, transfection was performed. Four groups were included: siRNA-treated cells, untreated controls, scramble (siRNA-negative control), and blank (medium-only). After 48 h of treatment, CCK-8 (Sigma, USA) reagent was added according to the manufacturer’s instructions, and the cells were incubated in the dark for 2 h. Absorbance was measured at 450 nm using a microplate reader. Data were normalized to the blank wells, and cell viability (%) was calculated relative to the control group. All experiments were performed in triplicate (three biological and three technical replicates). Results are presented as mean ± SD, and statistical significance was determined by one-way ANOVA.

Cell migration assay (wound healing assay)

Cell migration was assessed using a wound-healing assay. AGS and MKN-45 cells were seeded into 6-well plates and grown to 70–80% confluency. A straight scratch was made across the cell monolayer using a 100 µL pipette tip. Detached cells were removed by washing with culture medium, and fresh medium containing 1% FBS (to minimize cell proliferation) was added. Cells were then treated with siRNA. Initial wound images (0 h) were captured, and the plates were incubated at 37 °C with 5% CO₂. After 48 h, Images of the same regions were captured again. The wound closure percentage was quantified using ImageJ, and cell migration rates were calculated. Each experiment was performed in triplicate, and significance was determined using one-way ANOVA.

Colony formation assay

A colony formation assay was used to evaluate the colony-forming ability and stemness of GC cells. AGS and MKN-45 cells were seeded at approximately 500 cells per well in 6-well plates. After 24 h, TMEM105 knockdown was performed, and the cells were incubated for 7 days at 37 °C in a humidified atmosphere with 5% CO₂ to allow colony formation. For the treated groups, siRNA transfection was repeated every 48 h to maintain TMEM105 silencing. After incubation, the medium was removed, the cells were washed with PBS, fixed with methanol for 20 min, and stained with 0.5% crystal violet for 15 min. Plates were washed with water, air-dried, and colonies were counted using ImageJ software. Each condition was performed in triplicate, and the results are expressed as mean ± SD. Statistical significance was analyzed by one-way ANOVA.

RNA Extraction, cDNA Synthesis, and RT-qPCR

Total RNA was extracted using the TRIzol (Genius Gene, Iran) method according to the manufacturer’s protocol. RNA purity and concentration were evaluated by measuring absorbance at 260 nm and 280 nm. Equal RNA concentrations were used for cDNA synthesis, and potential DNA contamination was removed with DNase I (Sinaclon, Iran) treatment. cDNA was synthesized using the Yekta Tajhiz kit (Iran) with oligo(dT) and random hexamer primers and reverse transcriptase enzyme. Gene-specific primers were designed using Primer-BLAST and Oligo 7 software; their sequences are summarized in Table 2. Expression analysis of target genes was performed using SYBR Green dye and gene-specific primers. B2M was used as an internal control. Relative gene expression levels were calculated using the 2⁻^ΔΔCt method. All experiments were conducted in triplicate, results are presented as mean ± SD, and significance was assessed using one-way ANOVA.

Results

Increased expression ofTMEM105in GC and its association with advanced stage and poor patient survival

To investigate the association of TMEM105 with progression and malignancy in GC, its expression was first analyzed across public datasets. TCGA data showed that TMEM105 expression was significantly higher in tumor samples than in non-tumor tissues (Fig. 1A; FDR < 0.0001). Similarly, TMEM105 was upregulated in the GSE19826 and GSE54129 datasets (Fig. 1B and C; FDR < 0.0001). To confirm these findings, the expression level of TMEM105 in GC samples was compared with that in adjacent normal tissues using RT-qPCR. The ex vivo results demonstrated a significant increase in TMEM105 expression in tumor tissues compared to normal tissues (Fig. 1D; P = 0.006). Subsequently, the relationship between TMEM105 expression and clinical characteristics, as well as patient survival, was evaluated. Among the clinical features, data indicated that TMEM105 expression level was associated with tumor stage, while no significant correlation was found with TNM.M or TNM.T (Fig. 1E and G). The expression level of TMEM105 was significantly higher in samples with stage III and IV tumors than in those with lower stages (Fig. 1E; P = 0.02). Cox regression analysis revealed that high TMEM105 expression was associated with poor prognosis (hazard ratio = 1.29; P = 0.01). Moreover, Kaplan-Meier analysis indicated that samples with higher TMEM105 expression had a significantly lower survival rate (Fig. 1H; P = 0.02). These findings suggest that TMEM105 may be upregulated in GC and correlated with poor patient outcomes.

Fig. 1.

Increased expression of TMEM105 and its association with poor prognosis of patients in GC. A-C Changes in TMEM105 expression in public datasets, including TCGA, GSE19826, and GSE54129, are shown. D Ex vivo results for TMEM105 expression levels in 30 GC samples compared with adjacent healthy samples are presented using RT-qPCR. TMEM105 expression was significantly higher in GC samples. E-G The association between TMEM105 expression and patients’ clinical characteristics is demonstrated using TCGA data. The TMEM105 level was associated with the advanced stage. H The association between TMEM105 expression and patient survival is shown using TCGA data. Increased TMEM105 expression correlated with lower survival

Association of TMEM105 expression with cell proliferation-related pathways in GC

To identify potential pathways associated with TMEM105 in GC, WGCNA analyses were performed. In the first step, after removing genes with low variance from the TCGA data, the samples were clustered. The results showed that two samples were markedly different from the others and were excluded from further analysis (Fig. 2A). Different β values were tested for the WGCNA analysis. As shown in Fig. 2, the optimal β value was 10 (Fig. 2B). The genes in the expression matrix were modularized using the WGCNA method, and closely related modules were merged. As presented in Fig. 3A, a total of 25 modules were identified in the TCGA dataset. The identified modules showed no strong correlations among themselves (Fig. 3B). TMEM105 was located in the orange module, which contained 2752 genes (Fig. 3C). Pathway analysis of these 2752 genes revealed that a large number of them were significantly enriched in cell proliferation-related pathways such as E2F Targets, G2-M Checkpoint, Myc Targets, Mitotic Spindle, mTORC1 Signaling, and PI3K/AKT/mTOR Signaling (Fig. 3D; FDR < 0.05). These findings suggest that TMEM105 may be involved in the progression and malignancy of GC through the pathways above. Furthermore, the pathways associated with the TMEM105 module are directly involved in tumor development and malignancy across various cancers.

Fig. 2.

Selecting a power of 10 for the WGCNA network and removing outlier samples. A Hierarchical clustering of TCGA samples identifies outlier samples. B Scale-Free Topology Fit and Mean Connectivity plots are given for the optimal β value

Fig. 3.

Co-expression of TMEM105 with genes related to cell proliferation. A The dendrogram of the identified modules is shown. B The status of the identified modules is indicated by their proximity to each other based on ME. C Twenty-five modules were identified, and the number of genes in each module is shown. TMEM105 was located in the orange module, which contained 2752 genes. D The enrichment results for genes in the orange module are displayed using MsigDB data. The genes in the orange module were significantly involved in pathways related to cell proliferation

Silencing of TMEM105 reduces cell proliferation and proliferation markers

The results of the previous section indicated that TMEM105 may be involved in cell proliferation pathways and thus contribute to the pathogenesis of GC. To further investigate this, TMEM105 expression was modulated using siRNA in two GC cell lines, AGS and MKN-45, and cell proliferation was subsequently evaluated. TMEM105 expression decreased by up to 70% in both cell lines after knockdown (Fig. 4A and B; P < 0.01). The survival rate of GC cell lines was significantly reduced following TMEM105 knockdown compared with the control and scramble groups (Fig. 4C and D; P < 0.05).

Fig. 4.

Reduced survival of GC cell lines with TMEM10 knockdown. A and B The efficacy of TMEM105 knockdown is demonstrated in AGS and MKN-45. After transfection, the expression level of TMEM105 was downregulated by 70%. C and D The survival results of GC cell lines after TMEM105 knockdown are presented. TMEM105 silencing reduced the proliferation and survival of GC cell lines by 50%

To explore this further, the expression levels of three cell proliferation markers, including cyclin D1 (CCND1), E2F1, and Ki-67 (MKI67), were examined following TMEM105 knockdown. It is noteworthy that E2F1 was also identified as a pathway associated with TMEM105 in the previous section. A 50% reduction in CCND1 expression was observed in the TMEM105 knockdown group compared to the control and scramble groups in both cell lines (Fig. 5A and B; P < 0.05). Additionally, E2F1 expression was significantly decreased following TMEM105 silencing (Fig. 5C and D; P < 0.01). Similar results were obtained for MKI67, with reduced expression observed following TMEM105 knockdown (Fig. 5E and F; P < 0.05). These findings suggest that TMEM105 may contribute to the development and progression of GC by regulating genes associated with cell proliferation.

Fig. 5.

Decreased expression of cell proliferation markers in TMEM105 knockdown cell lines. A-F The expression levels of genes related to cell proliferation, including CCND1, E2F1, and MKI67, were measured in the TMEM105 knockdown group compared to the control and scramble groups. The levels of candidate genes associated with cell proliferation were significantly reduced following TMEM105 modification

Reduction of migration and colony formation following TMEM105 downregulation

Next, the association between TMEM105 expression and other key cancer cell characteristics, including migration and colony formation, was examined. The migration assay results showed that silencing TMEM105 reduced AGS cell migration by approximately 40% (Fig. 6A and B; P < 0.01). Similarly, TMEM105 knockdown significantly reduced the migration rate of MKN-45 cells by approximately 30% relative to the control and scramble groups (Fig. 6C and D; P < 0.001). The number of colonies formed in both cell lines was also significantly reduced after TMEM105 knockdown (Fig. 7A-C, P < 0.05). These findings suggest that TMEM105 expression levels may influence malignancy-related characteristics of GC cells, such as migration and colony formation.

Fig. 6.

Knockdown of TMEM105 could inhibit the migration of GC cell lines. A-D Qualitative and quantitative results of GC cell line migration in the studied groups are presented. Silencing TMEM105 was associated with decreased cell line migration

Fig. 7.

Decreased expression of TMEM105 is associated with decreased colony formation. A-C The results of the colony formation assay in two GC cell lines are shown. Decreased TMEM105 expression significantly reduced colony formation in the cell lines

Discussion

Extensive evidence indicates that lncRNAs possess strong potential as therapeutic, diagnostic, and prognostic targets in various cancers, including GC [27–29]. Moreover, antisense oligonucleotide drugs targeting these molecules have entered phase II clinical trials for certain diseases, revolutionizing therapeutic strategies [30]. In this study, we emphasized the significance of lncRNAs, particularly TMEM105, and investigated the association between its expression and the progression and malignancy of GC. Identifying such disease-associated lncRNAs enhances our understanding of the molecular mechanisms underlying cancer development.

Based on in silico and ex vivo findings, TMEM105 expression was notably higher in GC samples than in non-tumor tissues. A significant link was also observed between TMEM105 levels and advanced tumor stages. Additionally, higher TMEM105 expression correlated with poorer patient outcomes. In pancreatic cancer, high TMEM105 expression is linked to worse patient outcomes [16]. In cases of breast cancer, TMEM105 expression is additionally upregulated and is associated with an unfavorable prognosis as well as liver metastasis [17]. Increased TMEM105 expression has been reported in head and neck cancer, where its expression is associated with radiotherapy response [18]. Similarly, in colorectal cancer, upregulation of TMEM105 and its correlation with tumor stage are observed [31]. In addition, evidence suggests that TMEM105 expression is linked to mortality risk in bladder cancer [32]. These findings align with our results and suggest that TMEM105 is implicated in tumor progression and malignancy, not solely in GC, but also across a range of other cancer types. The importance of gene expression changes in predicting survival in patients with GC has also been demonstrated [33, 34]. Therefore, TMEM105 has the potential to be considered as a prognostic biomarker.

WGCNA analysis indicated that TMEM105 was co-expressed with genes associated with cell proliferation pathways, including E2F Targets, G2–M Checkpoint, Mitotic Spindle, Myc Targets, mTORC1 Signaling, and PI3K/AKT/mTOR Signaling. This suggests that TMEM105 may contribute to the development and progression of GC through these pathways. The pathways mentioned above play a critical role in the development and progression of various cancers, including GC, and are recognized as primary therapeutic targets [35–38]. These findings may be consistent with the role of oncogene TMEM105 in GC. In vitro results demonstrated that TMEM105 knockdown reduced cell viability in GC cell lines. Moreover, the expression levels of proliferation markers, including Cyclin D1, E2F1, and Ki-67, decreased following TMEM105 silencing. It is noteworthy that Cyclin D1 is a key gene in the G2–M Checkpoint pathway, E2F1 is a vital transcription factor in the E2F Targets pathway, and Ki-67 is a core gene in the Mitotic Spindle pathway [39, 40]. Previous studies have shown that TMEM105 overexpression promotes cell proliferation in pancreatic cancer via the MYC and β-catenin pathways [16]. Similarly, in breast cancer, TMEM105 has been reported to enhance cell proliferation by regulating the glycolysis pathway [17]. These findings are consistent with our data and suggest that TMEM105 may contribute to GC progression by modulating cell proliferation pathways.

This study found that TMEM105 knockdown significantly reduced cell migration and colony formation in GC cell lines. Similarly, previous studies on pancreatic and breast cancers have demonstrated that silencing TMEM105 reduces cancer cell migration and colony-forming ability [16, 17]. These results highlight the importance of TMEM105 across various cancer types, including GC.

Together, these results suggest the potential of TMEM105 as a therapeutic, diagnostic, and prognostic biomarker in GC. Our results demonstrated that TMEM105 may contribute to GC progression through pathways associated with cell proliferation, migration, and stemness, underscoring its multifaceted role in tumor development and malignancy. This comprehensive study, which integrates bioinformatics and experimental validation, has limitations. Although public datasets and in vitro assays have confirmed TMEM105 expression and potential role in GC, its molecular mechanisms remain unclear. No in vivo studies were conducted to verify TMEM105’s effects on tumor growth and metastasis, limiting translational relevance. The small ex vivo tissue sample size prevented a complete assessment of patient variability. The focus was primarily on proliferation and migration pathways, with little attention to processes such as apoptosis, immune modulation, and metabolism. While siRNA knockdown demonstrated functional effects, complementary methods, such as overexpression or CRISPR, would better establish causality. Addressing these issues is vital for understanding TMEM105’s oncogenic role and its potential as a therapeutic and prognostic biomarker.

Conclusion

In summary, our findings demonstrate that TMEM105 is significantly upregulated in GC tissues and cell lines, and its elevated expression correlates with advanced disease stage and poor patient prognosis. Through integrated bioinformatics and experimental analyses, we showed that TMEM105 is associated with cell proliferation–related pathways and that its silencing suppresses proliferation, migration, and colony formation in GC cells. These results suggest that TMEM105 may function as an oncogenic lncRNA contributing to GC progression. Further in-depth mechanistic and in vivo studies are required to validate its potential as a diagnostic, prognostic, and therapeutic target.

Abbreviations

GC

Gastric Cancer

lncRNA

Long Non-Coding RNA

TCGA

The Cancer Genome Atlas

FDR

False discovery rate

WGCNA

Weighted Gene Co-expression Network Analysis

Authors’ contributions

B.N., M.M., and A.Z. designed the study. B.N. and M.S. carried out data analysis and experiments. B.N. and M.M. handled data interpretation. B.N. and M.M. undertook bioinformatics analysis. B.N. drafted the manuscript. Finally, M.M. and A.Z. approved the manuscript.

Funding

Not available.

Data availability

No datasets were generated or analyzed during the current study. The present investigation examined information from publicly accessible databases.

Declarations

Ethical approval and consent to participate

The review board at Emam Khomeini Hospital validated all bioethical issues in accordance with Iran’s Ministry standards. Informed consent was obtained from participants. Ethical approval was granted by the Internal Ethics Committee of the Genius Gene Research Center (IR-GG-1402-018). This study involved only laboratory analysis of anonymized human samples and did not include any direct human intervention. This study was conducted in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki).

Consent for publication

All authors support the submission of the present manuscript to this journal.

Competing interests

The authors declare no competing interests.