TMEM171 is a potential biomarker associated with lung injury and lung adenocarcinoma

Abstract

Background

Lung cancer therapy frequently induces lung injury, leading to treatment interruption and poor prognosis; however, specific biomarkers linking cancer therapy and therapy-related lung injury remain poorly defined. This study aimed to identify a biomarker relevant to both lung cancer progression and lung injury, with emphasis on prognostic significance and immune infiltration.

Methods

RNA-sequencing data from Gene Expression Omnibus (GEO) (GSE166340, GSE27128) were used to identify lung injury-related genes (LIRGs), and The Cancer Genome Atlas (TCGA) data were analyzed to evaluate their expression in lung adenocarcinoma (LUAD). Single-cell RNA-sequencing data (PRJNA846488) and in vivo lung injury models were used to validate TMEM171 expression. Prognostic value was assessed using Kaplan-Meier analysis, univariate and multivariate Cox regression, and a least absolute shrinkage and selection operator (LASSO) prognostic model. Immune infiltration, immune checkpoint correlations, and pathway enrichment were analyzed using single-sample gene set enrichment analysis (ssGSEA). Functional effects of TMEM171 were examined by in vitro knockdown experiments.

Results

A total of 10 LIRGs were identified. TMEM171 was significantly upregulated in LUAD tissues and immunotherapy-induced lung injury models. High TMEM171 expression was an independent predictor of poor prognosis and was associated with TP53 mutations, apoptosis, and inflammatory response pathways. TMEM171 expression positively correlated with neutrophil infiltration and immune checkpoint expression. TMEM171 knockdown suppressed malignant behavior of LUAD cells and induced G1/S cell-cycle arrest in vitro.

Conclusions

TMEM171 represents a potential biomarker linking LUAD and therapy-related lung injury and may serve as a candidate for future mechanistic investigation.

Introduction

Lung cancer is a highly aggressive and common malignancy and remains the leading cause of cancer-related mortality worldwide. Recent epidemiological data estimate 2.2 million new cases and 1.8 million deaths occurring on an annual basis (1,2). Among its subtypes, lung adenocarcinoma (LUAD) is the most common type, accounting for 40% of all global lung cases (3). A series of therapies (including surgery and immunotherapy) are applied to improve the overall 5-year survival for LUAD (4). Immunotherapy is an emerging type of antineoplastic therapy based on mechanisms of the immune system aimed at facilitating immune function to kill cancer cells. However, cancer immunotherapies might increase the risk of adverse events such as immunotherapy-related lung injury (IRLI) (5). As a result of anti-programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) immune checkpoint therapy, IRLI occurs in approximately 19% of non-small cell lung cancer (NSCLC) patients receiving anti-PD-1/PD-L1 therapy (6,7); additionally, this condition is potentially linked to inflammatory cytokine imbalance (8). Immune checkpoint inhibitors (ICIs) promote reactivation of T cell to induce the release of cytokines, which leads to lung injury through recruitment of neutrophils, macrophages and lymphocytes. Research has confirmed marked neutrophil infiltration in the lung tissues of Lewis lung cancer tumor-bearing mouse model treated with PD-1 inhibitors (9). Unfortunately, there is limited sensitive biomarkers for early recognition of cancer therapy related lung injury. Most patients following exposure to IRLI are forced to terminate treatment and/or given systemic glucocorticoids to reduce inflammatory response. As a result, the efficacy of lung cancer treatment is reduced. Therefore, we urgently need to explore common markers that can serve as therapeutic targets for both lung cancer and lung injury. Few genetic and mechanistic studies have focused on the cancer therapy and lung injury, thereby highlighting the urgent need for further scientific research in this field. This study aimed to bridge this gap through bioinformatics and experimental validation to elucidate co-biomarkers in both LUAD and lung injury based on the Gene Expression Omnibus (GEO) dataset and The Cancer Genome Atlas (TCGA) database, with a focus on candidate gene TMEM171. We present this article in accordance with the MDAR and ARRIVE reporting checklists (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1116/rc).

Methods

Data acquisition and processing

Lung injury-related genes (LIRGs) were identified from the GSE27128 and GSE166340 datasets via the GEO database. The gene expression data and corresponding clinical information were extracted from the TCGA database (https://Portal.gdc.cancer.gov/repository, accessed on 12/5/2022). The analysis included a total of 59 normal samples and 516 LUAD samples. The “Limma” package was used to identify differentially expressed genes (DEGs) with the criteria of an adjusted P<0.05 and a |log₂ fold change (FC)| >1 (Table S1).

Single-cell RNA-sequencing analysis of PD-1 inhibitor-induced lung injury

Single-cell RNA-sequencing data from murine models of immunotherapy-induced lung injury were obtained from the Sequence Read Archive (SRA; accession No. PRJNA846488). Lung tissue samples from PD-1 inhibitor-treated mice and corresponding control mice were extracted for downstream analysis. Raw gene-barcode count matrices were processed using Seurat in R studio. Following quality control, counts were normalised and log-transformed, and highly variable genes were identified. The data were then scaled, and principal component analysis (PCA) was performed for dimensionality reduction. Clusters were visualised using uniform manifold approximation and projection (UMAP). To assess the distribution of TMEM171 in IRLI, the expression pattern of TMEM171 was extracted from the normalised expression matrix.

Functional enrichment and single-sample gene set enrichment analysis (ssGSEA)

In this study, we conducted a comprehensive analysis of the biological functions and signalling pathways related to the candidate genes via Gene Ontology (GO) (10) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses (11). Additionally, we utilised the R packages “clusterProfiler” and “GSVA” to perform ssGSEA and calculate Spearman correlations for various functional signalling pathways.

Identification of prognostic LIRGs in LUAD

To assess the prognostic value of LIRGs, Kaplan-Meier plot analysis and Cox regression were conducted to examine the relationships between the expression levels of each LIRGs and survival outcomes in the TCGA-LUAD cohort. Variables with potential prognostic significance identified by Cox regression analyses, together with TMEM171 expression, were incorporated into a prognostic nomogram for predicting overall survival (OS) in patients with LUAD. Log-rank tests were conducted, and the P values and hazard ratios (HRs), along with the corresponding 95% confidence intervals (CIs), were compiled. A threshold P<0.05 was considered to be the standard for prognostic gene classification.

Human LUAD tissue samples

Human LUAD tissue samples were obtained from patients who underwent surgical resection at Zhongshan Hospital, Fudan University. All tissue samples were collected immediately after resection and stored according to standard protocols for subsequent analysis. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of Zhongshan Hospital, Fudan University (No. B2021-128) and informed consent was obtained from all individual participants.

Experimental animals

Eight-week-old wild-type (WT) male C57BL/6 mice were purchased from GemPharmatech (Nanjing, China) and then housed under controlled environmental conditions with a 12 h light/dark cycle and had free access to water and food prior to experiments. The animal study was approved by the Animal Review Committee of Zhongshan Hospital, Fudan University (No. 2021-128). The experiments were performed in compliance with national and institutional guidelines for the care and use of animals.

In vivo lipopolysaccharide (LPS)-induced lung injury model

Eight-week-old male WT C57BL/6 mice were used to establish an LPS-induced acute lung injury (ALI) model. The mice were randomly divided into control and ALI groups. All animals had free access to water and food before the experiments began. To construct the ALI mouse model, the mice were first anesthetized by isoflurane inhalation, and then the skin and muscles were incised sequentially to expose the trachea, and 100 μL of 1 mg/kg LPS (0111:B4, L2630, Sigma, St. Louis, MO, USA) was slowly injected into the lungs from trachea using a microinjector; the control group was administrated with 100 μL phosphate-buffered saline (PBS). Forty-eight hours after LPS administration, the mice were euthanized by injecting 1% pentobarbital (50 mg/kg), and lung tissue were collected for further analysis.

Western blotting (WB) assay

Whole proteins were isolated using whole-cell lysis buffer supplemented with a protein phosphatase inhibitor cocktail (Pierce, Waltham, MA, USA). The concentration of the isolated proteins was determined via a Pierce BCA protein assay kit (Thermo Scientific, Waltham, MA, USA). A total of 20 μg of protein was loaded into each well. Following electrophoresis via 4–20% polyacrylamide gels, the proteins were transferred onto a nitrocellulose (NC) membrane. The membranes were subsequently incubated with primary antibodies, including TMEM171 (HUABIO, ER62285, Hangzhou, China) and β-actin (Yeasen, 30101ES, Shanghai, China) antibodies. After incubation with peroxidase-linked secondary antibodies (Yeasen), the membranes were washed and visualised using the Immobilon Western HRP Chemiluminescence Substrate (Millipore, Burlington, MA, USA).

Immune infiltration analysis

To evaluate the associations between TMEM171 expression and immune infiltration, we utilised immune cell analysis for B cells, CD4⁺ T cells, CD8⁺ T cells, macrophages, neutrophils and dendritic cells, as well as immune checkpoint correlation analysis for CD274, CTLA4, HAVCR2, LAG3, PDCD1, TIGIT and SIGLEC15, based on the Tumor Immune Estimation Resource (TIMER) database (12).

Development of risk models and nomograms based on prognostic LIRGs

A risk model for LUAD LIRGs was constructed using the least absolute shrinkage and selection operator (LASSO) regression algorithm. The LIRGs were utilised to compute the risk score [risk score = S (Exp_mRNAn × β_mRNAn)] (13). Kaplan-Meier curves were used to compare the high- and low-risk groups, with P values being calculated via log-rank tests. A forest plot was generated using the “forest plot” R package to present the P value, HR, and 95% CI for each variable. A nomogram was subsequently developed based on the multivariate Cox proportional hazards analysis of LUAD LIRGs.

Cell culture

The LUAD cell line known as A549, which was obtained from American Type Culture Collection (ATCC), was used in the present study (catalogue No. 86012804). A549 cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM; Gibco, Grand Island, NY, USA) supplemented with 10% foetal bovine serum (FBS; Gibco, USA) and 1% penicillin/streptomycin (100 units) at 37 °C in a humidified 5% CO₂ atmosphere. Additionally, small interfering RNAs (siRNAs) targeting TMEM171 were produced by Genomeditech, Inc. (Shanghai, China), and A549 cells were transfected with these siRNAs via a Lipofectamine^® 3000 Transfection Kit (L3000015; Invitrogen, Carlsbad, CA, USA). A scrambled siRNA was used as a negative control. The utilised target siRNA sequences were as follows— small interfering RNA targeting TMEM171 (siTMEM171) (5'-3'): GUAGGUGACUCGGUAAUAAtt; UUAUUACCGAGUCACCUACtt. The efficiency was validated via WB.

Cell Counting Kit-8 (CCK-8)

A total of 1,000 cells were seeded into 96 wells after transfection. The cells were cultured for 0, 1, 2, 3, or 4 days. The CCK-8 kit (NCM, Suzhou, China) reagent was added to the wells, and the optical density (OD) value was measured at 450 nm after incubation at 37 °C for 2 h.

Wound-healing assay

Wound-healing assays were conducted to evaluate the migratory capacity of A549 cells after transfection. The cells were plated in 6-well plates and allowed to grow until they reached approximately 70–80% confluence. A sterile 20 μL pipette tip was used to create a scratch across the cell monolayer. The detached cells and debris were removed via two washes with 1× PBS. The cells were then maintained in serum-free medium supplemented with the indicated treatments. Images of the wound area were captured, and the width of the wound was measured at 0 and 24 hours post-scratching.

Transwell assay

The transfected A549 cells were adjusted to a concentration of 10×10⁴/mL in serum-free DMEM. A 300 μL aliquot of the cell suspension was added to the upper compartment of the transwell chamber, whereas 600 μL of medium was added to the lower compartment. The transwell system, which was housed in a 24-well plate, was incubated at 37 °C for 24 hours. Images of the wound area were captured, and the width of the wound was measured at 0 and 24 hours.

Colony formation assay

A549 cells were seeded into 6-well plates at a density of 1×10³ cells/well after transfection. After incubation at 37 °C for 10 days, the cells were washed with PBS, after which they were fixed with 4% paraformaldehyde. After being stained with 0.1% crystal violet for 30 min at room temperature, the colonies were photographed and counted.

RNA sequencing and bioinformatics analysis

Total RNA was extracted from A549 cells infected with either siTMEM171 or si-scramble via TRIzol (Thermo Fisher). A NanoDrop 2000 spectrophotometer was used to detect RNA quality. After complementary DNA (cDNA) library construction, high-throughput sequencing was performed using the Illumina platform.

TMEM171-related DEGs were identified via the “limma” package of R software. The cut-off values were set at a P<0.05 and a |log₂ FC|>2. Other packages, including “ggplot2”, were utilised to visualise the results of the bioinformatics analysis.

Gene set enrichment analysis (GSEA)

Based on the TCGA datasets, GSEA was utilised to identify the enriched pathways underlying the interaction between TMEM171 expression and LUAD progression via the “clusterProfiler” package in R software. Normalised P<0.05 and false discovery rate (FDRs) <0.25 were considered to be statistically significant.

Cell cycle analysis

A549 cells infected with siRNAs expressing siTMEM171 or si-scramble were seeded in 6-well plates and cultured to 70–80% confluence. A549 cells were harvested, fixed with 70% precooled ethanol overnight, and subsequently incubated with PBS buffer containing RNase and propidium iodide (PI). A cell cycle assay was performed with a CytoFlex S flow cytometer (Beckman Coulter, Brea, CA, USA), and ModFit software was used for the data analysis.

Statistical analysis

Statistical analysis was performed using GraphPad Prism 9.0 (USA). For comparisons between two groups, Student’s t-test was used, whereas one-way analysis of variance (ANOVA) was applied for comparisons among multiple groups. For survival analyses, patients were stratified into high- and low-expression groups according to the median expression value of each gene within the corresponding cohort. Samples with expression values greater than or equal to the median were assigned to the high-expression group, whereas samples with expression values below the median were assigned to the low-expression group. All experiments were performed with at least three independent biological replicates. Data were presented as mean ± standard deviation (SD). A P value of less than 0.05 was considered statistically significant.

Results

Identification of LIRGs and validation in lung injury models

To explore the relationship between lung injury and LUAD, we identified 10 LIRGs based on the GEO datasets GSE27128 (radiation-induced lung injury) and GSE166340 (ventilator-induced lung injury). The expression patterns and genetic alterations of these genes were subsequently evaluated in the TCGA-LUAD cohort (Figure 1A). A total of 10 genes were identified as LIRGs according to both lung injury datasets. Among them, 6 genes were upregulated, 2 genes were downregulated, and 2 genes were dysregulated (Figure 1B). More specifically, CCDC86, STC2, KIF20B, TMEM171, FOSL1, AEN, and MMP1 were upregulated in LUAD; additionally, the expression of CSF2 and CFB was decreased at a level of FC > |2| and a P<0.05. Figure 1C shows the expression levels of 10 LIRGs in LUAD tissues compared with normal tissues. Functional enrichment analysis further demonstrated that these shared genes were predominantly enriched in immune- and inflammation-related biological processes and signaling pathways (Figure S1). To further explore the genetic alterations of these LIRGs in LUAD, we used the cBioPortal platform, with the results displayed in Figure 1D. These results indicated that the eight LIRGs exhibited a low rate of genetic alterations in LUAD.

Figure 1.

Identification and characterization of LIRGs. (A,B) Differentially expressed genes identified from GEO datasets GSE27128 and GSE166340. (C) Expression levels of LIRGs in the TCGA-LUAD cohort. (D) Genetic alteration frequencies of LIRGs in the TCGA-LUAD cohort. **, P<0.01; ***, P<0.001. DEGs, differentially expressed genes; GEO, Gene Expression Omnibus; LIRGs, lung injury-related genes; LUAD, lung adenocarcinoma; TCGA, The Cancer Genome Atlas.

To investigate TMEM171 expression in an ICI-associated lung injury setting, we re-analyzed the publicly available PRJNA846488 single-cell RNA sequencing dataset (9). Clustering analysis resolved 13 major pulmonary cell populations, including epithelial cells, endothelial cells, fibroblasts, mesenchymal cells, pneumocyte, and multiple immune cell subsets such as macrophages, monocytes, neutrophils, dendritic cells, B cells, T cells and NK cells (Figure 2A). Bar plot of cell type proportions revealed distinct variation patterns across samples showed that PD-1 inhibitor-induced lung injury mice exhibited a relative expansion of fibroblasts, B cells and NK cells populations compared with controls (Figure 2B), consistent with the high expression of TMEM171 in PD-1 inhibitor-induced lung injury groups, indicating its involvement in IRLI (Figure 2C). Taken together, these findings further supported TMEM171 as a robust LIRG that was activated across diverse etiologies, including immunotherapy-induced lung injury, thereby highlighting its potential as a shared biomarker linking LUAD and treatment-related lung toxicity.

Figure 2.

Single-cell transcriptomic analysis based on the publicly available PRJNA846488 dataset of PD-1 inhibitor-associated lung injury. (A) UMAP visualization of pulmonary cell populations. (B) Bar plot of cell type proportions of control and PD-1 inhibitor-induced lung injury groups. (C) Violin plot of TMEM171 expression in control and PD-1 inhibitor-induced lung injury groups. **, P<0.01. ICI, immune checkpoint inhibitor; PD-1, programmed cell death protein 1; UMAP, uniform manifold approximation and projection.

Determination of prognostic LUAD LIRGs

To identify prognostic LIRGs, the TCGA LUAD cohort was subjected to univariate Cox regression analysis. OS analysis revealed that high expression of CCDC86, CCL20, KIF20B, TMEM171, and FOSL1 was associated with increased risk (HR >1, Figure 3A). The predictive value of these LIRGs was further confirmed via Kaplan-Meier survival curves, which were used to compare high- and low-expression groups among LUAD patients (Figure S2). We also performed disease-specific survival (DSS) analysis and observed that the expression of CCDC86, KIF20B, TMEM171, and FOSL1 was positively correlated with increased risk (HRs >1) (Figure 3B). The Kaplan-Meier survival curves validated the differences in DSS between the high-expression and low-expression groups (Figure S3). These results indicated that CCDC86, KIF20B, TMEM171, and FOSL1 may demonstrate prognostic value in LUAD. Among the prognostic LIRGs, TMEM171 was selected for subsequent analyses and experimental validation. Although TMEM171 did not exhibit the highest coefficient in the LASSO model, it was significantly upregulated in LUAD and associated with poor patient survival. Moreover, TMEM171 expression was also elevated in the PD-1 inhibitor-associated lung injury single-cell RNA sequencing (scRNA-seq) dataset (PRJNA846488), suggesting a potential association with both lung injury and LUAD. In addition, compared with other candidate genes, the biological role of TMEM171 in lung diseases remains largely unexplored. Therefore, considering its prognostic significance, consistent dysregulation across both disease contexts, and potential biological relevance, TMEM171 was selected as the representative candidate gene for further investigation.

Figure 3.

Prognostic analysis of LIRGs in the TCGA-LUAD cohort. Univariate Cox regression analysis for OS (A) and DSS (B) was performed for each lung injury-related gene with a P<0.05. CI, confidence interval; DSS, disease-specific survival; LIRGs, lung injury-related genes; LUAD, lung adenocarcinoma; OS, overall survival; TCGA, The Cancer Genome Atlas.

Construction of a prognostic risk model

To further explore the relationship between LIRGs and LUAD prognosis, we applied time-dependent LASSO (tLASSO) Cox regression algorithm to identify the optimal λ value (Figure 4A,4B). Afterwards, a 6-gene signature was developed by using the optimal λ value, and the correlation between the expression levels of these genes and the calculated risk score is illustrated in Figure 4C. The risk score was calculated as follows: risk score= (0.0696) × TMEM171 + (−0.1164) × CSF2 + (0.1771) × FOSL1 + (0.1422) × CCDC86 + (−0.0754) × CFB + (0.1238) × CCL20. Based on the risk score, LUAD patients were divided into two groups. Kaplan-Meier survival analysis revealed that patients in the high-risk group had a poorer prognosis than those in the low-risk group (Figure 4D). Receiver operating characteristic (ROC) curve analysis of the LUAD cohort revealed that this prognostic model demonstrated good predictive efficacy [area under the curve (AUC) =0.692 for 1-year survival, 0.718 for 3-year survival, and 0.649 for 5-year survival] (Figure 4E). We further investigated the potential relationship between the prognostic risk model and immune cell infiltration in LUAD patients via the TIMER algorithm (Figure 5). Our analysis revealed that B-cell (Figure 5A), CD4⁺ T-cell (Figure 5B), and CD8⁺ T-cell (Figure 5C) infiltration were negatively correlated or not significantly correlated with the risk score, whereas neutrophil infiltration was positively correlated with the risk score (Figure 5D). No significant relationships were observed with macrophages (Figure 5E) or myeloid dendritic cells (Figure 5F). These findings suggest that the high-risk group exhibited distinct immune characteristics, including reduced lymphocyte infiltration and increased neutrophil infiltration, which may contribute to an immunosuppressive and inflammatory tumour microenvironment.

Figure 4.

Construction of a prognostic risk model using TCGA-LUAD transcriptomic and survival data. (A) LASSO coefficients of LIRGs represented by λ parameter. (B) The LASSO Cox regression model was applied to plot the partial likelihood deviance against log(λ). (C) The correlation of risk score with the survival status, and the expression of LIRGs in LUAD. The x-axis represents individual patients ranked by risk score, with tick marks indicating patient indices. (D) OS curves for LUAD patients in high and low risk groups. (E) ROC curves demonstrated the predictive efficiency of the risk score. AUC, area under the curve; CI, confidence interval; HR, hazard ratio; LASSO, least absolute shrinkage and selection operator; LIRGs, lung injury-related genes; LUAD, lung adenocarcinoma; OS, overall survival; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas.

Figure 5.

Correlation between the TCGA-LUAD-derived risk model and immune cell infiltration estimated using the TIMER algorithm. (A) B cell. (B) CD4⁺ T cell. (C) CD8⁺ T cell. (D) Neutrophil cell. (E) Macrophage. (F) Myeloid dendritic cell. CI, confidence interval; LUAD, lung adenocarcinoma; TCGA, The Cancer Genome Atlas; TIMER, Tumor Immune Estimation Resource.

Prognostic nomogram based on LIRGs and TMEM171

Univariate (Figure 6A) and multivariate Cox (Figure 6B) analyses were performed to evaluate the associations between each gene and patient prognosis. These findings revealed that TMEM171 was a risk factor in both univariate and multivariate Cox analyses, thus suggesting that TMEM171 may serve as an independent prognostic factor in LUAD. In this study, we constructed a prognostic nomogram based on LIRG expression levels to estimate the survival probability of LUAD patients. A nomogram incorporating TMEM171 expression levels was subsequently constructed based on these findings (Figure 6C). Therefore, TMEM171 was selected for nomogram construction as the representative prognostic LIRG. The predictive nomogram was able to accurately predict the 1-, 3-, and 5-year OS rates in the entire cohort (Figure 6D). Based on the prognostic analysis shown in Figure 5, our findings suggest that TMEM171 could be a pivotal LIRG in LUAD. Furthermore, an external independent validation was conducted using the GSE68465 dataset from the GEO database. Consistently, for LUAD patients, the high-risk group exhibited a significantly lower survival rate than the low-risk group (P<0.05) (Figure S4). Given that TMEM171 was selected as the representative candidate gene for subsequent experimental validation, we further evaluated its independent clinical utility by constructing a TMEM171-based prognostic nomogram integrating TMEM171 expression with clinicopathological variables.

Figure 6.

Construction of a TMEM171-based prognostic nomogram integrating TMEM171 expression and clinicopathological variables in the TCGA-LUAD cohort. (A) Univariate Cox regression analysis. (B) Multivariate Cox regression analysis. (C) Nomogram incorporating TMEM171 expression together with independent clinical prognostic factors for predicting 1-, 3-, and 5-year OS. (D) Calibration curves of the nomogram. CI, confidence interval; LUAD, lung adenocarcinoma; N, node; OS, overall survival; T, tumor; TCGA, The Cancer Genome Atlas.

Pan-cancer expression profile of TMEM171

The expression level of TMEM171 across cancers was examined using the TIMER2 database (Figure 7A). Compared with that in normal tissues, TMEM171 was upregulated in various tumour types, such as LUAD, cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), breast invasive carcinoma (BRCA), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), and uterine corpus endometrial carcinoma (UCEC). However, the expression levels of TMEM171 were decreased in colon adenocarcinoma (COAD), kidney chromophobe (KICH), kidney renal papillary cell carcinoma (KIRP), rectum adenocarcinoma (READ), and thyroid carcinoma (THCA). In LUAD, no significant association was observed between TMEM171 expression and tumour size (Figure 7B), whereas TMEM171 expression was positively correlated with lymph node metastasis (Figure 7C) but not distant metastasis (Figure 7D). The somatic mutation rate of TMEM171 was only 0.79% in LUAD patients (Figure 7E). Additionally, the expression of TMEM171 was associated with the TP53 mutation (Figure 7F); however, no significant relationship was observed with the KRAS mutation status (Figure S5). Furthermore, TMEM171 expression was significantly correlated with the infiltration of multiple immune cell populations and the expression of several immune checkpoint molecules in LUAD (Figure S6). ssGSEA further demonstrated that TMEM171 expression was associated with multiple biological pathways, including tumor inflammation, hypoxia, epithelial-mesenchymal transition (EMT), angiogenesis, DNA repair, and cell cycle regulation (Figure S7).

Figure 7.

Expression and genetic characteristics of TMEM171 based on TCGA-LUAD and TIMER2 databases. (A) TMEM171 expression in various tumor types was analyzed using the TIMER2. The association of TMEM171 expression with tumor size (B), lymph node status (C), and metastasis status (D). (E) The mutation rate of TMEM171. (F) TMEM171 expression was associated with TP53 mutation status. ns, not significant; *, P<0.05; **, P<0.01; ***, P<0.001. LUAD, lung adenocarcinoma; TCGA, The Cancer Genome Atlas; TNM, tumor-node-metastasis; WT, wild-type.

Genetic function and immune analysis of TMEM171 in LUAD

To further investigate the genetic function of TMEM171, patients with LUAD were categorised into two groups according to high and low expression of TMEM171 (Figure S2). To compare the two groups, KEGG and GO functional enrichment analyses were conducted. According to the KEGG analysis, the expression of TMEM171 was associated with the WNT signalling pathway, the IL-17 signalling pathway, and the MAPK signalling pathway (Figure S1A). GO analysis revealed that the expression of TMEM171 was correlated with tissue homeostasis, epidermis development, reproductive structure development, and skin development. However, decreased expression of TMEM171 was correlated with the JAK-STAT signalling pathway (Figure S1B). We conducted an additional analysis to examine the relationships between TMEM171 expression and different types of immune cell infiltration. Among the reported associations, TMEM171 was positively associated with neutrophil infiltration but negatively associated with CD4⁺ T-cell infiltration (Figure S6A). We also examined the correlations between TMEM171 and immune checkpoint-related genes. Notably, TMEM171 expression was strongly positively correlated with various immune checkpoints, including CD274, CTLA4, HAVCR2, LAG3, PDCD1, and SIGLEC15 (Figure S6B). GSEA further revealed that TMEM171 expression was positively associated with tumour inflammation signature (Figure S7A), cellular response to hypoxia (Figure S7B), tumour proliferation (Figure S7C), and EMT (Figure S7D), whereas it was negatively associated with extracellular matrix (ECM) degradation (Figure S7E). TMEM171 expression was also positively associated with angiogenesis (Figure S7F), apoptosis (Figure S7G), DNA repair (Figure S7H), G2M checkpoint regulation (Figure S7I), IL-10 anti-inflammatory signalling (Figure S7J), inflammatory response (Figure S7K), and ferroptosis (Figure S7L).

TMEM171 knockdown inhibited LUAD cell malignancy in vitro

To validate the biological function of TMEM171 in LUAD, a series of in vitro assays were performed. First, we examined the expression levels of TMEM171 in primary LUAD tissues. The results revealed higher expression of TMEM171 in primary LUAD tissues than in normal tissues (Figure 8A). To provide experimental validation of TMEM171 expression under inflammatory lung injury conditions, we established an LPS-induced ALI mouse model. Western blot analysis of lung tissues revealed a significant upregulation of TMEM171 expression in lung injury mice model compared to control groups (Figure 8B), primarily suggesting its key role in the pathogenesis of lung injury. Consequently, an siRNA targeting TMEM171 was designed and transfected into the A549 LUAD cell line. WB confirmed the knockdown efficacy of siTMEM171 (Figure 8C). A CCK-8 assay was then conducted to assess the effect of TMEM171 on tumour cell proliferation. Compared with that of the control cells, the growth rate of A549 cells following TMEM171 knockdown was lower (Figure 8D). Additionally, a colony formation assay was performed to detect the effect of TMEM171 on tumourigenic ability in vitro. Our results revealed that TMEM171 knockdown significantly inhibited colony formation ability (Figure 8E). Additionally, transwell (Figure 8F) and wound healing assays (Figure 8G) demonstrated that the migration ability of LUAD cells was decreased after siTMEM171 transfection. Collectively, these findings indicate that TMEM171 is upregulated in both lung injury and LUAD and promotes the malignancy of LUAD.

Figure 8.

TMEM171 inhibition suppressed key biological functions in LUAD cells. (A) Upregulation of TMEM171 was confirmed in LUAD tissues. (B) TMEM171 expression was elevated in LPS-induced lung injury tissues. (C) The knockdown efficiency of siTMEM171 was assessed via western blot. (D) Genetic knockdown of TMEM171 reduced LUAD cell proliferation. (E) TMEM171 inhibition decreased colony formation capacity. (F) Migration of A549 cells was impaired following siTMEM171 transfection, as demonstrated by the Transwell assay. (G) Wound healing assays further confirmed the reduced migration ability. **, P<0.01; ***, P<0.001; ****, P<0.0001. LPS, lipopolysaccharide; LUAD, lung adenocarcinoma; N, node; OD, optical density; siTMEM171, small interfering RNA targeting TMEM171; T, tumor.

TMEM171 knockdown induced G1/S cell cycle arrest

To investigate the function of TMEM171, RNA sequencing was used to detect TMEM171 in A549 cells transfected with TMEM171 siRNA (Figure 9A). First, the volcano map revealed a total of 426 and 685 genes that were differentially expressed between A549 cells treated with si-scramble and those treated with siTMEM171, respectively (Figure 9B,9C). GSEA revealed that the cell cycle and DNA replication were significantly enriched in TMEM171-associated pathways (Figure 9D,9E). A cell cycle assay revealed that, compared with the negative control, TMEM171 knockdown increased the percentage of A549 cells in the G1 phase, whereas the percentage of cells in the S phase was significantly decreased (Figure 9F-9H). Hence, we speculated that TMEM171 knockdown inhibited LUAD cell proliferation by inducing G1/S cell cycle arrest.

Figure 9.

TMEM171 knockdown inhibited LUAD cell proliferation by inducing G1/S cell cycle arrest. (A) Workflow for the RNA-sequencing analysis of A549 cells infected with siRNA expressing si-scrambled (negative control) and siTMEM171. (B,C) Volcano plots of DEGs between A549 cells treated with si-scrambled and siTMEM171. Dashed lines represent the cutoff thresholds (P<0.05 and |log₂ FC| >1). (D,E) GSEA enrichment analysis represented the enrichment of cell cycle and DNA replication pathway. (F-H) Flow cytometry analysis of cell cycle distribution in A549 cells treated with si-scrambled and siTMEM171. Data were presented as mean ± SD of three of separate experiments. ns, not significant; *, P<0.05; ****, P<0.0001. DEGs, differentially expressed genes; FC, fold change; GSEA, gene set enrichment analysis; LUAD, lung adenocarcinoma; PI, propidium iodide; SD, standard deviation; siRNA, small interfering RNA; siTMEM171, small interfering RNA targeting TMEM171.

Discussion

Our study identified LIRGs through integrated analyses of GEO and TCGA datasets. Among these genes, TMEM171 was consistently upregulated in lung injury-related datasets and LUAD clinical samples. These findings suggest that TMEM171 may serve as a potential biomarker linking lung injury and LUAD and warrants further investigation.

Previous studies have reported dysregulated TMEM171 expression in several malignancies, including triple-negative breast cancer, papillary thyroid carcinoma, and gastrointestinal cancers. However, the biological role of TMEM171 appears to be context-dependent and remains poorly understood, particularly in lung diseases (14-16). Our results indicated that TMEM171 was upregulated in LUAD. Notably, the selection of TMEM171 for further investigation was not based solely on its contribution to the prognostic model. Although TMEM171 did not exhibit the highest coefficient in the LASSO regression model, it demonstrated consistent dysregulation across both LUAD and lung injury-related datasets, including the PD-1 inhibitor-associated lung injury scRNA-seq dataset (PRJNA846488). Furthermore, compared with other candidate genes identified in the prognostic signature, the biological function of TMEM171 in lung diseases remains largely unexplored. Therefore, TMEM171 was considered a biologically relevant and relatively understudied candidate linking lung injury and LUAD, warranting further investigation. Furthermore, ssGSEA revealed that TMEM171 expression was positively associated with tumour proliferation signalling, EMT markers, the tumour inflammatory signature, and the inflammatory response. The pathway analysis results were similar with a previous study which indicated that the high expression of TMEM171 was associated with a poor prognosis for LUAD patients (17,18). Moreover, our study identified that TMEM171 expression was strongly positively correlated with various immune checkpoints, including CD274, CTLA4, HAVCR2, LAG3, PDCD1, and SIGLEC15. These findings suggest that TMEM171 may be involved in shaping the immune microenvironment of LUAD. However, whether TMEM171 directly regulates immune responses or influences the efficacy of immunotherapy requires further experimental validation. Previous studies elucidated that immune checkpoints shape an immunosuppressive microenvironment, thereby weakening, restricting, and exhausting antitumour T cell activity (19). Consistent with this, immune cell analysis revealed that TMEM171 expression was negatively correlated with CD4⁺ T cells and B cells but positively correlated with neutrophil infiltration. Previous studies had revealed that B cell depletion accelerates tumor progression in intraperitoneal ovarian carcinosarcomas (20); and enhancing CD4⁺ T cell help to improve cytotoxic T lymphocytes responses in cancer immunotherapy (21). Neutrophils could promote cancer cell proliferation, metastasis, and angiogenesis, as well as suppress antitumour T-cell responses (22). Hence, our results suggest that the high-TMEM171 group may be less responsive to immunotherapy due to an inflammatory and immunosuppressive microenvironment that promotes tumour progression. In accordance with the prior results, we further demonstrated that TMEM171 was an independent prognostic factor in LUAD via univariate and multivariate Cox analyses.

Neutrophils are first recruited to inflammation sites in response to activated macrophages and pulmonary epithelial and endothelial cells, where they release reactive oxygen species, granular enzymes, various proinflammatory cytokines, and neutrophil extracellular traps (NETs) (23). Excessive activated neutrophils and their products cause tissue damage, disrupting the alveolar-capillary interface and increasing pulmonary microvascular permeability (24). Moreover, previous report elucidated high concentration of neutrophils in the bronchoalveolar lavage fluid of patients with acute respiratory distress syndrome (ARDS) was related with the aggressiveness and poor outcome of ARDS (25). TMEM171 expression was positively associated with neutrophil infiltration in LUAD. Given the well-established role of neutrophils in inflammatory lung injury, this observation raises the possibility that TMEM171 may participate in immune and inflammatory processes relevant to lung injury. However, the current evidence is based primarily on bioinformatic analyses and expression studies. Therefore, whether TMEM171 directly regulates neutrophil recruitment or contributes to lung injury pathogenesis remains to be determined by future mechanistic investigations.

In summary, this research demonstrated the potential molecular mechanisms involved in lung injury and LUAD (Figure 10). TMEM171 may represent a promising biomarker associated with both lung injury and LUAD and provides new insights into the potential biological connections between these two conditions. However, whether TMEM171 represents a viable therapeutic target remains to be fully elucidated. Future studies using TMEM171 gain- and loss-of-function approaches are required to determine its mechanistic role and therapeutic potential.

Figure 10.

TMEM171 as a potential biomarker linking LUAD and lung injury. TMEM171 was upregulated in LUAD and lung injury-related datasets, correlated with immune checkpoint expression and neutrophil infiltration, and was associated with poor prognosis in LUAD. Red arrows indicate promotion/upregulation/activation; green arrows indicate inhibition/downregulation/suppression. LUAD, lung adenocarcinoma.

Several limitations of this study should be acknowledged. First, the LIRGs were identified from public datasets representing different types of lung injury rather than exclusively IRLI. Second, although TMEM171 expression was evaluated using a public PD-1 inhibitor-associated lung injury dataset, no independent patient cohort or ICI-induced lung injury animal model was available for validation. The LPS-induced ALI model was used in this study as a general model of inflammatory lung injury to validate TMEM171 expression at the experimental level. However, we acknowledge that this model does not fully reproduce the complex immune-mediated pathogenesis of IRLI. Therefore, the relevance of TMEM171 to IRLI requires further validation in dedicated clinical cohorts and experimental models. In addition, validation using clinical samples from patients receiving ICI therapy was not performed in the present study. Future studies incorporating prospective clinical cohorts and dedicated IRLI models will be necessary to further validate the clinical significance and biological role of TMEM171. Importantly, the present study was designed primarily as a biomarker discovery and validation study rather than a mechanistic investigation of IRLI. Although TMEM171 expression was elevated in both the PRJNA846488 dataset and the LPS-induced lung injury model, our findings do not establish a causal role for TMEM171 in the development or progression of lung injury. In vivo functional experiments and further studies using dedicated IRLI models and TMEM171 gain- and loss-of-function approaches will be required to determine whether TMEM171 directly contributes to lung injury pathogenesis. Third, the functional role of TMEM171 in lung injury was not directly investigated in the present study, and the mechanistic links between TMEM171, immune regulation, and lung injury require further experimental confirmation. In addition, the cellular source of TMEM171 in the PD-1 inhibitor-associated lung injury dataset was not investigated in the present study. Further single-cell and spatial transcriptomic analyses will be required to clarify the cell type-specific expression pattern of TMEM171 during lung injury. Therefore, additional clinical and experimental studies are warranted to validate the findings and clarify the biological functions of TMEM171.

Conclusions

1. TMEM171 was significantly upregulated in LUAD tissues, lung injury-related datasets, and a PD-1 inhibitor-associated lung injury scRNA-seq dataset.

2. High TMEM171 expression was associated with poor prognosis in LUAD.

3. TMEM171 expression was positively correlated with immune checkpoint-related genes and neutrophil infiltration.

4. TMEM171 may serve as a potential biomarker linking lung injury and LUAD and provides a foundation for future mechanistic and translational studies.

Supplementary

The article’s supplementary files as

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of Zhongshan Hospital, Fudan University (No. B2021-128) and informed consent was obtained from all individual participants. The animal study was approved by the Animal Review Committee of Zhongshan Hospital, Fudan University (No. 2021-128). The experiments were performed in compliance with national and institutional guidelines for the care and use of animals.

Data Sharing Statement

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DOI: 10.21037/jtd-2026-1116