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. 2025 Jan 10;25:62. doi: 10.1186/s12885-024-13415-y

Exploring the role of ELOVLs family in lung adenocarcinoma based on bioinformatic analysis and experimental validation

Zihan Wang 1,#, Wenjing Cui 2,3,#, Long Liang 1, Jingge Qu 1, Yuqiang Pei 1, Danyang Li 1, Ying Luo 1, Yue Zhang 1, Yifan Qiu 4, Yongchang Sun 1,✉
PMCID: PMC11720344  PMID: 39794751

Abstract

Background

The role of lipid metabolic reprogramming in the development of various types of cancer has already been established. However, the exact biological function and significance of the elongation of very-long-chain fatty acids (ELOVLs) gene family, which can affect fatty acid metabolism, is still not well understood in lung adenocarcinoma (LUAD). The aim of our study is to explore whether there are genes related to the pathogenesis of LUAD in the ELOVLs family, and even to guide clinical medication and potential prognostic indicators.

Methods

Gene expression profiling interactive analysis (GEPIA), human protein atlas (HPA), cBioPortal, Kaplan–Meier (KM) plotter, single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm and SubMap algorithms were utilized to analyze the role of ELOVLs in the LUAD. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis, cell counting kit-8 (CCK8), colony formation, wound healing, transwell migration assays and fatty acid metabolism detection were employed to confirm the significant role of ELOVL6 in vitro experiment.

Results

Our results revealed that mRNA expression levels of ELOVL2, ELOVL4 and ELOVL6 and protein expression levels of ELOVL5 and ELOVL6 were elevated in LUAD tissues compared to normal subjects. The low-expressing ELOVL6 group showed superior overall survival (OS) and disease-specific survival (DSS) versus the high-expressing group. Meanwhile, patients with low-ELOVL6 expression were more sensitive to the 4 representative chemotherapeutic agents. In vitro, we revealed that interfering with ELOVL6 could influence the viability, proliferation, migration capacity and fatty acid metabolism of LUAD cells (A549 and H1299).

Conclusions

Our study indicated that ELOVL6 could be used as an indicator to evaluate the prognosis and therapeutic effect, and even potential therapeutic target for patients with LUAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-024-13415-y.

Keywords: Lung adenocarcinoma, Fatty acid metabolism, ELOVLs, Personalized treatment, Prognosis

Introduction

Recent research indicated that lung cancer has surpassed breast cancer as the most frequently diagnosed cancer and continues to be the leading cause of cancer-related mortality. Among its histological subtypes, LUAD accounts for the majority of cases [1]. Moreover, LUAD is often diagnosed at an advanced stage, complicating treatment outcomes [2, 3]. Additionally, although with the progress of drug research and development, especially with the advent of the era of precision medicine [4], the therapeutic effect of LUAD varies from person to person, and the 5-year overall survival rate for LUAD remains at a very low level, only 23% [5]. Therefore, there is a crucial and urgent need to search for a risk assessment marker and innovative therapeutic targets to improve the prognosis for LUAD patients.

Accumulating evidence has substantiated that tumor cells undergo distinct metabolic reprogramming compared to normal cells, thereby facilitating uncontrolled proliferation, metastasis and adaptation to changes in the surrounding microenvironment [6, 7]. Lipid metabolism is one of the three main metabolisms of cells, and its metabolic change is a hallmark of tumor cells. To meet the needs of tumor cell growth and energy production, lipid metabolism is significantly upregulated in several tumors. As a section of lipid metabolism, fatty acid metabolism is also markedly altered in cancer cells, especially in lung cancer cells [8, 9].

Long and very long chain fatty acids serve as precursors for various molecules, including eicosanoid signaling molecules, ceramides and sphingolipids [10, 11], which are essential components of biological membrane formation. ELOVLs play a pivotal role in the synthesis of these fatty acids, and abnormal expression of this gene family can lead to various diseases. For instance, Vasiliki et al. [12] proved that knockout of the ELOVL1 can lead to decreased viability of MLL-AF4+ acute lymphoblastic leukemia cells. Ryan C et al. [13] found that the depletion of ELOVL2 can inhibit the proliferation and promote the apoptosis of glioblastoma cells. In prostate cancer, BRG1 promotes the metastasis of tumor cells by activating the ELOVL3 [14]. Besides, Francesco et al. [15] discovered that MYCN regulated lipid metabolism via ELOVL4 in neuroblastoma cells to affect disease progression. Margaret M et al. [16] found that depletion of ELOVL5 can result in the suppression of growth and migration of prostate cancer cells. Additionally, Feng et al. [17] confirmed that ELOVL6 is a poor prognostic predictor of breast cancer. Tamura et al. [18] revealed that knockdown of ELOVL7 resulted in drastic attenuation of prostate cancer cell growth. However, it remains unknown whether the members within the ELOVLs family could be used as prognostic predictors or therapeutic targets for LUAD patients.

The aim of our study is to investigate whether ELOVLs family genes are involved in the pathogenesis of LUAD and to evaluate their potential as clinical biomarkers and prognostic indicators. Through a comprehensive analysis of mRNA and protein levels of ELOVL family genes and clinical prognostic indicators, we found that ELOVL6 is upregulated in lung adenocarcinoma and associated with patient prognosis. Further investigation revealed that low expression of ELOVL6 is highly sensitive to four commonly used chemotherapy drugs in LUAD: 5-Fluorouracil, cisplatin, cytarabine and gemcitabine. We also developed a clinical predictive model incorporating ELOVL6 expression to estimate survival in LUAD patients, which was validated both internally and externally. Additionally, in vitro experiments confirmed that ELOVL6 promotes migration and proliferation of LUAD cell lines and influenced fatty acid metabolism. These findings could offer novel insights into the underlying mechanisms and potential personalized therapies for LUAD.

Materials and methods

Data collection

After removing duplicate samples, the RNA sequencing (RNA-seq) data and clinical information of 516 LUAD tissue samples and 59 adjacent normal tissue samples were obtained from the TCGA database (https://tcga-data.nci.nih.gov/tcga/). To further investigate the expression of the ELOVL family genes in LUAD, we conducted a pooled analysis of 12 datasets available in the GEO database (GSE3141, GSE8894, GSE14814, GSE29013, GSE30219, GSE31210, GSE31908, GSE37745, GSE40791, GSE43580, GSE50081 and GSE68465).

Gene variation analysis

The cBioPortal (https://cbioportal.org/) is a web tool that can be utilized to analyze the association between genes and clinical features online and to explore the genetic variation in specific tumors.

Comparison of protein expression between LUAD and normal tissues

HPA (https://www.proteinatlas.org/), an open-access source, can be used to explore the localization and expression of certain proteins or genes in certain fields such as cell lines, normal tissues, and pathological tissues. We collected the immunohistochemical staining images of normal and LUAD tissues of ELOVLs contained in this open-access source.

Single gene correlation analysis

The R “stat” package was utilized to screen the correlation of ELOVLs family members and other genes by the Spearman method. P < 0.05 was taken as the threshold, and the top 20 genes positively and negatively relevant to ELOVLs genes were selected.

Functional analysis of DEGs correlated with ELOVLs

The differentially expressed genes (DEGs) between LUAD and normal tissues were identified using the R package “DESeq2” with an adjusted P < 0.05 and an absolute log2 Fold Change (logFC) greater than 1. To further understand the functions of genes correlated with the ELOVLs family, we intersect the ELOVLs family-related genes with DEGs, the “clusterProfiler” R package was conducted to analyze the GO function and KEGG pathway of the above positively and negatively correlated genes.

Survival analysis

The R “survminer” package was employed to analyze the prognostic value of the ELOVLs family in LUAD. LUAD samples were divided into two groups according to the median expression value, and then OS or DSS were set as outcome predictors. To confirm prognosis outcomes, we further validated our findings using KM-plotter (https://kmplot.com/analysis/), an online site for prognosis analysis of available datasets.

Tumor microenvironment analysis

The 29 immune gene sets, comprising of 16 sets related to immune cell infiltration and 13 sets associated with immune functions, were extracted from previous studies [19, 20]. Subsequently, single sample gene set enrichment analysis (ssGSEA) was employed to elucidate the immunological status within low- and high-ELOVL6 expression subtypes.

Response to chemotherapy and immunotherapy analysis

Firstly, based on the Genomics of Drug Sensitivity in Cancer (GDSC) database, the effectiveness of chemotherapy prediction was evaluated by using the R package “pRRophetic”, in which the half-maximum inhibitory concentration (IC50) of each patient was assessed by Ridge’s regression, and predictive accuracy was assessed by 10-fold cross-validation [21]. In addition, the subclass mapping (SubMap) method was utilized to contrast gene expression similarity between ImmClusts and the responders of anti-PD-1 or anti-CTLA-4 therapy [22, 23].

Development and verification of a nomogram

A nomogram with age, gender, pathologic stage and ELOVL6 expression was performed using the “rms” R package based on the TCGA cohort for predicting OS in LUAD patients. The 422 LUAD patients with complete clinical information in the TCGA database were used to construct the nomogram, and randomly divided into 296 patients for training set and 126 patients for internal validation set according to a ratio of 7:3. Additionally, the eligible patients in GSE37745 were further used as an external validation test. Model performance for predicting the prognosis was evaluated through receiver operating characteristic (ROC) curves. The accuracy of the nomogram was assessed by calibration curves.

Cell culture and treatment

The present study employed 16HBE, A549 and H1299 cell lines that were procured from Shanghai Fuheng Biotechnology Co., Ltd. The 16HBE cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS) (Gibco, USA), while A549 and H1299 cells were grown in RPMI-1640 medium (HyClone, USA) containing 10% FBS. Additionally, we employed the INTERFERin® reagent (Polyplus-Transfection SA, France) for transfecting A549 and H1299 cells in accordance with the manufacturer’s instructions.

RNA isolation and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis

The Total RNA isolation was utilized the RNA Fast 200 RNA Extraction kit (Fastagen Biotech, China). The cDNA synthesis was performed using an Evo M-MLV Mix Kit with gDNA Clean for qPCR (Accurate Biology Co., Ltd., China), utilizing 1 µg of RNA as the starting material. Subsequently, RT-qPCR analysis was conducted employing the SYBR Green PCR Master Mix (Accurate Biotechnology Co., Ltd., China). The PCR primer of ELOVL6 used in this study was as follows: Human ELOVL6, forward: 5’-CAGATGCTGATGGGCTGTGT-3’, and reverse, 5’-GCAGAAGAGCACAAGGTAGC-3’. All samples were run in triplicate, and the mean values were used for quantification.

Cell proliferation assays

Cell proliferation assays were conducted using two methods: the Cell Counting Kit-8 (CCK-8; Dojindo, Kumamoto, Japan), which measures the number of viable cells, and the Colony Formation Assay, which measures the ability of cells to grow into colonies. For the CCK-8 assay, cells were cultured in 96-well plate at a dilution of 1 × 103 per well for 0, 24, 48, 72–96 h. After incubation, each well was supplemented with 10 µl of CCK8 solution and further incubated at 37℃ for 2 h. Subsequently, the absorbance at a wavelength of 450 nm was measured using a microplate reader. For the Colony Formation Assay, a total of 500 treated cells were uniformly inoculated into each well of a six-well plate and cultured for 10 days. Following fixation with 1 ml of 4% paraformaldehyde for 20 min, the cells were stained with an appropriate amount of crystal violet for 15 min. Subsequently, the staining solution was gently washed away using running water, and the cells were air-dried before colony counting under an optical microscope. All experiments were performed in triplicate to ensure experimental accuracy.

Cell migration assays

Cell migration was measured by two methods: wound healing assays and transwell migration assays. In wound healing assays, a scratch was made using a 200 µl pipette tip when cell confluence reached 90%. The cells were then incubated without FBS for 24 h and each scratch wound was recorded with a microscope at the same position at 0 h and 24 h. On the other hand, in transwell migration assays, 1 × 104 cells were suspended in low serum (5% FBS) medium and seeded into the upper chamber of transwell 24-well plates (Corning, USA) with 8 μm pore filters. The lower chamber was filled with a complete medium containing 10% FBS. After 12 h, the cells attached to the upper surface of the filter membranes were cleaned, and the migrated cells on the lower surface were stained with 0.5% crystal violet for several minutes. Pictures were taken under a microscope (100×).

Quantifcation of FFAs (free fatty acids) and TGs (triglycerides)

The cellular specimens were amassed in centrifuge tubes and subsequent to centrifugation, the supernatant was discarded. Sequentially, the appropriate reagents from the respective kits were introduced, and ultrasonication was employed to disrupt the cells for a duration of 1 min. The resultant samples were then subjected to centrifugation at 4 °C for 10 min, following which the supernatant was retained for subsequent experimentation. Quantification of FFAs and TGs contents was conducted using the “Amplex Red Free Fatty Acid Assay Kit” (Beyotime, China) and the “Triglyceride (TG) Content Assay Kit” (Solarbio, China) in accordance with the provided guidelines.Immunohistochemical staining.

Immunohistochemistry staining (IHC)

Histological sections were immersed in 3% hydrogen peroxide solution for endogenous peroxidase removal, and then subjected to antigenic thermal repair. Non-specific antigen blocking of the tissues, followed by overnight incubation at 4 °C with primary antibodies. The following day histological slices were hatched with secondary antibodies. The slices were sequestered and photographically imaged with an Olympus microscope.

Patients and samples

Samples of LUAD tissues and surrounding normal tissues were collected from 20 patients who had undergone curative surgery in Peking University Third Hospital. The research protocols were reviewed and approved by the Medical Ethics Committee of Peking University Third Hospital. Informed consent was obtained from all participants included in the study.

Statistical analysis

Wilcoxon rank sum test was used to analyze the differential expression of ELOVLs family genes in LUAD and normal samples in TCGA database, and ELOVL6 expression between LUAD and normal samples from our hospital, and the correlation between the degree of immune cell infiltration and the expression level of ELOVL6 in ssGSEA analysis, as well as drug sensitivity prediction. Kruskal-Wallis test was used to assess the differences in ELOVL6 expression among samples with varying clinical features. Spearman’s rank correlation analysis was performed to calculate correlation coefficients between two genes. Cox regression analysis was applied to assess the OS and DFS. The experimental data was expressed as the mean ± SEM and statistically analyzed using a t-test for two groups and one-way ANOVA for multiple groups. A significance threshold of P < 0.05 was used (ns, P > 0.05; *, P < 0.05; **, P < 0.01; ***, P < 0.001).

Results

The overall design of the study

The overall flow chart of the study is displayed Fig. 1.

Fig. 1.

Fig. 1

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The flow chat of the study

The mRNA expression, variance, and correlation of ELOVLs in LUAD tissues

The 516 LUAD tissues and 59 adjacent normal tissues from the TCGA database were included in the analysis. The results indicated that ELOVL2, ELOVL4 and ELOVL6 were markedly increased in LUAD tissues compared to the normal tissues, while ELOVL3 was significantly decreased in LUAD, the remaining genes showed no difference between the two tissues (Fig. 2(A)). Besides, after an integrated analysis of the 12 GEO LUAD datasets related to 12 GEO, some results were consistent with the preliminary TCGA analysis. Specifically, ELOVL2, ELOVL4, and ELOVL6 were found to be increased in LUAD tissues (Supplementary Figure S1). Figure 2(B) showed that ELOVL2 and ELOVL7 in the gene family had the highest variance rate in LUAD. The correlation of genes in this gene family in LUAD was shown in Fig. 2(C), the most significant positive correlated genes were observed between ELOVL6 and ELOVL7(R = 0.412), besides, the most significant negative correlated genes were observed between ELOVL1 and ELOVL2 (R= -0.229). However, the correlations between genes was weak, with absolute R < 0.05 (Supplementary Table S1 and S2).

Fig. 2.

Fig. 2

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The mRNA expression levels, mutation and correlation of ELOVLs in LUAD. (A) The mRNA expression levels of ELOVLs in LUAD from the TCGA database. (B) The mutation frequency and type of ELOVLs in LUAD. (C) Correlation between the expression levels of the ELOVLs in LUAD

The protein expression of ELOVLs in normal and LUAD lung tissues

We further investigated the protein expression of ELOVLs using the HPA. Immunohistochemical staining revealed that the protein levels of ELOVL5 and ELOVL6 were elevated in LUAD tissue compared to normal tissue. However, data for ELOVL2 and ELOVL4 were not available in the open-source database (Fig. 3A-G). Consequently, we performed IHC of ELOVL2 and ELOVL4 on clinical histological samples to examine their expression in LUAD and normal lung tissues. The results showed that expression levels of ELOVL2 and ELOVL4 were not significantly different between paracancerous tissues and LUAD (Supplementary Figure S2).

Fig. 3.

Fig. 3

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The representative protein expression levels of ELOVL1, ELOVL3, ELOVL5, ELOVL6 and ELOVL7 in normal and LUAD lung tissues from HPA database

Heatmap of the top 20 protein coding genes correlated with ELOVLs

Figure 4(A)-(G) showed the top 20 genes positively (The left side of each group) and negatively (The right side of each group) correlated with ELOVLs in LUAD. MED8 and TMEM54 were most positively correlated with ELOVL1, besides, ZNF594 and CEP128 were most negatively correlated with ELOVL1. ANGPT2 and SLC35S1 were most significantly positively correlated with ELOVL2, moreover, S100A6 and MST1R were most negatively correlated with ELOVL2. MESP2 and NHLRC1 were most positively correlated with ELOVL3, additionally, ARHGAP26 and AT8B1 were most negatively correlated with ELOVL3. SV2A and MLLT11 were most positively correlated with ELOVL4, besides, MUC1 and ELMO3 were most negatively correlated with ELOVL4. XPO and DEK were most positively correlated with ELOVL5, besides, ZNF524 and CLTB were most negatively correlated with ELOVL5. CCNA2 and MAD2L1 were most positively correlated with ELOVL6, besides, PBXIP1 and SELENBP1 were most negatively correlated with ELOVL6. DEPDC1B and FRMD5 were most positively correlated with ELOVL7, besides, FYCO1 and VPS54 were most negatively correlated with ELOVL7.

Fig. 4.

Fig. 4

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Heatmap of the top 20 protein coding genes positively (left) and negatively (right) correlated with ELOVLs

GO and KEGG analysis of genes correlated with ELOVLs

Firstly, a total of 5,393 DEGs were identified between LUAD and adjacent tissues in the TCGA database, using an adjusted P-value of less than 0.05 and absolute logFC greater than 1. Then, the intersection of genes positively and negatively correlated to ELOVLs and total DEGs was taken to show using the Venn diagram and upset diagram respectively Fig. 5(A). A total of 53 positively correlated and 31 negatively DEGs to ELOVLs were obtained finally. Subsequently, functional enrichment analysis of these two types of genes respectively. The results showed that the positively correlated DEGs to ELOVLs were mainly enriched in nuclear division, spindle, fatty acid synthase activity and p53 signaling pathway (Fig. 5(B)), and the negatively correlated DEGs were mainly enriched in the regulation of protein polymerization (Fig. 5(C)).

Fig. 5.

Fig. 5

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GO and KEGG analysis of DEGs correlated with ELOVLs. (A) The Venn and an upset diagram for interaction between DEGs, positively and negatively correlated with ELOVLs. (B) and (C) The representative GO and KEGG analysis of DEGs positively and negatively correlated with ELOVLs. BP, Biological Process; CC, Cellular Component; MF, Molecular Function; KEGG, Kyoto Encyclopedia of Genes and Genomes

Prognostic value of ELOVLs in patients with LUAD

Patients with high-expression ELOVL6 and ELOVL7 had a poorer prognosis compared to low-expression ones evaluated by the OS (P < 0.05). Furthermore, when the expression levels of ELOVL6 and ELOVL7 were further analyzed to assess patient prognosis based on DSS, only ELOVL6 expression was found to be significantly correlated with prognosis. (P < 0.05) (Fig. 6). Notably, the prognostic analysis using KM-plotter website toos, based on integrated 10 GEO datasets analysis for ELOVL6 was consistent with findings from the TCGA database, including GSE3141, GSE14814, GSE19188, GSE29013, GSE30219, GSE31210, GSE37745, GSE50081, GSE31908 and GSE68465 (Supplementary Figure S3).

Fig. 6.

Fig. 6

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The KM curve of ELOVLs for LUAD patients from TCGA database. (A)-(G) The KM curve with OS as the outcome index for ELOVLs. (H) and (I) The KM curve with DSS as the outcome index for ELOVL6 and ELOVL7

Immunity infiltration and response to immunotherapy and chemotherapy

Given the prognostic analysis indicating a negative correlation between ELOVL6 expression in lung tissue and LUAD prognosis, we conducted further investigations to elucidate the underlying mechanism through which ELOVL6 may impact LUAD prognosis. The patients were grouped based on the median expression of ELOVL6. The ssGSEA analysis revealed differential infiltration of activated dendritic cells (aDCs), mast cells, neutrophils, natural killer (NK) cells and Treg cells between the low- and high-expression groups of ELOVL6 (Fig. 7(A)). Furthermore, there were significant variations in immunologic functions such as APC co-inhibition, HLA expression, MHC class I presentation, parainflammation response, T cell co-inhibition and Type I/II IFN response between patients with low and high expression levels of ELOVL6. Furthermore, as widely acknowledged CTLA4 and PD-1 blockade is a promising treatment option in tumor immunotherapy. In order to predict the possibility of clinical response to PD-1 and CTLA4 inhibitors in the low and high-expression group of LUAD patients performed by SubMap algorithms. Presented in Fig. 7(B), regrettably, the study proved that there was no significant difference between the two groups in response to CTLA4 and PD-1 blockade. Moreover, four representative chemotherapy drugs (5-Fluorouracil, cisplatin, cytarabine and gemcitabine) on LUAD were selected to explore the diverse sensitivity of low- and high-expression ELOVL6 groups to them. The low-expression ELOVL6 group was more sensitive to these 4 drugs with P < 0.05 (Fig. 7(C)-7(F)).

Fig. 7.

Fig. 7

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Tumor immunity infiltration and response to immunotherapy and chemotherapy for low- and high-expression of ELOVL6. (A) Comparison of the ssGSEA scores between the low- and high-expression ELOVL6 groups based on immune cell infiltration. (B) Comparison of the ssGSEA scores between the low- and high-expression ELOVL6 groups based on immune function. (C-G) Response to immunotherapy and chemotherapy for low- and high-expression of ELOVL6 LUAD patients. Wilcoxon rank sum test was used to compare the two groups. P values were shown as ns, not significant; *P < 0.05; **P < 0.01; ***P < 0.001. iDC, immature dendritic cells; pDC, plasmacytoid dendritic cells; Tfh, follicular helper T cells; Treg, regulatory T cells; CCR, cytokine-cytokine receptor; APC, antigen-presenting cells; HLA, human leukocyte antigen; MHC, Major histocompatibility complex; IFN, interferon

Development and validation of a nomogram model for predicting survival for LUAD patients

A nomogram was constructed to predict OS for LUAD patients by combining the pathologic stage, age, sex and ELOVL6 expression based on training set (Fig. 8(A)). Our results demonstrated high area under the curve (AUC) scores for predicting the 1-, 3-, and 5-year survival rates of LUAD patients using the nomogram model in the training, internal validation, and external validation groups (Fig. 8(B)-(D)). The predictive performance of the nomogram was superior to that of ELOVL6 expression alone (Supplementary Figure S3(A)-(C)), indicating that this model possesses strong discriminative ability. The Calibration curves for the prediction of survival at 1-year, 3-year and 5-year validated the predictive accuracy of the nomogram (Supplementary Figure S3(D)-(F)). Similar findings were also observed in the internal and external validation cohorts (Supplementary Figure S3(G)-(L)).

Fig. 8.

Fig. 8

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Nomogram model for predicting the OS for LUAD. (A) Nomogram model predicting OS of patients from TCGA cohort. (B-D) Receiver operator characteristic (ROC) analysis of the nomogram model in the TCGA training, TCGA validation and GSE37745 cohorts

ELOVL6 could affect the proliferation, migration and fatty acid metabolism of LUAD cell lines

Firstly, we performed qPCR to compare the expression of ELOVL6 between LUAD tissues and their adjacent obtained from curative surgical procedures. Additionally, we examined the expression levels between two LUAD cell lines (A549 and H1299) and human normal bronchial epithelial (HBE) cells. The outcomes unequivocally revealed a marked upregulation of ELOVL6 expression in both LUAD tissues and cell lines (Fig. 9(A) and (B)), thereby reinforcing the results derived from our comprehensive analysis of the TCGA database. In order to further demonstrate the role of ELOVL6 in LUAD, we transfected both A549 and H1299 cells with siELOVL6. Subsequently, we selected si-ELOVL6 2 with the highest transfection efficiency for follow-up study (Fig. 9(C) and (D)). According to the CCK8 assays, it was observed that the viability of LUAD cell lines (A549 and H1299) decreased significantly after 96 h of low expression of ELOVL6, as compared to the normal group (Fig. 9(E) and (F)). Similarly, the colony formation assays also revealed that the cell proliferation capacity of LUAD cell lines reduced significantly after hypo-expression of ELOVL6, as compared to before (Fig. 9(G)-(I)). Moreover, the analysis of TCGA showed that the mRNA expression of ELOVL6 increased with the increase of T stages of LUAD patients, which further revealed that ELOVL6 could promote the proliferation of LUAD cells (Fig. 9(J)). In addition, transwell migration assay and wound healing experiments indicated that down-regulation of ELOVL6 impaired the migratory capacity of both A549 and H1299 cells (Fig. 9(K)-(O)). Furthermore, TCGA data analysis also provided further evidence for the involvement of ELOVL6 in promoting LUAD cell migration by revealing a positive correlation between mRNA expression levels of ELOVL6 and advanced N stages in patients with LUAD (Fig. 9(P)). Finally, by analyzing TG and FFA contents in LUAD cell lines, we observed a corresponding decrease in these lipid components upon downregulation of ELOVL6 expression (Fig. 9(Q) and (R)). This finding implied a potential role of ELOVL6 in modulating fatty acid metabolism within LUAD cells.

Fig. 9.

Fig. 9

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Inhibition of ELOVL6 can influence the proliferation, migration and fatty acid metabolism of LUAD cell lines. (A) The qPCR results of ELOVL6 expression in 20 patients with LUAD. (B) The mRNA expression levels of ELOVL6 in 16HBE, A549 and H1299 cells. (C) and (D) The mRNA expression levels of ELOVL6 after siRNA transfection in A549 and H1299 cells respectively. (E-I) Cell proliferation of A549 and H1299 cells were examined by CCK-8 assay and colony formation assay. (J) The relationship between pathologic T stage and the expression of the ELOVL6 in TCGA LUAD tissues. (K-O) Cell migration of A549 and H1299 cells were measured via transwell assay and wound healing assay. (P) The relationship between pathologic N stage and the expression of the ELOVL6 in TCGA LUAD tissues. (Q-R) Intracellular levels of TGs and FFAs were measured in A549 and H1299 cells in response to silencing ELOVL6. TPM transcripts per kilobase million; *P < 0.05; **P < 0.01; ***P < 0.001. Black bar, 50 μm

Discussion

In the past decade, significant advancements in lung cancer treatment have been made through the integration of target therapy and immunotherapy with chemotherapy or other modalities to extend patient survival, particularly in LUAD. However, given the rapid development characteristics and heterogeneity in treatment effectiveness among patients with LUAD, it is crucial to identify novel biomarkers that can accurately evaluate prognosis and guide personalized clinical management. With in-depth research on cancer cells, it is found that they have unique metabolic characteristics, which enable them to satisfy the biomass consumed by their rapid progression and resist the therapy and changes in their surroundings [24, 25]. Fatty acid metabolism, as one of the common cellular metabolisms, also plays an essential role in cancer cells, such as the synthesis of tumor cell membranes, the transmission of tumor signals and the construction of energy substrates (ATP and NADH) [26, 27]. Although existing studies have explored the role of fatty acid metabolism in LUAD [28, 29], a comprehensive and systematic exploration of the impact of ELOVLs, which are crucial for fatty acid elongation, remians lacking. Hence, the study of the prognostic value of ELOVLs is helpful to further understand the underlying disease mechanism, predict the prognosis and guide a more effective treatment strategy for LUAD.

Recently, there has been a surge in the exploration of genes related to the prognosis of LUAD [30, 31]. To date, this is an innovative study to explore the prognostic value of ELOVLs for LUAD, meanwhile, ELOVL6 with significant prognostic value was selected for further exploration of the mechanism. Firstly, we observed a significant increase in the mRNA expression levels of ELOVL2, ELOVL4 and ELOVL6 in LUAD tissues through analysis of TCGA and 12 integrated GEO data. However, IHC images from HPA database revealed an elevated protein expression of ELOVL5 and ELOVL6 compared to normal tissues. Both mRNA and protein analyses consistently demonstrated an upregulation of ELOVL6 in LUAD tissues, suggesting its potential involvement in the pathogenesis and progression of LUAD as well as its potential utility as a prognostic or diagnostic marker for patients with this disease. Furthermore, the functional enrichment analysis of genes positively correlated with ELOVLs revealed their significant enrichment in platinum drug resistance pathways according to KEGG analysis. This finding suggested a potential association between elevated expression levels of ELOVLs and these related genes with unfavorable prognosis and treatment outcomes in patients with LUAD. Additionally, OS was utilized as a prognostic outcome index to assess the prognostic significance of ELOVLs in patients with LUAD. The findings from TCGA and integrated GEO datasets revealed that only ELOVL6 could served as significant prognostic indicators for LUAD. Subsequent analysis using DSS, a more precise prognostic measure, demonstrated that only ELOVL6 exhibited meaningful potential for guiding prognosis.

In order to further explore how ELOVL6 affects the prognosis of LUAD patients, we divided LUAD patients into low and high-expression groups according to the expression level of ELOVL6 and conducted immunological analysis and drug sensitivity studies subsequently. Previous studies had revealed that the difference in tumor microenvironment among patients may affect the therapeutic effect [32–34]. Therefore, we conducted a comparative analysis of immune cell types and immune-related activities in tumor tissues between groups exhibiting low and high levels of ELOVL6 expression. The results revealed distinct variations in the infiltration levels of certain immune cell types, including aDCs, mast cells, neutrophils, NK cells and Treg cells within the tumor microenvironment of low- and high-ELOVL6 expression groups. Furthermore, significant disparities were observed between these two groups in terms of immune-related activities such as APC co-inhibition, parainflammation, type I IFN response and type II IFN response. For instance, the interaction of APCs and T cell receptors and ligands plays a crucial role in the activation of T cells, known as immune checkpoint signaling. The APCs can modulate T cell activity through both co-inhibitory and co-stimulatory mechanisms. However, the co-inhibitory effect of the interaction can suppress T cell activity and induce apoptosis, thereby contributing to the immune evasion of tumors [35]. Our findings indicated a significant reduction in APC co-inhibition within the low ELOVL6 group, potentially leading to enhanced T cell activation and augmenting the immune system’s cytotoxicity against tumor cells. Additionally, the Treg cells play an immunosuppressive role in our body, which can act as immunologic barriers against CD8+ T cell-mediated antitumor immune responses, the increase in the number of Treg cells in tumor tissues can also lead to the occurrence of tumor immune escape [36]. Our results revealed that the number of Treg cells in the low ELOVL6 expression group was decreased, which also confirmed the better prognosis of low ELOVL6.

Our analysis of the sensitivity of drug responses in low- and high-expression groups showed that low-expression ELOVL6 was more sensitive to four selected representative chemotherapy drugs, but no difference in the two immunotherapy drugs, indicating that the level of ELOVL6 can provide efficacy prediction for the chemotherapy treatment for patients with LUAD, and other markers are urgently needed to predict the patient’s sensitivity to immunology therapy. Notably, a recent study showed a correlation between high ELOVL6 expression and reduced sensitivity to certain anti-tumor drugs [37]. However, it should be noted that the prediction method employed in this study is based on cancer cell line studies in CellMiner, an online database [38]. We use “pRRophetic” package to combine LUAD patients’ genetic data with drug sensitivity prediction, which is more clinically instructive than predicting drug sensitivity based on cell line alone. Alongside this, a nomogram model was established to predict 1, 3 and 5-year survival rates in patients with LUAD by combining pathologic stage, age, sex and ELOVL6 expression in lung adenocarcinoma tissues. The model was further validated using both internal and external sets. Besides, the ROC and time-dependent calibration curves proved that the predictive method was rather reliable for LUAD patients. Finally, the vitro experiments confirmed that ELOVL6, as a prognostic gene of LUAD analyzed by bioinformatic analysis, could affect the progression of LUAD by affecting the proliferation and migration of tumor cells.

However, there are still some limitations to this study. Firstly, the limited and retrospective clinical sample volume could not exclude the possibility of a biased outcome and thus further validation of the role of ELOVL6 by prospective design is required. Furthermore, the effects of ELOVL6 in LUAD cell lines (A549 and H1299) in vitro have been demonstrated by our work, but LUAD is the result of a combination of many complex and diverse factors, and how ELOVL6 works in vivo still needs to be further explored. Last but not least, the specific mechanisms underlying the function of the ELOVL6 gene and its action require deeper excavation to provide a solid theoretical basis for our better understanding and treatment of LUAD.

Conclusion

In summary, this study comprehensively analyzed the mRNA and protein expression profiles, gene variances and prognostic value of ELOVLs in the context of LUAD. Bioinformatic analysis revealed that ELOVL6 holds promise as a potential prognostic marker for guiding personalized treatment strategies in LUAD patients. Furthermore, experimental validation demonstrated that ELOVL6 was elevated in LUAD tissues and cell lines, and downregulation of ELOVL6 significantly attenuated proliferation activity and migration ability in A549 and H1299 cells, and influenced the fatty acid metabolism, suggesting its potential as a therapeutic target for LUAD.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (11.9KB, xlsx)
Supplementary Material 2 (200.7KB, png)
Supplementary Material 3 (1.9MB, png)
Supplementary Material 4 (831.6KB, png)
Supplementary Material 5 (30.8MB, tif)

Acknowledgements

We sincerely appreciate the scientists who uploaded their research data on TCGA and GEO public database.

Abbreviations

ELOVLs

Elongation of very-long-chain fatty acids

LUAD

Lung adenocarcinoma

GEPIA

Gene expression profiling interactive analysis

HPA

Human protein atlas

KM

Kaplan–Meier

OS

Overall survival

DSS

Disease-specific survival

FBS

Fetal bovine serum

DMEM

Dulbecco’s modified Eagle’s medium

DEGs

Differentially expressed genes

RNA-seq

RNA sequencing

CCR

Cytokine-cytokine receptor

APC

Antigen-presenting cells

HLA

Human leukocyte antigen

ssGSEA

Single-sample Gene Set Enrichment Analysis

RT-qPCR

Reverse transcription-quantitative polymerase chain reaction

CCK-8

Cell Counting Kit-8

ANOVA

Analysis of variance

FFAs

Free fatty acids

TGs

Triglycerides

Author contributions

W.Z.H. and C.W.J. designed the study and wrote the main manuscript text. Q.J.G. and P.Y.Q. were involved in planning the work, L.L, L.D.Y., L.Y and Q.Y.F processed the experimental data, performed the analysis, and designed the figures. Z.Y aided in interpreting the results and worked on the manuscript. S.Y.C supervised the whole project. All authors discussed the results and commented on the manuscript.

Funding

YC.S. is supported by the National Natural Science Foundation of China (81970041, 82170048).

Data availability

The bioinformatic analysis concerned data originated from online TCGA and GEO databases. The datasets analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

The research protocols were reviewed and approved by the Medical Ethics Committee of Peking University Third Hospital [batch number: (2021) 572-02]. Informed consent was obtained from all participants included in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Zihan Wang and Wenjing Cui contributed equally to this work.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (11.9KB, xlsx)
Supplementary Material 2 (200.7KB, png)
Supplementary Material 3 (1.9MB, png)
Supplementary Material 4 (831.6KB, png)
Supplementary Material 5 (30.8MB, tif)

Data Availability Statement

The bioinformatic analysis concerned data originated from online TCGA and GEO databases. The datasets analyzed during the current study are available from the corresponding author upon reasonable request.

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