Lung cancer is one of the most common incident cancers and the leading cause of cancer-related death worldwide 1. As the most predominant pathological subtype, lung adenocarcinoma (LUAD) makes up more than 40% of lung cancer cases 2, 3. In recent decades, although we have made promising progress in the screening, diagnosis, and management of LUAD patients, it remains a lethal disease because a large fraction of patients is diagnosed at the advanced disease stage 4, 5. Approximately 30% of LUAD patients are diagnosed at early stage with limited disease symptoms, with surgical resection and post-operation adjuvant therapy being recommended for these patients according to various protocols 6, 7. Adjuvant treatment is not required for stage IA patients with negative tumor margins 6, 8. However, stage IB patients with high-risk factors and stage IIA patients need to receive adjuvant chemotherapy to avoid disease relapse as recommended by National Comprehensive Cancer Network (NCCN) Guidelines 8. Although surgery and adjuvant treatment could bring remarkable survival benefits for these individuals, some patients still cannot escape the fate of disease recurrence within five years 6, 9. Therefore, effective biomarkers that could monitor disease recurrence and progression are urgently needed for patients with early-stage LUAD.Lipids, a large class of metabolites composed of different fatty acids 10, are essential components of the biological membranes and structural units that make up cells 11. Besides, they are also used for energy storage and metabolism and serve as crucial signaling molecular roles in most cellular activities 11. Altered cellular metabolism and energetics are recognized hallmarks of cancer cells. Accumulating evidence elucidated that lipid metabolism disorder in the tumor microenvironment (TME) is significantly correlated with the malignant phenotypes of cancer cells 11. For instance, Hall et al. reported that MYC drives the production of specific eicosanoids, which are critical for lung cancer cell survival and proliferation 12. This phenomenon indicated that MYC expression drives aberrant lipid metabolism in lung cancer. Furthermore, Zhang et al. found that knockdown of MGLL (a key enzyme in lipid metabolism) inhibits the proliferation and metastasis of LUAD cell lines, supporting that lipid metabolism plays a pivotal role in LUAD progression and metastasis 13. As we all know, TME also serves an important role in cancer progression, metastasis, immune evasion, and treatment resistance.The crosstalk between altered lipid metabolism and TME can strongly impact other cancer hallmarks 14. Different components of TME have distinct metabolism programs 14. Lipid metabolism reprogramming of different immune cells could change the biological behavior of the tumor and affect the antitumor immune response 14, 15. Therefore, targeting lipid metabolism is considered as a new strategy for malignancy treatment. This study systematically evaluated the prognostic value of lipid metabolism-related genes (LMRGs) in early-stage LUAD. In this study, we developed a lipid metabolism-related gene prognostic index (LMRGPI) that based on the expression levels of six LMRGs to predict the prognosis and treatment response for early-stage LUAD patients. An independent external validation cohort was also used to evaluate its predictive ability and risk stratification ability. Besides, we also identified two different LMRGs subtypes that have very different prognosis and immune characteristics via non-negative Matrix Factorization (NMF) algorithm. We believe our findings will provide potential biomarkers and therapeutic targets for these individuals.
Materials and methods
Raw data acquisition and processing
The transcriptional, mutation and clinical data of 403 stage I-II LUAD samples and 43 adjacent tissues were downloaded from the TCGA database. Because there were no progression-free survival (PFS) and disease-free survival (DFS) records in the TCGA database, we obtained these data from UCSC Xena (https://xena.ucsc.edu/). There were three formats (count, FPKM, and FPKM-UQ) of RNA-seq data, and we selected the second one for further analysis. The human.gtf file was adopted to raw matrix annotation. Furthermore, the GSE68465 cohort was also downloaded from the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/) database, an independent external validation cohort, which including transcriptional data and clinical information of 371 early-stage LUAD patients. As previously reported, 776 LMRGs were obtained from the Molecular Signature Database v. 7.0 (MSigDB, http://www.gsea-msigdb.org/gsea/msigdb/). We used the “VLOOK UP” function in Microsoft Excel to match the gene expression matrix with clinical data according to their unique ID number to generate the merged matrix for later analysis. The detailed clinicopathological characteristics of patients in the TCGA and GSE68465 cohorts is presented in Table .
DELMRGs identification and functional enrichment analysis
First, we adopted intersecting analysis between the gene expression matrix of early-stage LUAD patients from the TCGA-LUAD cohort and the extracted LMRGs. Then, differential expression analysis was performed to filter differentially expressed lipid metabolism-related genes (DELMRGs) between early-stage LUAD and normal samples by using the R software, “limma” package, with |log2(Foldchange)| >1.0 and false discovery rate (FDR)< 0.05 being used as cut-off value. The volcano plot and heatmap were also generated to visualize the distribution of the identified DELMRGs using R software, “ggplot2” and “pheatmap” packages. Subsequently, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were exploited to investigate the most significantly enriched pathways and biological processes of the DELMRGs using R software, “clusterProfiler” package.
Sample clustering using NMF algorithm
After differential expression analysis, intersected analysis was performed to identify common DELMRGs in the TCGA-LUAD and GSE68465 cohorts. Then, NMF was carried to divide patients into different subtypes according to the following steps: (a) the univariate Cox regression analysis was performed to identify potential prognostic DELMRGs via R software, “survival” package; (b) sample clustering through “brunet” method in R software, “NMF” package; (c) according to parameters such as cophenetic, dispersion, silhouette, and sparseness, the optimal number of the cluster was identified to classify patients into different subtypes; and (d) the consensus heatmap was generated in accordance with the above optimal cluster number to view the distribution characteristic among different subtypes.Then, we also explored the relationship between different clusters and the prognosis of patients with early-stage LUAD, including OS, PFS, and DFS. The Kaplan-Meier survival curves were generated to depict the survival difference via R software, “survival” and “survminer” packages. The log-rank test was used to evaluate the statistical difference. Besides, the MCPcounter algorithm 16 was adopted to estimate the infiltration of the immune cells between different clusters. According to the previous study 17, six immune subtypes play tumor-promoting or suppressive effects in human cancer. We also investigated the association between different clusters and immune subtypes.
LMRGPI construction and validation
We used the TCGA-LUAD cohort to develop the LMRGPI to stratify patients into different risk groups and predict their OS and treatment response. The GSE68465 cohort was set as an independent external validation cohort to assess the performance of LMRGPI. First, we performed univariate Cox regression analysis to identify potential prognostic LMRGPI for early-stage LUAD. Variables with a P value<0.05 were selected into the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis to reduce the number of genes in the final risk model through R software, “glmnet” package. Ultimately, genes in the LASSO regression were selected into the multivariate Cox regression analysis and therefore constructed the LMRGPI according to the following formula:In the formula, “βi” represents the coefficient of the selected LMRG in the multivariate Cox analysis and “expi” refers to its expression value. Subsequently, all patients were divided into high- and low-LMRGPI groups according to the median value of LMRGPI. Survival curve and risk plot were generated to visualize the survival difference and status for each patient. Besides, the receiver operating characteristic (ROC) curve was also adopted to evaluate the performance of LMRGPI in predicting 1-, 3-, and 5-year OS of early-stage LUAD patients via R software, “survival”, “survminer”, and “timeROC” packages. Likewise, we performed the above analyses in the GSE68465 cohort to confirm whether the LMRGPI could be a potential prognostic factor by dividing patients into two groups according to the median of the LMRGPI in the TCGA cohort. Furthermore, cBioPortal (http://www.cbioportal.org/) database was exploited to summarize the mutation landscape of the identified LMRGs in the multivariate Cox analysis, with the “OncoPrint” module being used for visualization. Meanwhile, the Human Protein Atlas (HPA, https://www.proteinatlas.org/) was also adopted to analyze their protein expression level, and the original immunohistochemistry (IHC) Figures were obtained for further analysis. Next, we performed the univariate and multivariate Cox analyses in TCGA and GSE68465 cohorts to determine whether LMRGPI could be an independent prognostic factor for early-stage LUAD compared with other common clinicopathological parameters, such as age, gender, disease stage, T stage, and N stage.
Nomogram development and evaluation
Then, we used R software, “rms” and “regplot” packages to develop two nomograms to illustrate each patient's 1-, 3-, and 5-year survival probability by integrating LMRGPI and common clinicopathological variables. Calibration curves were also generated to evaluate the consistency between the predicted and the actual OS. Furthermore, the Kaplan-Meier survival curve and ROC curve were also used to compare the discrimination ability of LMRGPI and other preexisting prognostic scores in predicting the OS of early-stage LUAD. Additionally, we also calculated their concordance index (C-index) and RMS values through R software, “rms” and “survcomp” packages to further assess their predictive ability.
Clinical relevance, mutation landscape, and enrichment analysis between high- and low-LMRGPI groups
Next, we investigated the relationship between LMRGPI and clinicopathological characteristics, the identified clusters by NMF, and the previously defined immune subtypes using R software, “ComplexHeatmap” package. Two waterfall plots were generated to explore the detailed gene mutation characteristics between high- and low-LMRGPI groups via R software, “maftools” package. Gene set enrichment analysis (GSEA) was then performed to identify the most significantly enriched pathways between high- and low-LMRGPI groups using R software, “clusterProfiler” package. We used “c2.cp.kegg.v7.4.symbols.gmt” as a reference gene set and visualized the top five pathways in different groups.
Immune cells infiltration and immune function status between high- and low-LMRGPI groups
Then, single-sample gene set enrichment analysis (ssGSEA) 18 was adopted to estimate the infiltrating score of immune cells and the activity of immune-related pathways using R software, “GSVA” and “GSEABase” packages. The Wilcoxon rank-sum test was used to compare the statistical difference between high- and low-LMRGPI groups. Besides, we also investigated the correlation between LMRGPI and immune cells infiltration, tumor mutation burden (TMB), and immune checkpoint inhibitors (ICIs) related genes expression levels.
Chemotherapeutic and immunotherapy response rates between high- and low-LMRGPI groups
We then calculated the half inhibitory concentration (IC50) of commonly used antitumor drugs in the TCGA-LUAD dataset via R software, 'pRRophetic' package 19 to evaluate the clinical utility of LMRGPI for the treatment of early-stage LUAD. Meanwhile, the Wilcoxon signed-rank test was utilized to compare the difference in the IC50 between low- and high-LMRGPI groups. To further investigate the prognostic value of LMRGPI in predicting the OS of patients treated with ICIs, we downloaded the gene expression matrix and survival data of the IMvigor 210 cohort 20 and performed survival analysis. Besides, we calculated the area under the curve (AUC) of LMRGPI in predicting 1-, 3-, and 5-year OS of patients in the IMvigor 210 cohort. Tumor Immune Dysfunction and Exclusion (TIDE, http://tide.dfci.harvard.edu/) algorithm can predict anti-PD1 and anti-CTLA4 response across several melanoma datasets and a limited dataset of non-small cell lung cancer (NSCLC) 21. The TIDE score could help oncologists choose patients who are more suitable for ICIs therapy. In prospective clinical trials, the TIDE score will be of great significance in immunotherapy decision-making 21. With the help of the TIDE online webserver, we predicted the response rate of immunotherapy in high- and low-LMRGPI groups. Furthermore, we also explored the correlation between LMRGPI and TIDE score, microsatellite instability (MSI), immune exclusion score, and immune dysfunction score.
Statistical analysis
The statistical difference between the categorical variables was detected by the Chi-square test. The non-parameter Wilcoxon rank-sum test was used to examine the relationship of continuous variables between the two groups. The LASSO regression and Cox regression analyses were used for LMRGPI development. Kaplan-Meier survival analysis was used to test the survival difference between different risk groups. A log-rank test was adopted to examine the statistical difference. A two-sided P-value < 0.05 was considered significant. All analyses were conducted in R software (version 3.6.3) for windows 64.0.
Results
The detailed study process of this study is illustrated in Figure . There were 752 LMRGs in the TCGA-LUAD cohort after matching the gene expression matrix and LMRGs list (776 genes). A total of 105 genes were identified as DELMRGs after differential expression analysis (Figure ). Of these, 64 were up-regulated genes, while 51 were down-regulated genes (Figure ). Next, we conducted GO and KEGG enrichment analyses to investigate the most significantly enriched biological processes and pathways of the identified DELMRGs. Not surprisingly, GO analysis revealed that the DELMRGs were mainly enriched in the biological process that involved fatty acid metabolism (Figure ). KEGG analysis indicated that the DELMRGs were mainly enriched in the PPAR signaling pathway, glycerophospholipid metabolism pathway, and arachidonic acid metabolism pathway (Figure ).
Different molecular subtypes identification based on DELMRGs
First, we performed univariate Cox analysis to identify the most significant prognostic LMRGs in the TCGA-LUAD cohort. Then, we conducted NMF to divide patients into different clusters according to relevant parameters. In this analysis, we observed that the optimal number of clusters is two according to cophenetic, dispersion, silhouette, sparseness, and so on (). Figure showed the expression level of LMRGs related to the prognosis of patients with early-stage LUAD in different clusters. Besides, we compared the OS, PFS, and DFS between different clusters. Better OS, PFS, and DFS were identified with patients in cluster 2 than in cluster 1 (Figure ). Besides, we also investigated the relationship between different immune subtypes and clusters via the Sankey plot. It showed that patients in cluster 1 are mainly classified into Immune C1 (wound healing), Immune C2 (IFN-gamma dominant), and Immune C6 (TGF-beta dominant) subtypes (Figure ). On the contrary, patients in cluster 2 are mainly classified into Immune C3 (inflammatory) subtype (Figure ). The MCPcounter algorithm was used to estimate the infiltration of the immune cells in different clusters. We found that the infiltration levels of cytotoxic lymphocytes, fibroblasts, and NK cells were significantly higher in cluster 1 than in cluster 2 (Figure ). However, cluster 2 had a higher infiltration level of endothelial cells, myeloid dendritic cells, and neutrophils (Figure ).First, we performed univariate Cox regression analysis to identify potential prognostic LMRGPI for early-stage LUAD in the TCGA-LUAD cohort. We found that 17 genres were correlated with the prognosis of these patients. Second, we conducted LASSO regression analysis to reduce the number of genes in the final risk model through R software, “glmnet” package, with 15 genes were identified through this step (Figure ). Ultimately, six genes were recognized as independent prognostic LMRGs via multivariate Cox analysis, including ANGPTL4, NPAS2, SLCO1B3, ACOXL, ALOX15, and B3GALNT1. According to their coefficients, we calculated LMRGPI using the following formula: LMRGPI= expression level of ANGPTL4 * 0.108 + expression level of NPAS2* 0.265 + expression level of SLCO1B3 * 0.083 + expression level of ACOXL * (-0.261) + expression level of ALOX15 *(-0.191) + expression level of B3GALNT1 * 0.177. All patients in this cohort were divided into high- and low-LMRGPI groups according to the median value of LMRGPI. The survival curve showed that patients with high-LMRGPI were associated with the worse OS when compared with patients with low-LMRGPI (Figure ). The risk plot also showed detailed survival outcomes of each patient (Figure ). We used the GSE68465 cohort as an independent external validation cohort to further assess the performance of LMRGPI. Consistently, similar results were observed in the GSE68465 cohort (Figure ). Besides, we used ROC curves and calculated AUC values to evaluate the performance of LMRGPI in predicting 1-, 3-, and 5-year OS of early-stage LUAD patients. We observed that LMRGPI had good performance in predicting the OS in these individuals both in the TCGA-LUAD cohort (AUC for 1-, 3-, and 5-year OS: 0.701, 0.720, and 0.665; Figure ) and GSE68465 cohort (AUC for 1-, 3-, and 5-year OS: 0.680, 0.643, and 0.632; Figure ).Next, we used the cBioPortal database to summarize the mutation landscape of the identified LMRGs in the multivariate Cox analysis. We observed that 8% of patients harbored SLCO1B3 mutation, with amplification being the most predominant genetic alteration type (Figure ). Meanwhile, the HPA database was also adopted to analyze their protein expression level. We found that ALOX15 and NPAS2 protein were highly expressed in the LUAD samples (Figure ). In contrast, SLCO1B3 protein was not detected in the LUAD sample (Figure ). The protein expression level of other prognostic LMRGs is not available in the HPA database. Subsequently, we performed subgroup analysis to evaluate the prognostic significance of LMRGPI in different subgroups, including age (Figure ), gender (Figure ), disease stage (Figure ), T stage (Figure ), and N stage (Figure ). It indicated that except for patients with T1 (Figure ) and N1 (Figure ) stage disease, higher LMRGPI was significantly associated with poor OS in other subgroups. Ultimately, we performed single factor and multi-factor Cox analyses to determine whether LMRGPI could be an independent prognostic factor for early-stage LUAD compared with other common clinicopathological parameters. Not surprisingly, we observed that LMRGPI could serve as an independent prognostic index for these individuals (Figure ).
Nomograms development and assessment
Next, we developed two nomograms to illustrate each patient's 1-, 3-, and 5-year survival probability by integrating LMRGPI and common clinicopathological variables. We could easily calculate each patient's total points and the corresponding survival probability using the constructed nomogram (Figure ). Calibration curves indicated higher consistencies between the predicted OS and the actual OS rates in the TCGA-LUAD cohort (Figure ) and the GSE68465 cohort (Figure ). Furthermore, we compared the discrimination ability of LMRGPI and other preexisting prognostic scores in predicting the OS of early-stage LUAD. It showed that LMRPI had comparable risk stratification ability to other prognostic scores (). The C-index (Figure ) and RMS (Figure ) values also supported the above results.
Clinical relevance, mutation landscape, and enrichment analysis between high- and low- LMRGPI groups
Next, we investigated the relationship between LMRGPI and clinicopathological characteristics, different clusters, and immune subtypes. It showed that LMRGPI was significantly correlated with age, disease stage, T stage, N stage, cluster, and immune subtype (Figure ). Afterward, we generated two waterfall plots to explore the detailed gene mutation characteristics between high- and low-LMRGPI groups. We identified that TP53, TTN, MUC16 were the most frequently mutated genes in these groups (Figure ). Besides, we also observed that the high-LMRGPI group harbored a more frequent TP53 mutation rate than the low-LMRGPI group (Figure ). Furthermore, we performed GSEA analysis to identify the most significantly enriched pathways between the two groups. We found that genes in the high-LMRGPI significantly enriched in cell cycle, cytokine-cytokine receptor interaction, ECM receptor interaction, focal adhesion, and regulation of actin cytoskeleton (Figure ). However, genes in the low-LMRGPI significantly enriched in alpha-linolenic acid metabolism, arachidonic acid metabolism, proximal tubule bicarbonate reclamation, systemic lupus erythematosus, and vascular smooth muscle contraction (Figure ).
The immune function between high- and low-LMRGPI groups
We then adopted ssGSEA to estimate the infiltrating score of immune cells and the activity of immune-related pathways in different LMRGPI groups. The results demonstrated that the infiltration levels of B cells, iDCs, Macrophages, Mast cells, and NK cells were significantly different in the two groups (Figure ). Meanwhile, the two groups also had different scores of APC co-inhibition, MHC class I, parainflammation, and Type II IFN response (Figure ). Subsequently, we investigated the correlation between LMRGPI and immune cells infiltration, TMB value, and the expression level of common ICIs related genes. The results revealed that LMRGPI was positively correlated with the infiltration levels of cytotoxic lymphocytes and fibroblasts. In contrast, it was negatively correlated with the infiltration levels of T cells, myeloid dendritic cells, neutrophils, and endothelial cells (). Besides, we observed that LMRGPI was positively correlated with TMB value (). We found that higher LMRGPI was also significantly associated with up-regulation of CD274 (Figure ). Nevertheless, there was no significant statistical difference between LMRGPI and PDCD1 (Figure ), CTLA4 (Figure ), TIGIT (Figure ), and LAG3 () expression. Interestingly, it showed that LMRGPI also positively correlated with POLE2 expression ().Next, we investigated the association between LMRGPI and commonly used antitumor drugs sensitivity via R software, “pRRophetic” package to evaluate the clinical utility of LMRGPI for the treatment of early-stage LUAD. We found that lower LMRGPI was significantly correlated with higher IC50 of gefitinib (Figure ), erlotinib (Figure ), cisplatin (Figure ), and vinorelbine (Figure ). We also downloaded the gene expression matrix and survival data of the IMvigor 210 cohort to explore the prognostic value of LMRGPI in predicting the OS of patients treated with ICIs. It revealed that LMRGPI could also be served as a potential prognostic biomarker for these patients (Figure ). Besides, we used the TIDE algorithm to predict the response rate of immunotherapy in high- and low-LMRGPI groups. We observed that patients with higher immunotherapy responses presented with higher LMRGPI (Figure ). Ultimately, we explored the correlation between LMRGPI and TIDE score, MSI, immune exclusion score, and immune dysfunction score. The results demonstrated that lower LMRGPI was significantly associated with a high TIDE score (Figure ) and immune dysfunction score (Figure ). However, higher LMRGPI was correlated with a higher immune exclusion score (Figure ). There was no significant difference between LMRGPI and MSI (Figure ).
Discussion
The current study identified two different LMRGs subtypes based on the NMF algorithm and explored their association with patients' prognosis and immune cells infiltration. We observed very different prognostic and immune profiles between different subtypes. Most importantly, we developed a novel prognostic index, LMRGPI, based on the expression levels of six LMRGs. It could be used to predict the prognosis and treatment response of early-stage LUAD patients. Furthermore, the results from an independent external validation cohort validation also depict a similar predictive ability of LMRGPI.We observed that patients in cluster 1 suffered from worse OS, PFS, and DFS than patients in cluster 2. Besides, it showed that patients in cluster 1 are mainly classified into Immune C1, Immune C2, and Immune C6 subtypes, which are correlated with more aggressive immune infiltrates and worse prognosis 17, 22. On the contrary, patients in cluster 2 are mainly classified into the Immune C3 subtype, which is associated with a more favorable immune composition and better clinical outcomes 17, 22. Therefore, we furtherly estimated the infiltration of the immune cells in different clusters. We found that cluster 1 correlated with higher cytotoxic lymphocytes, fibroblasts, and NK cells infiltration levels than cluster 2. Accumulating studies have shown that cancer-associated fibroblasts (CAFs) could transfer lipid to the TME to support cancer cell growth 15, 23, 24. Recently, Gong et al. elucidated that reprogramming of lipid metabolism in CAFs potentiates migration of colorectal cancer cells through in vivo and in vitro experiments 15. Furthermore, dysfunctional CD8+ T cells increased their uptake and accumulation of specific long-chain fatty acids (FAs), thus resulting in T cell dysfunction, inhibition of mitochondrial function, and reduction of FA catabolism 14, 25. Lipids also affect cytotoxic NK cells, which are vital in the antitumor response 14, 26. A recent study revealed that lipid accumulation in NK cells attenuated its antitumor immunity and failed to reduce tumor growth in obesity 27. Therefore, lipid accumulation could reprogram immune cells in TME and support a tumor-promoting microenvironment.Next, six LMRGs are recognized as correlated with OS of early-stage LUAD through LASSO and Cox regression analyses, including ANGPTL4, NPAS2, SLCO1B3, ACOXL, ALOX15, and B3GALNT1. ANGPTL4 is a member of the angiopoietin family and acts as a regulator of lipid and glucose metabolism. Upregulation of ANGPTL4 is associated with malignant biological behavior in various malignancies 28-31. Yang et al. reported that ANGPTL4 regulates ferroptosis through NOX2, thus inducing cell death and chemoresistance in epithelial ovarian cancer 28. NPAS2 is the most significant circadian rhythm gene, has received extensive attention due to its sophisticated function in various diseases development. He et al. revealed that NPAS2 polymorphism is an independent prognostic marker for lung cancer patients 32. Besides, Yuan et al. indicated that overexpression of NPAS2 significantly promoted cell proliferation and inhibited mitochondria-dependent intrinsic apoptosis, and thus contributed to a worse prognosis of patients with liver cancer 33. SLCO1B3 is a liver-specific transporter and is physiologically involved in the uptake of bile acids 34. Although numerous studies explored its functional change and prognostic value in various malignancies, the molecular regulatory mechanism of SLCO1B3 is not well elucidated 35. Sekine et al. reported that the expression of SLCO1B3 is associated with intratumoral cholestasis and CTNNB1 mutations in liver cancer 34. ACOXL is a rate-limiting enzyme in peroxisomal fatty acids β-oxidation, and it could initiate the oxidative metabolism of long-chain fatty acids 36. Therefore, ACOXL plays a crucial role in lipid metabolism. He et al. found that ACOXL is overexpressed in prostate cancer cell lines and could be served as a novel biomarker for prostate cancer 37. Nevertheless, the biological function and prognostic significance of ACOXL in LUAD and other tumors are not studied 37. ALOX15 oxidizes polyunsaturated fatty acids to generate several bioactive lipid metabolites, and many studies have elucidated its importance in oxidative and inflammatory responses 38. Recently, Zhang et al. revealed that CAFs could secrete exosomal miR-522 to inhibit ferroptosis in gastric cancer cell lines and promote acquired chemoresistance by targeting ALOX15 and blocking lipid peroxides accumulation 39. B3GALNT1 is a galactosyltransferase that catalyzes the transfer of galactose 40. Umeyama et al. indicated that B3GALNT1 is a potential therapeutic target in lung cancer through bioinformatic analysis 40. However, the association between B3GALNT1 expression and cancer development and progression is not well discussed as well.Subsequently, all patients were divided into low- and high-LMRGPI groups based on the median value of risk score both in TCGA and GSE68465 cohorts. Subsequent analyses demonstrated that it could be used as an independent prognostic index for early-stage LUAD. Ultimately, we constructed two nomograms to predict each patient's 1-, 3-, and 5-year survival probability by integrating LMRGPI with other clinicopathological variables, with a series of tests being performed to evaluate their discrimination and calibration abilities. These results proved that LMRGPI is a reliable prognostic index, and the nomograms could be an effective tool to predict the prognosis of early-stage LUAD. We then investigated the gene mutation landscape, immune function, and treatment response in different LMRGPI groups. We identified that the high-LMRGPI group harbored a more frequent TP53 mutation rate than the low-LMRGPI group. Numerous studies identified that TP53 mutation is closely correlated with treatment resistance and lethal prognosis in lung cancer 41-43. However, many studies revealed that TP53 mutation was significantly correlated with remarkable clinical benefit from PD-1 inhibitors for patients with LUAD since it increases TMB, up-regulates PD-L1 expression, and remodels TME 43-45.In this study, we also evaluated the relationship between LMRGPI and chemotherapeutics efficacy, suggesting that lower LMRGPI was correlated with the sensitivity to vinorelbine and cisplatin and the first-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) (gefitinib and erlotinib). The results of lung adjuvant cisplatin evaluation (LACE) meta-analysis confirmed that adjuvant cisplatin plus vinorelbine can significantly improve the OS of early-stage LUAD after the operation 46. Because EGFR was frequently mutated in patients with LUAD, especially in Eastern Asia, numerous studies have attempted to apply EGFR-TKIs in early-stage LUAD treatment. The results from the ADAURA study reported that adjuvant osimertinib (a third-generation EGFR-TKI) could significantly prolong the DFS of resectable NSCLC with EGFR mutation 47. Besides, the EVIDENCE study also revealed that icotinib could significantly improve DFS and has a better tolerability profile in these patients 48. Our study provided a novel prognostic index that could stratify patients who may benefit from adjuvant chemotherapy or targeted therapy. With the promising effect of ICIs in advanced/ metastatic lung cancer treatment, more and more studies are investigating the possibility of immunotherapy in early-stage lung cancer. IMpower010 is a randomized multicentre phase 3 study that explores adjuvant atezolizumab (a PD-L1 inhibitor) versus best supportive care in early-stage NSCLC 49. The results showed that atezolizumab after adjuvant chemotherapy offers a promising treatment option for these patients 49. Given that PD-L1 and TMB are the most predominantly used biomarkers to predict the efficacy of immunotherapy in lung cancer, higher values predict better therapeutic efficacy. We investigated the relationship between LMRGPI and TMB value and PD-L1 expression. The results revealed that LMRGPI was positively correlated with TMB value and CD274 expression level. Besides, we used the TIDE algorithm to predict the response rate of immunotherapy in high- and low-LMRGPI groups. A lower TIDE score means a lower potential for immune evasion, suggesting patients may benefit from ICIs treatment 50. We observed that lower LMRGPI was significantly associated with a higher TIDE score and immune dysfunction score, indicating an immune dysfunction status. Our study showed that higher LMRGPI was associated with superior immunotherapy efficacy in early-stage LUAD, and it could be a novel biomarker to ICIs efficacy prediction. However, there are several inevitable limitations in our study. First, although LMRGPI could effectively predict the OS and treatment response of early-stage LUAD and an independent external cohort was used to validate its performance, all these results were obtained from the bioinformatic analysis. Second, we identified that CAFs were significantly correlated with LMRGPI and its infiltration level differed in two subtypes. However, these results are observed based on algorithm estimation. Third, searching for effective prognostic and predictive biomarkers for immunotherapy is an arduous task for us and needs a long way to go. Our study developed a novel biomarker and provided potential insights in this area. However, well-designed prospective studies are warranted in the future to address this issue.
Conclusions
To sum up, lipid metabolism plays a crucial role in the prognosis, TME, and antitumor immune response of early-stage LUAD. We identified two distinct population subtypes according to LMRGs, and they have very different prognoses and immune functions. Most importantly, we established a novel biomarker LMRGPI that could predict the OS and treatment response of these individuals. Taken together, LMRGPI is a promising biomarker for early-stage LUAD patients.Supplementary figures.Click here for additional data file.Supplementary table.Click here for additional data file.
Table 1
The detailed clinical characteristics of patients in the TCGA and GEO cohorts
Authors: David S Ettinger; Wallace Akerley; Gerold Bepler; Matthew G Blum; Andrew Chang; Richard T Cheney; Lucian R Chirieac; Thomas A D'Amico; Todd L Demmy; Apar Kishor P Ganti; Ramaswamy Govindan; Frederic W Grannis; Thierry Jahan; Mohammad Jahanzeb; David H Johnson; Anne Kessinger; Ritsuko Komaki; Feng-Ming Kong; Mark G Kris; Lee M Krug; Quynh-Thu Le; Inga T Lennes; Renato Martins; Janis O'Malley; Raymond U Osarogiagbon; Gregory A Otterson; Jyoti D Patel; Katherine M Pisters; Karen Reckamp; Gregory J Riely; Eric Rohren; George R Simon; Scott J Swanson; Douglas E Wood; Stephen C Yang Journal: J Natl Compr Canc Netw Date: 2010-07 Impact factor: 11.908
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