
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39227722
71482
10.1038/s41598-024-71482-4
Article
Identification of lysine lactylation (kla)-related lncRNA signatures using XGBoost to predict prognosis and immune microenvironment in breast cancer patients
Lin Feng 12
Li Hang 2
Liu Huan 3
Shen Jianlin 4
Zheng Lemin 6
Huang Shunyi 5
http://orcid.org/0009-0005-8459-0996
Chen Yu ptyychenyu@163.com

2
1 https://ror.org/050s6ns64 grid.256112.3 0000 0004 1797 9307 School of Clinical Medicine, Fujian Medical University, No. 1 Xuefu North Road, University New District, Fuzhou, 350122 Fujian China
2 https://ror.org/00jmsxk74 grid.440618.f 0000 0004 1757 7156 Department of Breast Surgery, Affiliated Hospital of Putian University, Putian, 351100 Fujian Province China
3 https://ror.org/00g2rqs52 grid.410578.f 0000 0001 1114 4286 Department of Orthopedics, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000 Sichuan China
4 https://ror.org/00jmsxk74 grid.440618.f 0000 0004 1757 7156 Department of Orthopedics, Affiliated Hospital of Putian University, Putian, 351100 Fujian China
5 https://ror.org/00my25942 grid.452404.3 0000 0004 1808 0942 Fudan University Shanghai Cancer Center Xiamen Hospital, Xiamen, China
6 grid.11135.37 0000 0001 2256 9319 The Institute of Cardiovascular Sciences, School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing Key Laboratory of Cardiovascular Receptors Research, Health Science Center, Peking University, Beijing, 100191 China
3 9 2024
3 9 2024
2024
14 204323 2 2024
28 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Breast cancer (BC) stands as a predominant global malignancy, significantly contributing to female mortality. Recently uncovered, histone lysine lactylation (kla) has assumed a crucial role in cancer progression. However, the correlation with lncRNAs remains ambiguous. Scrutinizing lncRNAs associated with Kla not only improves clinical breast cancer management but also establishes a groundwork for antitumor drug development. We procured breast tissue samples, encompassing both normal and cancerous specimens, from The Cancer Genome Atlas (TCGA) database. Utilizing Cox regression and XGBoost methods, we developed a prognostic model using identified kla-related lncRNAs. The model's predictive efficacy underwent validation across training, testing, and the overall cohort. Functional analysis concerning kla-related lncRNAs ensued. We identified and screened 8 kla-related lncRNAs to formulate the risk model. Pathway analysis disclosed the connection between immune-related pathways and the risk model of kla-related lncRNAs. Significantly, the risk scores exhibited a correlation with both immune cell infiltration and immune function, indicating a clear association. Noteworthy is the observation that patients with elevated risk scores demonstrated an increased tumor mutation burden (TMB) and decreased tumor immune dysfunction and exclusion (TIDE) scores, suggesting heightened responses to immune checkpoint blockade. Our study uncovers a potential link between Kla-related lncRNAs and BC, providing innovative therapeutic guidelines for BC management.

Keywords

Breast cancer
lncRNA
Lactylation
Kla
TIDE
TMB
Subject terms

Biochemistry
Cancer
Computational biology and bioinformatics
Genetics
Immunology
Molecular biology
Biomarkers
Diseases
Medical research
Molecular medicine
Oncology
Risk factors
the Key Project of the Natural Science Foundation of Fujian Province (2021J011374).2021J011374 Lin Feng he Key Project of the Natural Science Foundation of Fujian Province (2020J011254).2020J011254 Liu Huan issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

BC ranks among the most prevalent malignant tumors in women globally, with its incidence steadily rising. In 2020, BC became more prevalent than lung cancer, emerging as the most common malignancy1. Marked significant heterogeneity, BC exhibits variations in etiology, pathological manifestations, and prognosis among individuals. Clinical assessment of BC patient prognosis often considers tumor stage, histological grading, and molecular subtypes. However, these features inadequately predict prognostic information, leading to inaccuracies in diagnosis. This inaccuracy may result in unnecessary or excessive treatment for low-risk patients, while high-risk individuals may receive inappropriate interventions2,3. Hence, identifying key molecular biomarkers and therapeutic targets is essential for guiding clinical practices in the management of breast cancer.

Recent studies highlight the critical role of histone post-translational modifications in the progression of cancer, as well as in enhancing anti-tumor immunity and therapeutic approaches4,5. Histone lysine lactylation (kla), a novel modification discovered in 2019, entails the attachment of lactate molecules to lysine residues6. Further studies have reaffirmed kla’s significant role in lactate, with functions such as promoting tumor growth, regulating the nervous system, and controlling metabolism7–9. Tumor cells undergo metabolic reprogramming, promoting rapid growth and proliferation by altering metabolism, a characteristic of cancer10. Unique glucose metabolism patterns in tumor cells, favoring glycolysis over the tricarboxylic acid (TCA) cycle even under well-oxygenated conditions, are regulated by kla, especially in glucose metabolism. Alterations in the tumor microenvironment (TME) crucially impact carcinogenesis, and kla regulates the TME, offering potential avenues for tumor immunotherapy, antiangiogenic therapy, and targeted interventions11. Feng et al12. demonstrated that histone posttranslational modifications play roles in maintaining genome stability, transcription, DNA repair, and chromatin modulation in breast cancer. Ongoing clinical trials are investigating drugs targeting lactate metabolism. De et al. demonstrated enhanced effectiveness of the combination of clonidine (LND) and cisplatin over cisplatin alone in inhibiting tumor growth in MX-1 breast and A2780 ovarian cancers13.

Various prognostic models utilizing Kla-related genes have surfaced for the prediction of cancer prognosis. For instance, Cheng et al. used eight Kla-related genes to formulate a risk model for hepatocellular carcinoma (HCC) with robust predictive efficiency. Lower risk scores signify better responses to treatment with most targeted drugs and immunotherapies14. Our previous study screened 6 prognostic lactylation genes to establish a gastric cancer (GC) lactylation score. The findings indicated that GC patients with higher lactylation scores displayed an enhanced likelihood of immune escape and decreased responsiveness to immunotherapy. This implies that lactylation scores can serve as predictive indicators for patient responses to immune checkpoint inhibitor (ICI) therapy15.

However, no reports have emerged on establishing a prognostic signature with kla-related lncRNAs to predict breast cancer prognosis and response to immunotherapy. The distinctive attributes of lncRNAs, including elevated tissue-specificity, developmental stage-specificity, and cellular subtype-specificity, govern their distinct functions in cancer progression and the functions of the TME16. Concurrently, lncRNAs assume pivotal roles in diverse biological processes, encompassing cell proliferation, differentiation, invasion, apoptosis, and metastasis17. Enhanced comprehension of the functional role of lncRNAs associated with lactylation in BC could offer novel avenues for precise treatment and individualized management.

In this study, gene expression profiles from the TCGA were utilized, and co-expression analysis was conducted to identify lncRNAs associated with kla (kla-lncRNAs). Subsequently, risk scores were computed through univariate, XGBoost, and multivariate Cox regression analyses, with particular attention given to the chosen Kla-related lncRNAs. BC samples were categorized into low-risk and high-risk groups based on the median risk score. This stratification revealed variations in overall survival (OS), clinical characteristics, progression-free survival (PFS), immune infiltration, response to immune checkpoint blockade (ICB) treatment, and sensitivity to chemotherapeutic drugs. Importantly, no Kla-related lncRNA signatures capable of effectively predicting BC prognosis and treatment response have been documented thus far. This study addresses this knowledge gap, making a substantial contribution to the field.

Materials and methods

Data acquisition

We gathered 1098 BC samples and 113 normal controls from the TCGA data portal accessible at https://portal.gdc.cancer.gov/.The dataset comprises mRNA expression data (in FPKM format), lncRNA expression information, somatic mutation details, and clinical data. After downloading the data from TCGA, patients without clinical information were excluded. Next, we identified kla-related genes based on published literature14.

Identification of kla-related lncRNAs

We assessed the expression of 327 kla-related genes in 254 tumor samples (excluding normal samples) using the 'limma' R package. Additionally, co-expression analysis was utilized to detect Kla-related lncRNAs, considering |Cor|> 0.4 and P < 0.001.

Development and validation of prognostic risk assessment models

The assignment of patients to the training or validation cohort was done randomly, with a ratio of 7:3. In the training cohort, an initial screening of prognostically relevant long non-coding RNAs (lncRNAs) was performed through one-way Cox regression, with a significance threshold set at a p-value below 0.05. Following this, the XGBoost machine learning algorithm was utilized to pinpoint 10 noteworthy featured lncRNAs, mitigating the potential for overfitting. Ultimately, a multivariate (multi-Cox) proportional risk regression was utilized to formulate the risk model, incorporating the chosen set of 10 lncRNAs related to kla. The risk score was computed through the formula: risk score = (lncRNA1 expression × lncRNA1 coefficient) + (lncRNA2 expression × lncRNA2 coefficient) + … + (lncRNA8 expression × lncRNA8 coefficient). Based on the average risk scores, patients were categorized into low-risk and high-risk groups. Following Kaplan–Meier analyses, we investigated the relationship between risk scores and patient prognosis, encompassing OS and PFS in the training, validation, and overall cohorts. We evaluated the prognostic predictive capability of the risk scoring system using receiver operating characteristic (ROC) curves. Various R packages, such as "caret," " survival," "glmnet," "xgboost," " timeROC," and "survminer," were utilized for these analyses.

Developing and assessing a nomogram.

Through univariate and multivariate Cox regressions, we explored the independent prognostic significance of the risk score system and clarified other remaining prognostic factors. Following this, we constructed a nomogram to predict the 1-, 3-, and 5-year survival rates of patients. The accuracy of the nomogram was assessed using a calibration plot. R packages, such as " rms," "survival," and " regplot," were employed for these analyses.

Principal component analysis (PCA) and GSEA

We assessed the discriminatory capability of the risk model in distinguishing between high and low-risk BC groups through PCA. This analysis was executed utilizing the " scatterplot3d " and " limma " software packages. Distinct lncRNAs in the high and low-risk groups were uncovered through GSEA. We performed Gene Ontology (GO) analyses to investigate the relevant biological processes, identifying differentially expressed genes and pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) in both groups. The KEGG gene set (c2.cp.kegg.Hs.symbols.gmt) was employed to distinguish between low and high-risk BC groups52, and the GO gene set (c5.go.Hs.symbols.gmt) was retrieved from the website (https://www.gsea-msigdb.org/). We conducted GO and KEGG analyses for kla-related features18 utilizing the "enrichplot," and "clusterProfiler" software packages.

Microenvironment of tumors in low and high-risk groups

We employed the CIBERSORT algorithm to examine the correlation between risk scores and the presence of tumor-infiltrating immune cells (TIICs). Additionally, single-sample genomic enrichment analysis (ssGSEA) with the "GSVA" software package19 assessed immune infiltration across 16 cell types and 13 immune-related pathways within two distinct risk subgroups. The choice of potential immune checkpoint genes relied on previous literature. To assess variations in expression levels among different risk groups, we employed the Wilcoxon test for immune checkpoint genes.

Genetic alterations in kla-related genes and TIDE scores

We obtained somatic mutation files (TCGA.BRCA.varscan.DR-10.0.somatic) from the TCGA website. Subsequently, we categorized the raw mutation annotation format (MAF) based on the risk score. TMB scores were then calculated using somatic mutation data for every patient in both groups (Total number of mutations (including synonymous and non-synonymous point mutations, displacements, insertions, and deletion mutations) / Coding region size of the target region). The analysis mentioned depended on the maftools R software package. In evaluating potential responses to ICB, we employed the TIDE algorithm20.The TIDE method categorizes patients into high and low cytotoxic T lymphocytes (CTL) groups based on the median CTL level in each sample. For patients with elevated CTL levels, their TIDE score is determined by the Pearson correlation between their expression profile and the T-cell inactivation signature. Conversely, for patients with low CTL levels, their TIDE score is determined by the Pearson correlation between their expression profile and the T-cell rejection signature. Lastly, we employed the oncoPredict R package to ascertain the semi-inhibitory concentration (IC50) values for chemotherapeutic agents. This analysis used the databases Cancer Therapeutics Response Portal (CTRP, http://portals.broadinstitute.org/ctrp.v2.1/) and Genomics of Cancer Drug Sensitivity (GDSC, https://osf.io/c6tfx/files/osfstorage).

Statistical analysis

R, a programming language for statistical analysis and graphing, is extensively utilized in statistical computing and data visualization. Version 4.2.3 of R, along with its associated software packages, was employed for all statistical analysis and graphing tasks. To compare the two datasets, we initially conducted a normality test. If the data adhered to a normal distribution, a t-test was performed; otherwise, a nonparametric test was applied. Correlation between variables was evaluated using Pearson correlation analysis. For prognostic value assessment, we utilized the log-rank test, Cox regression analysis, and Kaplan–Meier curve analysis. Additionally, differences in immune checkpoints, tumor mutational burden, immune cell infiltration, and chemotherapeutic agent IC50 values between the two groups were assessed using the Wilcoxon rank-sum test. Statistical significance was denoted by *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, and ns indicating no significance.

Results

Collection of kla-related lncRNAs

Initially, we extracted 327 genes associated with kla from previously published literature. Subsequently, co-expression analysis employing the Pearson correlation algorithm identified 756 lncRNAs associated with kla in the TCGA database.

Construction of a prognostic risk assessment model

In this investigation, we enrolled 1,082 BC patients, excluding those with missing clinical data. Random division assigned them to a training set (n = 756) and a test set (n = 326) following a 7:3 ratio. Statistical analysis revealed no significant differences in demographics, including age, gender, or staging, between the two groups (Table 1). Initially, uni-Cox regression analysis identified 48 lncRNAs (Fig. 1A). Following this, XGBoost analysis screened 10 distinctive lncRNAs (Fig. 1B), with 8 being incorporated into the multivariate Cox proportional risk model. The risk score was determined through the application of the multivariate Cox regression formula as follows: risk score = MIR4435-2HG × (0.867022528575273) + EGOT × (-0.235669144798444) + ST7-AS1 × (-1.11551827892176) + PDCD6IP-DT × (− 0.750754400726742) + ERICH6-AS1 × (− 1.20438658732549) + LINC00310 × (0.882320522314723) + OTUD6B-AS1 × (0.423845118028363) + LINC01871 × (− 0.415778456572796). The resulting risk scores for each modeled lncRNA are presented in the three-line table (Supplementary Table 1). Simultaneously, Sankey diagrams depicted the correlation between the 8 lncRNAs and the initial set of 13 genes related to kla (Fig. 1C). People received risk scores through the scoring system and were then grouped into high-risk or low-risk categories according to the median risk score value. Table 1 Demographics of 1082 patients.

Covariates	Total (n = 1082) ,n(%)	Train (n = 756), n(%)	Test (n = 326), n(%)	P value	
Age	
 <  = 65	765 (70.7)	537 (71.03)	228 (69.94)	0.8	
 > 65	317 (29.3)	219 (28.97)	98 (30.06)	
Gender	
Female	1070(98.89)	746(98.68)	324 (99.39)	0.5	
Male	12 (1.11)	10 (1.32)	2 (0.61)	
Stage	
Stage I	182 (16.82)	127 (16.8)	55 (16.87)	0.3	
Stage II	618 (57.12)	429 (56.75)	189 (57.98)	
Stage III	249 (23.01)	171 (22.62)	78 (23.93)	
Stage IV	20 (1.85)	18 (2.38)	2 (0.61)	
Unknow	13 (1.2)	11 (1.46)	2 (0.61)	
T	
T1	277 (25.6)	189 (25)	88 (26.99)	0.8	
T2	627 (57.95)	439 (58.07)	188 (57.67)	
T3	138 (12.75)	98 (12.96)	40 (12.27)	
T4	37 (3.42)	28 (3.7)	9 (2.76)	
Unknow	3 (0.28)	2 (0.26)	1 (0.31)	
N	
N0	514 (47.5)	365 (48.28)	149 (45.71)	0.7	
N1	356 (32.9)	241 (31.88)	115 (35.28)	
N2	117 (10.81)	82 (10.85)	35 (10.74)	
N3	76 (7.02)	55(7.28)	21 (6.44)	
Unknow	19 (1.76)	13 (1.72)	6 (1.84)	
M	
M0	1003 (83.45)	623 (82.41)	280 (85.89)	0.1	
M1	20 (1.85)	18 (2.38)	2 (0.61)	
Unknow	159 (14.7)	115 (15.21)	44 (13.5)	
T, tumor; N, node; M, metastasis.

Fig. 1 Identification of prognostic kla‐related lncRNAs in BC. (A) Prognostic lncRNAs that were derived from the uni‐Cox regression analysis. (B) Negatively biased logarithms of the XGBoost algorithm for cox risk-proportional regression vary depending on the number of iterations. (C) Sankey diagram of the co-expression relationship between 8 kla-related lncRNAs and related mRNAs. (D) PCA between the high- and low-risk groups based on total genes. (E) PCA between the high- and low-risk groups based on kla-related genes. (F) PCA between the high- and low-risk groups based on kla-related lncRNAs. (G) PCA between the high- and low-risk groups based on signature lncRNAs.

Validation of prognostic risk assessment models

As depicted in Fig. 2A–C, our analysis indicated that individuals in the high-risk category experienced a less favorable prognosis compared to those in the low-risk group. Expanding on this finding, Fig. 2D–F demonstrate the validation of our risk assessment model. Consistent results from Kaplan–Meier analysis demonstrated that individuals in the high-risk category had a lower overall survival than those in the low-risk group (Fig. 2D). This trend persisted in both the test cohort and the overall cohort (Fig. 2E, F). In Fig. 3B–K we assessed the prognostic significance of eight kla-related lncRNAs across various patient subgroups (Age ≤ 65, Age > 65, Metastasis negative, Metastasis positive, node negative, node negative, Stage I–II, Stage III–IV, T1–2, T3–4). Our findings demonstrated the predictive potential of these features across all examined subgroups. Patients classified in the high-risk category exhibited poorer prognoses compared to those in the low-risk category. These results indicate that these eight kla-related lncRNAs hold predictive value for breast cancer prognosis across different clinical stages. The discriminatory efficacy of various gene sets was demonstrated using PCA, depicted in four plots (Fig. 1D–G). Our findings emphasize that among the four gene sets (signature lncRNAs, kla-related lncRNAs, kla-related genes, and total genes), signature lncRNAs exhibited the most robust discriminatory efficacy.Fig. 2 Prognosis of the risk model in the different sets. (A) Risk score distribution, patients’ survival status, and heatmap of 8‐lncRNA prognostic signature in the training set. (B) Risk score distribution, patients’ survival status, and heatmap of 8‐lncRNA prognostic signature in the test set. (C) Risk score distribution, patients’ survival status, and heatmap of 8‐lncRNA prognostic signature in the total set. (D–F) K‐M survival curves of OS of patients between the two groups in the training, test, and total set. (G–I) Time‐dependent ROC analysis for 1-, 3‐ and 5‐year survival probability by the 8‐lncRNA signature in the training set, test set, and total set.

Fig. 3 Risk curve under different clinical and pathological variables. (A) The correlation between 8 lncRNAs and clinicopathologic features was evaluated. (B, C)Age. (D, E)M. (F, G)N. (H, I)Stage. (J, K)T. Abbreviations: T, tumor; N, lymph node; M, metastasis.

Prognostic properties of 8 lncRNAs

We assessed the correlation between eight lncRNAs and clinicopathologic features. The low-risk profile group exhibited elevated expression levels of EGOT, ST7-AS1, PDCD6IP-DT, ERICH6-AS1, and LINC01871, whereas the high-risk group demonstrated increased expression of MIR4435-2HG, LINC00310, and OTUD6B-AS1 (Fig. 3A). Additionally, survival analysis of lactylation profiles was conducted across various subgroups with distinct clinical characteristics. Figure 3B–K depict the prognostic significance of the 8 lncRNAs in different patient subgroups. The findings indicate that prognostic characterization is applicable across all subgroups. Those in the high-risk category exhibited a less favorable prognosis compared to individuals in the low-risk group. These results imply that the 8 lncRNAs serve as predictive indicators for the prognosis of BC, although prognostic features may not be applicable to breast cancer cases with distant metastases.

The construction and validation of a nomogram

Illustrated in Fig. 4A, B, we utilized univariate and multivariate Cox regressions to evaluate the prognostic importance of variables. In both single-factor and multi-factor regressions, the p-value associated with our risk score was below 0.05, confirming the independent prognostic significance (HR = 1.408, 95% CI 1.268–1.562, p < 0.001). Age emerged as the sole remaining independent prognostic factor (HR = 1.033, 95% CI 1.014–1.051, p < 0.001). Furthermore, Kaplan–Meier analysis validated the prognostic significance of the PFS risk score (Fig. 4G). To evaluate the predictive capacity of independent prognostic factors, we employed the receiver operating characteristic curve (ROC). In the 3-year ROC model, the AUC of the risk score was 0.756, indicating a robust predictive ability in comparison to other clinicopathologic features (Fig. 4C). In the training set, the AUC values for the ROC curves at 1, 3, and 5 years were 0.691, 0.756, and 0.722, respectively. This affirms the predictive capacity of our risk score across different time frames (Fig. 2G). This discovery was consistently validated in both the test cohort and the overall cohort (Fig. 2H, I). Furthermore, the C-index demonstrated that our risk score displayed superior predictive accuracy in comparison to other factors (Fig. 4D). Consequently, we generated nomogram plots to facilitate quantitative prognosis prediction (Fig. 4E), and its predictive accuracy was confirmed through subsequent calibration (Fig. 4F).Fig. 4 Verification of a prognosis risk assessment model and a nomogram of construction. (A) Uni‐Cox analyses of clinicopathologic factors and risk score with OS. (B) Multi‐Cox analyses of clinicopathologic factors and risk score with OS. (C) The ROC curves of risk score and clinicopathologic features. (D) C-index demonstrated the predictive accuracy of the risk score was superior to other clinical parameters. (E) Nomogram for predicting overall survival. (F) A calibration plot to assess the prediction power of the nomogram. (G) Kaplan–Meier curves of PFS.

GSEA of high- and low-risk groups

We employed GSEA to investigate variations in GO and KEGG between the two groups. Within the high-risk subgroup, KEGG analysis confirmed enriched signaling pathways associated with tumor initiation and progression, including ECM receptor interaction, focal adhesion, and tight junction signaling pathways (Fig. 5D). Moreover, the high-risk subgroup exhibited enrichment genetically altered biological processes, such as cellular component assembly involved in morphogenesis and muscle cell development, as evidenced by the GO analysis (Fig. 5B). On the flip side, the low-risk subgroup exhibited enriched signaling pathways, including Graft-versus-host disease, allograft rejection, cytokine-cytokine receptor interaction, and Intestinal immune network for IgA production, all interconnected with immunity based on the KEGG analysis (Fig. 5C). Embedded biological processes that are associated with immune function were highlighted by the GO analysis, which included an adaptive immune response and T cell activation (Fig. 5A).Fig. 5 Differences between high- and low-risk groups in gene set enrichment analysis (GSEA) and tumor microenvironment (TME). The GSEA approach was utilized to detect and depict distinct Gene Ontology (GO) (A, B) as well as enrichment analyses for the Kyoto Encyclopedia of Genes and Genomes (KEGG) (C, D) in both the high- and low-risk groups. Results of infiltrating fractions of immune cells. (E) Results of immune functions. (F) The presence of immune cells was assessed in both low- and high-risk groups. (G) The expression of immune checkpoint inhibitors varied among the high- and low-risk groups. (H) The proposed model examined correlations between TIICs and 8 kla‐related lncRNAs. *p < 0.05, **p < 0.01, ***p < 0.001, ns (no significance).

Investigation of immunization characteristics of different risk groups

We delved deeper into exploring the potential correlation of kla-related lncRNA features with tumor immunity. We calculated the difference in the proportion of tumor-infiltrating immune cells exhibiting lactylation between low- and high-risk subgroups using multiple databases, such as TIMER, CIBERSORT, CIBERSORT-ABS, MCPCOUNTER, QUANTISEQ, EPIC, and XCEL immunization databases (Supplementary Fig. 1). Furthermore, we assessed the relative proportions of 22 tumor-infiltrating immune cells in each sample through CIBERSORT, unveiling distinctive abundances in the two risk groups (Fig. 5G). An intricate correlation between the TIICs and the 8 kla-related lncRNAs was observed (Fig. 5I). These results imply that the 8 lncRNAs incorporated into our risk model can discern different aspects of immune cell infiltration in breast cancer. We conducted a thorough quantification of 16 immune cells and their respective 13 immune pathways and functions using single-sample gene set enrichment analysis (ssGSEA). Exploring correlations between various risk scores and immune pathways, we identified significant associations between 11 immune functions and 15 immune cell types with Kla-related risk scores (Fig. 5E, F). Further analysis explored the connection between risk scores and immune checkpoint genes, revealing heightened expression of 23 gene types in the low-risk category, encompassing immunosuppressive molecules like CD274, CTLA4, BTLA, IDOI, and ICOS (Fig. 5H).

TIDE, TMB and therapeutic drug sensitivity

We investigated the prevalence of somatic mutations and copy number variations (CNVs) in genes associated with breast cancer, revealing missense mutations as the most common variant category, with single nucleotide polymorphisms (SNPs) being the most prevalent type. The most frequently observed SNV classification has been C > T (Supplementary Fig. 1). Subsequently, we explored the frequency and categories of mutations in two distinct risk groups. Mutations were identified in 453 (88.3%) out of 513 breast cancer samples in the low-risk subgroup and 454 (90.26%) out of 503 breast cancer samples in the high-risk subgroup. The predominant type of mutation observed was missense mutation. Within the high-risk subgroup, PIK3CA exhibited a notable mutation rate (32%), ranking second only to the TP53 mutation rate (41%). Meanwhile, in the low-risk subgroup, PIK3CA had the highest number of mutations (37%) (Fig. 6A, B). Moreover, in the computation of the tumor mutational burden (TMB) for each breast cancer patient, we observed increased TMB in patients associated with the high-risk group in comparison to those in the low-risk group (Fig. 6C). The survival curve based on the TMB illustrated that individuals with lower TMB had a more favorable prognosis (Fig. 6D p = 0.010). Individuals with both low risk and low tumor mutational burden in breast cancer demonstrated the most favorable prognosis compared to other groups (Fig. 6E). The TIDE score was notably lower in the high-risk group in comparison to the low-risk group (Fig. 6F). We analyzed drug sensitivity using the GDSC and CTRP databases with the R package "oncoPredict" to predict drug response in cancer patients based on cell line screening data. We observed variations in the IC50 value of the PLK1 inhibitor BI 2536 between the two groups. Breast cancer patients in the high-risk group exhibited increased sensitivity to this drug (Fig. 6G).Fig. 6 TMB, TIDE, and sensitivity to chemotherapeutics. (A) waterfall plot illustrating the top 20 mutant genes is generated for patients in the low-risk group. (B) Patients in the high-risk group face a heavier tumor mutational burden. (C) TMB between high‐ and low‐risk groups. (D) K–M survival curves between the low‐TMB and high‐TMB groups. (E) K‐M survival curves between the two groups. (F) TIDE score between the high‐risk and low‐risk groups. (G) IC50 difference in BI 2536.

Discussion

BC exhibits a high degree of complexity, presenting various morphologic, biological, and clinical phenotypes. The development of an effective prognostic and predictive classification system is imperative to unravel the diverse biological and clinical heterogeneity inherent in BC21. The activation of aerobic glycolysis is pivotal in the tumorigenesis and progression of breast cancer22. In general, this metabolic pathway leads to the accumulation of lactate in the tumor microenvironment (TME), correlating with histone kla and playing a pivotal role in cancer progression and tumor immunity23,24. Recent studies consistently demonstrate that abnormal kla levels are closely linked to tumorigenesis and malignant progression25,26. Furthermore, prior investigations have employed bioinformatics analyses to establish prognostic models for BC, integrating lactylation-associated genes. These studies affirm that features related to lactylation can serve as innovative prognostic biomarkers for BC27. Presently, lncRNAs are recognized participants in cancer-associated cellular pathways, displaying robust predictive capabilities in prognosis and diagnosis28,29. However, up to this point, the utility of kla-related lncRNAs in predicting OS in BC patients remains unexplored. The involvement of kla-related lncRNAs in immune regulation within the context of BC also remains uncertain.

In this investigation, we identified two distinct risk subtypes characterized by 8 kla-lncRNAs, showcasing variations in survival status, immune infiltration, and response to immunotherapy. The eight Kla-lncRNAs MIR4435-2HG, EGOT, ST7-AS1, PDCD6IP-DT, ERICH6-AS1, LINC00310, OTUD6B-AS1, and LINC01871 were used to categorize BC cases as high- and low-risk groups. This signature, validated in our study, demonstrated robust predictive capabilities for overall survival, mutational load, immune-related functions, and immunotherapy response across different risk strata in breast cancer. To validate the prognostic accuracy of the risk score, we employed ROC and C-index curves. Our results emphasize the effectiveness of the risk score as a dependable predictive and prognostic indicator for breast cancer. Furthermore, we created a nomogram diagram illustrating the prognosis of breast cancer patients, demonstrating remarkable concordance with the anticipated results. This affirms the credibility of the risk score in forecasting and assessing outcomes for breast cancer.

Anomalously increased levels of MIR4435-2HG hold potential diagnostic and prognostic value in cancer. Heightened expression has been associated with significantly decreased OS in diverse tumors, including esophageal squamous cell carcinoma, colorectal cancer, gastric cancer, and triple-negative breast cancer (TNBC)30. Suppression of MIR4435-2HG exhibits potential in hindering metastasis and invasion in BC cells via the Wnt/β-catenin pathway, indicating its candidacy as a therapeutic target31. RT-PCR analysis indicates that MIR4435-2HG may facilitate macrophage migration and induce M1 to M2 macrophage polarization, thereby promoting BC progression32. EGOT, identified as a BC genome instability-associated lncRNA, serves as a predictor for BC prognosis and immune checkpoint inhibitor efficacy33. Cao et al34. reported EGOT as an N7-methylguanosine-associated lncRNA and a favorable prognostic factor for BC patients. Diminished expression of ST7-AS1 suggests an unfavorable prognosis for BC, influencing the cell cycle, DNA repair, and the composition of infiltrating immune cells within the TME. It has been identified as both a protective prognostic marker and a plausible therapeutic target for BC35. ERICH6-AS1, employed as an immune-related lncRNA, contributes to forming a new signature for enhanced prognostic prediction in individuals with cervical squamous cell carcinoma36. Serum LINC00310 has demonstrated robust diagnostic ability as a potential biomarker for breast cancer37. Our results suggest that OTUD6B-AS1 functions as an lncRNA associated with angiogenesis and poses as a risk factor for breast cancer prognosis38. LINC01871, identified as a prognostic signature, could serve as an immunologically relevant therapeutic target for BC in the clinic39. Nevertheless, the prognostic significance of PDCD6IP-DT in breast cancer or other malignancies has not been investigated. The study suggests that seven out of the eight lncRNAs investigated are associated with BC prognosis, and further exploration of PDCD6IP-DT's potential role is warranted.

While breast cancer historically wasn’t considered highly immunogenic, the tumor immune microenvironment now impacts breast cancer subgroups. Some patients may benefit from immune checkpoint blockade therapeutic strategies40,41. However, the interplay between lactation and tumor-infiltrating immune cells has received limited attention. Building on these insights, we utilized the CIBERSORT and ssGSEA methods for immunoassays in our risk model. Employing the CIBERSORT algorithm, we assessed the percentage of immune cells infiltrating the tumor to explore whether kla-related features were linked to tumor immunity and immunotherapy. Immune cells responsible for tumor suppression, such as T cells CD8, activated memory T cells CD4, and activated NK cells, were significantly reduced in high-risk categories. Conversely, immune cells associated with tumor promotion, such as macrophages M0 and M2 linked to cancer progression, invasion, metastasis, and immunosuppression, exhibited a notable increase in high-risk categories42. In accordance with CIBERSORT, ssGSEA analysis indicated a significant reduction in CD8 + T cells, B cells, Th1 cells, tumor-infiltrating lymphocytes (TIL), and NK cells within the high-risk group. On the flip side, the high-risk group exhibited an increase in macrophages. Moreover, immune functions, including cytolytic activity, HLA, inflammation promotion, MHC class I, T cell co-inhibition, T cell co-stimulation, and type II IFN response, were more prominent in the low-risk category. Both CIBERSORT and ssGSEA methods underscored a strong correlation between lactylation function and the abundance of immune cells infiltrating breast cancer tumors. This notably highlighted a diminished presence of immune cells with tumor-killing capabilities in high-risk populations. Consequently, individuals with breast cancer categorized as high-risk displayed diminished immune checkpoint molecule levels and compromised immune functionality.

Our investigation demonstrates that patients at high risk display the highest mutation frequency in the TP53 gene. Previous investigations have indicated that TP53 mutations play a role in enhancing tumor immunogenicity by modulating TP53-related signaling pathways in breast cancer (BC)43,44. Despite the success of immunotherapy in certain tumor types, its efficacy is not universal for all breast cancer patients45. Hence, identifying appropriate biomarkers becomes crucial for determining which patients may exhibit greater responsiveness to immunotherapy. Subgroups with hypermutated breast cancer, characterized by a high TMB, may be more predisposed to benefiting from PD-1 inhibitors46. Elevated TMB has been linked to enhanced effectiveness of immune checkpoint blockade therapy (ICB)47,48. While immune checkpoint blockade therapy can provide enduring clinical advantages, only a minority of patients show a positive response. To predict the response to immune checkpoint blockade (ICB), we employed TIDE, a computational method that simulates the fundamental mechanisms of immune evasion by tumors20.Increased TIDE scores indicate reduced responses to immune checkpoint blockade and have demonstrated high accuracy in forecasting the survival outcomes of cancer patients undergoing ICB treatment49. Recent studies have supported its utility in forecasting treatment outcomes in ICB50,51. In our investigation, the TIDE score notably decreased within the high-risk group. In summary, when evaluating both TMB and TIDE scores, the high-risk category demonstrated increased sensitivity to ICB. Additionally, utilizing the oncoPredict R package, we calculated the IC50 values of chemotherapeutic agents, revealing heightened sensitivity to BI 2536 in patients with high-risk scores.

However, our study presents certain limitations. Initially, we exclusively relied on information from the TCGA database for internal validation. To enhance the robustness of our findings, additional datasets from other sources are essential for external validation, thereby expanding the scope of assessing the predicted signature's applicability. Moreover, it is crucial to experimentally validate and clarify the mechanism of lncRNAs associated with kla in breast cancer.

Conclusively, the lncRNA signature associated with kla independently anticipates the prognosis in BC patients. This not only furnishes prognostic insights but also substantiates potential mechanisms of lncRNAs linked to kla in BC and their responsiveness to clinical interventions.

Supplementary Information

Supplementary Figure 1.

Supplementary Figure 2.

Supplementary Table 1.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-71482-4.

Author contributions

(I) Conception and design: F.L., H.L., H.L.; (II) Administrative support: Y.C.; (III) Provision of study materials or patients: J.S., L.Z.，S.H.; (IV) Collection and assembly of data: F.L., H.L.; (V) Data analysis and interpretation: F.L., H.L.; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. F.L. and H.L. contributed equally to this work.

Funding

This work was supported by the Key Project of the Natural Science Foundation of Fujian Province (2020J011254).

Data availability

The datasets generated and/or analysed during the current study are available in the TCGA repository, http://cancergenome.nih.gov/abouttcga.

Competing interests

The authors declare no competing interests.

Publisher's note

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

These authors contributed equally: Feng Lin and Hang Li.
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