Eleven inflammation-related genes risk signature model predicts prognosis of patients with breast cancer

Introduction

Breast cancer has replaced lung cancer as the most commonly diagnosed cancer (1). With over 2.3 million new cases and 685,000 deaths occurring in 2020, by 2040, it is predicted that there will be over 3 million new cases and 1 million deaths due to breast cancer every year (1,2). Despite the availability of multiple therapies, including surgery, radiotherapy, chemotherapy, endocrine therapy, and immunotherapy, the median survival of patients with locally advanced or metastatic breast cancer is approximately 2–4 years. Currently, about 30–50% of patients who are diagnosed at earlier stages will progress to metastatic breast cancer (3,4). Therefore, breast cancer, as a genetic and molecular heterogeneous disease, is a serious global threat to women’s health. One of the hallmarks of malignant tumors is the change of gene expression (4,5). Genetic, epigenetic, or transcriptomic alterations are associated with the genesis and progression of breast cancer (6). Gene expression data has been analyzed to reveal distinct cancer subtypes, critical molecular drivers, and prognostic signatures (7), and to predict cancer vulnerability, therapy response, and mortality (8,9).

In 1863, the connections between inflammation and cancer were firstly recognized (10,11). On the one hand, inflammation causes DNA damage that leads to mutations, and in course of time accumulated mutations result in the occurrence of cancers (12). On the other hand, DNA damage contributes to inflammation, where tumors create their own local inflammation microenvironment to recruit various immune cells such as T lymphocytes, B cells, dendritic cells, macrophages, neutrophils, monocytes, and natural killer (NK) cells, thus promoting the growth of the tumors (10,13,14). The effects of cancer-associated inflammation not only comprise beneficial effects but also detrimental systemic effects (15). T-cell-mediated cytotoxicity leads to tumor suppression (16). The so-called B symptoms such as fevers, sweats, and weight loss as well as cachexia and a series of other paraneoplastic symptoms constitute the negative systemic reaction to malignancy (15,17,18).

As previously mentioned, the tumor microenvironment infiltrated by inflammatory cells is an indispensable participant in breast cancer development, progression, survival, and migration (13,19). Interleukin (IL)-6, as a pro-inflammatory cytokine, facilitates the survival of cancer cells through promoting endothelial cell migration and proliferation (20,21). Furthermore, the over-expression of the IL-6 family cytokines combining with vascular endothelial growth factor (VEGF) A leads to significantly worse survival in human epidermal growth factor receptor 2 (HER2)-negative breast cancer (22). The over-expression of IL-1β causes higher breast cancer stage and poorer prognosis (23,24). In triple-negative breast cancer (TNBC), A20/TNFAIP3 is up-regulated and its expression level is highly linked to a poor prognosis of metastasis-free patients (25,26). Recently, several predictive models based on inflammation-related genes (IRGs) have been developed and validated to evaluate prognosis as well as predicting survival probability in breast cancer patients. In 2019, Zhao et al. developed a 3-messenger RNA (mRNA) (TBX21, TGIF2, and CYCS) model of breast cancer by collecting clinical materials and mRNA data from The Cancer Genome Atlas (TCGA), a research team (Cat. #BR1504a, Alenabio Company, Shanxi, China), and Gene Expression Omnibus (GEO) (27). Zang et al. developed an inflammatory risk model to indicate prognosis and reflect the immune microenvironment in breast cancer using the databases from TCGA, GEO, and Molecular Signatures Database (MSigDB) (28). In 2022, a tumor immune-inflammation signature for breast cancer was explored by Liu et al. by extracting the datasets from The Cancer Genome Atlas-Breast Invasive Carcinoma (TCGA-BRCA), Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), and MSigDB (29). Although the models in the first and second studies lacked validations in vitro cell experiments, the second study identified expression levels of the 5-gene signature by quantitative real time polymerase chain reaction (qRT-PCR) in breast tissue samples. In present study, we systematically and comprehensively analyzed the expression levels of IRGs in breast cancer patients through the datasets from TCGA and GEO. We explored the links between expression of genes and sensitivity of drugs to screen possible potential biomarkers guiding the clinical application of targeted therapy for breast cancer. We constructed an IRG risk signature for patients with breast cancer by conducting least absolute shrinkage and selection operator (LASSO). We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-215/rc).

Methods

Data acquisition

We downloaded a list of 200 IRGs from the MSigDB database (http://www.gsea-msigdb.org/gsea/msigdb/index.jsp). RNA-sequence (RNA-seq) data, the simple nucleotide variation data from the “Masked Somatic Mutation” category, and the corresponding clinical information of 1,092 female breast cancer patients were obtained from TCGA (https://portal.gdc.cancer.gov). The dataset of GSE96058 was downloaded from GEO (https://www.ncbi.nlm.nih.gov/geo/).

Identification of differentially expressed IRGs (DEIRGs)

DESeq2 (R package version 1.34) in the R software (version 4.1.1; R Foundation for Statistical Computing, Vienna, Austria) was used to normalize raw read count of RNA-seq from TCGA and calculate differentially expressed genes (DEGs). DEGs were identified as genes with log₂(fold change) ≥1.0 and adjusted P value <0.05 (tumor tissues vs. normal tissues). Subsequently, the DEIRGs were identified from the DEGs. The heatmap was drawn by pheatmap package (R package version 1.0.12).

Functional enrichment analysis of DEIRGs

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were carried out to annotate the functions of the DEIRGs with clusterProfiler (R package version 4.0.5). A P value adjusted with Benjamini and Hochberg method less than 0.05 was considered statistically significant. To clarify the connections between genes and enriched GO terms, a network was constructed with Cytoscape (version 3.9.0; https://cytoscape.org/).

Construction and validation of multiple-IRG risk signature

We used LASSO regression analysis to construct the prognostic model while using the TCGA cohort as training dataset with “glmnet” package (R package version 4.1–7). The RNA-seq data was standardized by log₂(normalized read counts +1) transformation. With the addition of λ, LASSO tends to shrink the regression coefficients to zero. For 10-fold cross-validation, we chose a λ value that produced the minimum cross-validation error as our optimal λ value. We calculated a risk score of each sample as follows: risk score = sum of coefficients from the LASSO regression × normalized read counts. The median value of the risk score was used to divide the patients into high-risk or low-risk groups. We used the log-rank test with survival package (R package version 3.5-5) to analyze the overall survival (OS) of the patients. We drew time-dependent receiver operating characteristic (ROC) curves to calculate the area under the ROC curves (AUCs) to measure the predictive accuracy of the prognostic model using ROCR package (R package version 1.0-11). GSE96058 was used as test dataset to validate the multiple-IRG risk signature.

Construction and validation of nomogram

The univariate and multivariate Cox proportional hazards regression analyses were used to identify key factors associated with OS of breast cancer patients from risk score and other clinical factors. We used rms package (R package version 6.7-1) to draw a nomogram with the key factors as well as calibration curves. The calibration curves were used to assess whether the actual outcomes of 3-, 5-, and 10-year OS were similar to the predicted outcomes (30).

Gene set variation analysis (GSVA)

We obtained 50 hallmark pathways with msigdbr package (R package version 7.5.1) and removed the genes involved in two or more pathways (31). Then, GSVA was performed to assign pathway activity estimates to individual patients using GSVA package (R package version 1.40.1). Finally, we compared the pathway activity estimates between high- and low-risk patients with limma package (R package version 3.50.3).

Mutation analysis

We used maftools package (R package version 2.8.05) to compare mutation load between high- and low-risk patients.

Cell type estimation

Cell type estimation was performed using Estimation of Stromal and Immune cells in Malignant Tumor tissues (ESTIMATE) with TCGA dataset. We compared cell enrichments between high- and low-risk groups. The correlations between risk score and the enrichments of immune cells were analyzed by using Spearman’s correlation.

Drug response analysis

We used Cancer Therapeutics Response Portal (CTRP) (32) as a training dataset to predict imputed sensitivity score for TCGA dataset with oncoPredict (33) (R package version 0.2). The difference in drug response between high and low risk groups were assessed using Wilcoxon test. The correlations between expression levels of genes and imputed sensitivity scores were assessed using Spearman’s correlation. In the drug response analysis, we only included the approved drugs.

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Results

DEGs associated with inflammation

We obtained 65 DEIRGs after comparing 1,092 breast cancer tissues with 113 paracancerous tissues in TCGA (Figure 1A). Figure 1B displays the dissimilarity of expression levels of the 65 IRGs in breast cancer tissues and paracancerous tissues.

Figure 1 Identification of differences in 65 expressed IRGs. (A) Heatmap of 65 differentially expressed IRGs between breast cancer tissues and adjacent normal tissues. (B) Differential expressions of 65 IRGs were compared between breast cancer tissues and adjacent normal tissues. IRGs, inflammation-related genes.

Enrichment analysis of the DEIRGs

We applied GO and KEGG enrichment analysis to annotate the functions in all DEIRGs. The genes were annotated into three ontologies in GO annotation, including cellular component, molecular function, and biological process. As shown in Figure 2A, the DEIRGs were mainly distributed in endocytic vesicle, external side of plasma membrane, and endoplasmic reticulum lumen. The top 3 terms enriched molecular function were the activity of signaling receptor activator and cytokine as well as cytokine receptor binding. Cell chemotaxis, response to molecule of bacterial origin, and myeloid leukocyte migration were the top 3 terms enriched in biological process.

Figure 2 Enrichment analysis of the 65 DEIRGs. (A) The top 10 GO items of each ontology and the correlative IRGs. (B) Bubble plot of KEGG enrichment analysis on signal pathway of the 65 DEIRGs. Fold enrichment, the percentage of genes in IRGs belonging to a pathway, divided by the corresponding percentage in all human genes. FC, fold change; KEGG, Kyoto Encyclopedia of Genes and Genomes; TNF, tumor necrosis factor; IL, interleukin; COVID-19, coronavirus disease 2019; AGE, advanced glycation end product; RAGE, receptor for AGE; DEIRGs, differentially expressed inflammation-related genes; GO, Gene Ontology; IRGs, inflammation-related genes.

In the KEGG pathway analysis, the DEIRGs were mainly enriched in viral protein interaction with cytokine and cytokine receptor, cytokine-cytokine receptor interaction, tumor necrosis factor (TNF) signaling pathway, and chemokine signaling pathway (Figure 2B).

Construction and validation of the 11-IRGs risk signature

LASSO Cox regression analysis was performed to screen crucial IRGs from the 65 DEIRGs. Ultimately, 11 IRGs were obtained to construct the prognostic model (Figure 3A,3B). The 11 IRGs and their corresponding regression coefficients are shown in Figure 3C. We calculated risk scores for all patients, and separated the patients into low- or high-risk score groups. Survival analysis indicated that the low-risk group had fewer deaths and longer survival time (Figure 3D-3G). The median survival days of the low- and high-risk groups were 6,593 and 3,462, respectively [Figure 3G, hazard ratio (HR) =1.926, 95% confidence interval (CI): 1.382–2.684, P<0.001]. ROC curve analysis indicated that the AUCs for 3-, 5-, and 10-year survival were 0.638, 0.633, and 0.617, respectively (Figure 3H). These results manifested that the model could distinguish high-risk patients from low-risk patients.

Figure 3 Construction of 11-IRGs risk signature. (A) The LASSO Cox regression of DEIRGs. (B) LASSO coefficient gears of the 65 DEIRGs. (C) 11 IRGs with their regression coefficients. (D) The dispersion of 11-gene signature risk score. (E) The dispersion of patients’ survival time and status in low- and high-risk groups. (F) Heatmap of 11-gene signature. The blues represent lower expressions compared to the reds. (G) Relation between low- or high-risk groups and survival possibility of breast cancer patients. (H) Time-dependent ROC curve of 11-gene signature. In (D) and (E) dotted line stands for the median risk score. IRGs, inflammation-related genes; LASSO, least absolute shrinkage and selection operator; DEIRGs, differentially expressed inflammation-related genes; ROC, receiver operating characteristic.

The GSE96058 cohort was used to evaluate the performance of the 11-IRGs risk signature. We calculated risk score for each patient and then stratified them into high- and low-risk groups. Patients with low-risk scores had longer survival time and less deaths (Figure 4A-4C). The low-risk group had longer survival time than high-risk group (Figure 4D; HR =1.253, 95% CI: 1.016–1.545, P=0.03). In ROC curve analysis, the AUCs were 0.563, 0.530, and 0.528 for 3-, 5-, and 10-year survival (Figure 4E). We also investigated the basal characteristics of patients in the different risk groups. In the TCGA cohort, only estrogen receptor (ER) showed a significant difference between the low- and high-risk group (Table 1). In the GSE96058 cohort, the difference existed in age and grade (Table 1).

Figure 4 Validation of 11-IRGs risk signature in GSE96058. (A) The dispersion of 11-gene signature risk score in GSE96058. (B) Patients’ survival time and status in low- and high-risk groups in GSE96058. (C) Expression patterns of the 11-gene signature in GSE96058. (D) Relation between low- or high-risk groups and survival possibility of breast cancer patients in GSE96058. (E) Time-dependent ROC curve of 11-gene signature in GSE96058. In (A) and (B) dotted line stands for the median risk score. IRGs, inflammation-related genes; ROC, receiver operating characteristic.

Construction and validation of nomogram

In the univariate Cox analysis, six risk factors were significantly relevant with survival of breast cancer patients, including risk score, age, stage, tumor (T), node (N), and metastasis (M) (Figure 5A). Then, we carried out multivariate Cox analysis with the 6 factors, and found that risk score, age, and stage were the three independent prognostic factors (Figure 5B). Thereby, we used the three factors to develop a nomogram to predict the survival time of breast cancer patients (Figure 5C). As shown in Figure 5D-5F, calibration curves revealed that the nomogram model owned high accuracy in predicting 3-, 5-, and 10-year OS.

Figure 5 Survival feasibility of risk scores integrating clinical risk factors in breast cancer patients. (A) Univariate Cox analysis of TCGA. (B) Multivariate Cox analysis of TCGA. (C) Nomogram of predicting survival probability for 3, 5, and 10 years. (D) Calibration plot for the 3-year OS nomogram model. (E) Calibration plot for the 5-year OS nomogram model. (F) Calibration plot for the 10-year OS nomogram model. T, tumor; N, node; M, metastasis; HR, hazard ratio; CI, confidence interval; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2; OS, overall survival; TCGA, The Cancer Genome Atlas.

GSVA between high- and low-risk groups

Figure S1 shows the pathways significantly enriched in the high- and low-risk groups. Among the 50 pathways, 12 pathways had higher activities and 24 pathways had lower activities in the high-risk group compared with the low-risk group.

Correlation between gene mutation and risk score

Mutations in genes have been shown to play key roles in both the development of tumors and their response to therapy (34), thus we compared mutation load between high- and low-risk patients. However, no difference was observed between the two groups (Figure S2).

Status of immune infiltration

We predicted the composition of infiltrating immune cells in breast cancer tissues with TCGA cohort. We noted that the proportion of T cells showed a decreased trend according to the increase of risk score (Figure 6A). The differences between low- and high-risk groups in cell composition were observed in 15 types of immune cells (Figure 6B). Most of the immune cells in Figure 6B were enriched in the low-risk group, including T cells CD4 memory activated, T cells CD8, T cells follicular helper, and T cells regulatory Treg, and several cells showed an opposite trend, such as macrophages M2, which were also confirmed in the following Pearson correlation analysis (Figure 6C-6G).

Figure 6 Differences of immune infiltration between high- and low-risk group and the correlation between risk score and infiltration of immune cells. (A) The proportion distribution of 22 kinds of immune cells infiltration in high- and low-risk groups. The patients were sorted by their risk scores at x-axis. (B) Boxplot of immune-infiltrating cells showed significant differences between the low-risk and high-risk groups. (C) Scatter plots of correlation between risk score and macrophages M2. (D) Scatter plots of correlation between risk score and T cells CD4 memory activated. (E) Scatter plots of correlation between risk score and T cells CD8. (F) Scatter plots of correlation between risk score and T cells follicular helper. (G) Scatter plots of correlation between risk score and T cells regulatory Tregs. In figure (C-G), each black dot represents an individual sample and the solid blue line is a linear regression line. *, P<0.05; **, P<0.01; ****, P<0.0001. NK, natural killer.

The difference in drug response between high- and low-risk patients

We identified 67 approved drugs that showed a different effect between high- and low-risk patients (Figure 7A). Then, the correlations between expression levels of the 11 genes in the signature and imputed sensitivity scores were assessed using Spearman’s correlation. Totally, 72 gene-drug pairs with P<0.05 and absolute value of Pearson correlation coefficient >0.25 were identified (Figure 7B). The top 2 gene-drug pairs are shown in Figure 7C,7D.

Figure 7 Differences of drug response between high- and low-risk groups and the correlation between expression levels of the 11 genes in the signature and drug response. (A) Boxplot of the 67 differentially drug response in high- and low-risk groups. (B) Gene-drug interaction network of the 11 genes in the signature and drug response. (C) Scatter plots of correlation between IL-12B and sunitinib. (D) Scatter plots of correlation between SCARF1 and ruxolitinib. In figure (C,D), each black dot represents an individual sample and the solid blue line is a linear regression line. *, P<0.05; **, P<0.01; ***, P<0.001. IL-12B, interleukin 12B.

Discussion

Inflammation occurs when the body’s immune system responds to injury or infection to maintain homeostasis (35). Studies have shown that inflammation is involved in the initiation, growth, progression, and metastasis of tumors (36). Perhaps the most striking case is that ulcerative colitis, a type of inflammatory disease, increases the risk of bowel cancer (37). Analogously, hepatitis B and chronic gastritis with Helicobacter pylori increase the risks of liver and stomach cancer (38). It is noteworthy that chronic inflammation is remarkably associated with breast cancer recurrence (39) and uncontrolled inflammation kills more than a third of breast cancer patients (40).

In our study, we firstly obtained 65 DEIRGs from TCGA dataset. Then an 11-IRGs risk signature model was established to calculate risk score of breast cancer patients. We further constructed a nomogram to predict 3-, 5-, or 10-year survival of breast cancer patients. By now, several models of inflammation-related breast cancer prognosis have been published. In the 3-mRNA model constructed by Zhao et al., only 5-year survival was predicted for breast cancer patients (27). In the 5-inflammatory-gene signature developed by Zang et al., two validation sets were used to test the predictive feasibility of the model (28). They only analyzed risk score for differences in gene variation and signaling pathways. In our study, we explored the differences between high- and low-risk groups in genetic variation, genetic mutation, inflammatory cell infiltration, and drug sensitivity analysis.

Tumor microenvironment is a complex cellular ecosystem, in which inflammatory cells infiltration could interact with tumor cells, thus affecting tumor progression and therapeutic efficacy (41). The inflammatory cells recruited to the tumor site exhibited dual natures: some inhibited tumor growth, such as T cells and NK cells, whereas some promoted tumor progression (42). As a key immune cell in the tumor microenvironment, the abundance of macrophages correlates with adverse clinical outcomes, especially type M2 macrophages. Macrophage M2 have been shown to promote angiogenesis, invasion, movement, and infiltration at site of metastasis to stimulate the extravasation and continuous growth of tumor cells (43). Increased macrophage M2 infiltration has been associated with metastasis and poor prognosis in osteosarcoma (44). Macrophage M2 also accelerated lymphoma progression and deteriorates the patient’s prognosis (45). In addition, CD8⁺ T and CD4⁺ T cells have been shown to have possible anti-tumor effects on breast cancer (42). Herein, more macrophage cells infiltrated in the high-risk group indicated a worse prognosis. Our analysis of the correlation between risk scores and immune-infiltrating cells confirmed the above findings.

In drug sensitivity analysis between high- and low-risk patients, IL12B, of which high expression was negatively correlated with risk core, was associated with increased sensitivity to sunitinib. High expression of SCARF1 was negatively correlated with sensitivity of ruxolitinib. IL-12 is a multipotent cytokine encoded by IL12B that has been shown to be effective in anti-tumor therapy (46). Previous studies have shown that anti-tumor therapies combined with IL-12 gene therapy potentiated the efficacy of experimental cancer therapy. Co-injection of IL-12 in adenovirus vector and paclitaxel enhanced the therapeutic effect of melanoma (47). In mammary tumors, animals injected intratumorally with IL-2 and IL-12 had a higher rate of tumor regression (48). Sunitinib is a tyrosine kinase inhibitor (49) which has become the optimum first-line treatment for advanced renal cell carcinoma when united with immune checkpoint inhibitors (50). Together, they have the ability to antagonize vascular endothelial growth factor receptors (50). Another study elucidated that immune checkpoint inhibition in combination significantly prolonged OS compared with sunitinib alone in metastatic renal cell carcinoma (51). SCARF1 expresses in several cells, such as dendritic cells, epithelial cells, and macrophages (51). As a member of the scavenger receptor protein superfamily (52), it not only promotes the specific recruitment of pro-inflammatory CD4⁺ T cells to hepatocarcinoma tissues (53), but also binds and clears apoptotic cells (52). Moreover, high expression of SCARF1 was found to be associated with lung adenocarcinoma (54). Li et al. validated that ERβ2 promoted TNBC metastasis by binding SCARF1 (55). Ruxolitinib is a JAK inhibitor that has been shown to work by inhibiting Janus kinase, which in turn inhibits cytokine receptor signaling pathways (56). It has anti-inflammatory as well as immunosuppressive effects (57). During the treatment of myelofibrosis and high-risk polycythemia vera, ruxolitinib has been shown to affect immune function and increase the risk of infection (58). Therefore, the expressions of IL12B and SCARF1 may serve as potential targets for therapy of breast cancer, but the specific mechanism needs further investigation.

In this study, we provided a comprehensive tool to predict the prognosis of breast cancer. However, the research involved deficiencies, as follows. First, the prediction model was based on public databases and was not evaluated in a prospective cohort. Second, the model was only tested in one validation set, and the sample size was not large enough to validate the feasibility of the model. Third, the molecular mechanisms underlying IL12B and SCARF1 as prognostic biomarkers in breast cancer were unclear. Taken together, the roles of the IL12B and SCARF1 genes in breast cancer warrant further investigation.

Conclusions

We have provided a comprehensive prognostic tool for predicting breast cancer survival. Drug sensitivity analyses further hinted at the possibility that we use IL12B and SCARF1 as potential markers to predict therapeutic efficacy in breast cancer, although the mechanisms associated with them need to be further explored.

Acknowledgments

Funding: This work was supported by Nanjing Medical Science and Technique Development Foundation (No. ZKX21048), Wu Jieping Medical Foundation Clinical Research Fund (No. 320.6750.2023-17-1), and the Young Talents Program of Jiangsu Cancer Hospital (No. QL201810).

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-215/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-215/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-215/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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