Identification of a Potential sialylation-related pattern for the Prediction of Prognosis and Immunotherapy Response in in Small Cell Lung Cancer

doi:10.21203/rs.3.rs-3397963/v1

Download PDF

Research Article

Identification of a Potential sialylation-related pattern for the Prediction of Prognosis and Immunotherapy Response in in Small Cell Lung Cancer

https://doi.org/10.21203/rs.3.rs-3397963/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background: Our study aimed to establish a novel system for quantifying sialylation patterns and comprehensively analyze their relationship with immune cell infiltration (ICI) characterization, prognosis, and therapeutic sensitivity in small cell lung cancer (SCLC).

Methods: We conducted a thorough assessment of the sialylation patterns in 100 patients diagnosed with SCLC. Our primary focus was on analyzing the expression levels of 7 prognostic sialylation-related genes (SRGs). To evaluate and quantify these sialylation patterns, we devised a sialylation score (SS) using principal component analysis algorithms. Prognostic value and therapeutic sensitivities were then evaluated using multiple methods. The GSE176307 was used to verify the predictive ability of SS for immunotherapy.

Results: Our study identified two distinct clusters based on sialylation patterns. Sialylation cluster B exhibited a lower level of induced ICI therapy and immune-related signaling enrichment, which was associated with a poorer prognosis. Furthermore, there were significant differences in prognosis, response to targeted inhibitors, and immunotherapy between the high and low SS groups. Patients with high SS were characterized by decreased immune cell infiltration, chemokine and immune checkpoint expression and poorer response to immunotherapy, while the low SS group was more likely to benefit from immunotherapy.

Conclusion: This work showed that the evaluation of sialylation subtypes will help to gain insight into the heterogeneity of SCLC. The quantification of sialylation patterns played a non-negligible role in the prediction of ICI characterization, prognosis and individualized therapy strategies.

Small Cell Lung Cancer

Potential sialylation-related

prognosis

Immunotherapy sensitivity

Immune cell infiltration

Lung cancer is one of the most commonly diagnosed cancers and the leading cause of cancer-related mortality worldwide[1]. Small cell lung cancer (SCLC) is a highly aggressive neuroendocrine tumor which accounts for approximately 13% of all lung cancers[2]. SCLC is a malignant tumor characterized by rapid growth, early invasion, and high dependence on chemotherapy. However, there are significant differences in patient prognosis and sensitivity to immunotherapy in clinical practice. To date, there are no effective targeted drugs for SCLC, which further limits therapeutic options. Platinum-etoposide chemotherapy is still the backbone of standard treatment for SCLC. However, the prognosis of SCLC is the worst among all types of lung cancer, with a 2-year survival rate of less than 5%[3]. Even combining the anti-PD-L1 monoclonal antibodies atezolizumab and durvalumab with standard chemotherapy in extensive SCLC only prolonged median overall survival about 2 months[4, 5]. Therefore, it is particularly important to search for biomarkers that can predict the prognosis and response to immunotherapy in SCLC. One potential biomarker of interest is sialylation patterns, which refer to the modification of glycoproteins and glycolipids by sialic acid. Sialylation is known to play crucial roles in various biological processes, including immune responses and cancer progression. Recently, studies have suggested that alterations in sialylation patterns may be associated with tumor heterogeneity and therapeutic outcomes in different cancer types[6]. Sialylation is involved in many pathological conditions such as embryonic development, neurodevelopment, reprogramming, oncogenesis and immune responses[7]. Several studies have identified that sialylation is involved in the development of many cancers such as pancreatic cancer, colorectal cancer, papillary thyroid cancer and ovarian cancer[8–11]. Furthermore, tumor sialylation has been found to correlate with immune suppression and reduced survival in clear cell kidney cancer (KIRC) and squamous cell lung cancer (LUSC)[12]. Experiments in vivo and vitro indicate that the β-galactoside α2-6-sialyltransferase 1 (ST6Gal-I) may mediate the invasiveness and tumorigenicity of NSCLC cells through the Notch1/Hes1/MMPs pathway[13]. However, the relationship between sialylation and SCLC is still unclear. Therefore, sialylation-related genes might be valuable for predicting survival outcomes in SCLC patients.

In this study, the integration of genes related to sialylation (SRGs) was carried out, and sialylation-related subtypes in SCLC were identified from the Gene Expression Omnibus (GEO) databases. Remarkably, our findings showed distinct immune cell infiltration (ICI) characteristics among the different sialylation-related subtypes. The findings indicated that sialylation plays a fundamental role in influencing the tumor microenvironment (TME). Consequently, a scoring model was developed to quantify sialylation characteristics, and the relationship between the sialylation score (SS) and immune infiltration, prognosis, and treatment sensitivity was analyzed. The outcomes of our study present an innovative and accurate approach to forecast the prognosis and potential treatment advantages for SCLC.

Data sources used for analysis

The SCLC bulk RNA-sequencing datasets (GSE60052[14] and GSE30219[15]) and clinical information were downloaded from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). The GSE60052 dataset includes 79 SCLC patients, of which 49 have prognostic information; and GSE30219 dataset contains 21 SCLC patients. Two Datasets were integrated into the entire set and batch effects were corrected using the R package “sva”[16]. Principal component analysis (PCA) was used to assess the distribution of patients before and after removal of the batch effect. The sialylation-related genes were downloaded as previous described[9] (Table S1).

Consensus molecular clustering based on sialylation-related genes

On the basis of the sialylation-related genes, we performed a single Cox regression study, and the genes with a significance level (P) of 0.05 or lower were chosen for unsupervised grouping using the "ConsensusClusterPlus" R package.

Functional enrichment analysis

Using the "GSVA" R package, gene group variation study was carried out to investigate biologic processes[17]. The Molecular Signatures Database (MSigDB)was utilized to retrieve the gene sets needed for GSVA. Using the "clusterProfiler" package in R, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes, or KEGG, analysis were used to discover probable pathways[18].

Estimation of immune cell infiltration

The assessment of stroma and immune cells in dangerous tissues of tumors information on expression (ESTIMATE) algorithm were used to determine the immunological level, stromal level, and ESTIMATE level of SCLC patients[19]. Furthermore, we performed the single sample Gene Set Enrichment Analysis (ssGSEA) with the R package "GSVA" to assess the immunological infiltration between various subgroups.

Development of a sialylation-related Prognostic System

Differential analysis (|log2FC|>1.5 and P < 0.05) between two clusters was conducted utilizing the R package "limma"[20]. Finding DEGs with prognostic value required applying a univariate Cox regression analysis. We uesd the PCA to develop a scoring machinery for each patient depending on the prognostic genes in SCLC clusters with P < 0.001, called the sialylation score (SS), according to the formula: sialylation score = PC1-PC2, where PC1 reflects the greatest proportion of the variance in the initial expression lineage to be decayed, accompanied by PC2. To verify the efficacy of the Risk score, we looked at the distribution variations in the clinical characteristics and tumor immune microenvironment.

Immunotherapeutic effect and drug sensitivity analysis

GSE176307[21] (including ICB treatment data of metastatic urothelial cancer) was used to validate the prediction of immunotherapy efficacy using the risk model. The “pRRophetic” package[22] was utilized to assess the therapeutic response based on the 50% maximum inhibitory concentration (IC50).

Statistical analysis

R software 4.2.2 was used to conduct all statistical analyses. For statistical comparisons between several groups, non-parametric tests were employed. Kaplan-Meier curves and log-rank tests were used to evaluate survival distinctions between groups. Correlations were determined through the method of Pearson correlation. Statistics were deemed meaningful if P < 0.05.

Removal of Batch Effects

The ComBat function of “sva” package was used to remove batch effects from various platforms and dataset batches. Before batch effects were eliminated, samples were grouped by batch using the initial two principal components (PC) of the unnormalized expression values (Figure S1A). In contrast, batch processing impacts from various platforms were eliminated, according to a principal component analysis (PCA) based on normalized expression (Figure S1B). These outcomes proved that batch impacts were effectively avoided by cross-platform standardization.

Identification of sialylation-related cluster

We used the sialylation-related genes and survival data of SCLC patients to perform univariate Cox regression analysis, and 7 genes (NEU2, CTSA, PASP, ST3GAL2, GM2A, GCNT4 and SIGLEC10) were associated with survival (P < 0.05). The R package “ConsensusClusterPlus” was used for consistent clustering based on the 7 prognostic genes and identified two clusters: Cluster A and Cluster B (Fig. 1A). PCA revealed significant differences in prognostic sialylation-related genes between the 2 clusters (Fig. 1B). CTSA, PASP, ST3GAL2, GM2A and SIGLEC10 were significantly downregulated in cluster B (Fig. 1C). Kaplan-Meier survival analysis showed that Cluster B had worse outcomes than Cluster A (Fig. 1D). The relationship between clinical features, gene expression and clusters were shown in the heat map (Fig. 1E). The analysis of the ICI showed that except type 17 helper cell, the cluster A highly infiltrated immune cells such as activated B cells, activated CD4 + and CD8 + T cells, eosinophil, myeloid-derived suppressor cells (MDSCs), macrophages, mast cells, regulatory T cells (Tregs) as well as type 1 and type 2T helper cells and so on (Fig. 1F). The ESTIMATE algorithm results showed that the stromal score, immune score and estimated score were all significantly higher in the cluster A (P < 0.001) (Fig. 1G), indicating increased abundance of infiltrating immune cells.

Functional enrichment analyses in Distinct clusters

To explore the differences in the biological behaviors between the sialylation clusters, we conducted hallmark-, KEGG- and Reactome-related GSVAs. The results showed that oncogene- and immune-related signals such as P53 pathway, coagulation, interleukins pathways, PD-1 signaling and JAK/STAT signaling pathway were positively enriched in sialylation cluster A. (Fig. 2). We explored the DEGs between the two clusters and 242 DEGs were found (Fig. 3A). GO analysis showed that DEGs were enriched in immune-related biological processes such as adaptive immune response, leukocyte cell-cell adhesion, leukocyte mediated immunity, positive regulation of leukocyte activation and cell activation (Fig. 3B). The cellular component analysis indicated that DEGs were primarily enriched in collagen-containing extracellular matrix, external side of plasma membrane and endocytic vesicle (Fig. 3C). The molecular function suggested that DEGs was enriched for immune receptor activity, receptor ligand activity, signaling receptor activator activity (Fig. 3D). The top five KEGG pathway analysis showed that DEGs were related to complement and coagulation cascades, phagosome, rheumatoid arthritis, staphylococcus aureus infection and viral myocarditis (Fig. 3E).

Construction and validation of a sialylation-related prognostic model

Following multivariate Cox evaluation of the DEGs, 131 genes with prognostic significance were identified. Then, based on the 131 genes, we selected the 16 DEGs with P value < 0.001 to build a sialylation-related prognostic model (Fig. 4A). The individuals were classified into two gene clusters (gene cluster A and B) in order to further investigate these genes using an unsupervised cluster analysis based on the 16 DEGs (Fig. 4B). The individuals in gene cluster A had the best prognosis (P < 0.001) according to our clustering approach (Fig. 4C). The vast majority of patients in cluster A belonged to gene cluster A, whereas patients in cluster B with a generally dismal prognosis belonged to gene cluster B (Fig. 4D). Within the various gene clusters, there were considerable variations in the expression of the 16 sialylation-related genes (Fig. 4E). The risk score for individuals were determined using a principal component analysis based on the 16 genes as follows: Score of sialylation (SS) = PC1-PC2. Depending on the median SS, we separated the patients into two different risk levels groups. Patients with higher SS had a worse outcome, according to a survival study (Fig. 5A). In comparison to patients who were still alive, individuals who had died had significantly greater sialylation scores (P = 0.019) (Fig. 5B). The high-risk category included a greater proportion of individuals with a poorer outcome (Fig. 5C). Each patient'sclusters, gene clusters, sialylation score and prognosis were displayed using a Sankey plot (Fig. 5D). Figure 5E depicts the association between the sialylation score and the majority of infiltrating immune cells. The information demonstrated a substantial inverse relationship between the sialylation score and the majority of infiltrating immune cells.

Differences in immune cell infiltration and immune gene expression between high- and low-risk groups

Since immunity is essential for the development of tumors and the multiplication of cancerous cells, we hypothesized that the tumor immune microenvironment would differ depending on the type of patient. According to the examination of the tumor immune microenvironment, chemokines as CCL18, CCR1, CXCL10, and XCL1 had considerably higher expression levels in the low-risk category (Fig. 6A). The high category had far more decreased levels of the immune components, whereas the low-risk category showed rising expression of interleukins, receptors, and certain other inflammatory substances (Fig. 6A). The sialylation score was found to be adversely correlated with allograft rejection, IL2/STAT5 signaling, IL6/JAK/STAT3 signaling, interferon response, apoptosis, KRAS signaling, P53 pathway, and other characteristic pathways by GSVA (Fig. 6B). The immune molecules CD274 (PD-L1), CTLA-4, PDCD1 (PD-1) and TIGIT, which show underlying targets for immunotherapy, were more expressed in the low-risk patients as compared to the high category (Fig. 6C).

Prediction of therapy efficacy in high- and low-risk groups

Individuals in the low-risk category had longer overall survival and progression-free survival than patients in the high category (Fig. 7A-B) (P < 0.05). Complete response/partial response (CR/PR) individuals made up a much higher percentage of the low-risk category than the high category (Fig. 7C). We also looked into the anti-cancer drug IC50 levels in the different risk levels groups (Fig. 8). According to the findings, individuals in the high-risk category had higher IC50 values for the medications that were specifically targeting them, such as sunitinib, pazopanib, lapatinib, sorafenib, tipifarnib, and bosutinib. Additionally, the IC50 for the chemotherapeutic medicines bortezomib, cytarabine, cyclopamine, gemcitabine, midostaurin, and paclitaxel was higher in the high-risk category of patients. These findings suggested that the risk levels might serve as a predictor when choosing an anti-cancer medication.

SCLC, a highly lethal type of cancer, is primarily characterized by early invasion and an extreme sensitivity to primary chemotherapy. While it initially responds well to treatment, nearly all patients eventually develop resistance, resulting in the progression of the disease. In this study, we have unveiled two distinct groups based on the expression of 7 genes related to sialylation (Sialylation-Related Genes – SRGs), which also possess prognostic value. These two groups exhibit contrasting patterns in terms of immune infiltration and SRG expression. Analysis reveals that cluster A demonstrates a higher presence of immune cells and longer survival time. Furthermore, we have discovered two clusters of DEGs (Differentially Expressed Genes) related to sialylation that are also strongly correlated with immune activation. Lastly, we have developed a scoring system, known as SS (Sialylation Score), which allows for individualized prognosis and precise treatment guidance for patients with SCLC.

In recent years, the field of cancer treatment has witnessed a remarkable diversification of strategies. One such advancement is the development of immune checkpoint inhibitors (ICIs), which have shown unprecedented progress in targeting and eliminating cancer cells by harnessing the power of the body’s own immune system to enhance anti-tumor immunity[23]. In recent years, immune checkpoint inhibitors (ICIs) targeting programmed death-1 (PD-1), programmed death-1 ligand (PD-L1), and cytotoxic T lymphocyte antigen-4 (CTLA-4) have been developed to block the immune regulatory checkpoints present on tumor cells and immune cells. This strategy has shown significant efficacy in the treatment of non-small cell lung cancer (NSCLC), leading to the approval of certain ICIs for clinical use [24, 25]. Although the use of ICIs has improved the survival status of SCLC patients, it is only a subset of patients benefits from immunotherapy[4, 26–28]. High tumor mutational burden (TMB) may correlate with immunotherapy response in SCLC[29]. There is increasing recognition that discrete molecular subtypes of SCLC exist in preclinical models of the disease that may differ in their response to different therapies, providing new perspectives for the treatment of SCLC[30]. In our analysis, patients with high and low sialylation scores showed significant differences in immune cell infiltration and immune checkpoint gene expression, suggesting that identification of the distinct subtypes of sialylation may help us identify patients with SCLC who may benefit from treatment with ICIs.

According to a recent investigation, the sialoglycan-siglec axis emerges as a novel immune checkpoint with potential for regulating innate and adaptive immune responses against cancerous growths[31]. In advanced solid tumors, molecules that block siglecs, such as antibodies against siglec-15, are considered promising for normalising the immune system[32, 33]. In addition, other investigators have shown that local removal of sialic acids in the tumor microenvironment leads to increased infiltration of NK and infiltration of NK and cytotoxic T cells, while reducing suppressive myeloid cells[34]. Siglec-9 has also been found on effector memory T-cell subsets in the tumor microenvironment, suggesting that sialic acid removal may affect T-cell function[35]. Zhou et al. have developed a sialylation lncRNA-based signature for predicting immune landscape and chemotherapeutic response to guide the prognosis for patients with colorectal cancer[9]. We have successfully developed a sialylation scoring system for the identification of patients who may benefit from immunotherapy as a basis for further studies, implying that sialylation-related genes were potential targets for SCLC therapy.

Despite the new insights, the present study has several limitations. Firstly, it is essential to conduct further independent cohorts focusing on immunotherapy to strengthen the robustness and consistency of the SS prognosis. Secondly, it is crucial to conduct additional in vivo and in vitro experiments to validate the function of regulatory molecules responsible for sialylation in SCLC. Lastly, given that this study mostly relied on retrospective data sourced from online databases, prospective double-blind trials involving large sample sizes and multi-centers are needed for future validation and confirmation.

In summary, this study extensively examined the relationship between sialylation and ICI in SCLC. Diving into the various subtypes of sialylation will enhance understanding of SCLC’s diversity. By creating a quantification process for sialylation patterns, the SS has the potential to serve as a robust biomarker for evaluating prognosis and predicting immunotherapy responses. This research’s outcomes have paved the way forward for determining sialylation immunophenotypes, prognostic forecasting, and providing personalized immunotherapy in SCLC.

Acknowledgements

Guarantor statement

All authors read and approved the final manuscript.

Author contributions

Yu Y developed the research idea, performed data collection, performed data analysis, validated data collection, prepared the first manuscript draft, refined the research idea, and edited manuscripts. Shang Y and He YW validated data collection, prepared the first manuscript draft, and edited manuscripts. Wang Q, Ma J, Wang MH, Shi S and Shi WC performed data analysis. Chen H developed the research idea, refined the research idea, validated data collection and edited manuscripts. All authors read and approved the final manuscript. Chen H is the guarantor of the manuscript. All authors contributed to the article and approved the submitted version.

Names of collaborators

Not applicable.

Financial/Non-financial disclosures

Not applicable.

Ethics approval and consent to participate

Not applicable.

Role of the sponsors

Not applicable.

Funding

Not applicable.

Availability of data and materials

The datasets used and/or analyzed during the current study are available in the GEO repository, and related links are as follows:

GSE60052[13], https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60052;

GSE30219[14], https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30219;

GSE176307[20], https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE176307.

Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. Ca-a cancer journal for clinicians 2021, 71, 209–249.
Wang, W.Z.; Shulman, A.; Amann, J.M.; Carbone, D.P.; Tsichlis, P.N. Small cell lung cancer: Subtypes and therapeutic implications. Seminars in cancer biology 2022, 86, 543–554.
George, J.; Lim, J.S.; Jang, S.J.; Cun, Y.; Ozretić, L.; Kong, G.; Leenders, F.; Lu, X.; Fernández-Cuesta, L.; Bosco, G.; et al. Comprehensive genomic profiles of small cell lung cancer. Nature 2015, 524, 47–53.
Horn, L.; Mansfield, A.S.; Szczęsna, A.; Havel, L.; Krzakowski, M.; Hochmair, M.J.; Huemer, F.; Losonczy, G.; Johnson, M.L.; Nishio, M.; et al. First-Line Atezolizumab plus Chemotherapy in Extensive-Stage Small-Cell Lung Cancer. New england journal of medicine 2018, 379, 2220–2229.
Mathieu, L.; Shah, S.; Pai-Scherf, L.; Larkins, E.; Vallejo, J.; Li, X.; Rodriguez, L.; Mishra-Kalyani, P.; Goldberg, K.B.; Kluetz, P.G.; et al. FDA Approval Summary: Atezolizumab and Durvalumab in Combination with Platinum-Based Chemotherapy in Extensive Stage Small Cell Lung Cancer. Oncologist 2021, 26, 433–438
Vajaria, Bhairavi N et al. “Sialylation: an Avenue to Target Cancer Cells.” Pathology oncology research: POR vol. 22,3 (2016): 443–7
Li, F.; Ding, J. Sialylation is involved in cell fate decision during development, reprogramming and cancer progression. Protein & Cell 2019, 10, 550–565.
Cao, K.; Zhang, G.; Yang, M.; Wang, Y.; He, M.; Zhang, C.; Huang, Y.; Lu, J.; Liu, H. Attenuation of sialylation augments antitumor immunity and improves response to immunotherapy in ovarian cancer. Cancer research 2023, null, null.
Rodriguez, E.; Boelaars, K.; Brown, K.; Eveline Li, R.J.; Kruijssen, L.; Bruijns, S.C.M.; van Ee, T.; Schetters, S.T.T.; Crommentuijn, M.H.W.; van der Horst, J.C.; et al. Sialic acids in pancreatic cancer cells drive tumour-associated macrophage differentiation via the Siglec receptors Siglec-7 and Siglec-9. Nature communications 2021, 12, 1270.
Zhou, M.; Lv, S.; Hou, Y.; Zhang, R.; Wang, W.; Yan, Z.; Li, T.; Gan, W.; Zeng, Z.; Zhang, F.; et al. Characterization of sialylation-related long noncoding RNAs to develop a novel signature for predicting prognosis, immune landscape, and chemotherapy response in colorectal cancer. Frontiers in immunology 2022, 13, 994874.
Cao, Z.; Zhang, Z.; Liu, R.; Wu, M.; Li, Z.; Xu, X.; Liu, Z. Serum Linkage-Specific Sialylation Changes Are Potential Biomarkers for Monitoring and Predicting the Recurrence of Papillary Thyroid Cancer Following Thyroidectomy. Frontiers in endocrinology 2022, 13, 858325.
Stanczak, M.A.; Rodrigues Mantuano, N.; Kirchhammer, N.; Sanin, D.E.; Jacob, F.; Coelho, R.; Everest-Dass, A.V.; Wang, J.; Trefny, M.P.; Monaco, G.; et al. Targeting cancer glycosylation repolarizes tumor-associated macrophages allowing effective immune checkpoint blockade. Science translational medicine 2022, 14, eabj1270.
Yuan, Q.; Chen, X.; Han, Y.; Lei, T.; Wu, Q.; Yu, X.; Wang, L.; Fan, Z.; Wang, S. Modification of α2,6-sialylation mediates the invasiveness and tumorigenicity of non-small cell lung cancer cells in vitro and in vivo via Notch1/Hes1/MMPs pathway. International journal of cancer 2018, 143, 2319–2330.
Jiang, L.; Huang, J.; Higgs, B.W.; Hu, Z.; Xiao, Z.; Yao, X.; Conley, S.; Zhong, H.; Liu, Z.; Brohawn, P.; et al. Genomic Landscape Survey Identifies SRSF1 as a Key Oncodriver in Small Cell Lung Cancer. PLoS genetics 2016, 12, e1005895.
Rousseaux, S.; Debernardi, A.; Jacquiau, B.; Vitte, A.L.; Vesin, A.; Nagy-Mignotte, H.; Moro-Sibilot, D.; Brichon, P.Y.; Lantuejoul, S.; Hainaut, P.; et al. Ectopic activation of germline and placental genes identifies aggressive metastasis-prone lung cancers. Science translational medicine 2013, 5, 186ra166.
Leek, J.T.; Johnson, W.E.; Parker, H.S.; Jaffe, A.E.; Storey, J.D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics (Oxford, England) 2012, 28, 882–883.
Hanzelmann, S.; Castelo, R.; Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC bioinformatics 2013, 14, 7, doi:10.1186/1471-2105-14-7.
Yu, G.; Wang, L.G.; Han, Y.; He, Q.Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 2012, 16, 284–287.
Chakraborty, H.; Hossain, A. R package to estimate intracluster correlation coefficient with confidence interval for binary data. Computer methods and programs in biomedicine 2018, 155, 85–92.
Ritchie, M.E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015, 43, e47, doi:10.1093/nar/gkv007.
Rose, T.L.; Weir, W.H.; Mayhew, G.M.; Shibata, Y.; Eulitt, P.; Uronis, J.M.; Zhou, M.; Nielsen, M.; Smith, A.B.; Woods, M.; et al. Fibroblast growth factor receptor 3 alterations and response to immune checkpoint inhibition in metastatic urothelial cancer: a real world experience. British journal of cancer 2021, 125, 1251–1260.
Geeleher, P.; Cox, N.; Huang, R.S. pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One 2014, 9, e107468.
Ribas, A.; Wolchok, J.D. Cancer immunotherapy using checkpoint blockade. Science 2018, 359, 1350–1355.
Topalian, S.L.; Drake, C.G.; Pardoll, D.M. Immune checkpoint blockade: a common denominator approach to cancer therapy. Cancer cell 2015, 27, 450–461.
Reck, M.; Rodríguez-Abreu, D.; Robinson, A.G.; Hui, R.; Csőszi, T.; Fülöp, A.; Gottfried, M.; Peled, N.; Tafreshi, A.; Cuffe, S.; et al. Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer. New england journal of medicine 2016, 375, 1823–1833.
Ku, G.Y.; Yuan, J.; Page, D.B.; Schroeder, S.E.; Panageas, K.S.; Carvajal, R.D.; Chapman, P.B.; Schwartz, G.K.; Allison, J.P.; Wolchok, J.D. Single-institution experience with ipilimumab in advanced melanoma patients in the compassionate use setting: lymphocyte count after 2 doses correlates with survival. Cancer 2010, 116, 1767–1775.
Ott, P.A.; Elez, E.; Hiret, S.; Kim, D.W.; Morosky, A.; Saraf, S.; Piperdi, B.; Mehnert, J.M. Pembrolizumab in Patients With Extensive-Stage Small-Cell Lung Cancer: Results From the Phase Ib KEYNOTE-028 Study. Journal of clinical oncology 2017, 35, 3823–3829.
Antonia, S.J.; López-Martin, J.A.; Bendell, J.; Ott, P.A.; Taylor, M.; Eder, J.P.; Jäger, D.; Pietanza, M.C.; Le, D.T.; de Braud, F.; et al. Nivolumab alone and nivolumab plus ipilimumab in recurrent small-cell lung cancer (CheckMate 032): a multicentre, open-label, phase 1/2 trial. Lancet oncology 2016, 17, 883–895.
Hellmann, M.D.; Callahan, M.K.; Awad, M.M.; Calvo, E.; Ascierto, P.A.; Atmaca, A.; Rizvi, N.A.; Hirsch, F.R.; Selvaggi, G.; Szustakowski, J.D.; et al. Tumor Mutational Burden and Efficacy of Nivolumab Monotherapy and in Combination with Ipilimumab in Small-Cell Lung Cancer. Cancer cell 2018, 33, 853–861.e854.
Rudin, C.M.; Poirier, J.T.; Byers, L.A.; Dive, C.; Dowlati, A.; George, J.; Heymach, J.V.; Johnson, J.E.; Lehman, J.M.; MacPherson, D.; et al. Molecular subtypes of small cell lung cancer: a synthesis of human and mouse model data. Nat rev cancer 2019, 19, 289–297.
Ibarlucea-Benitez, I.; Weitzenfeld, P.; Smith, P.; Ravetch, J.V. Siglecs-7/9 function as inhibitory immune checkpoints in vivo and can be targeted to enhance therapeutic antitumor immunity. P natl acad sci USA 2021, 118, null.
Hiruma, Y.; Hirai, T.; Tsuda, E. Siglec-15, a member of the sialic acid-binding lectin, is a novel regulator for osteoclast differentiation. Biochem bioph res co 2011, 409, 424–429.
Wang, J.; Sun, J.; Liu, L.N.; Flies, D.B.; Nie, X.; Toki, M.; Zhang, J.; Song, C.; Zarr, M.; Zhou, X.; et al. Siglec-15 as an immune suppressor and potential target for normalization cancer immunotherapy. Nat med 2019, 25, 656–666.
Büll, C.; Boltje, T.J.; Balneger, N.; Weischer, S.M.; Wassink, M.; van Gemst, J.J.; Bloemendal, V.R.; Boon, L.; van der Vlag, J.; Heise, T.; et al. Sialic Acid Blockade Suppresses Tumor Growth by Enhancing T-cell-Mediated Tumor Immunity. Cancer research 2018, 78, 3574–3588.
Haas, Q.; Boligan, K.F.; Jandus, C.; Schneider, C.; Simillion, C.; Stanczak, M.A.; Haubitz, M.; Seyed Jafari, S.M.; Zippelius, A.; Baerlocher, G.M.; et al. Siglec-9 Regulates an Effector Memory CD8 + T-cell Subset That Congregates in the Melanoma Tumor Microenvironment. Cancer Immunology Research 2019, 7, 707–718.

Supplementary Figure 1 is not available with this version

No competing interests reported.

TableS1.xlsx
Supplementary Table 1. The sialylation-related genes used in this study.

Download PDF

Version 1

posted

You are reading this latest preprint version

Identification of a Potential sialylation-related pattern for the Prediction of Prognosis and Immunotherapy Response in in Small Cell Lung Cancer

Status:

Version 1

Abstract

Figures

Introduction

Materials and methods

Data sources used for analysis

Consensus molecular clustering based on sialylation-related genes

Functional enrichment analysis

Estimation of immune cell infiltration

Development of a sialylation-related Prognostic System

Immunotherapeutic effect and drug sensitivity analysis

Statistical analysis

Results

Removal of Batch Effects

Identification of sialylation-related cluster

Functional enrichment analyses in Distinct clusters

Construction and validation of a sialylation-related prognostic model

Differences in immune cell infiltration and immune gene expression between high- and low-risk groups

Prediction of therapy efficacy in high- and low-risk groups

Discussion

Declarations

References

Supplementary Figure

Additional Declarations

Supplementary Files

Status:

Version 1