Interleukin-11: A Potential Biomarker and Molecular Therapeutic Target in Non-Small Cell Lung Cancer

Blocking GP130 binding in interleukin-11 through site-specific PEGylation attenuates bleomycin-induced pulmonary fibrosis in mice

Recent insights have identified interleukin-11 (IL-11) as a pivotal profibrotic cytokine, with its signaling through IL-11Rα and GP130 receptors emerging as a promising therapeutic target for fibrotic diseases. Herein, we developed receptor-biased IL-11 via site-specific PEGylation at the GP130 binding interface, aiming to explore its therapeutic potential for bleomycin-induced pulmonary fibrosis in mice. By conducting single site-directed cysteine mutagenesis at site II or site III of IL-11, we refined the conjugation site, demonstrating that mutation at site III exhibits heightened sensitivity to GP130 binding and signaling. Cysteine-based PEGylation substantially attenuated the ability of GP130 to bind to IL-11 W147C, while almost entirely preserving its IL-11Rα binding ability. These PEGylated IL-11 W147C analogs showed potent inhibition of TF-1 cell proliferation and significant antagonism to TGF-β1-induced human lung fibroblasts (HLFs) differentiation into myofibroblasts. Moreover, PEGylation significantly prolonged the half-life of IL-11 W147C in healthy rats. Subcutaneous administration of PEGylated analogs, particularly PEG20/40 k-IL-11 W147C, effectively mitigated extracellular matrix deposition, preserved alveolar architecture, and attenuated the progression of pulmonary fibrosis in mice. The finding of this study not only underscores the therapeutic potential of IL-11 modulation, but also provides a general strategy for the design of cytokine-based biased antagonists and agonists targeting these multifaceted signaling pathways.

A novel prediction model for the prognosis of non-small cell lung cancer with clinical routine laboratory indicators: a machine learning approach

BACKGROUND

Lung cancer is characterized by high morbidity and mortality due to the lack of practical early diagnostic and prognostic tools. The present study uses machine learning algorithms to construct a clinical predictive model for non-small cell lung cancer (NSCLC) patients.

METHODS

Laboratory indices of the NSCLC patients at their initial visit were collected for quality control and exploratory analysis. By comparing the levels of the above indices between the survival and death groups, the statistically significant indices were selected for subsequent machine learning modeling. Ten machine learning algorithms were then employed to develop the predictive models with survival and recurrence as outcomes, respectively. Moreover, regression models were constructed using the random survival forest algorithm by incorporating the survival time dimension. Finally, critical variables in the optimal model were screened based on the interpretable algorithms to build a decision tree to facilitate clinical application.

RESULTS

682 patients were enrolled according to the inclusion and exclusion criteria. The preliminary comparison results revealed that except for fast blood glucose, CD3+T cell proportion, NK cell proportion, and CA72-4, there were significant statistical differences in other tumor markers, inflammation, metabolism, and immune-related indices between the survival and death groups (p < 0.01). Subsequently, indices with statistical differences were incorporated into machine learning modeling and evaluation. The results showed that among the ten prognostic models constructed using survival status as the outcome, the neural network model obtained the best predictive performance, with accuracy, sensitivity, specificity, AUC, and precision values of 0.993, 0.987, 1.000, 0.994, and 1.000, respectively. The corresponding SHAP16 algorithm revealed that the top five variables in terms of importance were interleukin6 (IL-6), soluble interleukin2 receptor (sIL-2R), cholesterol, CEA, and Cy211, respectively. The random survival forest model also confirmed the critical role of CEA, sIL-2R, and IL-6 in predicting the prognosis of NSCLC patients. A decision tree model with seven cut-off points based on the above three indices was eventually built for clinical application.

CONCLUSION

The neural network model exhibited ideal predictive performance in the survival status of NSCLC patients, and the decision tree model constructed based on selected important variables was conducive to rapid bedside prognosis assessment and decision-making.

Circulating Interleukins as Biomarkers in Non-Small Cell Lung Cancer Patients: A Pilot Study Compared to Normal Individuals

Identifying biomarkers in non-small cell lung cancer (NSCLC) can improve diagnosis and patient stratification. We evaluated plasmas and sera for interleukins (IL)-11, IL-6, IL-8, IL-17A, and IL-33 as biomarkers in primary NSCLC patients undergoing surgical treatment against normal volunteers. Exhaled-breath condensates (EBCs), a potential source without invasive procedures, were explored in normal individuals. Due to separate recruitment criteria and intrinsic cohort differences, the NSCLC and control cohorts were not well matched for age (median age: 65 vs. 40 years; p < 0.0001) and smoking status (p = 0.0058). Interleukins were first assessed through conventional ELISA. IL-11 was elevated in NSCLC plasma compared to controls (49.71 ± 16.90 vs. 27.67 ± 14.06 pg/mL, respectively, p < 0.0001) but undetectable in sera and EBCs by conventional ELISA. Therefore, high-sensitivity PCR-based IL-11 ELISA was repeated, albeit with concentration discrepancies. IL11 gene and protein upregulation by RT-qPCR and immunohistochemistry, respectively, were validated in NSCLC tumors. The lack of detection sensitivity across IL-6, IL-8, IL-17A, and IL-33 suggests the need for further, precise assays. Surprisingly, biomarker concentrations can be dissimilar across paired plasmas and sera. Our results identified a need to optimize detection limits for biomarker detection and caution against over-reliance on just one form of blood sample for biomarker assessment.

Characterization of the angiomodulatory effects of Interleukin 11 cis- and trans-signaling in the retina

BACKGROUND

The IL-6 cytokine family, with its crucial and pleiotropic intracellular signaling pathway STAT3, is a promising target for treating vasoproliferative retinal diseases. Previous research has shown that IL-6 cis-signaling (via membrane-bound receptors) and trans-signaling (via soluble receptors) can have distinct effects on target cells, leading to their application in various disease treatments. While IL-6 has been extensively studied, less is known about the angiogenic effects of IL-11, another member of the IL-6 family, in the retina. Therefore, the aim of this study was to characterize the effects of IL-11 on retinal angiogenesis.

MAIN TEXT

In vitreous samples from proliferative diabetic retinopathy (PDR) patients, elevated levels of IL-11Rα, but not IL-11, were detected. In vitro studies using vascular endothelial cells revealed distinct effects of cis- and trans-signaling: cis-signaling (IL-11 alone) had antiangiogenic effects, while trans-signaling (IL-11 + sIL-11Rα) had proangiogenic and pro-migratory effects. These differences can be attributed to their individual signaling responses and associated transcriptomic changes. Notably, no differences in cis- and trans-signaling were detected in primary mouse Müller cell cultures. STAT3 and STAT1 siRNA knockdown experiments revealed opposing effects on IL-11 signaling, with STAT3 functioning as an antiproliferative and proapoptotic player while STAT1 acts in opposition to STAT3. In vivo, both IL-11 and IL-11 + sIL-11Rα led to a reduction in retinal neovascularization. Immunohistochemical staining revealed Müller cell activation in response to treatment, suggesting that IL-11 affects multiple retinal cell types in vivo beyond vascular endothelial cells.

CONCLUSIONS

Cis- and trans-signaling by IL-11 have contrasting angiomodulatory effects on endothelial cells in vitro. In vivo, cis- and trans-signaling also influence Müller cells, ultimately determining the overall angiomodulatory impact on the retina, highlighting the intricate interplay between vascular and glial cells in the retina.

IL-22: A key inflammatory mediator as a biomarker and potential therapeutic target for lung cancer

Lung cancer, one of the most prevalent cancers worldwide, stands as the primary cause of cancer-related deaths. As is well-known, the utmost crucial risk factor contributing to lung cancer is smoking. In recent years, remarkable progress has been made in treating lung cancer, particularly non-small cell lung cancer (NSCLC). Nevertheless, the absence of effective and accurate biomarkers for diagnosing and treating lung cancer remains a pressing issue. Interleukin 22 (IL-22) is a member of the IL-10 cytokine family. It exerts biological functions (including induction of proliferation and anti-apoptotic signaling pathways, enhancement of tissue regeneration and immunity defense) by binding to heterodimeric receptors containing type 1 receptor chain (R1) and type 2 receptor chain (R2). IL-22 has been identified as a pro-cancer factor since dysregulation of the IL-22-IL-22R system has been implicated in the development of different cancers, including lung, breast, gastric, pancreatic, and colon cancers. In this review, we discuss the differential expression, regulatory role, and potential clinical significance of IL-22 in lung cancer, while shedding light on innovative approaches for the future.

Exploring the relationship between the interleukin family and lung adenocarcinoma through Mendelian randomization and RNA sequencing analysis

BACKGROUND

Lung adenocarcinoma (LUAD) is still one of the most prevalent malignancies. Interleukin factors are closely associated with the initiation and progression of cancer. However, the relationship between interleukin factors and LUAD has not been fully elucidated. This study aimed to use Mendelian randomization (MR) and RNA sequencing (RNA-seq) analyses to identify the interleukin factors associated with the onset and progression of LUAD.

METHODS

Exposure-related instrumental variables were selected from interleukin factor summary datasets. The LUAD summary dataset from FINGENE served as the outcome. MR and sensitivity analyses were conducted to screen for interleukin factors associated with LUAD occurrence. Transcriptome analyses revealed the role of interleukin factors in lung tissues. The results were validated through Western blotting and further confirmed with driver gene-negative patients from multiple centers. Potential mechanisms influencing LUAD occurrence and development were explored using bulk RNA-seq and single-cell RNA-seq data.

RESULTS

MR analysis indicated that elevated plasma levels of IL6RB, IL27RA, IL22RA1, and IL16 are causally associated with increased LUAD risk, while IL18R1 and IL11RA exhibit the opposite effect. Transcriptome analyses revealed that IL11RA, IL18R1, and IL16 were downregulated in tumor tissues compared with normal lung tissue, but only higher expression of IL11RA correlated with improved prognosis in patients with LUAD from different centers and persisted even in driver-gene negative patients. The IL11RA protein level was lower in various LUAD cell lines than in human bronchial epithelial cells. The genes co-expressed with IL11RA were enriched in the Ras signaling pathway and glycosylation processes. Fibroblasts were the primary IL11RA-expressing cell population, with IL11RA+fibroblasts exhibiting a more immature state. The genes differentially expressed between IL11RA+and IL11RA- fibroblasts were involved in the PI3K-Akt/TNF signaling pathway.

CONCLUSION

According to the MR and transcriptome analyses, the downregulation of IL11RA was closely related to the occurrence and development of LUAD.

Interleukin-11 Suppresses Ocular Surface Inflammation and Accelerates Wound Healing

Purpose

Regulation of inflammation is critical for achieving favorable outcomes in wound healing. In this study, we determine the functional role and mechanism of action of IL-11, an immunomodulatory cytokine, in regulating inflammatory response at the ocular surface.

Methods

Corneal injury was induced by mechanical removal of the epithelium and anterior stroma using an AlgerBrush II. Transcript and protein levels of IL-11 in injured cornea were quantified using real-time PCR and ELISA analysis. Corneal inflammation was assessed by measuring frequencies of total CD45+ inflammatory cells, CD11b+Ly6G+ polymorphonuclear cells (neutrophils), and CD11b+Ly6G- mononuclear cells (macrophages, monocytes) at the ocular surface using flow cytometry. To assess the effect of IL-11 on innate immune cell function, cell activation marker and inflammatory cytokines including major histocompatibility complex (MHC) class II, myeloperoxidase (MPO), TNFα, and inducible nitric oxide synthase (iNOS) were measured following recombinant IL-11 treatment (1 µg/mL). Injured corneas were topically treated with IL-11 (1 µg/mL), and wound healing was evaluated using corneal fluorescein staining.

Results

Corneal injury resulted in increased levels of IL-11 in the cornea, particularly in the stroma. Neutrophils and CD11b+ mononuclear cells (macrophages, monocytes) substantially expressed IL-11 receptor. Interestingly, IL-11 significantly downregulated the activation of immune cells, as evidenced by the lower expression of MHC II and TNFα by CD11b+ mononuclear cells and lower levels of MPO by neutrophils. Topical administration of IL-11 to injured corneas led to faster wound healing and better retention of tissue architecture.

Conclusions

Our findings demonstrate IL-11 is a key modulator of ocular surface inflammation and provide novel evidence of IL-11 as a potential therapeutic to control inflammatory damage and accelerate wound repair following injury.

An engineered interleukin-11 decoy cytokine inhibits receptor signaling and proliferation in lung adenocarcinoma

The cytokine interleukin (IL)-11 has been shown to play a role in promoting fibrosis and cancer, including lung adenocarcinoma, garnering interest as an attractive target for therapeutic intervention. We used combinatorial methods to engineer an IL-11 variant that binds with higher affinity to the IL-11 receptor and stimulates enhanced receptor-mediated cell signaling. Introduction of two additional point mutations ablates IL-11 ligand/receptor association with the gp130 coreceptor signaling complex, resulting in a high-affinity receptor antagonist. Unlike wild-type IL-11, this engineered variant potently blocks IL-11-mediated cell signaling and slows tumor growth in a mouse model of lung cancer. Our approach highlights a strategy where native ligands can be engineered and exploited to create potent receptor antagonists.

Sanikovich V, S. Rusanov A, N. Pirogova Y, Boroda A, Rozhkov A, Kemfang Ngowa JD, N. Bagmet L, I. Sekacheva M. Angiogenesis in Lung Cancer: Understanding the Roles of Growth Factors

Research has shown the role of growth factors in lung cancer angiogenesis. Angiogenesis promotes lung cancer progression by stimulating tumor growth, enhancing tumor invasion, contributing to metastasis, and modifying immune system responses within the tumor microenvironment. As a result, new treatment techniques based on the anti-angiogenic characteristics of compounds have been developed. These compounds selectively block the growth factors themselves, their receptors, or the downstream signaling pathways activated by these growth factors. The EGF and VEGF families are the primary targets in this approach, and several studies are being conducted to propose anti-angiogenic drugs that are increasingly suitable for the treatment of lung cancer, either as monotherapy or as combined therapy. The efficacy of the results are encouraging, but caution must be placed on the higher risk of toxicity, outlining the importance of personalized follow-up in the management of these patients.

Machine learning for prediction of immunotherapeutic outcome in non-small-cell lung cancer based on circulating cytokine signatures

BACKGROUND

Immune checkpoint inhibitor (ICI) therapy has substantially improved the overall survival (OS) in patients with non-small-cell lung cancer (NSCLC); however, its response rate is still modest. In this study, we developed a machine learning-based platform, namely the Cytokine-based ICI Response Index (CIRI), to predict the ICI response of patients with NSCLC based on the peripheral blood cytokine profiles.

METHODS

We enrolled 123 and 99 patients with NSCLC who received anti-PD-1/PD-L1 monotherapy or combined chemotherapy in the training and validation cohorts, respectively. The plasma concentrations of 93 cytokines were examined in the peripheral blood obtained from patients at baseline (pre) and 6 weeks after treatment (early during treatment: edt). Ensemble learning random survival forest classifiers were developed to select feature cytokines and predict the OS of patients undergoing ICI therapy.

RESULTS

Fourteen and 19 cytokines at baseline and on treatment, respectively, were selected to generate CIRI models (namely preCIRI14 and edtCIRI19), both of which successfully identified patients with worse OS in two completely independent cohorts. At the population level, the prediction accuracies of preCIRI14 and edtCIRI19, as indicated by the concordance indices (C-indices), were 0.700 and 0.751 in the validation cohort, respectively. At the individual level, patients with higher CIRI scores demonstrated worse OS [hazard ratio (HR): 0.274 and 0.163, and p<0.0001 and p=0.0044 in preCIRI14 and edtCIRI19, respectively]. By including other circulating and clinical features, improved prediction efficacy was observed in advanced models (preCIRI21 and edtCIRI27). The C-indices in the validation cohort were 0.764 and 0.757, respectively, whereas the HRs of preCIRI21 and edtCIRI27 were 0.141 (p<0.0001) and 0.158 (p=0.038), respectively.

CONCLUSIONS

The CIRI model is highly accurate and reproducible in determining the patients with NSCLC who would benefit from anti-PD-1/PD-L1 therapy with prolonged OS and may aid in clinical decision-making before and/or at the early stage of treatment.

A biotin-guided near-infrared fluorescent probe for imaging hydrogen sulfide and differentiating cancer cells

Imaging cancer specific biomarkers with near-infrared (NIR) fluorescent probes can help inaccurate diagnosis. Hydrogen sulfide (H₂S) has been reported to be involved in many physiological and pathological processes and is considered as one of the key gasotransmitters during the development of cancer. To achieve specific H₂S detection in cancer cells, we reported a biotin-guided NIR fluorescent sensor P1 targeting a cancer cell surface biomarker, based on the H₂S-specific thiolysis of the NBD-amine-hemicyanine conjugate. The probe showed a fast turn-on signal at 754 nm upon H₂S activation and good selectivity towards H₂S over millimolar levels of other biothiols. We successfully employed P1 to image endogenous H₂S and demonstrated its tumor-targeting ability in live cells. P1 could differentiate multiple cancer cells with various levels of H₂S from normal cells, indicating its potential for cancer imaging.

Insights into the Relationship between Pentraxin-3 and Cancer

Although cancer can be cured if detected early and treated effectively, it is still a leading cause of death worldwide. Tumor development can be limited by an appropiate immune response, but it can be promoted by chronic extensive inflammation through metabolic dysregulation and angiogenesis. In the past decade, numerous efforts have been made in order to identify novel candidates with predictive values in cancer diagnostics. In line with this, researchers have investigated the involvement of pentraxin-3 (PTX-3) in cellular proliferation and immune escape in various types of cancers, although it has not been clearly elucidated. PTX-3 is a member of the long pentraxin subfamily which plays an important role in regulating inflammation, innate immunity response, angiogenesis, and tissue remodeling. Increased synthesis of inflammatory biomarkers and activation of different cellular mechanisms can induce PTX-3 expression in various types of cells (neutrophils, monocytes, lymphocytes, myeloid dendritic cells, fibroblasts, and epithelial cells). PTX-3 has both pro- and anti-tumor functions, thus dual functions in oncogenesis. This review elucidates the potential usefulness of PTX-3 as a serum biomarker in cancer. While future investigations are needed, PTX-3 is emerging as a promising tool for cancer's diagnosis and prognosis, and also treatment monitoring.

Pathways of Angiogenic and Inflammatory Cytokines in Multiple Myeloma: Role in Plasma Cell Clonal Expansion and Drug Resistance

Multiple myeloma (MM) is the second most common hematological malignancy, and despite the introduction of innovative therapies, remains an incurable disease. Identifying early and minimally or non-invasive biomarkers for predicting clinical outcomes and therapeutic responses is an active field of investigation. Malignant plasma cells (PCs) reside in the bone marrow (BM) microenvironment (BMME) which comprises cells (e.g., tumour, immune, stromal cells), components of the extracellular matrix (ECM) and vesicular and non-vesicular (soluble) molecules, all factors that support PCs’ survival and proliferation. The interaction between PCs and BM stromal cells (BMSCs), a hallmark of MM progression, is based not only on intercellular interactions but also on autocrine and paracrine circuits mediated by soluble or vesicular components. In fact, PCs and BMSCs secrete various cytokines, including angiogenic cytokines, essential for the formation of specialized niches called “osteoblastic and vascular niches”, thus supporting neovascularization and bone disease, vital processes that modulate the pathophysiological PCs–BMME interactions, and ultimately promoting disease progression. Here, we aim to discuss the roles of cytokines and growth factors in pathogenetic pathways in MM and as prognostic and predictive biomarkers. We also discuss the potential of targeted drugs that simultaneously block PCs’ proliferation and survival, PCs–BMSCs interactions and BMSCs activity, which may represent the future goal of MM therapy.