Gene expression and prognosis of insulin‑like growth factor‑binding protein family members in non‑small cell lung cancer

Mechanism and recent updates on insulin-related disorders

Insulin, a small protein with 51 amino acids synthesized by pancreatic β-cells, is crucial to sustain glucose homeostasis at biochemical and molecular levels. Numerous metabolic dysfunctions are related to insulin-mediated altered glucose homeostasis. One of the significant pathophysiological conditions linked to the insulin associated disorder is diabetes mellitus (DM) (type 1, type 2, and gestational). Insulin resistance (IR) is one of the major underlying causes of metabolic disorders despite its association with several physiological conditions. Metabolic syndrome (MS) is another pathophysiological condition that is associated with IR, hypertension, and obesity. Further, several other pathophysiological disorders/diseases are associated with the insulin malfunctioning, which include polycystic ovary syndrome, neuronal disorders, and cancer. Insulinomas are an uncommon type of pancreatic β-cell-derived neuroendocrine tumor that makes up 2% of all pancreatic neoplasms. Literature revealed that different biochemical events, molecular signaling pathways, microRNAs, and microbiota act as connecting links between insulin disorder and associated pathophysiology such as DM, insuloma, neurological disorder, MS, and cancer. In this review, we focus on the insulin-related disorders and the underlying mechanisms associated with the pathophysiology.

The identification of genes associated T-cell exhaustion and construction of prognostic signature to predict immunotherapy response in lung adenocarcinoma

T-cell exhaustion (Tex) is considered to be a reason for immunotherapy resistance and poor prognosis in lung adenocarcinoma. Therefore, we used weighted correlation network analysis to identify Tex-related genes in the cancer genome atlas (TCGA). Unsupervised clustering approach based on Tex-related genes divided patients into cluster 1 and cluster 2. Then, we utilized random forest and the least absolute shrinkage and selection operator to identify nine key genes to construct a riskscore. Patients were classified as low or high-risk groups. The multivariate cox analysis showed the riskscore was an independent prognostic factor in TCGA and GSE72094 cohorts. Moreover, patients in cluster 2 with high riskscore had the worst prognosis. The immune response prediction analysis showed the low-risk group had higher immune, stromal, estimate scores, higher immunophenscore (IPS), and lower tumor immune dysfunction and exclusion score which suggested a better response to immune checkpoint inhibitors (ICIs) therapy in the low-risk group. In the meantime, we included two independent immunotherapy cohorts that also confirmed a better response to ICIs treatment in the low-risk group. Besides, we discovered differences in chemotherapy and targeted drug sensitivity between two groups. Finally, a nomogram was built to facilitate clinical decision making.

Insulin-like growth factor binding protein 5: Diverse roles in cancer

Insulin-like growth factor binding proteins (IGFBPs) and the associated signaling components in the insulin-like growth factor (IGF) pathway regulate cell differentiation, proliferation, apoptosis, and adhesion. Of the IGFBPs, insulin-like growth factor binding protein 5 (IGFBP5) is the most evolutionarily conserved with a dynamic range of IGF-dependent and -independent functions, and studies on the actions of IGFBP5 in cancer have been somewhat paradoxical. In cancer, the IGFBPs respond to external stimuli to modulate disease progression and therapeutic responsiveness in a context specific manner. This review discusses the different roles of IGF signaling and IGFBP5 in disease with an emphasis on discoveries within the last twenty years, which underscore a need to clarify the IGF-independent actions of IGFBP5, the impact of its subcellular localization, the differential activities of each of the subdomains, and the response to elements of the tumor microenvironment (TME). Additionally, recent advances addressing the role of IGFBP5 in resistance to cancer therapeutics will be discussed. A better understanding of the contexts in which IGFBP5 functions will facilitate the discovery of new mechanisms of cancer progression that may lead to novel therapeutic opportunities.

IGFBP3 inhibits tumor growth and invasion of lung cancer cells and is associated with improved survival in lung cancer patients

The insulin-like growth factor (IGF)-pathway is involved in tumor cell proliferation, metastasis, and survival. We aimed to find out what effects IGF binding protein 3 (IGFBP3) exerted on H1299 lung cancer (LC) cells in terms of tumor growth and invasion and whether IGFBP3 was associated with clinical and pathological parameters in a prospective cohort of LC patients. H1299 cells were transfected with an IGFBP3-expressing vector. Its influence on apoptosis induction via flow cytometry annexin V FITC assay, cell proliferation in 2D and 3D cell culture, and invasion were examined. Expression of several matrix metalloproteinases (MMPs) and inhibitors (TIMP-1) were also investigated in IGFBP3-transfected LC cells. Further, data on LC patients (n = 131), tumor characteristics, and survival were prospectively collected and correlated with IGFBP3 plasma levels. IGFBP3 did not influence apoptosis induction and 2D cell proliferation. However, both spheroid growth (3D proliferation) and invasion of IGFBP3-transfected cells planted in an extracellular matrix-based gel were significantly inhibited. IGFBP3 inhibited MMP-1 release, and the total MMP activity. In LC patients, higher IGFBP3 plasma levels correlated with both lower clinical tumor stage, grading, Ki-67 staining, and the absence of necrosis (P < 0.05, respectively). Increased IGFBP3 plasma levels were associated with improved overall survival (hazard ratio 0.37, P = 0.01). In conclusion, overexpressed IGFBP3 in a LC cell line inhibited tumor growth and invasion. Translating from bench to bedside, investigation of clinicopathological parameters confirmed these experimental results showing that higher IGFBP3 plasma levels were associated with less aggressive tumor growth, reduced tumor spread, and improved survival of LC patients.

Comprehensive Analysis of Prognostic Value and Immune Infiltration of IGFBP Family Members in Glioblastoma

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. The insulin-like growth factor-binding protein (IGFBP) family is involved in tumorigenesis and the development of multiple cancers. However, little is known about the prognostic value and regulatory mechanisms of IGFBPs in GBM. Oncomine, Gene Expression Profiling Interactive Analysis, PrognoScan, cBioPortal, LinkedOmics, TIMER, and TISIDB were used to analyze the differential expression, prognostic value, genetic alteration, biological function, and immune cell infiltration of IGFBPs in GBM. We observed that IGFBP1, IGFBP2, IGFBP3, IGFBP4, and IGFBP5 mRNA expression was significantly upregulated in patients with GBM, whereas IGFBP6 was downregulated; this difference in mRNA expression was statistically insignificant. Subsequent investigations showed that IGFBP4 and IGFBP6 mRNA levels were significantly associated with overall survival in patients with GBM. Functional Gene Ontology Annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that genes coexpressed with IGFBP4 and IGFBP6 were mainly enriched in immune-related pathways. These results were validated using the TIMER and TSMIDB databases. This study demonstrated that the IGFBP family has prognostic value in patients with GBM. IGFBP4 and IGFBP6 are two members of the IGFBP family that had the highest prognostic value; thus, they have the potential to serve as survival predictors and immunotherapeutic targets in GBM.

Nitrogen Metabolism Disorder Accelerates Occurrence and Development of Lung Adenocarcinoma: A Bioinformatic Analysis and In Vitro Experiments

Background

Nitrogen metabolism (NM) plays a pivotal role in immune regulation and the occurrence and development of cancers. The aim of this study was to construct a prognostic model and nomogram using NM-related genes for the evaluation of patients with lung adenocarcinoma (LUAD).

Methods

The differentially expressed genes (DEGs) related to NM were acquired from The Cancer Genome Atlas (TCGA) database. Consistent clustering analysis was used to divide them into different modules, and differentially expressed genes and survival analysis were performed. The survival information of patients was combined with the expressing levels of NM-related genes that extracted from TCGA and Gene Expression Omnibus (GEO) databases. Subsequently, univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) regression were used to build a prognostic model. GO and KEGG analysis were elaborated in relation with the mechanisms of NM disorder (NMD). Meanwhile, immune cells and immune functions related to NMD were discussed. A nomogram was built according to the univariate and multivariate Cox analysis to identify independent risk factors. Finally, real-time fluorescent quantitative PCR (RT-PCR) and Western bolt (WB) were used to verify the expression level of hub genes.

Results

There were 138 differential NM-related genes that were divided into two gene modules. Sixteen NM-related genes were used to build a prognostic model and the receiver operating characteristic curve (ROC) showed that the efficiency was reliable. GO and KEGG analysis suggested that NMD accelerated development of LUAD through the Wnt signaling pathway. The level of activated dendritic cells (aDCs) and type II interferon response in the low-risk group was higher than that of the high-risk group. A nomogram was constructed based on ABCC2, HMGA2, and TN stages, which was identified as four independent risk factors. Finally, RT-PCR and WB showed that CDH17, IGF2BP1, IGFBP1, ABCC2, and HMGA2 were differently expressed between human lung fibroblast (HLF) cells and cancer cells.

Conclusions

High NM levels were revealed as a poor prognosis of LUAD. NMD regulates immune system through affecting aDCs and type II interferon response. The prognostic model with NM-related genes could be used to effectively evaluate the outcomes of patients.

Different roles of the insulin-like growth factor (IGF) axis in non-small cell lung cancer

Abstract:

Non-small cell lung cancer (NSCLC) remains one of the deadliest malignant diseases, with high incidence and mortality worldwide. The insulin-like growth factor (IGF) axis, consisting of IGF-1, IGF-2, related receptors (IGF-1R, -2R), and high-affinity binding proteins (IGFBP 1–6), is associated with promoting fetal development, tissue growth, and metabolism. Emerging studies have also identified the role of the IGF axis in NSCLC, including cancer growth, invasion, and metastasis. Upregulation of IGE-1 and IGF-2, overexpression of IGF-1R, and dysregulation of downstream signaling molecules involved in the PI-3K/Akt and MAPK pathways jointly increase the risk of cancer growth and migration in NSCLC. At the genetic level, some noncoding RNAs could influence the proliferation and differentiation of tumor cells through the IGF signaling pathway. The resistance to some promising drugs might be partially attributed to the IGF axis. Therapeutic strategies targeting the IGF axis have been evaluated, and some have shown promising efficacy. In this review, we summarize the biological roles of the IGF axis in NSCLC, including the expression and prognostic significance of the related components, noncoding RNA regulation, involvement in drug resistance, and therapeutic application. This review offers comprehensive understanding of NSCLC and provides insightful ideas for future research.

Establishing a Macrophage Phenotypic Switch-Associated Signature-Based Risk Model for Predicting the Prognoses of Lung Adenocarcinoma

Background

Tumor-associated macrophages are important components of the tumor microenvironment, and the macrophage phenotypic switch has been shown to correlate with tumor development. However, the use of a macrophage phenotypic switch-related gene (MRG)-based prognosis signature for lung adenocarcinoma (LADC) has not yet been investigated.

Methods

In total, 1,114 LADC cases from two different databases were collected. The samples from TCGA were used as the training set (N = 490), whereas two independent datasets (GSE31210 and GSE72094) from the GEO database were used as the validation sets (N = 624). A robust MRG signature that predicted clinical outcomes of LADC patients was identified through multivariate COX and Lasso regression analysis. Gene set enrichment analysis was applied to analyze molecular pathways associated with the MRG signature. Moreover, the fractions of 22 immune cells were estimated using CIBERSORT algorithm.

Results

An eight MRG-based signature comprising CTSL, ECT2, HCFC2, HNRNPK, LRIG1, OSBPL5, P4HA1, and TUBA4A was used to estimate the LADC patients’ overall survival. The MRG model was capable of distinguishing high-risk patients from low-risk patients and accurately predict survival in both the training and validation cohorts. Subsequently, the eight MRG-based signature and other features were used to construct a nomogram to better predict the survival of LADC patients. Calibration plots and decision curve analysis exhibited good consistency between the nomogram predictions and actual observation. ROC curves displayed that the signature had good robustness to predict LADC patients’ prognostic outcome.

Conclusions

We identified a phenotypic switch-related signature for predicting the survival of patients with LADC.

The role of IGF-pathway biomarkers in determining risks, screening, and prognosis in lung cancer

Background: Detection rates of early-stage lung cancer are traditionally low, which contributes to inconsistent treatment responses and high rates of annual cancer deaths. Currently, low-dose computed tomography (LDCT) screening produces a high false discovery rate. This limitation has prompted research to identify biomarkers to more clearly define eligible patients for LDCT screening, differentiate indeterminate pulmonary nodules, and select individualized cancer therapy. Biomarkers within the Insulin-like Growth Factor (IGF) family have come to the forefront of this research.

Main Body: Multiple biomarkers within the IGF family have been investigated, most notably IGF-I and IGF binding protein 3. However, newer studies seek to expand this search to other molecules within the IGF axis. Certain studies have demonstrated these biomarkers are useful when used in combination with lung cancer screening, but other findings were not as conclusive, possibly owing to measurement bias and non-standardized assay techniques. Research also has suggested IGF biomarkers may be beneficial in the prognostication and subsequent treatment via systemic therapy. Despite these advances, additional knowledge of complex regulatory mechanisms inherent to this system are necessary to more fully harness the potential clinical utility for diagnostic and therapeutic purposes.

Conclusions: The IGF system likely plays a role in multiple phases of lung cancer; however, there is a surplus of conflicting data, especially prior to development of the disease and during early stages of detection. IGF biomarkers may be valuable in the screening, prognosis, and treatment of lung cancer, though their exact application requires further study.

An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Adenocarcinoma

Aging is an inevitable time-dependent process associated with a gradual decline in many physiological functions. Importantly, some studies have supported that aging may be involved in the development of lung adenocarcinoma (LUAD). However, no studies have described an aging-related gene (ARG)-based prognosis signature for LUAD. Accordingly, in this study, we analyzed ARG expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). After LASSO and Cox regression analyses, a six ARG-based signature (APOC3, EPOR, H2AFX, MXD1, PLCG2, and YWHAZ) was constructed using TCGA dataset that significantly stratified cases into high- and low-risk groups in terms of overall survival (OS). Cox regression analysis indicated that the ARG signature was an independent prognostic factor in LUAD. A nomogram based on the ARG signature and clinicopathological factors was developed in TCGA cohort and validated in the GEO dataset. Moreover, to visualize the prediction results, we established a web-based calculator yurong.shinyapps.io/ARGs_LUAD/. Calibration plots showed good consistency between the prediction of the nomogram and actual observations. Receiver operating characteristic curve and decision curve analyses indicated that the ARG nomogram had better OS prediction and clinical net benefit than the staging system. Taken together, these results established a genetic signature for LUAD based on ARGs, which may promote individualized treatment and provide promising novel molecular markers for immunotherapy.

Development and validation of a prognostic nomogram for patients with lung adenocarcinoma based on a novel 6-DNA repair-related gene signature

DNA repair-related genes (DRGs) have attracted much attention in the field of oncology. However, the prognostic role of DRGs and their biological function in lung adenocarcinoma (LUAD) remains rudimentary and inconclusive. In this study, 716 LUAD cases from two different cohorts were collected. Samples from The Cancer Genome Atlas (TCGA) were used as the training set, and data from Gene Expression Omnibus (GEO) datasets were used for validation. Using multivariate Cox analysis and LASSO regression, we constructed a DRG signature and used it, together with clinical indices, to develop a nomogram to predict 1-, 3-, and 5-year survival rates. We identified a six-DRG signature to estimate the survival of LUAD patients, which distinguished high-risk from low-risk patients with LUAD in both the training and validation cohorts. We also observed elevated levels of infiltrating CD4 memory activated T cells, resting NK cells, M0 and M1 macrophages, and activated mast cells in the high-risk group. Finally, a nomogram incorporating the signature and clinical parameters was superior to the American Joint Committee on Cancer (AJCC) staging system in predicting the survival of LUAD patients. The DRG prognostic signature and integrated nomogram could be a useful tool to predict prognosis in patients with LUAD.

Systematic construction and validation of an epithelial-mesenchymal transition risk model to predict prognosis of lung adenocarcinoma

Epithelial–mesenchymal transition (EMT) has been shown to be linked to a poor prognosis, particularly in patients with non-small-cell lung cancer. Nevertheless, little is known regarding the existence of EMT-related gene signatures and their prognostic values in lung adenocarcinoma (LUAD). In the current study, we systematically profiled the mRNA expression data of patients with LUAD in The Cancer Genome Atlas and Gene Expression Omnibus databases using a total of 1,184 EMT-related genes. The prognostic values of the EMT-related genes used to develop risk score models for overall survival were determined using LASSO and Cox regression analyses. A prognostic signature that consisted of nine unique EMT-related genes was generated using a training set. A nomogram, incorporating this EMT-related gene signature and clinical features of patients with LUAD, was constructed for potential clinical use. Calibration plots, decision-making curves, and receiver operating characteristic curve analysis showed that this model had a good ability to predict the survival of patients with LUAD. The EMT-associated gene signature and prognostic nomogram established in this study were reliable in predicting the survival of patients with LUAD. Thus, we first identified a novel EMT-related gene signature and developed a nomogram for predicting the prognosis of patients with LUAD.

Development and validation of a nomogram with an epigenetic signature for predicting survival in patients with lung adenocarcinoma

Epigenetic factors play crucial roles in carcinogenesis by modifying chromatin architecture. Here, we established an epigenetic biosignature-based model for examining survival in patients with lung adenocarcinoma (LUAD). We retrieved gene-expression profiles and clinical data from The Cancer Genome Atlas and Gene Expression Omnibus and clustered the data into training (n = 490) and Validation (n = 226) datasets, respectively. To establish an epigenetic model, we identified prognostic epigenetic regulation-related genes by LASSO and Cox regression analyses, and established a novel 11-gene signature, including EPC1, GADD45A, HCFC2, RCOR1, SMARCAL1, TLE2, TRIM28, and ZNF516, for predicting LUAD overall survival (OS). The biosignature performed optimally in both the training and validation sets according to receiver operating characteristic and calibration plots. Moreover, the biosignature classified patients into high- and low-risk clusters with distinct survival times, with Cox regression analysis revealing the biosignature as an independent LUAD prognostic index. Furthermore, the generated nomogram integrating the prognostic gene biosignature and clinical indices predicted LUAD OS with high efficiency and outperformed tumor-node-metastasis staging in LUAD survival prediction. These results demonstrated the efficacy of the epigenetic signature prognostic nomogram for reliably predicting LUAD OS and its potential application for informing clinical decision making and individualized treatment.

Computational assessment of MCM2 transcriptional expression and identification of the prognostic biomarker for human breast cancer

Minichromosome maintenance protein 2 (MCM2) is a highly conserved protein from the MCM protein family that plays an important role in eukaryotic DNA replication as well as in cell cycle progression. In addition, it maintains the ploidy level consistency in eukaryotic cells, hence, mutations or alteration of this protein could result in the disintegration of the fine-tuned molecular machinery that can lead to uncontrolled cell proliferation. Moreover, MCM2 has been found to be an important marker for progression and prognosis in different cancers. Therefore, we aimed to analyze the MCM2 expression and the associated outcome in breast cancer (BC) patients based on the publicly available online databases. In this study, server-based gene expression analyses indicate the upregulation of MCM2 (p < 10^-6; fold change>2.0) in various BC subtypes as compared to the respective normal tissues. Besides, the evaluation of histological sections from healthy and cancer tissues showed strong staining signals indicating higher expression of MCM2 protein. The overexpression of MCM2 was significantly correlated to promoter methylation and was related to patients' clinical features. Further, mutation analysis suggested missense as the predominant type of mutation (71.4%) with 18 copy-number alterations and 0.2% mutation frequency in the MCM2 gene. This study revealed a significant correlation (Cox p ≤ 0.05) between the higher MCM2 expression and lower patient survival. Finally, we identified the co-expressed genes with gene ontological features and signaling pathways associated in BC development. We believe that this study will provide a basis for MCM2 to be a significant biomarker for human BC prognosis.