Analysis of expression differences of immune genes in non-small cell lung cancer based on TCGA and ImmPort data sets and the application of a prognostic model

Construction of an immune-related gene prognostic model with experimental validation and analysis of immune cell infiltration in lung adenocarcinoma

There is a correlation between tumors and immunity with the degree of immune cell infiltration in tumors being closely related to tumor growth and progression. Therefore, the present study identified immune-related prognostic genes and evaluated the immune infiltration level in lung adenocarcinoma (LUAD). This study performed Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and Gene Set Enrichment Analysis (GSEA) enrichment analyses on differential immune-associated genes. A risk model was created and validated using six immune-related prognostic genes. Reverse transcription-quantitative PCR was used to assess the prognostic gene expression in non-small cell lung cancer cells. Immune cell infiltration in LUAD was analyzed using the CIBERSORT method. Single sample GSEA was used to compare Tumor Immune Dysfunction and Exclusion (TIDE) scores between high and low-risk groups and to assess the activation of thirteen immune-related pathways. Multifactor Cox proportional hazards model analysis identified six prognostic risk genes (S100A16, FURIN, FGF2, LGR4, TNFRSF11A and VIPR1) to construct a risk model. The survival and receiver operating characteristic curves indicated that patients with higher risk scores had lower overall survival rates. The expression levels of prognostic genes S100A16, FURIN, LGR4, TNFRSF11A and VIPR1 were significantly increased in LUAD. B cells naive, plasma cells, T cells CD4 memory activated, T cells follicular helper, T cells regulatory, NK cells activated, macrophages M1, macrophages M2, and Dendritic cells resting cells showed elevated expression in LUAD. The prognostic genes were differentially associated with individual immune cells. Immune-related function scores, such as those for antigen presenting cell (APC) co-stimulation, APC co-inhibition, check-point, Cytolytic-activity, chemokine receptor, parainflammation, major histocompatibility complex-class-I, type-I-IFN-reponse and T-cell-co-inhibition, were higher in the high-risk group compared with the low-risk group. Furthermore, the TIDE score of the high-risk group was significantly lower than the low-risk group. This immune-related gene prognostic model has the potential to predict the prognosis of LUAD patients, supporting the development of a personalized clinical diagnosis and treatment plan.

Effects of the SEMA4B gene on hexavalent chromium [Cr(VI)]-induced malignant transformation of human bronchial epithelial cells

Our previous study identified the potential of SEMA4B methylation level as a biomarker for hexavalent chromium [Cr(VI)] exposure. This study aimed to investigate the role of the SEMA4B gene in Cr(VI)-mediated malignant transformation of human bronchial epithelial (BEAS-2B) cells. In our population survey of workers, the geometric mean [95% confidence intervals (CIs)] of Cr in blood was 3.80 (0.42, 26.56) μg/L. Following treatment with various doses of Cr(VI), it was found that 0.5 μM had negligible effects on the cell viability of BEAS-2B cells. The expression of SEMA4B was observed to decrease in BEAS-2B cells after 7 days of treatment with 0.5 μM Cr(VI), and this downregulation continued with increasing passages of Cr(VI) treatment. Chronic exposure to 0.5 μM Cr(VI) enhanced the anchorage-independent growth ability of BEAS-2B cells. Furthermore, the use of a methylation inhibitor suppressed the Cr(VI)-mediated anchorage-independent growth in BEAS-2B cells. Considering that Cr levels exceeding 0.5 μM can be found in human blood due to occupational exposure, the results suggested a potential carcinogenic risk associated with occupational Cr(VI) exposure through the promotion of malignant transformation. The in vitro study further demonstrated that Cr(VI) exposure might inhibit the expression of the SEMA4B gene to promote the malignant transformation of BEAS-2B cells.

Integrated multi-omics analysis for lung adenocarcinoma in Xuanwei, China

BACKGROUND

Xuanwei lung cancer (XWLC) is well-known for its high incidence and mortality. However, the molecular mechanism is still unclear.

METHODS

We performed a comprehensive transcriptomic, proteomic, and phosphoproteomic characterization of tumors and matched normal adjacent tissues from three XWLC patients with lung adenocarcinoma (LUAD).

RESULTS

Integrated transcriptome and proteome analysis revealed dysregulated molecules and pathways in tumors and identified enhanced metabolic-disease coupling. Non-coding RNAs were widely involved in post-transcriptional regulatory mechanisms to coordinate the progress of LUAD and partially explained the molecular differences between RNA and protein expression patterns. Phosphoproteome provided evidence support for new phosphate sites, reporting the potential roles of core kinase family members and key kinase pathways involved in metabolism, immunity, and homeostasis. In addition, by comparing with the previous LUAD researches, we emphasized the higher degree of oxidative phosphorylation in Xuanwei LUAD and pointed that VIPR1 deficiency aggravated metabolic dysfunction.

CONCLUSION

Our integrated multi-omics analysis provided a powerful resource for a systematic understanding of the molecular structure of XWLC and proposed therapeutic opportunities based on redox metabolism.

Discovery of potential biomarkers for lung cancer classification based on human proteome microarrays using Stochastic Gradient Boosting approach

PURPOSE

Early identification of lung cancer (LC) will considerably facilitate the intervention and prevention of LC. The human proteome micro-arrays approach can be used as a "liquid biopsy" to diagnose LC to complement conventional diagnosis, which needs advanced bioinformatics methods such as feature selection (FS) and refined machine learning models.

METHODS

A two-stage FS methodology by infusing Pearson's Correlation (PC) with a univariate filter (SBF) or recursive feature elimination (RFE) was used to reduce the redundancy of the original dataset. The Stochastic Gradient Boosting (SGB), Random Forest (RF), and Support Vector Machine (SVM) techniques were applied to build ensemble classifiers based on four subsets. The synthetic minority oversampling technique (SMOTE) was used in the preprocessing of imbalanced data.

RESULTS

FS approach with SBF and RFE extracted 25 and 55 features, respectively, with 14 overlapped ones. All three ensemble models demonstrate superior accuracy (ranging from 0.867 to 0.967) and sensitivity (0.917 to 1.00) in the test datasets with SGB of SBF subset outperforming others. The SMOTE technique has improved the model performance in the training process. Three of the top selected candidate biomarkers (LGR4, CDC34, and GHRHR) were highly suggested to play a role in lung tumorigenesis.

CONCLUSION

A novel hybrid FS method with classical ensemble machine learning algorithms was first used in the classification of protein microarray data. The parsimony model constructed by the SGB algorithm with the appropriate FS and SMOTE approach performs well in the classification task with higher sensitivity and specificity. Standardization and innovation of bioinformatics approach for protein microarray analysis need further exploration and validation.

An Update on S100A16 in Human Cancer

S100A16 is a member of the S100 protein family. S100A16 is expressed in a variety of human tissues, although at varying levels. S100A16 expression is especially high in tissues rich in epithelial cells. mRNA and protein levels of S100A16 have been reported to be differentially expressed in the majority of human cancers. Functionally, S100A16 has been linked to several aspects of tumorigenesis, for example, cell proliferation, differentiation, migration, invasion, and epithelial-mesenchymal transition (EMT). Accordingly, S100A16 has been suggested to have both tumour-promoting and suppressive roles in human cancers. S100A16-mediated cellular functions are suggested to be mediated by the regulation of various signaling pathways/proteins including EMT-related proteins E-cadherin and Vimentin, PI3K-AKT, p53, MMP1-1, MMP-2, MMP-9, JNK/p38, etc. In addition to the functional roles, expression of S100A16 has been suggested to have prognostic potential in various cancer types. The aims of this review are to summarise the expression profile, identify common molecular partners and functional roles, and explore the prognostic potential of S100A16 in human cancers.

Integrated bioinformatics analysis for conducting a prognostic model and identifying immunotherapeutic targets in gastric cancer

BACKGROUND

Gastric cancer is the third leading cause of death from cancer worldwide and has a poor prognosis. Practical risk scores and prognostic models for gastric cancer are lacking. While immunotherapy has succeeded in some cancers, few gastric cancer patients benefit from immunotherapy. Immune genes and the tumor microenvironment (TME) are essential for cancer progression and immunotherapy response. However, the roles of immune genes and the tumor microenvironment in immunotherapy remain unclear. The study aimed to construct a prognostic prediction model and identify immunotherapeutic targets for gastric cancer (GC) patients by exploring immune genes and the tumor microenvironment.

RESULTS

An immune-related risk score (IRRS) model, including APOH, RNASE2, F2R, DEFB126, CXCL6, and CXCL3 genes, was constructed for risk stratification. Patients in the low-risk group, which was characterized by elevated tumor mutation burden (TMB) have higher survival rate. The risk level was remarkably correlated with tumor-infiltrating immune cells (TIICs), the immune checkpoint molecule expression, and immunophenoscore (IPS). CXCL3 and CXCL6 were significantly upregulated in gastric cancer tissues compared with normal tissues using the UALCAN database and RT-qPCR. The nomogram showed good calibration and moderate discrimination in predicting overall survival (OS) at 1-, 3-, and 5- year for gastric cancer patients using risk-level and clinical characteristics.

CONCLUSION

Our findings provided a risk stratification and prognosis prediction tool for gastric cancer patients and further the research into immunotherapy in gastric cancer.

Identification of the pyroptosis-related gene signature and risk score model for esophageal squamous cell carcinoma

Advanced esophageal squamous cell carcinoma (ESCC) still has a dismal prognostic outcome. However, the current approaches are unable to evaluate patient survival. Pyroptosis represents a novel programmed cell death type which widely investigated in various disorders and can influence tumor growth, migration, and invasion. Furthermore, few existing studies have used pyroptosis-related genes (PRGs) to construct a model for predicting ESCC survival. Therefore, the present study utilized bioinformatics approaches for analyzing ESCC patient data obtained from the TCGA database to construct the prognostic risk model and applied it to the GSE53625 dataset for validation. There were 12 differentially expressed PRGs in healthy and ESCC tissue samples, among which eight were selected through univariate and LASSO cox regression for constructing the prognostic risk model. According to K-M and ROC curve analyses, our eight-gene model might be useful in predicting ESCC prognostic outcomes. Based on the cell validation analysis, C2, CD14, RTP4, FCER3A, and SLC7A7 were expressed higher in KYSE410 and KYSE510 than in normal cells (HET-1A). Hence, ESCC patient prognostic outcomes can be assessed by our PRGs-based risk model. Further, these PRGs may also serve as therapeutic targets.

A new immune signature for survival prediction and immune checkpoint molecules in non-small cell lung cancer

Background

Immune checkpoint blockade (ICB) therapy has brought remarkable clinical benefits to patients with advanced non-small cell lung carcinoma (NSCLC). However, the prognosis remains largely variable.

Methods

The profiles of immune-related genes for patients with NSCLC were extracted from TCGA database, ImmPort dataset, and IMGT/GENE-DB database. Coexpression modules were constructed using WGCNA and 4 modules were identified. The hub genes of the module with the highest correlations with tumor samples were identified. Then integrative bioinformatics analyses were performed to unveil the hub genes participating in tumor progression and cancer-associated immunology of NSCLC. Cox regression and Lasso regression analyses were conducted to screen prognostic signature and to develop a risk model.

Results

Functional analysis showed that immune-related hub genes were involved in the migration, activation, response, and cytokine-cytokine receptor interaction of immune cells. Most of the hub genes had a high frequency of gene amplifications. MASP1 and SEMA5A presented the highest mutation rate. The ratio of M2 macrophages and naïve B cells revealed a strong negative association while the ratio of CD8 T cells and activated CD4 memory T cells showed a strong positive association. Resting mast cells predicted superior overall survival. Interactions including protein-protein, lncRNA and transcription factor interactions were analyzed and 9 genes were selected by LASSO regression analysis to construct and verify a prognostic signature. Unsupervised hub genes clustering resulted in 2 distinct NSCLC subgroups. The TIDE score and the drug sensitivity of gemcitabine, cisplatin, docetaxel, erlotinib and paclitaxel were significantly different between the 2 immune-related hub gene subgroups.

Conclusions

These findings suggested that our immune-related genes can provide clinical guidance for the diagnosis and prognosis of different immunophenotypes and facilitate the management of immunotherapy in NSCLC.

Identification of a novel gene signature of lung adenocarcinoma based on epidermal growth factor receptor-tyrosine kinase inhibitor resistance

INTRODUCTION

Tyrosine kinase inhibitors (TKIs) that target epidermal growth factor receptor (EGFR) mutations are commonly administered to EGFR-positive lung cancer patients. However, resistance to EGFR-TKIs (mostly gefitinib and erlotinib) is presently a significant problem. Limited studies have focused on an EGFR-TKI resistance-related gene signature (ERS) in lung adenocarcinoma (LUAD).

METHODS

Gefitinib and erlotinib resistance-related genes were obtained through the differential analyses of three Gene Expression Omnibus datasets. These genes were investigated further in LUAD patients from The Cancer Genome Atlas (TCGA). Patients in the TCGA-LUAD cohort were split into two groups: one for training and one for testing. The training cohort was used to build the ERS, and the testing cohort was used to test it. GO and KEGG analyses were explored for the enriched pathways between the high-risk and low-risk groups. Various software, mainly CIBERSORT and ssGSEA, were used for immune infiltration profiles. Somatic mutation and drug sensitivity analyses were also explored.

RESULTS

An ERS based on five genes (FGD3, PCDH7, DEPDC1B, SATB2, and S100P) was constructed and validated using the TCGA-LUAD cohort, resulting in the significant stratification of LUAD patients into high-risk and low-risk groups. Multivariable Cox analyses confirmed that ERS had an independent prognostic value in LUAD. The pathway enrichment analyses showed that most of the genes that were different between the two risk groups were related to the immune system. Further immune infiltration results revealed that a lower immune infiltration score was observed in high-risk patients, and that various leukocytes were significantly related to the ERS. Importantly, samples from the high-risk group showed lower levels of PD-1, PD-L1, and CTLA-4, which are important biomarkers for immunotherapy responses. Patients in the high-risk group also had more gene mutation changes and were more sensitive to chemotherapy drugs like docetaxel and sorafenib. The ERS was also validated in the GSE30219, GSE11969 and GSE72094, and showed a favorable prognostic value for LUAD patients.

DISCUSSION

The ERS established during this study was able to predict a poor prognosis for LUAD patients and had great potential for predicting drug responses.

A new risk model for CSTA, FAM83A, and MYCT1 predicts poor prognosis and is related to immune infiltration in lung squamous cell carcinoma

OBJECTIVES

To create a prognostic model based on differentially expressed genes (DEGs) in early lung squamous cell carcinoma (LUSC) and characterize the relationship between risk scores and tumor immune infiltration.

METHODS

We identified DEGs in normal and tumor tissues that overlapped between LUSC-related data sets from the Gene Expression Omnibus and the Cancer Genome Atlas and evaluated their roles in the diagnosis and prognosis of LUSC by Kaplan-Meier survival analysis, receiver operating characteristic (ROC) analysis, meta-analysis and nomogram analysis. We then constructed a risk model based on Cox regression analysis and the Akaike information criterion and identified the relationship between LUSC risk scores and immune infiltration.

RESULTS

Sixty-two overlapping DEGs were involved with keratinocyte differentiation, epidermal cell differentiation, neutrophil migration, granulocyte chemotaxis, granulocyte migration, leukocyte aggregation, and positive regulation of nuclear factor-κB (NF-κB) activity. Overexpression of family with sequence similarity 83 member A (FAM83A) and MYC target 1 (MYCT1), kallikrein related peptidase 8 (KLK8), and downregulation of ADP ribosylation factor like GTPase 14 (ARL14), caspase recruitment domain family member 14 (CARD14), cystatin A (CSTA), dickkopf WNT signaling pathway inhibitor 4 (DKK4), desmoglein 3 (DSG3), and keratin 6B (KRT6B) were associated with a poor prognosis in LUSC and had significant value for LUSC diagnosis. The expression of CSTA, FAM83A, and MYCT1 and high-risk scores were independent risk factors for a poor prognosis in LUSC. A risk nomogram revealed that risk scores could predict the prognosis of LUSC. The risk score was associated with neutrophils, naive B cells, helper follicular T cells, and activated dendritic cells.

CONCLUSIONS

The expression levels of CSTA, FAM83A, and MYCT1 are related to the diagnosis and prognosis of LUSC and may have potential as therapeutic targets in LUSC. A risk model and nomogram based on CSTA, FAM83A, and MYCT1 can predict the prognosis of LUSC.

A feature selection-based framework to identify biomarkers for cancer diagnosis: A focus on lung adenocarcinoma

Lung cancer (LC) represents most of the cancer incidences in the world. There are many types of LC, but Lung Adenocarcinoma (LUAD) is the most common type. Although RNA-seq and microarray data provide a vast amount of gene expression data, most of the genes are insignificant to clinical diagnosis. Feature selection (FS) techniques overcome the high dimensionality and sparsity issues of the large-scale data. We propose a framework that applies an ensemble of feature selection techniques to identify genes highly correlated to LUAD. Utilizing LUAD RNA-seq data from the Cancer Genome Atlas (TCGA), we employed mutual information (MI) and recursive feature elimination (RFE) feature selection techniques along with support vector machine (SVM) classification model. We have also utilized Random Forest (RF) as an embedded FS technique. The results were integrated and candidate biomarker genes across all techniques were identified. The proposed framework has identified 12 potential biomarkers that are highly correlated with different LC types, especially LUAD. A predictive model has been trained utilizing the identified biomarker expression profiling and performance of 97.99% was achieved. In addition, upon performing differential gene expression analysis, we could find that all 12 genes were significantly differentially expressed between normal and LUAD tissues, and strongly correlated with LUAD according to previous reports. We here propose that using multiple feature selection methods effectively reduces the number of identified biomarkers and directly affects their biological relevance.

[VIPR1 promoter methylation promotes transcription factor AP-2α binding to inhibit VIPR1 expression and promote hepatocellular carcinoma cell growth in vitro]

OBJECTIVE

To explore the transcriptional regulation mechanism and biological function of low expression of vasoactive intestinal peptide receptor 1 (VIPR1) in hepatocellular carcinoma (HCC).

METHODS

We constructed plasmids carrying wild-type VIPR1 promoter or two mutant VIPR1 promoter sequences for transfection of the HCC cell lines Hep3B and Huh7, and examined the effect of AP-2α expression on VIPR1 promoter activity using dual-luciferase reporter assay. Pyrosequencing was performed to detect the changes in VIPR1 promoter methylation level in HCC cells treated with a DNA methyltransferase inhibitor (DAC). Chromatin immunoprecipitation was used to evaluate the binding ability of AP-2α to VIPR1 promoter. Western blotting was used to assess the effect of AP-2α knockdown on VIPR1 expression and examine the differential expression of VIPR1 in the two cell lines. The effects of VIPR1 overexpression and knockdown on the proliferation, cell cycle and apoptosis of HCC cells were analyzed using CCK8 assay and flow cytometry. We also observed the growth of HCC xenograft with lentivirus-mediated over-expression of VIPR1 in nude mice.

RESULTS

Compared with the wild-type VIPR1 promoter group, co-transfection with the vector carrying two promoter mutations and the AP-2α-over-expressing plasmid obviously restored the luciferase activity in HCC cells (P < 0.05). DAC treatment of the cells significantly decreased the methylation level of VIPR1 promoter and inhibited the binding of AP-2α to VIPR1 promoter (P < 0.01). The HCC cells with AP-2α knockdown showed increased VIPR1 expression, which was lower in Huh7 cells than in Hep3B cells. VIPR1 overexpression in HCC cells caused significant cell cycle arrest in G2/M phase (P < 0.01), promoted cell apoptosis (P < 0.001), and inhibited cell proliferation (P < 0.001), while VIPR1 knockdown produced the opposite effects. In the tumor-bearing nude mice, VIPR1 overexpression in the HCC cells significantly suppressed the increase of tumor volume (P < 0.001) and weight (P < 0.05).

CONCLUSION

VIPR1 promoter methylation in HCC promotes the binding of AP-2α and inhibits VIPR1 expression, while VIPR1 overexpression causes cell cycle arrest, promotes cell apoptosis, and inhibits cell proliferation and tumor growth.

Semaphorin 4B promotes tumor progression and associates with immune infiltrates in lung adenocarcinoma

BACKGROUND

Semaphorins have been found to play important roles in multiple malignancy-related processes. However, the role of Semaphorin 4B (SEMA4B) in lung cancer remains unclear. Here, we aimed to explore the biological functions of SEMA4B in through bioinformatic analysis, in vitro and in vivo assays. In the present study, the possible mechanism by which SEMA4B affected the tumor growth and microenvironment of lung adenocarcinoma (LUAD) were investigated.

METHODS

The expression of SEMA4B in LUAD was analyzed by bioinformatic analysis and verified by the immunohistochemistry staining. The prognostic value of SEMA4B in LUAD was investigated using the Kaplan-Meier survival and Cox's regression model. After silencing SEMA4B expression, the functions of SEMA4B in LUAD cells were investigated by in vitro experiments, including CCK-8 and plate clone formation. And the effect of SEMA4B on tumor growth and immune infiltration was explored in C57BL/6 mice tumor-bearing models.

RESULTS

SEMA4B expression was upregulated in LUAD tissues and correlated with later pathological stages and poor prognosis of LUAD patients. Further study found that SEMA4B silencing suppressed the proliferation of lung cancer cells both in vitro and in vivo. Bioinformatic analysis showed that SEMA4B expression was correlated with the increased infiltration of myeloid-derived suppressor cells (MDSCs), T-regs and the decreased infiltration of CD8⁺ T cell in LUAD. Importantly, in vivo study verified that the infiltration of T-regs and MDSCs in tumor microenvironment (TME) of Xenograft tissues was decreased after SEMA4B silencing.

CONCLUSIONS

These findings demonstrated SEMA4B might play an oncogenic role in LUAD progression, and be a promising therapeutic target for lung cancer.

Emerging Roles for LGR4 in Organ Development, Energy Metabolism and Carcinogenesis

The leucine-rich repeats containing G protein-coupled receptor 4 (LGR4) belonging to G protein-coupled receptors (GPCRs) family, had various regulatory roles at multiple cellular types and numerous targeting sites, and aberrant LGR4 signaling played crucial roles in diseases and carcinogenesis. On the basis of these facts, LGR4 may become an appealing therapeutic target for the treatment of diseases and tumors. However, a comprehensive investigation of its functions and applications was still lacking. Hence, this paper provided an overview of the molecular characteristics and signaling mechanisms of LGR4, its involvement in multiple organ development and participation in the modulation of immunology related diseases, metabolic diseases, and oxidative stress damage along with cancer progression. Given that GPCRs accounted for almost a third of current clinical drug targets, the in-depth understanding of the sophisticated connections of LGR4 and its ligands would not only enrich their regulatory networks, but also shed new light on designing novel molecular targeted drugs and small molecule blockers for revolutionizing the treatment of various diseases and tumors.

A prognostic model of non small cell lung cancer based on TCGA and ImmPort databases

Bioinformatics methods are used to construct an immune gene prognosis assessment model for patients with non-small cell lung cancer (NSCLC), and to screen biomarkers that affect the occurrence and prognosis of NSCLC. The transcriptomic data and clinicopathological data of NSCLC and cancer-adjacent normal tissues were downloaded from the Cancer Genome Atlas (TCGA) database and the immune-related genes were obtained from the IMMPORT database (http://www.immport.org/); then, the differentially expressed immune genes were screened out. Based on these genes, an immune gene prognosis model was constructed. The Cox proportional hazards regression model was used for univariate and multivariate analyses. Further, the correlations among the risk score, clinicopathological characteristics, tumor microenvironment, and the prognosis of NSCLC were analyzed. A total of 193 differentially expressed immune genes related to NSCLC were screened based on the "wilcox.test" in R language, and Cox single factor analysis showed that 19 differentially expressed immune genes were associated with the prognosis of NSCLC (P < 0.05). After including 19 differentially expressed immune genes with P < 0.05 into the Cox multivariate analysis, an immune gene prognosis model of NSCLC was constructed (it included 13 differentially expressed immune genes). Based on the risk score, the samples were divided into the high-risk and low-risk groups. The Kaplan–Meier survival curve results showed that the 5-year overall survival rate in the high-risk group was 32.4%, and the 5-year overall survival rate in the low-risk group was 53.7%. The receiver operating characteristic model curve confirmed that the prediction model had a certain accuracy (AUC = 0.673). After incorporating multiple variables into the Cox regression analysis, the results showed that the immune gene prognostic risk score was an independent predictor of the prognosis of NSCLC patients. There was a certain correlation between the risk score and degree of neutrophil infiltration in the tumor microenvironment. The NSCLC immune gene prognosis assessment model was constructed based on bioinformatics methods, and it can be used to calculate the prognostic risk score of NSCLC patients. Further, this model is expected to provide help for clinical judgment of the prognosis of NSCLC patients.

Circulating microvesicles and exosomes in small cell lung cancer by quantitative proteomics

BACKGROUND

Early detection of small cell lung cancer (SCLC) crucially demands highly reliable markers. Growing evidence suggests that extracellular vesicles carry tumor cell-specific cargo suitable as protein markers in cancer. Quantitative proteomic profiling of circulating microvesicles and exosomes can be a high-throughput platform for discovery of novel molecular insights and putative markers. Hence, this study aimed to investigate proteome dynamics of plasma-derived microvesicles and exosomes in newly diagnosed SCLC patients to improve early detection.

METHODS

Plasma-derived microvesicles and exosomes from 24 healthy controls and 24 SCLC patients were isolated from plasma by either high-speed- or ultracentrifugation. Proteins derived from these extracellular vesicles were quantified using label-free mass spectrometry and statistical analysis was carried out aiming at identifying significantly altered protein expressions between SCLC patients and healthy controls. Furthermore, significantly expressed proteins were subjected to functional enrichment analysis to identify biological pathways implicated in SCLC pathogenesis.

RESULTS

Based on fold change (FC) ≥ 2 or ≤ 0.5 and AUC ≥ 0.70 (p < 0.05), we identified 10 common and 16 and 17 unique proteins for microvesicles and exosomes, respectively. Among these proteins, we found dysregulation of coagulation factor XIII A (Log₂ FC =  - 1.1, p = 0.0003, AUC = 0.82, 95% CI: 0.69-0.96) and complement factor H-related protein 4 (Log₂ FC = 1.2, p = 0.0005, AUC = 0.82, 95% CI; 0.67-0.97) in SCLC patients compared to healthy individuals. Our data may indicate a novel tumor-suppressing role of blood coagulation and involvement of complement activation in SCLC pathogenesis.

CONCLUSIONS

In comparing SCLC patients and healthy individuals, several differentially expressed proteins were identified. This is the first study showing that circulating extracellular vesicles may encompass specific proteins with potential diagnostic attributes for SCLC, thereby opening new opportunities as novel non-invasive markers.

A novel 14-gene signature for overall survival in lung adenocarcinoma based on the Bayesian hierarchical Cox proportional hazards model

There have been few investigations of cancer prognosis models based on Bayesian hierarchical models. In this study, we used a novel Bayesian method to screen mRNAs and estimate the effects of mRNAs on the prognosis of patients with lung adenocarcinoma. Based on the identified mRNAs, we can build a prognostic model combining mRNAs and clinical features, allowing us to explore new molecules with the potential to predict the prognosis of lung adenocarcinoma. The mRNA data (n = 594) and clinical data (n = 470) for lung adenocarcinoma were obtained from the TCGA database. Gene set enrichment analysis (GSEA), univariate Cox proportional hazards regression, and the Bayesian hierarchical Cox proportional hazards model were used to explore the mRNAs related to the prognosis of lung adenocarcinoma. Multivariate Cox proportional hazard regression was used to identify independent markers. The prediction performance of the prognostic model was evaluated not only by the internal cross-validation but also by the external validation based on the GEO dataset (n = 437). With the Bayesian hierarchical Cox proportional hazards model, a 14-gene signature that included CPS1, CTPS2, DARS2, IGFBP3, MCM5, MCM7, NME4, NT5E, PLK1, POLR3G, PTTG1, SERPINB5, TXNRD1, and TYMS was established to predict overall survival in lung adenocarcinoma. Multivariate analysis demonstrated that the 14-gene signature (HR 3.960, 95% CI 2.710–5.786), T classification (T₁, reference; T₃, HR 1.925, 95% CI 1.104–3.355) and N classification (N₀, reference; N₁, HR 2.212, 95% CI 1.520–3.220; N₂, HR 2.260, 95% CI 1.499–3.409) were independent predictors. The C-index of the model was 0.733 and 0.735, respectively, after performing cross-validation and external validation, a nomogram was provided for better prediction in clinical application. Bayesian hierarchical Cox proportional hazards models can be used to integrate high-dimensional omics information into a prediction model for lung adenocarcinoma to improve the prognostic prediction and discover potential targets. This approach may be a powerful predictive tool for clinicians treating malignant tumours.

Identification, Verification and Pathway Enrichment Analysis of Prognosis-Related Immune Genes in Patients With Hepatocellular Carcinoma

Hepatocellular carcinoma is a common malignant tumor with poor prognosis, poor treatment effect, and lack of effective biomarkers. In this study, bioinformatics analysis of immune-related genes of hepatocellular carcinoma was used to construct a multi-gene combined marker that can predict the prognosis of patients. The RNA expression data of hepatocellular carcinoma were downloaded from The Cancer Genome Atlas (TCGA) database, and immune-related genes were obtained from the IMMPORT database. Differential analysis was performed by Wilcox test to obtain differentially expressed genes. Univariate Cox regression analysis, lasso regression analysis and multivariate Cox regression analysis were performed to establish a prognostic model of immune genes, a total of 5 genes (HDAC1, BIRC5, SPP1, STC2, NR6A1) were identified to construct the models. The expression levels of 5 genes in HCC tissues were significantly different from those in paracancerous tissues. The Kaplan-Meier survival curve showed that the risk score calculated according to the prognostic model was significantly related to the overall survival (OS) of HCC. The receiver operating characteristic (ROC) curve confirmed that the prognostic model had high accuracy. Independent prognostic analysis was performed to prove that the risk value can be used as an independent prognostic factor. Then, the gene expression data of hepatocellular carcinoma in the ICGC database was used as a validation data set for the verification of the above steps. In addition, we used the CIBERSORT software and TIMER database to conduct immune infiltration research, and the results showed that the five genes of the model and the risk score have a certain correlation with the content of immune cells. Moreover, through Gene Set Enrichment Analysis (GSEA) and the construction of protein interaction networks, we found that the p53-mediated signal transduction pathway is a potentially important signal pathway for hepatocellular carcinoma and is positively regulated by certain genes in the prognostic model. In conclusion, this study provides potential targets for predicting the prognosis and treatment of hepatocellular carcinoma patients, and also provides new ideas about the correlation between immune genes and potential pathways of hepatocellular carcinoma.

Establishment of an Immune-Related Gene Signature for Risk Stratification for Patients with Glioma

Glioma is a frequently seen primary malignant intracranial tumor, characterized by poor prognosis. The study is aimed at constructing a prognostic model for risk stratification in patients suffering from glioma. Weighted gene coexpression network analysis (WGCNA), integrated transcriptome analysis, and combining immune-related genes (IRGs) were used to identify core differentially expressed IRGs (DE IRGs). Subsequently, univariate and multivariate Cox regression analyses were utilized to establish an immune-related risk score (IRRS) model for risk stratification for glioma patients. Furthermore, a nomogram was developed for predicting glioma patients' overall survival (OS). The turquoise module (cor = 0.67; P < 0.001) and its genes (n = 1092) were significantly pertinent to glioma progression. Ultimately, multivariate Cox regression analysis constructed an IRRS model based on VEGFA, SOCS3, SPP1, and TGFB2 core DE IRGs, with a C-index of 0.811 (95% CI: 0.786-0.836). Then, Kaplan-Meier (KM) survival curves revealed that patients presenting high risk had a dismal outcome (P < 0.0001). Also, this IRRS model was found to be an independent prognostic indicator of gliomas' survival prediction, with HR of 1.89 (95% CI: 1.252-2.85) and 2.17 (95% CI: 1.493-3.14) in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, respectively. We established the IRRS prognostic model, capable of effectively stratifying glioma population, convenient for decision-making in clinical practice.

The S100 Protein Family as Players and Therapeutic Targets in Pulmonary Diseases

The S100 protein family consists of over 20 members in humans that are involved in many intracellular and extracellular processes, including proliferation, differentiation, apoptosis, Ca₂⁺ homeostasis, energy metabolism, inflammation, tissue repair, and migration/invasion. Although there are structural similarities between each member, they are not functionally interchangeable. The S100 proteins function both as intracellular Ca²⁺ sensors and as extracellular factors. Dysregulated responses of multiple members of the S100 family are observed in several diseases, including the lungs (asthma, chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, cystic fibrosis, pulmonary hypertension, and lung cancer). To this degree, extensive research was undertaken to identify their roles in pulmonary disease pathogenesis and the identification of inhibitors for several S100 family members that have progressed to clinical trials in patients for nonpulmonary conditions. This review outlines the potential role of each S100 protein in pulmonary diseases, details the possible mechanisms observed in diseases, and outlines potential therapeutic strategies for treatment.

A Combined RNA Signature Predicts Recurrence Risk of Stage I-IIIA Lung Squamous Cell Carcinoma

Objective

Recurrence remains the main cause of the poor prognosis in stage I-IIIA lung squamous cell carcinoma (LUSC) after surgical resection. In the present study, we aimed to identify the long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) related to the recurrence of stage I-IIIA LUSC. Moreover, we constructed a risk assessment model to predict the recurrence of LUSC patients.

Methods

RNA sequencing data (including miRNAs, lncRNAs, and mRNAs) and relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) database. The differentially expressed lncRNAs, miRNAs, and mRNAs were identified using the “DESeq2” package of the R language. Univariate Cox proportional hazards regression analysis and Kaplan-Meier curve were used to identify recurrence-related genes. Stepwise multivariate Cox regression analysis was carried out to establish a risk model for predicting recurrence in the training cohort. Moreover, Kaplan-Meier curves and receiver operating characteristic (ROC) curves were adopted to examine the predictive performance of the signature in the training cohort, validation cohort, and entire cohort.

Results

Based on the TCGA database, we analyzed the differentially expressed genes (DEGs) among 27 patients with recurrent stage I-IIIA LUSC and 134 patients with non-recurrent stage I-IIIA LUSC, and identified 431 lncRNAs, 36 miRNAs, and 746 mRNAs with different expression levels. Out of these DEGs, the optimal combination of DEGs was finally determined, and a nine-joint RNA molecular signature was constructed for clinical prediction of recurrence, including LINC02683, AC244517.5, LINC02418, LINC01322, AC011468.3, hsa-mir-6825, AC020637.1, AC027117.2, and SERPINB12. The ROC curve proved that the model had good predictive performance in predicting recurrence. The area under the curve (AUC) of the prognostic model for recurrence-free survival (RFS) was 0.989 at 3 years and 0.958 at 5 years (in the training set). The combined RNA signature also revealed good predictive performance in predicting the recurrence in the validation cohort and entire cohort.

Conclusions

In the present study, we constructed a nine-joint RNA molecular signature for recurrence prediction of stage I-IIIA LUSC. Collectively, our findings provided new and valuable clinical evidence for predicting the recurrence and targeted treatment of stage I-IIIA LUSC.

Integrated analysis identifies S100A16 as a potential prognostic marker for pancreatic cancer

BACKGROUND

The new S100 protein family member S100A16 is functionally expressed in various cancers. This study explored the prognostic value and potential role of S100A16 in pancreatic cancer (PC).

METHODS

RNA-seq and clinical data were obtained from The Cancer Genome Atlas-Pancreatic Adenocarcinoma (TCGA-PAAD) dataset to compare the expression level of S100A16 between groups. The genes co-expressed with S100A16 in TCGA-PAAD were analyzed using cBioPortal. Gene Ontology and Kyoto Encyclopedia of Genes and genomes enrichment analyses were also performed on these genes. Pathways related to S100A16 expression dysregulation were explored using gene set enrichment analysis. The Tumor Immune Estimation Resource was used to analyze the correlation between S100A16 and infiltrating immune cells. The Kaplan-Meier method and Cox analyses were used to assess the prognostic significance of S100A16 for PC.

RESULTS

The S100A16 expression level was high in PC and increased with the degree of malignancy. The S100A16 functions in PC were mainly enriched in the immune modules, but negatively correlated with the immune activity (T-cell, cytokine, immune, co-receptor, signaling adaptor, cell adhesion molecule, chemokine, and JAK/STAT signaling) and infiltration level (T cells and macrophages). The strongest negative correlation was observed between the expression of CD8⁺ T cells and S100A16. Furthermore, high S100A16 expression also indicated worse overall survival and, therefore, worse prognosis of PC.

CONCLUSION

S100A16 is a potential independent prognostic marker and immunotherapy target for PC. Mechanistically, S100A16 potentially affects prognosis by extensive immunosuppression, including the inhibition of the anti-tumor immune response of CD8⁺ T cells.

LncRNA HOTAIRM1 knockdown inhibits cell glycolysis metabolism and tumor progression by miR-498/ABCE1 axis in non-small cell lung cancer

BACKGROUND

Non-small cell lung cancer (NSCLC) is a major contributor of cancer-related mortality. Long non-coding RNAs (lncRNAs) are indicated to participate in the pathogenesis of NSCLC.

OBJECTIVE

In this research, the effects of lncRNA HOXA transcript antisense RNA, myeloid-specific 1 (HOTAIRM1) on NSCLC progression and underlying mechanism were revealed.

METHODS

The expression levels of HOTAIRM1 and microRNA-498 (miR-498) were detected by quantitative real time polymerase chain reaction (qRT-PCR) in NSCLC tissues, cells or exosomes. The protein expression of CD63, CD81, hexokinase 2 (HK2) and ATP binding cassette subfamily E member 1 (ABCE1) was determined by western blot. Cell viability, apoptosis, migration and invasion were investigated by cell counting kit-8 (CCK-8), flow cytometry, transwell migration and invasion assays, respectively. Cell glycolysis metabolism was revealed by glucose uptake and lactate production assays and western blot analysis. The binding relationship between miR-498 and HOTAIRM1 or ABCE1 was predicted by DIANA-LncBase v2 and starBase online database, and identified by dual-luciferase reporter assay. The effects of HOTAIRM1 on NSCLC growth in vivo were revealed by in vivo tumor formation assay.

RESULTS

HOTAIRM1 expression was dramatically upregulated, whereas miR-498 expression was significantly downregulated in NSCLC tissues cells or exosomes as compared to control groups. Mechanistically, HOTAIRM1 knockdown repressed cell viability, migration, invasion and glycolysis metabolism, whereas induced cell apoptosis in NSCLC; however, miR-498 inhibitor hindered these effects. Functionally, HOTAIRM1 functioned as a sponge of miR-498 and miR-498 targeted ABCE1. In addition, HOTAIRM1 silencing inhibited NSCLC growth in vivo by downregulating ABCE1 and upregulating miR-498 expression.

CONCLUSIONS

HOTAIRM1 knockdown repressed cell glycolysis metabolism and tumor development by reducing ABCE1 expression through sponging miR-498 in NSCLC, which provided a theoretical basis for further studying NSCLC progression.

Identification of immune-related lncRNA signature for predicting immune checkpoint blockade and prognosis in hepatocellular carcinoma

BACKGROUND

An increasing body of evidence has supported that long non-coding RNAs (lncRNAs) can play as essential roles of various physiological process and pathological diseases. We aimed to construct a robust immune-associated lncRNA signature associated with the prognosis for HCC survival prediction.

METHODS

7 immune-associated lncRNAs presenting significant correlation with survival were screened through stepwise univariate Cox regression and LASSO algorithm, and multivariate Cox regression. Kaplan-Meier analysis, proportional hazards model, and ROC analyses further conducted. Gene set enrichment analysis (GSEA) was applied for functional annotation. We conducted quantitative real-time polymerase chain reaction to determine NRAV expression and preliminarily explored the latent role of NRAV in prognosis of HCC patients.

RESULTS

Finally, 7 immune-related lncRNA signature composed of AC007405.3, AC023157.3, NRAV, CASC19, MSC-AS1, GASAL1, and LINC00942 were validated. This lncRNAs signature can serve as an independent predictive biomolecular factor. This signature was further confirmed in the validation group and the entire cohort. We demonstrated that NRAV was significantly upregulated in HCC cell lines and it may serve as a key regulator in HCC. Our signature was associated to apoptosis and immunologic characteristics. This signature mediated immune cell infiltration (i.e., Dendritic, etc.,) and immune checkpoint blockade (ICB) immunotherapy-related molecules (i.e., CD274, etc.,).

CONCLUSION

This immune-related lncRNA signature possesses promising prognostic value in HCC and may have the potentiality to predict clinical outcome of ICB immunotherapy.

A novel immune-related genes prognosis biomarker for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is closely associated with the immune microenvironment. To identify the effective population before administering treatment, the establishment of prognostic immune biomarkers is crucial for early HCC diagnosis and treatment.

RESULTS

A total of 335 IRGs identified from 788 overlapping IRGs were associated with the survival of HCC. A prognostic immunoscore model was identified. The Kaplan-Meier survival curves and time-dependent ROC analysis revealed a powerful prognostic performance of immunoscore signature via multi validation. Besides, the immunoscore signature exhibited a better predictive power compared to other prognostic signatures. Gene set enrichment analysis showed multiple signaling differences between the high and low immunoscore group. Furthermore, immunoscore was significantly associated with multiple immune cells and immune infiltration in the tumor microenvironment.

CONCLUSIONS

We identified the immunoscore as a robust marker for predicting HCC patient survival.

METHODS

Three sets of immune-related genes (IRGs) were integrated to identify the overlapping IRGs. Weighted gene co-expression network analysis was performed to obtain the survival-related IRGs. Further, the prognostic immunoscore model was constructed via LASSO-penalized Cox regression analysis. Then the prognostic performance of immunoscore was evaluated. In addition, ESTIMATE and CIBERSORT algorithms were applied to explore the relationship between immunoscore and tumor immune microenvironment.

Identification of a three-miRNA signature as a novel prognostic model for papillary renal cell carcinoma

Background

Papillary renal cell carcinoma (pRCC) accounting for near 20% of renal cell carcinoma is the second most common histological subtype. MiRNAs have been demonstrated to played significant roles on predicting prognosis of patients with tumors. An appropriate and comprehensive miRNAs analysis based on a great deal of pRCC samples from The Cancer Genome Atlas (TCGA) will provide perspective in this field.

Methods

We integrated the expression of mRNAs, miRNAs and the relevant clinical data of 321 pRCC patients recorded in the TCGA database. The survival-related differential expressed miRNAs (sDEmiRs) were estimated by COX regression analysis. The high-risk group and the low-risk group were separated by the median risk score of the risk score model (RSM) based on three screened sDEmiRs. The target genes, underlying molecular mechanisms of these sDEmiRs were explored by computational biology. The expression levels of the three sDEmiRs and their correlations with clinicopathological parameters were further validated by qPCR.

Results

Based on univariate COX analysis (P < 0.001), eighteen differential expressed miRNAs (DEmiRs) were remarkably related with the overall survival (OS) of pRCC patients. Three sDEmiRs with the most significant prognostic values (miR-34a-5p, miR-410-3p and miR-6720-3p) were employed to establish the RSM which was certified as an independent prognosis factor and closely correlated with OS. In the verification of clinical samples, the overexpression of miR-410-3p and miR-6720-3p were detected to be associated with the advanced T-stages, while miR-34a-5p showed the reversed results.

Conclusion

The study developed a RSM based on the identified sDEmiRs with significant prognosis prediction values for pRCC patients. The results pave the avenue for establishing and optimizing a reliable and referable risk assessing model and provide novel insight into the researches of biomarkers and clinical treatment strategies.