Systematic assessment of the clinicopathological prognostic significance of tissue cytokine expression for lung adenocarcinoma based on integrative analysis of TCGA data

An immune-related gene signature predicts the 28-day mortality in patients with sepsis

Introduction

Sepsis is the leading cause of death in intensive care units and is characterized by multiple organ failure, including dysfunction of the immune system. In the present study, we performed an integrative analysis on publicly available datasets to identify immune-related genes (IRGs) that may play vital role in the pathological process of sepsis, based on which a prognostic IRG signature for 28-day mortality prediction in patients with sepsis was developed and validated.

Methods

Weighted gene co-expression network analysis (WGCNA), Cox regression analysis and least absolute shrinkage and selection operator (LASSO) estimation were used to identify functional IRGs and construct a model for predicting the 28-day mortality. The prognostic value of the model was validated in internal and external sepsis datasets. The correlations of the IRG signature with immunological characteristics, including immune cell infiltration and cytokine expression, were explored. We finally validated the expression of the three IRG signature genes in blood samples from 12 sepsis patients and 12 healthy controls using qPCR.

Results

We established a prognostic IRG signature comprising three gene members (LTB4R, HLA-DMB and IL4R). The IRG signature demonstrated good predictive performance for 28-day mortality on the internal and external validation datasets. The immune infiltration and cytokine analyses revealed that the IRG signature was significantly associated with multiple immune cells and cytokines. The molecular pathway analysis uncovered ontology enrichment in myeloid cell differentiation and iron ion homeostasis, providing clues regarding the underlying biological mechanisms of the IRG signature. Finally, qPCR detection verified the differential expression of the three IRG signature genes in blood samples from 12 sepsis patients and 12 healthy controls.

Discussion

This study presents an innovative IRG signature for 28-day mortality prediction in sepsis patients, which may be used to facilitate stratification of risky sepsis patients and evaluate patients' immune state.

Construction of an HLA Classifier for Early Diagnosis, Prognosis, and Recognition of Immunosuppression in Sepsis by Multiple Transcriptome Datasets

Background: Sepsis is a clinical syndrome, due to a dysregulated inflammatory response to infection. Accumulating evidence shows that human leukocyte antigen (HLA) genes play a key role in the immune responses to sepsis. Nevertheless, the effects of HLA genes in sepsis have still not been comprehensively understood.

Methods: A systematical search was performed in the Gene Expression Omnibus (GEO) and ArrayExpress databases from inception to 10 September 2021. Random forest (RF) and modified Lasso penalized regression were conducted to identify hub genes in multi-transcriptome data, thus we constructed a prediction model, namely the HLA classifier. ArrayExpress databases, as external validation, were utilized to evaluate its diagnostic, prognostic, and predictive performance. Immune cell infiltration score was calculated via CIBERSORTx tools and single-sample gene set enrichment analysis (ssGSEA). Gene set variation analysis (GSVA) and ssGSEA were conducted to determine the pathways that are significantly enriched in different subgroups. Next, we systematically correlated the HLA classifier with immunological characteristics from multiple perspectives, such as immune-related cell infiltration, pivotal molecular pathways, and cytokine expression. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to validate the expression level of HLA genes in clinical samples.

Results: A total of nine datasets comprising 1,251 patients were included. Based on RF and modified Lasso penalized regression in multi-transcriptome datasets, five HLA genes (B2M, HLA-DQA1, HLA-DPA1, TAP1, and TAP2) were identified as hub genes, which were used to construct an HLA classifier. In the discovery cohort, the HLA classifier exhibited superior diagnostic value (AUC = 0.997) and performed better in predicting mortality (AUC = 0.716) than clinical characteristics or endotypes. Encouragingly, similar results were observed in the ArrayExpress databases. In the E-MTAB-7581 dataset, the use of hydrocortisone in the HLA high-risk subgroup (OR: 2.84, 95% CI 1.07–7.57, p = 0.037) was associated with increased risk of mortality, but not in the HLA low-risk subgroup. Additionally, immune infiltration analysis by CIBERSORTx and ssGSEA revealed that B cells, activated dendritic cells, NK cells, T helper cells, and infiltrating lymphocytes (ILs) were significantly richer in HLA low-risk phenotypes, while Tregs and myeloid-derived suppressor cells (MDSCs) were more abundant in HLA high-risk phenotypes. The HLA classifier was significantly negatively correlated with B cells, activated dendritic cells, NK cells, T helper cells, and ILs, yet was significantly positively correlated with Tregs and MDSCs. Subsequently, molecular pathways analysis uncovered that cytokine-cytokine receptor (CCR) interaction, human leukocyte antigen (HLA), and antigen-presenting cell (APC) co-stimulation were significantly enriched in HLA low-risk endotypes, which was significantly negatively correlated with the HLA classifier in multi-transcriptome data. Finally, the expression levels of several cytokines (IL-10, IFNG, TNF) were significantly different between the HLA subgroups, and the ratio of IL-10/TNF was significantly positively correlated with HLA score in multi-transcriptome data. Results of qRT-PCR validated the higher expression level of B2M as well as lower expression level of HLA-DQA1, HLA-DPA1, TAP1, and TAP2 in sepsis samples compared to control sample.

Conclusion: Based on five HLA genes, a diagnostic and prognostic model, namely the HLA classifier, was established, which is closely correlated with responses to hydrocortisone and immunosuppression status and might facilitate personalized counseling for specific therapy.

Characterization of immune-related genes andimmune infiltration features for early diagnosis, prognosis and recognition of immunosuppression in sepsis

Among the body systems, the immune system plays a fundamental role in the pathophysiology of sepsis. The effects of immunogenomic and immune cell infiltration in sepsis were still not been systematically understood. Based on modified Lasso penalized regression and RF, 8 DEIRGs (ADM, CX3CR1, DEFA4, HLA-DPA1, MAPK14, ORM1, RETN, and SLPI) were combined to construct an IRG classifier. In the discovery cohort, IRG classifier exhibited superior diagnostic efficacy and performed better in predicting mortality than clinical characteristics or MARS/SRS endotypes. Encouragingly, similar results were observed in the ArrayExpress databases. The use of hydrocortisone in IRG high-risk subgroup was associated with increased risk of mortality. In IRG low-risk phenotypes, NK cells, T helper cells, and infiltrating lymphocyte (IL) are significantly richer, while T cells regulatory (Tregs) and myeloid-derived suppressor cells (MDSC) are more abundant in IRG high-risk phenotypes. IRG score were significantly negatively correlated with Cytokine cytokine receptor interaction (CCR) and human leukocyte antigen (HLA). Between the IRG subgroups, the expression levels of several cytokines (IL-10, IFNG, TNF) were significantly different, and IRG score was significantly positively correlated with ratio of IL-10/TNF. Results of qRT-PCR validated that higher expression level of ADM, DEFA4, MAPK14, ORM1, RETN, and SLPI as well as lower expression level of CX3CR1 and HLA-DPA1 in sepsis samples compared to control sample. A diagnostic and prognostic model, namely IRG classifier, was established based on 8 IRGs that is closely correlated with responses to hydrocortisone and immunosuppression status and might facilitate personalized counseling for specific therapy.

A situational awareness Bayesian network approach for accurate and credible personalized adaptive radiotherapy outcomes prediction in lung cancer patients

PURPOSE

A situational awareness Bayesian network (SA-BN) approach is developed to improve physicians' trust in the prediction of radiation outcomes and evaluate its performance for personalized adaptive radiotherapy (pART).

METHODS

118 non-small-cell lung cancer patients with their biophysical features were employed for discovery (n = 68) and validation (n = 50) of radiation outcomes prediction modeling. Patients' important characteristics identified by radiation experts to predict individual's tumor local control (LC) or radiation pneumonitis with grade ≥ 2 (RP2) were incorporated as expert knowledge (EK). Besides generating an EK-based naïve BN (EK-NBN), an SA-BN was developed by incorporating the EK features into pure data-driven BN (PD-BN) methods to improve the credibility of LC or / and RP2 prediction. After using area under the free-response receiver operating characteristics curve (AU-FROC) to assess the joint prediction of these outcomes, their prediction performances were compared with a regression approach based on the expert yielded estimates (EYE) penalty and its variants.

RESULTS

In addition to improving the credibility of radiation outcomes prediction, the SA-BN approach outperformed the EYE penalty and its variants in terms of the joint prediction of LC and RP2. The value of AU-FROC improves from 0.70 (95% CI: 0.54-0.76) using EK-NBN, to 0.75 (0.65-0.82) using a variant of EYE penalty, to 0.83 (0.75-0.93) using PD-BN and 0.83 (0.77-0.90) using SA-BN; with similar trends in the validation cohort.

CONCLUSIONS

The SA-BN approach can provide an accurate and credible human-machine interface to gain physicians' trust in clinical decision-making, which has the potential to be an important component of pART.

Identification and analysis of genes associated with lung adenocarcinoma by integrated bioinformatics methods

Lung adenocarcinoma (LUAD) is one of the most common forms of lung cancer, with a very high mortality rate. Although the treatments available for LUAD have become more effective in recent years, significant improvement is still needed. Advances in sequencing technologies and bioinformatics analysis have enabled new approaches to be developed for identifying drug targets. In this work we utilized data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify hub genes related to LUAD through Weighted Gene Correlation Network Analysis (WGCNA) and other bioinformatics methods, with the goal of identifying new drug targets for cancer treatment.

The Diverse Roles of Heme Oxygenase-1 in Tumor Progression

Heme oxygenase-1 (HO-1) is an inducible intracellular enzyme that is expressed in response to a variety of stimuli to degrade heme, which generates the biologically active catabolites carbon monoxide (CO), biliverdin and ferrous iron (Fe²⁺). HO-1 is expressed across a range of cancers and has been demonstrated to promote tumor progression through a variety of mechanisms. HO-1 can be expressed in a variety of cells within the tumor microenvironment (TME), including both the malignant tumor cells as well as stromal cell populations such as macrophages, dendritic cells and regulatory T-cells. Intrinsically to the cell, HO-1 activity provides antioxidant, anti-apoptotic and cytoprotective effects via its catabolites as well as clearing toxic intracellular heme. However, the catabolites of heme degradation can also diffuse outside of the cell to extrinsically modulate the wider TME, influencing cellular functionality and biological processes which promote tumor progression, such as facilitating angiogenesis and metastasis, as well as promoting anti-inflammation and immune suppression. Pharmacological inhibition of HO-1 has been demonstrated to be a promising therapeutic approach to promote anti-tumor immune responses and inhibit metastasis. However, these biological functions might be context, TME and cell type-dependent as there is also conflicting reports for HO-1 activity facilitating anti-tumoral processes. This review will consider our current understanding of the role of HO-1 in cancer progression and as a therapeutic target in cancer.

Low Expression of IL-15 and NKT in Tumor Microenvironment Predicts Poor Outcome of MYCN-Non-Amplified Neuroblastoma

Immune tumor microenvironment (TME) in neuroblastoma (NBL) contributes to tumor behavior and treatment response. T cells and natural killer (NK) cells have been shown to play important roles in the neuroblastoma TME. However, few reports address the clinical relevance of natural killer T cells (NKTs) and interleukin-15 (IL-15), one of the crucial cytokines controlling the activation and expansion of NK/NKT cells, in NBL. In this study, we examined NKT immunoscores and IL-15 expression in both MYCN-amplified and MYCN-non-amplified NBL to correlate with clinical outcomes such as event-free survival (EFS) and overall survival (OS). From Gene Expression Omnibus (GEO) datasets GSE45480 (n = 643) and GSE49711 (n = 493), we found that NKT immunoscore and IL-15 expression were both significantly lower in MYCN-amplified NBL, and similar results were observed using our clinical NBL samples (n = 53). Moreover, NBL patients (GEO dataset GSE49711 and our clinical samples) with both lower NKT immunoscore and IL-15 expression exhibited decreased EFS and OS regardless of MYCN gene amplification status. Multivariate analysis further showed that the combination of low NKT immunoscore and low IL-15 expression level was an independent prognostic factor for poor EFS and OS in our NBL patients. These findings provide the rationale for the development of strategy to incorporate IL-15 and NKT cell therapy into the treatment regimen for neuroblastoma.

Heat shock factor 5 correlated with immune infiltration serves as a prognostic biomarker in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the predominant subtype of lung cancer with a relatively poor prognosis. The dramatic improvements of new immunotherapy strategies have shown promising results in lung cancer patients. This study aimed to elucidate the functions of immune-associated genes in LUAD prognosis and pathogenesis by analyzing public databases. We obtained expression profiles of LUAD patients from The Cancer Genome Atlas (TCGA) database and applied the ESTIMATE algorithm to calculate immune scores and stromal scores. A series of microenvironment-related genes with prognostic value was then identified. Of note, heat shock factor 5 (HSF5) was found to be decreased in LUAD patients and positively correlated with overall survival, which was further confirmed in the Gene Expression Omnibus (GEO) database. Moreover, Gene Ontology (GO) analysis based on the correlated genes of HSF5 demonstrated that HSF5 expression was significantly associated with the immune response and inflammatory activities. Based on the Tumor IMmune Estimation Resource (TIMER) and Gene Expression Profiling Interactive Analysis (GEPIA) datasets, HSF5 expression showed strong correlations with various immune cell infiltration and diverse immune marker sets. These findings suggest that HSF5 can be used as a promising biomarker for determining prognosis and immune infiltration in LUAD patients.

Potential Genes Associated with the Survival of Lung Adenocarcinoma Were Identified by Methylation

Background

Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. The purpose of this study is to search for genes related to the prognosis of LUAD through methylation based on a linear mixed model (LMM).

Methods

Gene expression, methylation, and survival data of LUAD patients were downloaded from the TCGA database. Based on the LMM model, the GEMMA algorithm was used to screen the predictive genes related to LUAD survival. The Cox model was used to further screen the predicted genes, and then, protein-protein interaction (PPI) network was constructed. Through the software plugin Cytoscape MCODE 3.8.0, the most closely related genes in the PPI network module were selected for in-depth biological function analysis to further explore the interaction and correlation between genes.

Results

We screened out 97 predictive genes from 18,834 genes and eliminated one gene associated with lung squamous cell carcinoma from previous studies, leaving 96 genes. The MCODE and the Kaplan-Meier curve analysis were used to finally identify two genes ASB16 and NEDD4 that are related to the prognosis of LUAD.

Conclusions

The newly identified two genes associated with the prognosis of LUAD may provide a basis for the treatment of patients.

High expression of keratin 6C is associated with poor prognosis and accelerates cancer proliferation and migration by modulating epithelial-mesenchymal transition in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is a more frequent subtype of lung cancer and most cases are discovered in the late stages. The proliferation and metastasis of LUAD are pivotal for disease progression. Despite unremitting deeper understanding of LUAD biology, the mechanisms involved in the proliferation and metastasis of LUAD remain unclear. The objective of our article was to inquiry the expression and the function of keratin 6C (KRT6C) in LUAD cells.

METHODS

First, the expression level and prognostic value of KRT6C in LUAD tissues were analyzed on the basis of the data acquired from TCGA database. Through qRT-PCR, the expression level of KRT6C on LUAD cell lines (A549, H1299, PC-9) and human normal lung cell line MRC-5 was tested. After that, CCK8 and colony formation assays was utilized to detect cell proliferation. In addition, to explore the influence of KRT6C on LUAD migration and invasion ability, scratch wound healing and transwell assays were utilized. Through western blotting, the protein expression levels of KRT6C, PCNA, E-cadherin, N-cadherin, Snail and Vimentin were detected.

RESULTS

The outcomes revealed that KRT6C was highly expressed in LUAD tissues and cell lines. Besides, elevated level of KRT6C was related to worse prognosis in LUAD patients. Ablation of KRT6C restrained proliferation, migration and invasion of A549 cells. KRT6C deficiency augmented the expression of E-cadherin as well as reduced the expression of N-cadherin, Snail and Vimentin.

CONCLUSION

Above all, these consequences indicated that depletion of KRT6C suppressed A549 cell proliferation, migration and invasion, which might be achieved by regulating EMT. In general, KRT6C is identified as a potential therapeutic target for LUAD.