Comprehensive analysis of a chemokine- and chemokine receptor family-based signature for patients with lung adenocarcinoma

Chemokines in the tumor microenvironment: implications for lung cancer and immunotherapy

The tumor microenvironment (TME) is a complex interconnected network of immune cells, fibroblasts, blood vessels, and extracellular matrix surrounding the tumor. Because of its immunosuppressive nature, the TME can pose a challenge for cancer immunotherapies targeting solid tumors. Chemokines have emerged as a crucial element in enhancing the efficacy of cancer immunotherapy, playing a direct role in immune cell signaling within the TME and facilitating immune cell migration towards cancer cells. However, chemokine ligands and their receptors exhibit context-dependent diversity, necessitating evaluation of their tumor-promoting or inhibitory effects based on tumor type and immune cell characteristics. This review explores the role of chemokines in tumor immunity and metastasis in the context of the TME. We also discuss current chemokine-related advances in cancer immunotherapy research, with a particular focus on lung cancer, a common cancer with a low survival rate and limited immunotherapy options.

Chemokine‑ and chemokine receptor-based subtypes predict prognosis, immunotherapy and chemotherapy response in colorectal cancer patients

BACKGROUND

The clinical significance and comprehensive characteristics of chemokines and chemokine receptors in colorectal cancer (CRC) have not been previously reported. Our study aims to investigate the expression profiles of chemokines and chemokine receptors, as well as establish subtypes in CRC.

METHODS

1009 CRC samples were enrolled in our study. Consensus unsupervised clustering analysis was conducted to establish subtypes, and a risk score model was developed using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses. 36 pairs of tissue specimens of CRC patients and two CRC cell lines were used to validate the subtypes and risk score in vitro. Quantitative real-time PCR and western blotting were employed to validate mRNA and protein expression levels, respectively. Flow cytometry was utilized for analyzing cell apoptosis, while cell viability assay and EdU assay were conducted to assess cell proliferation ability.

RESULTS

The Cluster B group shares similarities with the low-risk group in terms of exhibiting a higher level of immune cell infiltration and belonging to hot tumor. Patients CRC in the Cluster B group demonstrate a more favorable prognosis and exhibit better response to immunotherapy and chemotherapy. On the other hand, the Cluster A group resembles the high-risk group as it displays lower levels of immune cell infiltration, indicating a cold tumor phenotype. CRC patients in the Cluster A group have poorer prognoses and show less therapeutic efficacy towards immunotherapy and chemotherapy. Furthermore, we utilized a total of 36 pairs of tissue samples obtained from patients with CRC, along with two CRC cell lines for validation in vitro. This comprehensive approach further enhances the scientific validity and reliability of the identified subtypes and risk score in their ability to predict prognosis, response to immunotherapy, and response to chemotherapy among CRC patients.

CONCLUSION

We first established robust prognostic subtypes based on chemokines and chemokine receptors, which could potentially serve as a novel biomarker for guiding individualized treatment in patients with CRC undergoing immunotherapy and chemotherapy.

Identifying a survival-associated cell type based on multi-level transcriptome analysis in idiopathic pulmonary fibrosis

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a progressive disease with a five-year survival rate of less than 40%. There is significant variability in survival time among IPF patients, but the underlying mechanisms for this are not clear yet.

METHODS AND RESULTS

We collected single-cell RNA sequence data of 13,223 epithelial cells taken from 32 IPF patients and bulk RNA sequence data from 456 IPF patients in GEO. Based on unsupervised clustering analysis at the single-cell level and deconvolution algorithm at bulk RNA sequence data, we discovered a special alveolar type 2 cell subtype characterized by high expression of CCL20 (referred to as ATII-CCL20), and found that IPF patients with a higher proportion of ATII-CCL20 had worse prognoses. Furthermore, we uncovered the upregulation of immune cell infiltration and metabolic functions in IPF patients with a higher proportion of ATII-CCL20. Finally, the comprehensive decision tree and nomogram were constructed to optimize the risk stratification of IPF patients and provide a reference for accurate prognosis evaluation.

CONCLUSIONS

Our study by integrating single-cell and bulk RNA sequence data from IPF patients identified a special subtype of ATII cells, ATII-CCL20, which was found to be a risk cell subtype associated with poor prognosis in IPF patients. More importantly, the ATII-CCL20 cell subtype was linked with metabolic functions and immune infiltration.

An Exosome-Related Long Non-coding RNA (lncRNA)-Based Signature for Prognosis and Therapeutic Interventions in Lung Adenocarcinoma

Background The poor prognosis of lung adenocarcinoma (LUAD) has been confirmed by a large number of studies, so it is necessary to construct a prognosis model. In addition, exosome is closely related to tumors, but there are few studies on exosome-related long non-coding RNA (lncRNA) (ExolncRNA). Methods In this study, we designed a prognostic model, exosome-related lncRNA-based signature (ExoLncSig), using ExolncRNA expression profiles of LUAD patients from The Cancer Genome Atlas (TCGA). ExolncRNAs were identified through univariate and multivariate and Lasso analyses. Subsequently, based on the ExoLncSig, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, immune function and immunotherapy analysis, drug screening, and so on were performed. Results AC026355.2, AC108136.1, AL590428.1, and LINC01312 were examined to establish the ExoLncSig. Gene enrichment analysis identified potential prognostic markers and therapeutic targets, including human leukocyte antigen (HLA), parainflammation, chemokine receptor (CCR), antigen-presenting cell (APC) co-inhibition, cancer-associated fibroblast (CAF), and myeloid-derived suppressor cell (MDSC). Moreover, we ascertained that the high-risk subgroup exhibits heightened susceptibility to pharmaceutical agents. Conclusion Our findings indicate that ExoLncSig holds promise as a valuable prognostic marker in LUAD. Furthermore, the immunogenic properties of ExolncRNAs may pave the way for the development of a therapeutic vaccine against LUAD.

High CX3CR1 expression predicts poor prognosis in paediatric acute myeloid leukaemia undergoing hyperleukocytosis

INTRODUCTION

Paediatric AML patients with hyperleukocytosis have a poor prognosis and higher early mortality. Therefore, more studies are needed to explore relevant prognostic indicators and develop effective prevention strategies for this type of childhood AML.

METHODS

All original data were obtained from the TARGET database. First, we explored meaningful differentially expressed genes (DEGs) between the hyperleukocytosis group and the non-hyperleukocytosis group. Next, we screened and identified valuable target genes using univariate Cox regression, Cytoscape software, and Kaplan-Meier survival curves. Finally, the coexpressed genes, functional networks, and immune-related activities associated with the target gene were deeply analysed by the GeneMANIA, LinkedOmics, GEPIA2021, TISIDB, and GSCA databases.

RESULTS

We selected 1229 DEGs between the hyperleukocytosis group and the non-hyperleukocytosis group in paediatric AML patients. Among them, 495 DEGs were significantly linked with the overall survival of paediatric AML patients. Further, we discovered that CX3CR1 was a promising target gene. Meanwhile, we identified CX3CR1 as an independent prognostic predictor. Besides, we showed that CX3CR1 had strong physical interactions with CX3CL1. Additionally, functional network analysis suggested that CX3CR1 and its coexpressed genes modulated immune response pathways. Subsequent analysis found that immune cells with a high median value of CX3CR1 were monocytes, resting NK cells and CD8 T cells. Finally, we observed that CX3CR1 expression correlated with infiltrating levels of immune cells and immune signatures.

CONCLUSION

Elevated CX3CR1 expression may be an adverse prognostic indicator in paediatric AML patients undergoing hyperleukocytosis. Moreover, CX3CR1 may serve as an immunotherapeutic target for AML with hyperleukocytosis in children.

A signature based on glycosyltransferase genes provides a promising tool for the prediction of prognosis and immunotherapy responsiveness in ovarian cancer

BACKGROUND

Ovarian cancer (OC) is the most fatal gynaecological malignancy and has a poor prognosis. Glycosylation, the biosynthetic process that depends on specific glycosyltransferases (GTs), has recently attracted increasing importance due to the vital role it plays in cancer. In this study, we aimed to determine whether OC patients could be stratified by glycosyltransferase gene profiles to better predict the prognosis and efficiency of immune checkpoint blockade therapies (ICBs).

METHODS

We retrieved transcriptome data across 420 OC and 88 normal tissue samples using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, respectively. An external validation dataset containing 185 OC samples was downloaded from the Gene Expression Omnibus (GEO) database. Knockdown and pathway prediction of B4GALT5 were conducted to investigate the function and mechanism of B4GALT5 in OC proliferation, migration and invasion.

RESULTS

A total of 50 differentially expressed GT genes were identified between OC and normal ovarian tissues. Two clusters were stratified by operating consensus clustering, but no significant prognostic value was observed. By applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, a 6-gene signature was built that classified OC patients in the TCGA cohort into a low- or high-risk group. Patients with high scores had a worse prognosis than those with low scores. This risk signature was further validated in an external GEO dataset. Furthermore, the risk score was an independent risk predictor, and a nomogram was created to improve the accuracy of prognostic classification. Notably, the low-risk OC patients exhibited a higher degree of antitumor immune cell infiltration and a superior response to ICBs. B4GALT5, one of six hub genes, was identified as a regulator of proliferation, migration and invasion in OC.

CONCLUSION

Taken together, we established a reliable GT-gene-based signature to predict prognosis, immune status and identify OC patients who would benefit from ICBs. GT genes might be a promising biomarker for OC progression and a potential therapeutic target for OC.

Tumor Immunogenic Cell Death as a Mediator of Intratumor CD8 T-Cell Recruitment

The success of anticancer treatments relies on a long-term response which can be mediated by the immune system. Thus, the concept of immunogenic cell death (ICD) describes the capacity of dying cancer cells, under chemotherapy or physical stress, to express or release danger-associated molecular patterns (DAMPs). These DAMPs are essential to activate dendritic cells (DCs) and to stimulate an antigen presentation to CD8 cytotoxic cells. Then, activated CD8 T cells exert their antitumor effects through cytotoxic molecules, an effect which is transitory due to the establishment of a feedback loop leading to T-cell exhaustion. This phenomenon can be reversed using immune checkpoint blockers (ICBs), such as anti-PD-1, PD-L1 or CTLA-4 Abs. However, the blockade of these checkpoints is efficient only if the CD8 T cells are recruited within the tumor. The CD8 T-cell chemoattraction is mediated by chemokines. Hence, an important question is whether the ICD can not only influence the DC activation and resulting CD8 T-cell activation but can also favor the chemokine production at the tumor site, thus triggering their recruitment. This is the aim of this review, in which we will decipher the role of some chemokines (and their specific receptors), shown to be released during ICD, on the CD8 T-cell recruitment and antitumor response. We will also analyze the clinical applications of these chemokines as predictive or prognostic markers or as new targets which should be used to improve patients' response.

Prognostic signature related to the immune environment of oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) prognosis remains poor. Here we aimed to identify an effective prognostic signature for predicting the survival of patients with OSCC. Gene-expression and clinical data were obtained from the Cancer Genome Atlas database. Immune microenvironment-associated genes were identified using bioinformatics. Subtype and risk-score analyses were performed for these genes. Kaplan–Meier analysis and immune cell infiltration level were explored in different subtypes and risk-score groups. The prognostic ability, independent prognosis, and clinical features of the risk score were assessed. Furthermore, immunotherapy response based on the risk score was explored. Finally, a conjoint analysis of the subtype and risk-score groups was performed to determine the best prognostic combination. We found 11 potential prognostic genes and constructed a risk-score model. The subtype cluster 2 and a high-risk group showed the worst overall survival; differences in survival status might be due to the different immune cell infiltration levels. The risk score showed good performance, independent prognostic value, and valuable clinical application. Higher risk scores showed higher Tumor Immune Dysfunction and Exclusion scores, indicating that patients with a high-risk score were less likely to benefit from immunotherapy. Finally, conjoint analysis for the subgroups and risk groups showed the best predictive ability.

Determination of a prediction model for therapeutic response and prognosis based on chemokine signaling-related genes in stage I–III lung squamous cell carcinoma

Background: The chemokine signaling pathway plays an essential role in the development, progression, and immune surveillance of lung squamous cell carcinoma (LUSC). Our study aimed to systematically analyze chemokine signaling-related genes (CSRGs) in LUSC patients with stage I–III disease and develop a prediction model to predict the prognosis and therapeutic response.

Methods: A total of 610 LUSC patients with stage I–III disease from three independent cohorts were included in our study. Least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox regression analyses were used to develop a CSRG-related signature. GSVA and GSEA were performed to identify potential biological pathways. The ESTIMATE algorithm, ssGSEA method, and CIBERSORT analyses were applied to explore the correlation between the CSRG signature and the tumor immune microenvironment. The TCIA database and pRRophetic algorithm were utilized to predict responses to immunochemotherapy and targeted therapy.

Results: A signature based on three CSRGs (CCL15, CXCL7, and VAV2) was developed in the TCGA training set and validated in the TCGA testing set and GEO external validation sets. A Kaplan–Meier survival analysis revealed that patients in the high-risk group had significantly shorter survival than those in the low-risk group. A nomogram combined with clinical parameters was established for clinical OS prediction. The calibration and DCA curves confirmed that the prognostic nomogram had good discrimination and accuracy. An immune cell landscape analysis demonstrated that immune score and immune-related functions were abundant in the high-risk group. Interestingly, the proportion of CD8 T-cells was higher in the low-risk group than in the high-risk group. Immunotherapy response prediction indicated that patients in the high-risk group had a better response to CTLA-4 inhibitors. We also found that patients in the low-risk group were more sensitive to first-line chemotherapeutic treatment and EGFR tyrosine kinase inhibitors. In addition, the expression of genes in the CSRG signature was validated by qRT‒PCR in clinical tumor specimens.

Conclusion: In the present study, we developed a CSRG-related signature that could predict the prognosis and sensitivity to immunochemotherapy and targeted therapy in LUSC patients with stage I–III disease. Our study provides an insight into the multifaceted role of the chemokine signaling pathway in LUSC and may help clinicians implement optimal individualized treatment for patients.

CCL20 promotes lung adenocarcinoma progression by driving epithelial-mesenchymal transition

C-C motif chemokine ligand 20 (CCL20) participates in multiple oncogenic processes, but its role in lung adenocarcinoma (LUAD) is unclear. Herein, we explored the mechanism by which CCL20 works in LUAD progression. We performed bioinformatical analyses based on the complete transcriptome sequencing data from 1544 LUAD cases in 4 independent cohorts to evaluate signaling pathways regulated by CCL20. We established A549 and H358 cell lines with CCL20 knockdown to explore how CCL20 promotes tumor progression in vitro and in vivo experiments. Using another independent cohort of 348 urothelial carcinoma patients treated with the anti-PD-L1 agent (atezolizumab), we explored the synergistic effect of CCL20 and TGF-β on immunotherapy efficacy. High CCL20 expression is a poor prognostic marker for LUAD patients, and is associated with enhanced epithelial-mesenchymal transition (EMT), inflammatory response, and activated TNF pathway in LUAD. CCL20 knockdown restrained the EMT process and cell proliferation of LUAD cells in vitro and in vivo. Low CCL20 expression blocked the detrimental effects of high TGF-β on survival and effectively improved patients' response to anti-PD-L1 therapy. Collectively, we revealed the underlying mechanisms by which CCL20 promotes LUAD progression based on the largest sample size. The synergistic inhibitory effect of CCL20 and TGF-β on immune-checkpoint blockade therapy efficacy provides new views of immunotherapy resistance.

Bioinformatics Analysis of Prognostic Significance and Immune Characteristics of CXC Chemokine Family in Patients with Lung Adenocarcinoma

Objective

To screen CXC chemokines related to the risk of lung adenocarcinoma (LUAD) using bioinformatics and construct a CXC-based prognostic risk model to improve the diagnosis and treatment of LUAD patients.

Methods

The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database were searched to obtain mRNA expression data and clinicopathological information of LUAD patients. CXC genes differentially expressed in LUAD were screened using the R packages. Further, risk factors significantly associated with the survival of LUAD patients were obtained by the univariate Cox proportional hazard regression, LASSO regression, and multivariate Cox proportional hazard regression analysis, following which a risk prediction model was constructed. The performance of the CXCL13-based model in predicting the prognosis of low-risk and high-risk effect LUAD patients was verified, and the association between the model and the degree of tumor immune cell infiltration was investigated.

Results

CXCL13 was significantly highly expressed in the cancer tissues of LUAD patients. The risk of death in patients with highly expressed CXCL13 was about 1.5 times higher than in those with lowly expressed CXCL13 (HR = 1.5153357). CXCL13-based risk scoring showed that the high-risk score of LUAD patients was significantly correlated with poor prognosis, but no relation between the two was found in the GEO validation sets, suggesting that this risk model may not be accurate enough. In addition, activated B cells, CD4+ T cells, CD8+ T cells, and dendritic cells were significantly positively correlated with the high risk of LUAD.

Conclusions

Although we found that a high expression of CXCL13 was associated with a high risk of death and immune cell infiltration and activation in LUAD patients, the CXCL13-based risk model was not accurate enough for predicting the prognosis of LUAD patients.

Comprehensive Analysis of a Cancer-Immunity Cycle-Based Signature for Predicting Prognosis and Immunotherapy Response in Patients With Colorectal Cancer

Immune checkpoint blockade (ICB) has been recognized as a promising immunotherapy for colorectal cancer (CRC); however, most patients have little or no clinical benefit. This study aimed to develop a novel cancer-immunity cycle–based signature to stratify prognosis of patients with CRC and predict efficacy of immunotherapy. CRC samples from The Cancer Genome Atlas (TCGA) were used as the training set, while the RNA data from Gene Expression Omnibus (GEO) data sets and real-time quantitative PCR (RT-qPCR) data from paired frozen tissues were used for validation. We built a least absolute shrinkage and selection operator (LASSO)-Cox regression model of the cancer-immunity cycle–related gene signature in CRC. Patients who scored low on the risk scale had a better prognosis than those who scored high. Notably, the signature was an independent prognostic factor in multivariate analyses, and to improve prognostic classification and forecast accuracy for individual patients, a scoring nomogram was created. The comprehensive results revealed that the low-risk patients exhibited a higher degree of immune infiltration, a higher immunoreactivity phenotype, stronger expression of immune checkpoint–associated genes, and a superior response to ICB therapy. Furthermore, the risk model was closely related to the response to multiple chemotherapeutic drugs. Overall, we developed a reliable cancer-immunity cycle–based risk model to predict the prognosis, the molecular and immune status, and the immune benefit from ICB therapy, which may contribute greatly to accurate stratification and precise immunotherapy for patients with CRC.

Clinical Significance and Immunometabolism Landscapes of a Novel Recurrence-Associated Lipid Metabolism Signature In Early-Stage Lung Adenocarcinoma: A Comprehensive Analysis

Background

The early-stage lung adenocarcinoma (LUAD) rate has increased with heightened public awareness and lung cancer screening implementation. Lipid metabolism abnormalities are associated with lung cancer initiation and progression. However, the comprehensive features and clinical significance of the immunometabolism landscape and lipid metabolism-related genes (LMRGs) in cancer recurrence for early-stage LUAD remain obscure.

Methods

LMRGs were extracted from Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Samples from The Cancer Genome Atlas (TCGA) were used as training cohort, and samples from four Gene Expression Omnibus (GEO) datasets were used as validation cohorts. The LUAD recurrence-associated LMRG molecular pattern and signature was constructed through unsupervised consensus clustering, time-dependent receiver operating characteristic (ROC), and least absolute shrinkage and selection operator (LASSO) analyses. Kaplan-Meier, ROC, and multivariate Cox regression analyses and prognostic meta-analysis were used to test the suitability and stability of the signature. We used Gene Ontology (GO), KEGG pathway, immune cell infiltration, chemotherapy response analyses, gene set variation analysis (GSVA), and GSEA to explore molecular mechanisms and immune landscapes related to the signature and the potential of the signature to predict immunotherapy or chemotherapy response.

Results

First, two LMRG molecular patterns were established, which showed diverse prognoses and immune infiltration statuses. Then, a 12-gene signature was identified, and a risk model was built. The signature remained an independent prognostic parameter in multivariate Cox regression and prognostic meta-analysis. In addition, this signature stratified patients into high- and low-risk groups with significantly different recurrence rates and was well validated in different clinical subgroups and several independent validation cohorts. The results of GO and KEGG analyses and GSEA showed that there were differences in multiple lipid metabolism, immune response, and drug metabolism pathways between the high- and low-risk groups. Further analyses revealed that the signature-based risk model was related to distinct immune cell proportions, immune checkpoint parameters, and immunotherapy and chemotherapy response, consistent with the GO, KEGG, and GSEA results.

Conclusions

This is the first lipid metabolism-based signature for predicting recurrence, and it could provide vital guidance to achieve optimized antitumor for immunotherapy or chemotherapy for early-stage LUAD.

Identification of an Autophagy-Related lncRNA Prognostic Signature and Related Tumor Immunity Research in Lung Adenocarcinoma

Autophagy is closely associated with the tumor immune microenvironment (TIME) and prognosis of patients with lung adenocarcinoma (LUAD). In the present study, we established a signature on the basis of long noncoding RNAs (lncRNAs) related to autophagy (ARlncRNAs) to investigate the TIME and survival of patients with LUAD. We selected ARlncRNAs associated with prognosis to construct a model and divided each sample into different groups on the basis of risk score. The ARlncRNA signature could be recognized as an independent prognostic factor for patients with LUAD, and patients in the low-risk group had a greater survival advantage. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analysis suggested that several immune functions and pathways were enriched in different groups. A high-risk score correlated significantly negatively with high abundance of immune cells and stromal cells around the tumor and high tumor mutational burden. Low-risk patients had a higher PD-1, CTLA-4, and HAVCR2 expression and had a better efficacy of immune checkpoint inhibitors, including PD-1/CTLA-4 inhibitor. A reliable signature on the basis of ARlncRNAs was constructed to explore the TIME and prognosis of patients with LUAD, which could provide valuable information for individualized LUAD treatment.

Single-Cell Transcriptomics Reveals the Expression of Aging- and Senescence-Associated Genes in Distinct Cancer Cell Populations

The human aging process is associated with molecular changes and cellular degeneration, resulting in a significant increase in cancer incidence with age. Despite their potential correlation, the relationship between cancer- and ageing-related transcriptional changes is largely unknown. In this study, we aimed to analyze aging-associated transcriptional patterns in publicly available bulk mRNA-seq and single-cell RNA-seq (scRNA-seq) datasets for chronic myelogenous leukemia (CML), colorectal cancer (CRC), hepatocellular carcinoma (HCC), lung cancer (LC), and pancreatic ductal adenocarcinoma (PDAC). Indeed, we detected that various aging/senescence-induced genes (ASIGs) were upregulated in malignant diseases compared to healthy control samples. To elucidate the importance of ASIGs during cell development, pseudotime analyses were performed, which revealed a late enrichment of distinct cancer-specific ASIG signatures. Notably, we were able to demonstrate that all cancer entities analyzed in this study comprised cell populations expressing ASIGs. While only minor correlations were detected between ASIGs and transcriptome-wide changes in PDAC, a high proportion of ASIGs was induced in CML, CRC, HCC, and LC samples. These unique cellular subpopulations could serve as a basis for future studies on the role of aging and senescence in human malignancies.