Establishment of a Macrophage Phenotypic Switch Related Prognostic Signature in Patients With Pancreatic Cancer

Identification of M2-like macrophage-related signature for predicting the prognosis, ecosystem and immunotherapy response in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma is one of the most common malignancies worldwide, representing a big health-care challenge globally. M2-like macrophages are significantly correlated with tumor progression, metastasis and treatment resistance.

METHODS

Integrative 10 machine learning algorithms were performed to developed a M2-like macrophage related prognostic signature (MRPS). Single-cell RNA-sequencing analysis was performed to dissect the ecosystem of HCC. Several approaches, including TIDE score, immunophenoscore, TMB score and tumor escape score were used to evaluate the predictive role of MRPS in immunology response.

RESULTS

The optimal MRPS constructed by the combination of stepCox + superPC algorithm served as an independent risk factor and showed stable and powerful performances in predicting the overall survival rate of HCC patients with 2-, 3-, and 4-year AUCs of 0. 763, 0.751, and 0.699 in TCGA cohort. HCC patients with low risk score possessed a more interaction of immunoactivated cells, including NK, CD8+ cytotoxic T, and activated B, and a less interaction of immunosuppressive cells, including Treg, CD4+ exhauster T, and M2-like macrophage. Low risk score indicated a higher PD1&CTLA4 immunophenoscore, higher TMB score, lower TIDE score and lower tumor escape score in HCC, suggesting a better immunotherapy response. The IC50 value of docetaxel, gemcitabine, crizotinib and Osimertinib in HCC with high risk score were lower versus that with low risk score. HCC patients with high risk score had a higher score of cancer-related hallmarks, including angiogenesis, DNA repair, EMT, glycolysis, and NOTCH signaling.

CONCLUSION

Our study proposed a novel MRPS for predicting the prognosis, ecosystem and immunotherapy response in HCC.

Integrative analysis of the SOX family-related prognostic signature and immunological infiltration in prostate cancer

Background

Prostate cancer (PCa) remains a major prevalent cancer worldwide and has a poor prognosis. The sex-determining region Y (SRY)-related high-mobility group (HMG) box (SOX) family is a series of transcription factors (TFs) involved in regulating many biological processes (BPs). In tumors, however, SOX genes are frequently deregulated. Tumorigenic deregulation took place at the transcriptional, translational, and posttranslational levels. This leads them to be correlated to tumor progression and poor clinical outcomes in PCa. Nevertheless, the SOX family prognostic role in PCa still needs further investigation.

Methods

A SOX family-related prognostic signature was developed by performing LASSO (Least absolute shrinkage and selection operator) Cox regression analysis. The construction of a lncRNA-miRNA-mRNA regulatory axis for PCa was performed using a ceRNA network.

Results

Upregulation was observed in the expression of SOX4/8/11/12/14, while downregulation was observed for SOX2/5/7/13/15/30 in PCa. Consensus clustering identified four clusters of PCa patients based on these differentially expressed SOX family members. The constructed SOX family-related prognostic signature, which includes five SOX family members (SOX5/8/11/12/30), performed well in predicting PCa-patient prognosis. B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cell immune infiltration levels had a significant association with PCa-patient risk scores. Based on additional analysis, a significant association was also suggested between SOX family expression and tumor mutational burden (TMB), microsatellite instability (MSI), and drug sensitivity. By constructing a ceRNA network, a lncRNA SGMS1-AS1/miR-194-5p/SOX5 regulatory axis was developed for PCa.

Conclusions

Herein, a SOX family-related prognostic signature was identified and was found to perform well in predicting PCa-patient prognosis. A lncRNA SGMS1-AS1/miR-194-5p/SOX5 regulatory axis was also identified for PCa progression.

Type IV P-Type ATPases: Recent Updates in Cancer Development, Progression, and Treatment

Adaptations of cancer cells for survival are remarkable. One of the most significant properties of cancer cells to prevent the immune system response and resist chemotherapy is the altered lipid metabolism and resulting irregular cell membrane composition. The phospholipid distribution in the plasma membrane of normal animal cells is distinctly asymmetric. Lipid flippases are a family of enzymes regulating membrane asymmetry, and the main class of flippases are type IV P-type ATPases (P4-ATPases). Alteration in the function of flippases results in changes to membrane organization. For some lipids, such as phosphatidylserine, the changes are so drastic that they are considered cancer biomarkers. This review will analyze and discuss recent publications highlighting the role that P4-ATPases play in the development and progression of various cancer types, as well as prospects of targeting P4-ATPases for anti-cancer treatment.

Immune gene signatures as prognostic criteria for cancer patients

Recently, the possibility of using immune gene signatures (IGSs) has been considered as a novel prognostic tool for numerous cancer types. State-of-the-art methods of genomic, transcriptomic, and protein analysis have allowed the identification of a number of immune signatures correlated to disease outcome. The major adaptive and innate immune components are the T lymphocytes and macrophages, respectively. Herein, we collected essential data on IGSs consisting of subsets of T cells and tumor-associated macrophages and indicating cancer patient outcomes. We discuss factors that can introduce errors in the recognition of immune cell types and explain why the significance of immune signatures can be interpreted with uncertainty. The unidirectional functions of cell types should be entirely addressed in the signatures constructed by the combination of innate and adaptive immune cells. The state of the antitumor immune response is the key basis for IGSs and should be considered in gene signature construction. We also analyzed immune signatures for the prediction of immunotherapy response. Finally, we attempted to explain the present-day limitations in the use of immune signatures as robust criteria for prognosis.

BIN1 in cancer: biomarker and therapeutic target

BACKGROUND

The bridging integrator 1 (BIN1) protein was originally identified as a pro-apoptotic tumor suppressor that binds to and inhibits oncogenic MYC transcription factors. BIN1 has complex physiological functions participating in endocytosis, membrane cycling, cytoskeletal regulation, DNA repair deficiency, cell-cycle arrest, and apoptosis. The expression of BIN1 is closely related to the development of various diseases such as cancer, Alzheimer's disease, myopathy, heart failure, and inflammation.

PURPOSE

Because BIN1 is commonly expressed in terminally differentiated normal tissues and is usually undetectable in refractory or metastatic cancer tissues, this differential expression has led us to focus on human cancers associated with BIN1. In this review, we discuss the potential pathological mechanisms of BIN1 during cancer development and its feasibility as a prognostic marker and therapeutic target for related diseases based on recent findings on its molecular, cellular, and physiological roles.

CONCLUSION

BIN1 is a tumor suppressor that regulates cancer development through a series of signals in tumor progression and microenvironment. It also makes BIN1 a feasible early diagnostic or prognostic marker for cancer.

Novel estrogen-responsive genes (ERGs) for the evaluation of estrogenic activity

Estrogen action is mediated by various genes, including estrogen-responsive genes (ERGs). ERGs have been used as reporter-genes and markers for gene expression. Gene expression profiling using a set of ERGs has been used to examine statistically reliable transcriptomic assays such as DNA microarray assays and RNA sequencing (RNA-seq). However, the quality of ERGs has not been extensively examined. Here, we obtained a set of 300 ERGs that were newly identified by six sets of RNA-seq data from estrogen-treated and control human breast cancer MCF-7 cells. The ERGs exhibited statistical stability, which was based on the coefficient of variation (CV) analysis, correlation analysis, and examination of the functional association with estrogen action using database searches. A set of the top 30 genes based on CV ranking were further evaluated quantitatively by RT-PCR and qualitatively by a functional analysis using the GO and KEGG databases and by a mechanistic analysis to classify ERα/β-dependent or ER-independent types of transcriptional regulation. The 30 ERGs were characterized according to (1) the enzymes, such as metabolic enzymes, proteases, and protein kinases, (2) the genes with specific cell functions, such as cell-signaling mediators, tumor-suppressors, and the roles in breast cancer, (3) the association with transcriptional regulation, and (4) estrogen-responsiveness. Therefore, the ERGs identified here represent various cell functions and cell signaling pathways, including estrogen signaling, and thus, may be useful to evaluate estrogenic activity.

Exploring the Potential of Pyroptosis-Related Genes in Predicting Prognosis and Immunological Characteristics of Pancreatic Cancer From the Perspective of Genome and Transcriptome

Objective

To probe into the role of pyroptosis-related genes in pancreatic carcinoma.

Methods

Herein, we conducted a comprehensive bioinformatics analysis to evaluate tumor-immune infiltration and tumor mutation burden, the correlations between PRGs, and microsatellite instability and found that 33 PRGS were up- or down-regulated in PC. Then we built the PPI network, which was downloaded from the STRING database. Using TCGA cohort median risk score, PC subjects from the Gene Expression Composite cohort (GEO) data resource were stratified into two risk categories, with the low-PC risk group harboring a higher overall survival (OS) (P = 0.011). We employed the ssGSEA approach to quantify immune cell abundance in separate risk groups separated by risk signature while assessing variations in immune cell invasion. Chemotherapeutic drugs were retrieved from the Genomics of Drug Sensitivity in Cancer (GDSC) data resource.

Results

Eight prognostic PRG models (CASP4, GSDMC, IL-18, NLRP1, NLRP2, PLCG1, TIRAP, and TNF) were established via LASSO Cox regression to estimate the OS of PC subjects with medium-to-high accuracy.

Conclusion

Our study is the first to identify a pyroptotic-related prognostic gene feature for PC, providing more options for the prognostic prediction of PC.

Role of ELMO1 in inflammation and cancer-clinical implications

BACKGROUND

Engulfment and cell motility protein 1 (ELMO1) is a key protein for innate immunity since it is required for the clearance of apoptotic cells and pathogenic bacteria as well as for the control of inflammatory responses. ELMO1, through binding with Dock180 and activation of the Rac1 signaling pathway, plays a significant role in cellular shaping and motility. Rac-mediated actin cytoskeletal rearrangement is essential for bacterial phagocytosis, but also plays a crucial role in processes such as cancer cell invasion and metastasis. While the role of ELMO1 in bacterial infection and inflammatory responses is well established, its implication in cancer is not widely explored yet. Molecular changes or epigenetic alterations such as DNA methylation, which ultimately leads to alterations in gene expression and deregulation of cellular signaling, has been reported for ELMO1 in different cancer types.

CONCLUSIONS

In this review, we provide an updated and comprehensive summary of the roles of ELMO1 in infection, inflammatory diseases and cancer. We highlight the possible mechanisms regulated by ELMO1 that are relevant for cancer development and progression and provide insight into the possible use of ELMO1 as a diagnostic biomarker and therapeutic target.

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

Background

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

Methods

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

Results

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

Conclusions

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

Systematic Analysis of an Invasion-Related 3-Gene Signature and Its Validation as a Prognostic Model for Pancreatic Cancer

Background

Pancreatic adenocarcinoma (PAAD) is a malignant tumor of the digestive system that is associated with a poor prognosis in patients owing to its rapid progression and high invasiveness.

Methods

Ninety-seven invasive-related genes obtained from the CancerSEA database were clustered to obtain the molecular subtype of pancreatic cancer based on the RNA-sequencing (RNA-seq) data of The Cancer Genome Atlas (TCGA). The differentially expressed genes (DEGs) between subtypes were obtained using the limma package in R, and the multi-gene risk model based on DEGs was constructed by Lasso regression analysis. Independent datasets GSE57495 and GSE62452 were used to validate the prognostic value of the risk model. To further explore the expression of the hub genes, immunohistochemistry was performed on PAAD tissues obtained from a large cohort.

Results

The TCGA-PAAD samples were divided into two subtypes based on the expression of the invasion-related genes: C1 and C2. Most genes were overexpressed in the C1 subtype. The C1 subtype was mainly enriched in tumor-related signaling pathways, and the prognosis of patients with the C1 subtype was significantly worse than those with the C2 subtype. A 3-gene signature consisting of LY6D, BCAT1, and ITGB6 based on 538 DEGs between both subtypes serves as a stable prognostic marker in patients with pancreatic cancer across multiple cohorts. LY6D, BCAT1, and ITGB6 were over-expressed in 120 PAAD samples compared to normal samples.

Conclusions

The constructed 3-gene signature can be used as a molecular marker to assess the prognostic risk in patients with PAAD.

The Cellular and Biological Impact of Extracellular Vesicles in Pancreatic Cancer

Simple Summary

The increased incidence and global failure of ongoing therapies project pancreatic cancer as the second deadliest cancer worldwide. While our knowledge of pancreatic cancer cells’ abilities and specificities has drastically improved based on multi-scaled omics, one must consider that much more remains to be uncovered on the role and impact of stromal cells and the established network of communication with tumor cells. This review article discusses how tumor cells communicate with the various cells composing the stroma and its implication in tumor cells’ abilities, PDA (pancreatic ductal adenocarcinoma) carcinogenesis and therapeutic response. We will focus on extracellular vesicles-mediated crosstalk and how this multifaceted dialogue impacts both cellular compartments and its subsequent impact on PDA biology.

Abstract

Deciphering the interactions between tumor and stromal cells is a growing field of research to improve pancreatic cancer-associated therapies and patients’ care. Indeed, while accounting for 50 to 90% of the tumor mass, many pieces of evidence reported that beyond their structural role, the non-tumoral cells composing the intra-tumoral microenvironment influence tumor cells’ proliferation, metabolism, cell death and resistance to therapies, among others. Simultaneously, tumor cells can influence non-tumoral neighboring or distant cells in order to shape a tumor-supportive and immunosuppressive environment as well as influencing the formation of metastatic niches. Among intercellular modes of communication, extracellular vesicles can simultaneously transfer the largest variety of signals and were recently reported as key effectors of cell–cell communication in pancreatic cancer, from its development to its evolution as well as its ability to resist available treatments. This review focuses on extracellular vesicles-mediated communication between different cellular components of pancreatic tumors, from the modulation of cellular activities and abilities to their biological and physiological relevance. Taking into consideration the intra-tumoral microenvironment and its extracellular-mediated crosstalk as main drivers of pancreatic cancer development should open up new therapeutic windows.