An Integrated Analysis of C5AR2 Related to Malignant Properties and Immune Infiltration of Breast Cancer

The Yin Yang of Complement and Cancer

Cancer-related inflammation is a crucial component of the tumor microenvironment (TME). Complement activation occurs in cancer and supports the development of an inflammatory microenvironment. Complement has traditionally been considered a mechanism of immune resistance against cancer, and its activation is known to contribute to the cytolytic effects of antibody-based immunotherapeutic treatments. However, several studies have recently revealed that complement activation may exert protumoral functions by sustaining cancer-related inflammation and immunosuppression through different molecular mechanisms, targeting both the TME and cancer cells. These new discoveries have revealed that complement manipulation can be considered a new strategy for cancer therapies. Here we summarize our current understanding of the mechanisms by which the different elements of the complement system exert antitumor or protumor functions, both in preclinical studies and in human tumorigenesis. Complement components can serve as disease biomarkers for cancer stratification and prognosis and be exploited for tumor treatment.

C5aR2 Regulates STING-Mediated Interferon Beta Production in Human Macrophages

The complement system mediates diverse regulatory immunological functions. C5aR2, an enigmatic receptor for anaphylatoxin C5a, has been shown to modulate PRR-dependent pro-inflammatory cytokine secretion in human macrophages. However, the specific downstream targets and underlying molecular mechanisms are less clear. In this study, CRISPR-Cas9 was used to generate macrophage models lacking C5aR2, which were used to probe the role of C5aR2 in the context of PRR stimulation. cGAS and STING-induced IFN-β secretion was significantly increased in C5aR2 KO THP-1 cells and C5aR2-edited primary human monocyte-derived macrophages, and STING and IRF3 expression were increased, albeit not significantly, in C5aR2 KO cell lines implicating C5aR2 as a regulator of the IFN-β response to cGAS-STING pathway activation. Transcriptomic analysis by RNAseq revealed that nucleic acid sensing and antiviral signalling pathways were significantly up-regulated in C5aR2 KO THP-1 cells. Altogether, these data suggest a link between C5aR2 and nucleic acid sensing in human macrophages. With further characterisation, this relationship may yield therapeutic options in interferon-related pathologies.

ForestSubtype: a cancer subtype identifying approach based on high-dimensional genomic data and a parallel random forest

BACKGROUND

Cancer subtype classification is helpful for personalized cancer treatment. Although, some approaches have been developed to classifying caner subtype based on high dimensional gene expression data, it is difficult to obtain satisfactory classification results. Meanwhile, some cancers have been well studied and classified to some subtypes, which are adopt by most researchers. Hence, this priori knowledge is significant for further identifying new meaningful subtypes.

RESULTS

In this paper, we present a combined parallel random forest and autoencoder approach for cancer subtype identification based on high dimensional gene expression data, ForestSubtype. ForestSubtype first adopts the parallel RF and the priori knowledge of cancer subtype to train a module and extract significant candidate features. Second, ForestSubtype uses a random forest as the base module and ten parallel random forests to compute each feature weight and rank them separately. Then, the intersection of the features with the larger weights output by the ten parallel random forests is taken as our subsequent candidate features. Third, ForestSubtype uses an autoencoder to condenses the selected features into a two-dimensional data. Fourth, ForestSubtype utilizes k-means++ to obtain new cancer subtype identification results. In this paper, the breast cancer gene expression data obtained from The Cancer Genome Atlas are used for training and validation, and an independent breast cancer dataset from the Molecular Taxonomy of Breast Cancer International Consortium is used for testing. Additionally, we use two other cancer datasets for validating the generalizability of ForestSubtype. ForestSubtype outperforms the other two methods in terms of the distribution of clusters, internal and external metric results. The open-source code is available at https://github.com/lffyd/ForestSubtype .

CONCLUSIONS

Our work shows that the combination of high-dimensional gene expression data and parallel random forests and autoencoder, guided by a priori knowledge, can identify new subtypes more effectively than existing methods of cancer subtype classification.

Complementing Testicular Immune Regulation: The Relationship between Sertoli Cells, Complement, and the Immune Response

Sertoli cells within the testis are instrumental in providing an environment for spermatogenesis and protecting the developing germ cells from detrimental immune responses which could affect fertility. Though these immune responses consist of many immune processes, this review focuses on the understudied complement system. Complement consists of 50+ proteins including regulatory proteins, immune receptors, and a cascade of proteolytic cleavages resulting in target cell destruction. In the testis, Sertoli cells protect the germ cells from autoimmune destruction by creating an immunoregulatory environment. Most studies on Sertoli cells and complement have been conducted in transplantation models, which are effective in studying immune regulation during robust rejection responses. In grafts, Sertoli cells survive activated complement, have decreased deposition of complement fragments, and express many complement inhibitors. Moreover, the grafts have delayed infiltration of immune cells and contain increased infiltration of immunosuppressive regulatory T cells as compared to rejecting grafts. Additionally, anti-sperm antibodies and lymphocyte infiltration have been detected in up to 50% and 30% of infertile testes, respectively. This review seeks to provide an updated overview of the complement system, describe its relationship with immune cells, and explain how Sertoli cells may regulate complement in immunoprotection. Identifying the mechanism Sertoli cells use to protect themselves and germ cells against complement and immune destruction is relevant for male reproduction, autoimmunity, and transplantation.

Identification of immune-related and autophagy-related genes for the prediction of survival in bladder cancer

Background

Bladder cancer has the characteristics of high morbidity and mortality, and the prevalence of bladder cancer has been increasing in recent years. Immune and autophagy related genes play important roles in cancer, but there are few studies on their effects on the prognosis of bladder cancer patients.

Methods

Using gene expression data from the TCGA-BLCA database, we clustered bladder cancer samples into 6 immune-related and autophagy-related molecular subtypes with different prognostic outcomes based on 2208 immune-related and autophagy-related genes. Six subtypes were divided into two groups which had significantly different prognosis. Differential expression analysis was used to explore genes closely related to the progression of bladder cancer. Then we used Cox stepwise regression to define a combination of gene expression levels and immune infiltration indexes to construct the risk model. Finally, we built a Nomogram which consist of risk score and several other prognosis-related clinical indicators.

Results

The risk model suggested that high expression of C5AR2, CSF3R, FBXW10, FCAR, GHR, OLR1, PGLYRP3, RASGRP4, S100A12 was associated with poor prognosis, while high expression level of CD96, IL10, MEFV pointed to a better prognosis. Validation by internal and external dataset suggested that our risk model had a high ability to discriminate between the outcomes of patients with bladder cancer. The immunohistochemical results basically confirmed our results. The C-Index value and Calibration curves verified the robustness of Nomogram.

Conclusions

Our study constructed a model that included a risk score for patients with bladder cancer, which provided a lot of helps to predict the prognosis of patients with bladder cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01073-7.

Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration

Accurate molecular subtypes prediction of cancer patients is significant for personalized cancer diagnosis and treatments. Large amount of multi-omics data and the advancement of data-driven methods are expected to facilitate molecular subtyping of cancer. Most existing machine learning–based methods usually classify samples according to single omics data, fail to integrate multi-omics data to learn comprehensive representations of the samples, and ignore that information transfer and aggregation among samples can better represent them and ultimately help in classification. We propose a novel framework named multi-omics graph convolutional network (M-GCN) for molecular subtyping based on robust graph convolutional networks integrating multi-omics data. We first apply the Hilbert–Schmidt independence criterion least absolute shrinkage and selection operator (HSIC Lasso) to select the molecular subtype-related transcriptomic features and then construct a sample–sample similarity graph with low noise by using these features. Next, we take the selected gene expression, single nucleotide variants (SNV), and copy number variation (CNV) data as input and learn the multi-view representations of samples. On this basis, a robust variant of graph convolutional network (GCN) model is finally developed to obtain samples’ new representations by aggregating their subgraphs. Experimental results of breast and stomach cancer demonstrate that the classification performance of M-GCN is superior to other existing methods. Moreover, the identified subtype-specific biomarkers are highly consistent with current clinical understanding and promising to assist accurate diagnosis and targeted drug development.