Complement and coagulation cascades pathway-related signature as a predictor of immunotherapy in metastatic urothelial cancer

Interplay between the complement system and other immune pathways in the tumor microenvironment

Tumor growth and spread are sustained by the tumor microenvironment. Inflammatory cells and pathways have a fundamental role in the tumor microenvironment, driving or conditioning the functional activation of other leukocyte subsets and favoring evasion of anti-tumor immunity. One of the inflammatory pathways contributing to cancer-related inflammation is the complement system. Complement has long been considered an immune mechanism associated with immunosurveillance. More recently it emerged as a tumor promoting pathway, due to direct effects on cancer cells or indirect effects via immunosuppression driven by myeloid cells. The role of complement in cancer is complex and ambiguous, and depends on the tumor type and stage, as well as other factors including oncogenic drivers, leukocyte infiltration, interactions with other tumor microenvironment components or tumor cells. Other factors of complexity include the source of complement molecules, its canonical or non-canonical extracellular functions, its potential intracellular activation, and the interaction with other systems, such as the coagulation or the microbiome. Preclinical studies generally demonstrate the involvement of complement activation in smouldering inflammation in cancer and promotion of an immunosuppressive environment. These studies paved the way for clinical trials aimed at enhancing the potential of immunotherapy, in particular by targeting complement-dependent myeloid-sustained immunosuppression. However, the complex role of complement in cancer and the multiplicity of complement players may represent stumbling blocks and account for failures of clinical trials, and suggest that further studies are required to identify patient subsets who may benefit from specific complement molecule targeting in combination with conventional therapies or immunotherapy. Here, we will discuss the anti- or pro-tumor role of complement activation in cancer, focusing on the interactions of complement with immune cells within the tumor microenvironment, in particular the myeloid compartment. Furthermore, we will examine the potential of complement targeting in cancer treatment, particularly in the context of macrophage reprogramming.

Clinical Significance of Complement and Coagulation Cascades Genes for Patients With Acute Lymphoblastic Leukemia

INTRODUCTION

Acute lymphoblastic leukemia (ALL) is the second most common acute leukemia in adults and the 5-year survival remains low.

METHODS

We analyzed the gene expression profiles of the complement and coagulation cascades pathway (CCCP) in 998 bone marrow (BM) and 122 peripheral blood (PB) samples of ALL patients and healthy individuals obtained from the TCGA database and evaluated their clinical significance in terms of being diagnostic and prognostic biomarkers.

RESULTS

We identified 18 CCCP genes (SERPINA1, C5AR1, F5, CD55, PLAUR, C3AR1, THBD, CD59, PLAU, VWF, CFD, F13A1, C1QA, C1QB, C1QC, A2M, SERPINE1 and CR2) differentially expressed in the BM samples of ALL patients compared to healthy individuals. The expression levels of CD55, F13A1 and CR2 in BM were linked with the overall survival of ALL patients. While in PB only 11 CCCP genes (e.g., SERPINA1, C5AR1, F5, PLAUR, C3AR1, THBD, CFD, F13A1, C1QA, SERPINE1, and CR2) were differentially expressed and F13A1 was significantly associated with ALL patient survival. Machine learning enabled us to predict ALL using the CCCP genes and the accuracy can reach 0.9701 and 0.9167 using the BM and PB, respectively. Furthermore, using single-cell RNA sequencing, we found that the differential expression of CCCP genes was found with diversity in the BM-derived immune cells of ALL patients.

CONCLUSION

Our findings suggest that the CCCP genes may play a key role in the progression of ALL and can be used as potential therapeutic targets and diagnostic markers.

Single-cell RNA sequencing and spatial transcriptome reveal potential molecular mechanisms of lung cancer brain metastasis

BACKGROUND

Lung cancer is a highly aggressive and prevalent disease worldwide. By the time it is first diagnosed, distant metastases have usually already occurred. Among them, the prognosis of patients with brain metastasis from lung cancer is very poor. Therefore, it is particularly important to identify the evolutionary status of tumor cells during lung cancer brain metastases and discover the underlying mechanisms of lung cancer brain metastases.

METHODS

In this study, we analysed three types of data: single-cell RNA sequencing, bulk RNA sequencing, and spatial transcriptome. Firstly, we identified early metastatic epithelial cell clusters (EMEC) using CNV and trajectory analysis in scRNA-seq data. Secondly, we integrated scRNA-seq and spatial transcriptome data with the help of MIA (Multimodal intersection analysis) to explore the biological characteristics of EMEC. Finally, we used bulk RNA-seq data to validate the molecular characteristics of EMEC.

RESULT

A total of 55,763 single cells were obtained and divided into 9 cell types. In brain metastasis, we found a significantly higher proportion of epithelial cells. In addition, we identified a specific subpopulation of epithelial cells, which was named as "early metastatic epithelial cell clusters (EMEC)". It is enriched in oxidative phosphorylation, coagulation, complement. Moreover, we also found that EMEC underwent cellular communication with other immune cells through ligand-receptor pairs such as MIF-(CD74 + CXCR4) and MIF-(CD74 + CD44). Next, we validated that EMEC were associated with poor clinical prognosis using three independent external datasets. Finally, spatial transcriptome analysis revealed specificity in the spatial distribution of EMEC, which shifted from the peripheral regions to the central regions of the tumour as the depth of tumor invasion progressed.

CONCLUSION

This study reveals the potential molecular mechanisms of lung cancer brain metastasis from both single-cell and spatial transcriptomic perspectives, providing biological insights and clinical reference value for detecting patients suffering from lung cancer brain metastasis.

Identification of novel diagnostic biomarkers associated with liver metastasis in colon adenocarcinoma by machine learning

BACKGROUND

Liver metastasis is one of the primary causes of poor prognosis in colon adenocarcinoma (COAD) patients, but there are few studies on its biomarkers.

METHODS

The Cancer Genome Atlas (TCGA)-COAD, GSE41258, and GSE49355 datasets were acquired from the public database. Differentially expressed genes (DEGs) between liver metastasis and primary tumor samples in COAD were identified by limma, and functional enrichment analysis were performed. MuTect2 and maftools were used to measure somatic mutation rates, while ADTEx was used to measure copy number variations (CNVs). The intersection of three machine learning methods, support vector machine (SVM), Random Forest, and least absolute shrinkage and selection operator (LASSO), is utilized to screen biomarkers, and their diagnostic performance is subsequently validated. The correlation between biomarkers and immune cells infiltration was analyzed by Spearman method.

RESULTS

47 DEGs between liver metastasis and primary tumor samples in COAD were obtained, which were mainly enriched in the complement and coagulation, extracellular matrix (ECM), and peptidase regulator activity, etc. 38 out of 47 DEGs had mutations and exhibited a high frequency of CNV amplification or deletion. Furthermore, 3 biomarkers (MMP3, MAB21L2, and COLEC11) were screened, which showed good diagnostic performance. The proportion of multiple immune cells, such as B cells naive, T cells CD4 naive, Monocytes, and Dendritic cells resting, was higher in liver metastasis samples than that in primary tumor samples. Meanwhile, MMP3, MAB21L2, and COLEC11 exhibited an outstanding correlation with immune cells infiltration.

CONCLUSION

In short, 3 biomarkers with good diagnostic efficacy were identified, providing a new perspective of therapeutic targets for liver metastasis in COAD.

Antitumor Effects and the Potential Mechanism of 10-HDA against SU-DHL-2 Cells

10-hydroxy-2-decenoic acid (10-HDA), which is a unique bioactive fatty acid of royal jelly synthesized by nurse bees for larvae and adult queen bees, is recognized for its dual utility in medicinal and nutritional applications. Previous research has indicated that 10-HDA exerts antitumor effects on numerous tumor cell lines, including colon cancer cells, A549 human lung cancer cells, and human hepatoma cells. The present study extends this inquiry to lymphoma, specifically evaluating the impact of 10-HDA on the SU-DHL-2 cell line. Our findings revealed dose-dependent suppression of SU-DHL-2 cell survival, with an IC50 of 496.8 μg/mL at a density of 3 × 106 cells/well after 24 h. For normal liver LO2 cells and human fibroblasts (HSFs), the IC50 values were approximately 1000 μg/mL and over 1000 μg/mL, respectively. The results of label-free proteomics revealed 147 upregulated and 347 downregulated differentially expressed proteins that were significantly enriched in the complement and coagulation cascades pathway (adjusted p-value = 0.012), including the differentially expressed proteins prothrombin, plasminogen, plasminogen, carboxypeptidase B2, fibrinogen beta chain, fibrinogen gamma chain, and coagulation factor V. The top three hub proteins, ribosomal protein L5, tumor protein p53, and ribosomal protein L24, were identified via protein-protein interaction (PPI) analysis. This result showed that the complement and coagulation cascade pathways might play a key role in the antitumor process of 10-HDA, suggesting a potential therapeutic avenue for lymphoma treatment. However, the specificity of the effect of 10-HDA on SU-DHL-2 cells warrants further investigation.

PD1/PD-L1 blockade in clear cell renal cell carcinoma: mechanistic insights, clinical efficacy, and future perspectives

The advent of PD1/PD-L1 inhibitors has significantly transformed the therapeutic landscape for clear cell renal cell carcinoma (ccRCC). This review provides an in-depth analysis of the biological functions and regulatory mechanisms of PD1 and PD-L1 in ccRCC, emphasizing their role in tumor immune evasion. We comprehensively evaluate the clinical efficacy and safety profiles of PD1/PD-L1 inhibitors, such as Nivolumab and Pembrolizumab, through a critical examination of recent clinical trial data. Furthermore, we discuss the challenges posed by resistance mechanisms to these therapies and potential strategies to overcome them. We also explores the synergistic potential of combination therapies, integrating PD1/PD-L1 inhibitors with other immunotherapies, targeted therapies, and conventional modalities such as chemotherapy and radiotherapy. In addition, we examine emerging predictive biomarkers for response to PD1/PD-L1 blockade and biomarkers indicative of resistance, providing a foundation for personalized therapeutic approaches. Finally, we outline future research directions, highlighting the need for novel therapeutic strategies, deeper mechanistic insights, and the development of individualized treatment regimens. Our work summarizes the latest knowledge and progress in this field, aiming to provide a valuable reference for improving clinical efficacy and guiding future research on the application of PD1/PD-L1 inhibitors in ccRCC.

Proteomic profiling and biomarker discovery for predicting the response to PD-1 inhibitor immunotherapy in gastric cancer patients

Background: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment; however, a significant proportion of gastric cancer (GC) patients do not respond to this therapy. Consequently, there is an urgent need to elucidate the mechanisms underlying resistance to ICIs and identify robust biomarkers capable of predicting the response to ICIs at treatment initiation. Methods: In this study, we collected GC tissues from 28 patients prior to the administration of anti-programmed death 1 (PD-1) immunotherapy and conducted protein quantification using high-resolution mass spectrometry (MS). Subsequently, we analyzed differences in protein expression, pathways, and the tumor microenvironment (TME) between responders and non-responders. Furthermore, we explored the potential of these differences as predictive indicators. Finally, using machine learning algorithms, we screened for biomarkers and constructed a predictive model. Results: Our proteomics-based analysis revealed that low activity in the complement and coagulation cascades pathway (CCCP) and a high abundance of activated CD8 T cells are positive signals corresponding to ICIs. By using machine learning, we successfully identified a set of 10 protein biomarkers, and the constructed model demonstrated excellent performance in predicting the response in an independent validation set (N = 14; area under the curve [AUC] = 0.959). Conclusion: In summary, our proteomic analyses unveiled unique potential biomarkers for predicting the response to PD-1 inhibitor immunotherapy in GC patients, which may provide the impetus for precision immunotherapy.

Investigation of cellular communication and signaling pathways in tumor microenvironment for high TP53-expressing osteosarcoma cells through single-cell RNA sequencing

Osteosarcoma (OS) stands as the most prevalent primary bone cancer in children and adolescents, and its limited treatment options often result in unsatisfactory outcomes, particularly for metastatic cases. The tumor microenvironment (TME) has been recognized as a crucial determinant in OS progression. However, the intercellular dynamics between high TP53-expressing OS cells and neighboring cell types within the TME are yet to be thoroughly understood. In our study, we harnessed the single-cell RNA sequencing (scRNA-seq) technology in combination with the computational tool-Cellchat, aiming to elucidate the intercellular communication networks present within OS. Through meticulous quantitative inference and subsequent analysis of these networks, we succeeded in identifying significant signaling pathways connecting high TP53-expressing OS cells with proximate cell types, namely Macrophages, Monocytes, Endothelial Cells, and PVLs. This research brings forth a nuanced understanding of the intricate patterns and coordination involved in the TME's intercellular communication signals. These findings not only provide profound insights into the molecular mechanisms underpinning OS but also indicate potential therapeutic targets that could revolutionize treatment strategies.

A pan-cancer analysis of TNFAIP8L1 in human tumors

TNFAIP8L1, as a recently identified member in TNFAIP8 family, plays an important role in tumorigenesis. However, a pan-cancer analysis of TNFAIP8L1 in human tumors has not been conducted until now. The main purpose of study is to investigate TNFAIP8L1 during 33 different types of human tumors by using TCGA and GTEx. The pan-cancer analysis showed that TNFAIP8L1 was significantly over-expressed in 15 cancers and low-expressed in 9 cancers. There were distinct relations between TNFAIP8L1 expression and prognosis of patients with cancer. Furthermore, we also found that DNA methylation and RNA modification of TNFAIP8L1 were associated with many cancers. And then, we detected that TNFAIP8L1 level was positively associated with cancer-associated fibroblasts (CAFs) in many tumors. And, we obtained that TNFAIP8L1 expression was related with most of immune inhibitory and stimulatory genes in multiple types of tumors. We also found TNFAIP8L1 expression was correlated with most of chemokine, receptor, MHC, immunoinhibitor and immunostimulator gens in most of cancers. Moreover, we detected TNFAIP8L1 expression was associated with TMB and MSI in several tumors. Finally, TNFAIP8L1 gene had a significant positive association with 5 genes including BCL6B, DLL4, PCDH12, COL4A1 and DLL4 in the majority of tumors. GO enrichment and KEGG pathway analyses showed that TNFAIP8L1 in thepathogenesis of cancer may be related to "purine nucleoside binding," "purine ribonucleoside binding," "ECM-receptor interaction," etc. Our first pan-cancer study may provide a deep comprehending of TNFAIP8L1 in tumoeigenesis from different tumors.