CIBERSORT analysis of TCGA and METABRIC identifies subgroups with better outcomes in triple negative breast cancer

Transcriptional regulation of DLGAP5 by AR suppresses p53 signaling and inhibits CD8+T cell infiltration in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a challenging subtype with unclear biological mechanisms. Recently, the transcription factor androgen receptor (AR) and its regulation of the DLGAP5 gene have gained attention in TNBC pathogenesis. In this study, we found a positive correlation between high AR expression and TNBC cell proliferation and growth. Furthermore, we confirmed DLGAP5 as a critical downstream regulator of AR with high expression in TNBC tissues. Knockdown of DLGAP5 significantly inhibited TNBC cell proliferation, migration, and invasion. AR was observed to directly bind to the DLGAP5 promoter, enhancing its transcriptional activity and suppressing the activation of the p53 signaling pathway. In vivo experiments further validated that downregulation of AR or DLGAP5 inhibited tumor growth and enhanced CD8+T cell infiltration. This study highlights the crucial roles of AR and DLGAP5 in TNBC growth and immune cell infiltration. Taken together, AR inhibits the p53 signaling pathway by promoting DLGAP5 expression, thereby impacting CD8+T cell infiltration in TNBC.

Bioinformatics data combined with single-cell analysis reveals patterns of immunoinflammatory infiltration and cell death in melanoma

BACKGRUOND

Melanoma is a common cancer in dermatology, but its molecular mechanisms remain poorly explained.

AIM

Utilizing single-cell analytics and bioinformatics, the work sought to discover the immunological infiltration and cellular molecular mechanisms of melanoma.

METHODS

Melanoma genes databases were downloaded from GeneCards, and gene expression profiles were chosen from the Gene Expression Omnibus (GSE244889). Establishing and analyzing protein-protein interaction networks for functional enrichment made use of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases. The process assesses the immunological cell infiltration variations between normal and malignant samples by Immune Cell AI software program. Different cell type differences were clarified by cell quality control, filtration, removal of batch effects and cell clustering analysis using single cell analysis techniques.

RESULTS

Using a variety of machine learning techniques, 20 differentially expressed hub genes were found; among these, TP53, HSP90AB1, HSPA4, RHOA, CCND1, CYCS, PPARG, NFKBIA, CAV1, ANXA5, ENO1, ITGAM, YWHAZ, RELA, SOD1, and VDAC1 were found to be significantly significant. The results of enrichment analysis demonstrated that immune response and inflammatory response were strongly associated with melanoma. Animal mitophagy, ferroptosis, the PI3K-Akt signaling pathway, and the HIF-1 signaling pathway were the primary signaling pathways implicated. Cells of immunity, T-cells, lymphocytes, B-cells, NK-cells, monocytes, and macrophages were shown to be significantly infiltrated in melanoma patients, according to analysis. Single cell analysis also demonstrated that ferroptosis is a significant mechanism of cell death that contributes to the advancement of melanoma and that macrophages are important in the disease.

CONCLUSION

In summary, different immune cell infiltrations-particularly macrophages-have a significant impact on the onset and course of melanoma, and our findings may help direct future investigations into melanoma macrophages.

LEF1 is associated with immunosuppressive microenvironment of patients with lung adenocarcinoma

Wnt/β-Catenin pathway plays an important role in the occurrence and progression of malignant tumors, especially PD-L1-mediated tumor immune evasion. However, the role of TCF/LEF, an important member of the Wnt/β-catenin pathway, in the tumor immunosuppressive microenvironment of lung adenocarcinoma (LUAD) remains unknown. LUAD tissue-coding RNA expression data from The Cancer Genome Atlas and TIMER databases were used to analyze the expression of TCF/LEF transcription factors and their correlation with various immune cell infiltration. Immunohistochemistry and immunofluorescence were used to detect tissue protein staining in 105 patients with LUAD. LEF1, TCF7, TCF7L1 and TCF7L2 were all aberrantly expressed in the tumor tissues of LUAD patients with the data from The Cancer Genome Atlas (TCGA) database, tumor immune estimation resource (TIMER) database and results of immunohistochemistry, but only LEF1 expression was associated with 5-year overall survival in LUAD patients. LEF1 protein expression was associated with advanced tumor node metastasis (TNM) stage, lymphatic metastasis and local invasion in 105 cases LUAD patients. At the same time, LEF1 mRNA expression was also associated with immunosuppressive microenvironment in LUAD patients with the data from TCGA database and TIMER database. Results of immunohistochemistry and immunofluorescence in tumor tissues of 105 cases LUAD patients showed that there was a positively correlation between LEF1 protein expression and the infiltration of M2 macrophages and Treg cells. LEF1 was highly expressed in tumor tissues of LUAD patients, and highly expressed LEF1 was associated with the immunosuppressive microenvironment of LUAD patients.

Cancer cell - Fibroblast crosstalk via HB-EGF, EGFR, and MAPK signaling promotes the expression of macrophage chemo-attractants in squamous cell carcinoma

Interactions between cells in the tumor microenvironment (TME) shape cancer progression and patient prognosis. To gain insights into how the TME influences cancer outcomes, we derive gene expression signatures indicative of signaling between stromal fibroblasts and cancer cells, and demonstrate their prognostic significance in multiple and independent squamous cell carcinoma cohorts. By leveraging information within the signatures, we discover that the HB-EGF/EGFR/MAPK axis represents a hub of tumor-stroma crosstalk, promoting the expression of CSF2 and LIF and favoring the recruitment of macrophages. Together, these analyses demonstrate the utility of our approach for interrogating the extent and consequences of TME crosstalk.

Plasma Cytokines Pattern as a Prognostic Marker for Esophageal Squamous Cell Carcinoma via Unsupervised Clustering Analyses

INTRODUCTION

Cytokines such as tumor necrosis factor-alpha (TNF-α), interleukin 6 (IL6), interferon-gamma (IFN-γ), interleukin 17-alpha (IL17-α), and interleukin 33 (IL33) play critical roles in immune responses and may impact cancer prognosis in future. However, few studies have simultaneously explored the prognostic impact of these cytokines for cancer. In this study, we aim to apply the unsupervised clustering analysis to approach the correlation between the expression of these cytokines and the subsequent prognosis of patients with esophageal squamous cell carcinoma (ESCC).

METHODS

A robust clustering algorithm was used, the Gaussian mixture method (GMM), through the mclust R package to group patients based on the expression of their cytokines in plasma or tumors. The 324 NTU patients were grouped into 4 clusters, and the 179 GSE53625 patients were grouped into 3 clusters based on expression in plasma and tumors, respectively. Five- and 3-year overall survival (OS) and progression-free survival (PFS) curves of each cluster were compared. Univariate and multivariate Cox regression analyses were also performed.

RESULTS

We successfully distinguished the multimodal distribution of cytokines through GMM clustering and discovered the relationship between cytokines and clinical outcomes. We observed that NTU-G3 and NTU-G4 subgroups showed most variation in 5-, 3-year OS and 5-, 3-year PFS with NTU-G3 being associated with poorer prognosis compared to NTU-G4 (p = 0.016, 0.0052, 0.0575, and 0.0168, respectively). NTU-G3 was characterized with higher TNF-α (median = 3.855, N = 78) and lower IL33 (median = 0.000, N = 78), while NTU-G4 showed lower TNF-α (median = 1.76, N = 51) and higher IL33 (median = 1.070, N = 51). The difference was statistically significant for TNF-α and IL33, with p = 0.0002 and p < 0.0001, respectively. A multivariate Cox-regression analysis revealed that GMM clustering and T/N stage were independent factors for prognosis, suggesting that the prognosis might be dependent on these cytokines.

CONCLUSIONS

Our data suggest that expression patterns of IL33 and TNF-α in plasma might serve as a convenient marker to predict the prognosis of ESCC in the future.

Syn-COM: A Multi-Level Predictive Synergy Framework for Innovative Drug Combinations

Drug prediction and treatment using bioinformatics and large-scale modeling have emerged as pivotal research areas. This study proposes a novel multi-level collaboration framework named Syn-COM for feature extraction and data integration of diseases and drugs. The framework aims to explore optimal drug combinations and interactions by integrating molecular virtuality, similarity clustering, overlap area, and network distance. It uniquely combines the characteristics of Chinese herbal medicine with clinical experience and innovatively assesses drug interaction and correlation through a synergy matrix. Gouty arthritis (GA) was used as a case study to validate the framework's reliability, leading to the identification of an effective drug combination for GA treatment, comprising Tamaricis Cacumen (Si = 0.73), Cuscutae Semen (Si = 0.68), Artemisiae Annuae Herba (Si = 0.62), Schizonepetae Herba (Si = 0.73), Gleditsiae Spina (Si = 0.89), Prunellae Spica (Si = 0.75), and Achyranthis Bidentatae Radix (Si = 0.62). The efficacy of the identified drug combination was confirmed through animal experiments and traditional Chinese medicine (TCM) component analysis. Results demonstrated significant reductions in the blood inflammatory factors IL1A, IL6, and uric acid, as well as downregulation of TGFB1, PTGS2, and MMP3 expression (p < 0.05), along with improvements in ankle joint swelling in GA mice. This drug combination notably enhances therapeutic outcomes in GA by targeting key genes, underscoring the potential of integrating traditional medicine with modern bioinformatics for effective disease treatment.

Evolutionary Mechanism Based Conserved Gene Expression Biclustering Module Analysis for Breast Cancer Genomics

The identification of significant gene biclusters with particular expression patterns and the elucidation of functionally related genes within gene expression data has become a critical concern due to the vast amount of gene expression data generated by RNA sequencing technology. In this paper, a Conserved Gene Expression Module based on Genetic Algorithm (CGEMGA) is proposed. Breast cancer data from the TCGA database is used as the subject of this study. The p-values from Fisher's exact test are used as evaluation metrics to demonstrate the significance of different algorithms, including the Cheng and Church algorithm, CGEM algorithm, etc. In addition, the F-test is used to investigate the difference between our method and the CGEM algorithm. The computational cost of the different algorithms is further investigated by calculating the running time of each algorithm. Finally, the established driver genes and cancer-related pathways are used to validate the process. The results of 10 independent runs demonstrate that CGEMGA has a superior average p-value of 1.54 × 10-4 ± 3.06 × 10-5 compared to all other algorithms. Furthermore, our approach exhibits consistent performance across all methods. The F-test yields a p-value of 0.039, indicating a significant difference between our approach and the CGEM. Computational cost statistics also demonstrate that our approach has a significantly shorter average runtime of 5.22 × 100 ± 1.65 × 10-1 s compared to the other algorithms. Enrichment analysis indicates that the genes in our approach are significantly enriched for driver genes. Our algorithm is fast and robust, efficiently extracting co-expressed genes and associated co-expression condition biclusters from RNA-seq data.

Analysis of macrophage polarization and regulation characteristics in ovarian tissues of polycystic ovary syndrome

Background

Polycystic ovary syndrome (PCOS) can lead to infertility and increase the risk of endometrial cancer. Analyzing the macrophage polarization characteristics in ovarian tissues of PCOS is crucial for clinical treatment.

Methods

We obtained 13 PCOS and nine control ovarian samples from the CEO database and analyzed differentially expressed genes (DEGs). Macrophage polarization-related genes (MPRGs) were sourced from the GeneCards and MSigDB databases. Intersection of DEGs with MPRGs identified DEGs associated with macrophage polarization (MPRDEGs). Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Protein-protein interaction (PPI) Network analysis were conducted on MPRDEGs. Moreover, the top 10 genes from three algorithms were identified as the hub genes of MPRGs. In addition, miRNAs, transcription factors (TFs), and drugs were retrieved from relevant databases for regulatory network analysis of mRNA-miRNA, mRNA-TF, and mRNA-Drug interactions. Immune cell composition analysis between the PCOS and control groups was performed using the CIBERSORT algorithm to calculate correlations across 22 immune cell types.

Results

A total of 13 PCOS samples and nine control ovarian samples were obtained in this study. We identified 714 DEGs between the two groups, with 394 up-regulated and 320 down-regulated. Additionally, we identified 774 MPRGs, from which we derived 30 MPRDEGs by intersecting with DEGs, among which 21 exhibited interaction relationships. GO and KEGG analyses revealed the enrichment of MPRDEGs in five biological processes, five cell components, five molecular functions, and three biological pathways. Immune infiltration analysis indicated a strong positive correlation between activated nature killer (NK) cells and memory B cells, while neutrophils and monocytes showed the strongest negative correlation. Further investigation of MPRDEGs identified nine hub genes associated with 41 TFs, 82 miRNAs, and 44 drugs or molecular compounds. Additionally, qRT-PCR results demonstrated overexpression of the CD163, TREM1, and TREM2 genes in ovarian tissues from the PCOS group.

Conclusion

This study elucidated the polarization status and regulatory characteristics of macrophages in ovarian tissues of the PCOS subjects, confirming significant overexpression of CD163, TREM1, and TREM2. These findings contribute to a deeper understanding of the pathogenesis of PCOS.

Sex-specific associations of Notch signaling with chronic HBV infection: a study from Taiwan Biobank

BACKGROUND

Hepatitis B, a liver infection caused by the hepatitis B virus (HBV), can develop into a chronic infection that puts patients at high risk of death from cirrhosis and liver cancer. In this study, we aimed to investigate the difference of reactome pre-Notch expression and processing between males and females by using gene to function analysis in FUMA.

METHODS

We analyzed Taiwan Biobank (TWB) data pertaining to 48,874 women and 23,178 men individuals which were collected from 2008 to 2019. According to hepatitis B surface antigen (HBsAg) status in hematology, positive and negative were classified into case and control in the genome-wide association study (GWAS) analysis.

RESULTS

We found 4715 women and 2656 men HBV cases. The genomic risk loci were different between males and females. In male, three risk loci (rs3732421, rs1884575 and Affx-28516147) were detected while eight risk loci (Affx-4564106, rs932745, rs7574865, rs34050244, rs77041685, rs107822, rs2296651 and rs12599402) were found in female. In addition, sex also presented different results. In females, the most significant SNPs are gathered in chromosome 6. However, except for chromosome 6, significant HBV infection SNPs also could be found in chromosome 3 among males. We further investigated gene function in FUMA to identify the difference in reactome pre-Notch expression and processing between males and females. We found that POGLUT1 and HIST1H2BC only appeared in men but not in women.

CONCLUSION

According to our study, the reactome pre-Notch expression including POGLUT1 and HIST1H2BC was associated with a risk of Hepatitis B in Taiwanese men when compared to women.

Experimental confirmation and bioinformatics reveal biomarkers of immune system infiltration and hypertrophy ligamentum flavum

Background

Hypertrophy ligamentum flavum is a prevalent chronic spinal condition that affects middle-aged and older adults. However, the molecular pathways behind this disease are not well comprehended.

Objective

The objective of this work is to implement bioinformatics techniques in order to identify crucial biological markers and immune infiltration that are linked to hypertrophy ligamentum flavum. Further, the study aims to experimentally confirm the molecular mechanisms that underlie the hypertrophy ligamentum flavum.

Methods

The corresponding gene expression profiles (GSE113212) were selected from a comprehensive gene expression database. The gene dataset for hypertrophy ligamentum flavum was acquired from GeneCards. A network of interactions between proteins was created, and an analysis of functional enrichment was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases. An study of hub genes was performed to evaluate the infiltration of immune cells in patient samples compared to tissues from the control group. Finally, samples of the ligamentum flavum were taken with the purpose of validating the expression of important genes in a clinical setting.

Results

Overall, 27 hub genes that were differently expressed were found through molecular biology. The hub genes were found to be enriched in immune response, chemokine-mediated signaling pathways, inflammation, ossification, and fibrosis processes, as demonstrated by GO and KEGG studies. The main signaling pathways involved include the TNF signaling pathway, cytokine-cytokine receptor interaction, and TGF-β signaling pathway. An examination of immunocell infiltration showed notable disparities in B cells (naïve and memory) and activated T cells (CD4 memory) between patients with hypertrophic ligamentum flavum and the control group of healthy individuals. The in vitro validation revealed markedly elevated levels of ossification and fibrosis-related components in the hypertrophy ligamentum flavum group, as compared to the normal group.

Conclusion

The TGF-β signaling pathway, TNF signaling pathway, and related hub genes play crucial roles in the progression of ligamentum flavum hypertrophic. Our study may guide future research on fibrosis of the ligamentum flavum.

Differences in T cell-associated serum markers between ischemic cardiomyopathy and dilated cardiomyopathy

Background

Ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM) have similar clinical manifestations but differ in pathogenesis. We aimed to identify T cell-associated serum markers that can be used to distinguish between ICM and DCM.

Methods

We identified differentially expressed genes (DEGs) with transcriptome sequencing data in GSE116250, and then conducted enrichment analysis of DEGs in the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Protein-protein interaction (PPI) networks were used to analyze the relationship between T cells-related genes and identify hub genes. Enzyme-linked immunosorbent assay (ELISA) kits were used to detect T cell-associated proteins in serum, and receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of these serum markers.

Results

Using the limma package and Venn plots, we found that the non-failing donors (NFD) and DCM groups shared many of the same DEGs and DEGs-enriched functions compared to the ICM group, which were involved in T cell activation and differentiation, among other functions. Subsequently, the immune cell score showed no difference between NFD and DCM, but they were significantly different from ICM patients in CD8 T cells CD4 T cells memory resting and activated, T cells follicular helper, and M1 macrophage. After analyzing T cell-associated DEGs, it was found that 4 DEGs encoding secreted proteins were highly expressed in the ICM group compared with the NFD and DCM groups, namely chemokine (C-C motif) ligand 21 (CCL21), interleukin (IL)-1β, lymphocyte-activation gene 3 (LAG3), and vascular cell adhesion molecule-1 (VCAM-1). Importantly, the serum levels of CCL21, IL-1β, LAG3, and VCAM-1 in ICM patients were all significantly higher than those in DCM patients. The ROC curves showed that the area under the curve (AUC) values of serum CCL21, IL-1β, LAG3, and VCAM-1 were 0.775, 0.868, 0.934, and 0.903, respectively.

Conclusions

We have identified four T cell-associated serum markers, CCL21, IL-1β, LAG3, and VCAM-1, as potential diagnostic serum markers that differentiate ICM from DCM.

MAPKAP1 orchestrates macrophage polarization and lipid metabolism in fatty liver-enhanced colorectal cancer

Various factors, including fatty liver and macrophage alterations, influence colorectal cancer (CRC). This study explores the mechanistic role of fatty liver in CRC progression, focusing on macrophage polarization and lipid metabolism. A murine fatty liver model was created with a high-fat diet (HFD), and CRC was induced using AOM and DSS. Single-cell transcriptome sequencing (scRNA-seq) identified MAPKAP1 as a critical gene promoting CRC via M2 macrophage polarization and lipid metabolism reprogramming. Prognosis analysis on the TCGA-CRC dataset confirmed MAPKAP1's significance. In vitro and in vivo experiments demonstrated that EVs from fatty liver cells enhanced MAPKAP1 expression, accelerating CRC development and metastasis. HFD exacerbated CRC, but fatty acid inhibitors delayed progression. Fatty liver upregulates MAPKAP1, driving M2 macrophage polarization and lipid metabolism changes, worsening CRC. These findings suggest potential therapeutic strategies for CRC, particularly targeting lipid metabolism and macrophage-mediated tumor promotion.

Expression and function of cytokine interleukin-22 gene in the tumor microenvironment of triple negative breast cancer

BACKGROUND

The tumor microenvironment (TME) and interleukin-22 (IL-22) in cytokines have recently attracted much attention due to their potential impact on tumor biology. However, the role of IL-22 in triple negative breast cancer (TNBC) TME is still poorly understood. This article investigated the gene expression and function of IL-22 in TNBC TME.

METHODS

Tumor samples from TNBC patients were collected, and adjacent noncancerous tissues were used as controls. A functional test was performed to evaluate the impact of IL-22 for TNBC cells, including proliferation, migration, and apoptosis.

RESULTS

IL-22 gene expression in TNBC tumor samples was markedly higher relative to adjacent non-cancerous tissues (P < 0.05). In addition, it was also observed that IL-22facilitated proliferation and migration of TNBC cells, and inhibit apoptosis. This article reveals the role of IL-22 in the TME of TNBC. The up-regulation of IL-22 gene expression in TNBC tumors and its promoting effect on cancer cell invasiveness highlight its potential as a therapeutic target in TNBC treatment strategies.

CONCLUSION

The findings suggested that targeting IL-22 and its related pathways can offer new insights for developing effective therapies for TNBC.

Advances in Integrated Multi-omics Analysis for Drug-Target Identification

As an essential component of modern drug discovery, the role of drug-target identification is growing increasingly prominent. Additionally, single-omics technologies have been widely utilized in the process of discovering drug targets. However, it is difficult for any single-omics level to clearly expound the causal connection between drugs and how they give rise to the emergence of complex phenotypes. With the progress of large-scale sequencing and the development of high-throughput technologies, the tendency in drug-target identification has shifted towards integrated multi-omics techniques, gradually replacing traditional single-omics techniques. Herein, this review centers on the recent advancements in the domain of integrated multi-omics techniques for target identification, highlights the common multi-omics analysis strategies, briefly summarizes the selection of multi-omics analysis tools, and explores the challenges of existing multi-omics analyses, as well as the applications of multi-omics technology in drug-target identification.

Identification of Immune-Related Gene Signature in Schizophrenia

BACKGROUND

Schizophrenia (SCZ) is a type of psychiatric disorder characterized by multiple symptoms. Our aim is to decipher the relevant mechanisms of immune-related gene signatures in SCZ.

METHODS

The SCZ dataset and its associated immunoregulatory genes were retrieved using Gene Expression Omnibus (GEO) and single-sample gene set enrichment analysis (ssGSEA). Co-expressed gene modules were determined through weighted gene correlation network analysis (WGCNA). To elucidate the functional characteristics of these clusters, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used. Additionally, gene set enrichment analysis (GSEA) and Gene Set Variation Analysis (GSVA) were conducted to identify enriched pathways for the immune subgroups. A protein-protein interaction (PPI) network analysis was performed to identify core genes relevant to SCZ.

RESULTS

A significantly higher immune score was observed in SCZ compared to control samples. Seven distinct gene modules were identified, with genes highlighted in green selected for further analysis. Using the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) method, degrees of immune cell adhesion and accumulation related to 22 different immune cell types were calculated. Significantly enriched bioprocesses concerning the immunoregulatory genes with differential expressions included interferon-beta, IgG binding, and response to interferon-gamma, according to GO and KEGG analyses. Eleven hub genes related to immune infiltration emerged as key players among the three top-ranked GO terms.

CONCLUSIONS

This study underscores the involvement of immunoregulatory reactions in SCZ development. Eleven immune-related genes (IFITM1 (interferon induced transmembrane protein 1), GBP1 (guanylate binding protein 1), BST2 (bone marrow stromal cell antigen 2), IFITM3 (interferon induced transmembrane protein 3), GBP2 (guanylate binding protein 2), CD44 (CD44 molecule), FCER1G (Fc epsilon receptor Ig), HLA-DRA (major histocompatibility complex, class II, DR alpha), FCGR2A (Fc gamma receptor IIa), IFI16 (interferon gamma inducible protein 16), and FCGR3B (Fc gamma receptor IIIb)) were identified as hub genes, representing potential biomarkers and therapeutic targets associated with the immune response in SCZ patients.

Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges

Single-cell RNA sequencing (scRNA-Seq) is a recent technology that allows for the measurement of the expression of all genes in each individual cell contained in a sample. Information at the single-cell level has been shown to be extremely useful in many areas. However, performing single-cell experiments is expensive. Although cellular deconvolution cannot provide the same comprehensive information as single-cell experiments, it can extract cell-type information from bulk RNA data, and therefore it allows researchers to conduct studies at cell-type resolution from existing bulk datasets. For these reasons, a great effort has been made to develop such methods for cellular deconvolution. The large number of methods available, the requirement of coding skills, inadequate documentation, and lack of performance assessment all make it extremely difficult for life scientists to choose a suitable method for their experiment. This paper aims to fill this gap by providing a comprehensive review of 53 deconvolution methods regarding their methodology, applications, performance, and outstanding challenges. More importantly, the article presents a benchmarking of all these 53 methods using 283 cell types from 30 tissues of 63 individuals. We also provide an R package named DeconBenchmark that allows readers to execute and benchmark the reviewed methods (https://github.com/tinnlab/DeconBenchmark).

Transcriptome profiling of immune rejection mechanisms in a porcine vascularized composite allotransplantation model

Background

Vascularized composite allotransplantation (VCA) offers the potential for a biological, functional reconstruction in individuals with limb loss or facial disfigurement. Yet, it faces substantial challenges due to heightened immune rejection rates compared to solid organ transplants. A deep understanding of the genetic and immunological drivers of VCA rejection is essential to improve VCA outcomes.

Methods

Heterotopic porcine hindlimb VCA models were established and followed until reaching the endpoint. Skin and muscle samples were obtained from VCA transplant recipient pigs for histological assessments and RNA sequencing analysis. The rejection groups included recipients with moderate pathological rejection, treated locally with tacrolimus encapsulated in triglycerol-monostearate gel (TGMS-TAC), as well as recipients with severe end-stage rejection presenting evident necrosis. Healthy donor tissue served as controls. Bioinformatics analysis, immunofluorescence, and electron microscopy were utilized to examine gene expression patterns and the expression of immune response markers.

Results

Our comprehensive analyses encompassed differentially expressed genes, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes pathways, spanning various composite tissues including skin and muscle, in comparison to the healthy control group. The analysis revealed a consistency and reproducibility in alignment with the pathological rejection grading. Genes and pathways associated with innate immunity, notably pattern recognition receptors (PRRs), damage-associated molecular patterns (DAMPs), and antigen processing and presentation pathways, exhibited upregulation in the VCA rejection groups compared to the healthy controls. Our investigation identified significant shifts in gene expression related to cytokines, chemokines, complement pathways, and diverse immune cell types, with CD8 T cells and macrophages notably enriched in the VCA rejection tissues. Mechanisms of cell death, such as apoptosis, necroptosis and ferroptosis were observed and coexisted in rejected tissues.

Conclusion

Our study provides insights into the genetic profile of tissue rejection in the porcine VCA model. We comprehensively analyze the molecular landscape of immune rejection mechanisms, from innate immunity activation to critical stages such as antigen recognition, cytotoxic rejection, and cell death. This research advances our understanding of graft rejection mechanisms and offers potential for improving diagnostic and therapeutic strategies to enhance the long-term success of VCA.

Comprehensive bioinformatics analytics and in vivo validation reveal SLC31A1 as an emerging diagnostic biomarker for acute myocardial infarction

BACKGROUND

Globally, Acute Myocardial Infarction (AMI) is a common cause of heart failure (HF), which has been a leading cause of mortality resulting from non-communicable diseases. On the other hand, increasing evidence suggests that the role of energy production within the mitochondria strongly links to the development and progression of heart diseases, while Cuproptosis, a newly identified cell death mechanism, has not yet been comprehensively analyzed from the aspect of cardiovascular medicine.

MATERIALS AND METHODS

8 transcriptome profiles curated from the GEO database were integrated, from which a diagnostic model based on the Stacking algorithm was established. The efficacy of the model was evaluated in a multifaced manner (i.e., by Precision-Recall curve, Receiver Operative Characteristic curve, etc.). We also sequenced our animal models at the bulk RNA level and conducted qPCR and immunohistochemical staining, with which we further validated the expression of the key contributor gene to the model. Finally, we explored the immune implications of the key contributor gene.

RESULTS

A merged machine learning model containing 4 Cuproptosis-related genes (i.e., PDHB, CDKN2A, GLS, and SLC31A1) for robust AMI diagnosis was developed, in which SLC31A1 served as the key contributor. Through in vivo modeling, we validated the aberrant overexpression of SLC31A1 in AMI. Besides, further transcriptome analysis revealed that its high expression was correlated with significant potential immunological implications in the infiltration of many immune cell types, especially monocyte.

CONCLUSIONS

We constructed an AMI diagnostic model based on Cuproptosis-related genes and validated the key contributor gene in animal modeling. We also analyzed the effects on the immune system for its overexpression in AMI.

Identification of GMFG as a novel biomarker in IgA nephropathy based on comprehensive bioinformatics analysis

Background

IgA nephropathy (IgAN) stands as the most prevalent form of glomerulonephritis and ranks among the leading causes of end-stage renal disease worldwide. Regrettably, we continue to grapple with the absence of dependable diagnostic markers and specific therapeutic agents for IgAN. Therefore, this study endeavors to explore novel biomarkers and potential therapeutic targets in IgAN, while also considering their relevance in the context of tumors.

Methods

We gathered IgAN datasets from the Gene Expression Omnibus (GEO) database. Subsequently, leveraging these datasets, we conducted an array of analyses, encompassing differential gene expression, weighted gene co-expression network analysis (WGCNA), machine learning, receiver operator characteristic (ROC) curve analysis, gene expression validation, clinical correlations, and immune infiltration. Finally, we carried out pan-cancer analysis based on hub gene.

Results

We obtained 1391 differentially expressed genes (DEGs) in GSE93798 and 783 DGEs in GSE14795, respectively. identifying 69 common genes for further investigation. Subsequently, GMFG was identified the hub gene based on machine learning. In the verification set and the training set, the GMFG was higher in the IgAN group than in the healthy group and all of the GMFG area under the curve (AUC) was more 0.8. In addition, GMFG has a close relationship with the prognosis of malignancies and a range of immune cells.

Conclusions

Our study suggests that GMFG could serve as a promising novel biomarker and potential therapeutic target for both IgAN and certain types of tumors.

Bioinformatics-based discovery of intervertebral disc degeneration biomarkers and immune-inflammatory infiltrates

Background

Intervertebral disc degeneration (IVDD) is a common chronic disease in orthopedics, and its molecular mechanisms are still not well explained.

Aim

This study's objective was to bioinformatics-based discovery of IVDD biomarkers and immune-inflammatory infiltrates.

Materials and Methods

The IVDD illness gene collection was gathered from GeneCards, DisGeNet, and gene expression profiles were chosen from the extensive Gene Expression Omnibus database (GSE124272, GSE150408, and GSE153761). The STRING database was used to create a network of protein-protein interactions, while the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases were used for functional enrichment analysis. Using hub genes, the immune cell infiltration between IVDD patient samples and control tissues was examined. Finally, quantitative polymerase chain reaction and Western blot experiments were used to verify the expression of hub genes.

Results

A total of 27 differentially expressed hub genes were identified by bioinformatics. According to GO and KEGG analyses, hub genes were prominent in immunological responses, chemokine-mediated signaling pathways, and inflammatory responses, with the key signaling pathways engaged in cellular senescence, apoptosis, Th1 and Th2 cell differentiation, and Th17 cell differentiation. Immune cell infiltration research revealed that T cells, lymphocytes, B cells, and NK cells were decreased in IVDD patients while monocytes, neutrophils, and CD8 T cells were increased. The expression levels of the senescence hub genes SP1, VEGFA, IL-6, and the apoptosis key gene CASP3 were considerably greater in the IVDD model group than in the control group, according to in vitro validation.

Conclusion

In conclusion, the cellular senescence signaling pathway, the apoptosis signaling pathway, and associated hub genes play significant roles in the development and progression of IVDD, this finding may help direct future research on the senescence signaling route in IVDD.

Identification of somatic mutation-driven enhancers and their clinical utility in breast cancer

Somatic mutations contribute to cancer development by altering the activity of enhancers. In the study, a total of 135 mutation-driven enhancers, which displayed significant chromatin accessibility changes, were identified as candidate risk factors for breast cancer (BRCA). Furthermore, we identified four mutation-driven enhancers as independent prognostic factors for BRCA subtypes. In Her2 subtype, enhancer G > C mutation was associated with poorer prognosis through influencing its potential target genes FBXW9, TRIR, and WDR83. We identified aminoglutethimide and quinpirole as candidate drugs targeting the mutated enhancer. In normal subtype, enhancer G > A mutation was associated with poorer prognosis through influencing its target genes ALOX15B, LINC00324, and MPDU1. We identified eight candidate drugs such as erastin, colforsin, and STOCK1N-35874 targeting the mutated enhancer. Our findings suggest that somatic mutations contribute to breast cancer subtype progression by altering enhancer activity, which could be potential candidates for cancer therapy.

Gene-Expression Profiling to Decipher Breast Cancer Inter- and Intratumor Heterogeneity

Breast cancer is heterogeneous and differs substantially across different tumors (intertumor heterogeneity) and even within an individual tumor (intratumor heterogeneity). Gene-expression profiling has considerably impacted our understanding of breast cancer biology. Four main "intrinsic subtypes" of breast cancer (i.e., luminal A, luminal B, HER2-enriched, and basal-like) have been consistently identified by gene expression, showing significant prognostic and predictive value in multiple clinical scenarios. Thanks to the molecular profiling of breast tumors, breast cancer is a paradigm of treatment personalization. Several standardized prognostic gene-expression assays are presently being used in the clinic to guide treatment decisions. Moreover, the development of single-cell-level resolution molecular profiling has allowed us to appreciate that breast cancer is also heterogeneous within a single tumor. There is an evident functional heterogeneity within the neoplastic and tumor microenvironment cells. Finally, emerging insights from these studies suggest a substantial cellular organization of neoplastic and tumor microenvironment cells, thus defining breast cancer ecosystems and highlighting the importance of spatial localizations.

Metabolic reprogramming involves in transition of activated/resting CD4+ memory T cells and prognosis of gastric cancer

Background

Little is known on how metabolic reprogramming potentially prompts transition of activated and resting CD4+ memory T cells infiltration in tumor microenvironment of gastric cancer (GC). The study aimed to evaluate their interactions and develop a risk model for predicting prognosis in GC.

Methods

Expression profiles were obtained from TCGA and GEO databases. An immunotherapeutic IMvigor210 cohort was also enrolled. CIBERSORT algorithm was used to evaluate the infiltration of immune cells. The ssGSEA method was performed to assess levels of 114 metabolism pathways. Prognosis and correlation analysis were conducted to identify metabolism pathways and genes correlated with activated CD4+ memory T cells ratio (AR) and prognosis. An AR-related metabolism gene (ARMG) risk model was constructed and validated in different cohorts. Flow cytometry was applied to validate the effect of all-trans retinoic acid (ATRA) on CD4+ memory T cells.

Results

Since significantly inverse prognostic value and negative correlation of resting and activated CD4+ memory T cells, high AR level was associated with favorable overall survival (OS) in GC. Meanwhile, 15 metabolism pathways including retinoic acid metabolism pathway were significantly correlated with AR and prognosis. The ARMG risk model could classify GC patients with different outcomes, treatment responses, genomic and immune landscape. The prognostic value of the model was also confirmed in the additional validation, immunotherapy and pan-cancer cohorts. Functional analyses revealed that the ARMG model was positively correlated with pro-tumorigenic pathways. In vitro experiments showed that ATRA could inhibit levels of activated CD4+ memory T cells and AR.

Conclusion

Our study showed that metabolic reprogramming including retinoic acid metabolism could contribute to transition of activated and resting CD4+ memory T cells, and affect prognosis of GC patients. The ARMG risk model could serve as a new tool for GC patients by accurately predicting prognosis and response to treatment.

Head and neck squamous cell carcinoma-specific prognostic signature and drug sensitive subtypes based on programmed cell death-related genes

Background

As a complex group of malignancies, head and neck squamous cell carcinoma (HNSC) is one of the leading causes of cancer mortality. This study aims to establish a reliable clinical classification and gene signature for HNSC prognostic prediction and precision treatments.

Methods

A consensus clustering analysis was performed to group HNSC patients in The Cancer Genome Atlas (TCGA) database based on genes linked to programmed cell death (PCD). Differentially expressed genes (DEGs) between subtypes were identified using the "limma" R package. The TCGA prognostic signature and PCD-related prognostic genes were found using a least absolute shrinkage and selection operator (LASSO) regression analysis and univariate Cox regression analysis. The robustness of the LASSO analysis was validated using datasets GSE65858 and GSE41613. A cell counting kit-8 (CCK-8) test, Western blot, and real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) were used to evaluate the expression and viability of prognostic genes.

Results

Four molecular subtypes were identified in PCD-related genes. Subtype C4 had the best prognosis and the highest immune score, while subtype C1 exhibited the most unfavorable outcomes. Three hundred shared DEGs were identified among the four subtypes, and four prognostic genes (CTLA4, CAMK2N1, PLAU and CALML5) were used to construct a TCGA-HNSC prognostic model. High-risk patients manifested poorer prognosis, more inflammatory pathway enrichment, and lower immune cell infiltration. High-risk patients were more prone to immune escape and were more likely to be resistant to Cisplatin and 5-Fluorouracil. Prognosis prediction was validated in external datasets. The expression of CTLA4, CAMK2N1, PLAU and CALML5 was enhanced in CAL-27 and SCC-25 cell lines, and CALML5 inhibited CAL-27 and SCC-25 cell viability.

Conclusion

This study shares novel insights into HNSC classification and provides a reliable PCD-related prognostic signature for prognosis prediction and treatment for patients with HNSC.

Roles of DSCC1 and GINS1 in gastric cancer

Gastric carcinoma is a common malignant tumor originating from gastric mucosal epithelium. However, role of DS-cell cycle-dependent protein 1 (DSCC1) and GINS1 in gastric carcinoma remains unclear. The gastric carcinoma datasets GSE79973 and GSE118916 were downloaded from gene expression omnibus. Multiple datasets were merged and batched. Differentially expressed genes (DEGs) were screened and weighted gene co-expression network analysis was performed. Functional enrichment analysis, gene set enrichment analysis and immune infiltration analysis were performed. Construction and analysis of protein-protein interaction Network. Survival analysis and comparative toxicogenomics database were performed. A heat map of gene expression was drawn. Target Scan screen miRNAs regulating DEGs. Two thousand forty-four DEGs were identified. According to gene ontology analysis, in biological process, they were mainly enriched in cell migration, transforming growth factor β receptor signaling pathway, angiogenesis, and steroid metabolism process. In cellular component, they were mainly enriched in extracellular vesicles, basement membrane, endoplasmic reticulum lumen, and extracellular space. In molecular function, they focused on extracellular matrix structural components, protein binding, platelet-derived growth factor binding, and catalytic activity. In Kyoto encyclopedia of genes and genomes, they were mainly enriched in protein digestion and absorption, metabolic pathways, fatty acid degradation, Glycerophospholipid metabolism, ether lipid metabolism. Gene set enrichment analysis showed that DEGs were mainly enriched in transforming growth factor β receptor signaling pathway, steroid metabolism process, basement membrane, endoplasmic reticulum lumen, structural components of extracellular matrix, platelet-derived growth factor binding, Glycerophospholipid metabolism, ether lipid metabolism. The results of immune infiltration analysis showed that expression of T cell CD4 memory resting was lower in the samples of gastric cancer. The core genes (TRIP13, CHEK1, DSCC1, GINS1) are protective factors, their expression shows a downward trend with increase of risk score. Comparative toxicogenomics database analysis showed that TRIP13, CHEK1, DSCC1, GINS1 were related to gastric tumors, gastric diseases, tumors, inflammation, and necrosis. DSCC1 and GINS1 are highly expressed in gastric cancer. Higher expression levels of DSCC1 and GINS1, worse the prognosis.

Establishment and validation of a novel risk model based on CD8T cell marker genes to predict prognosis in thyroid cancer by integrated analysis of single-cell and bulk RNA-sequencing

Papillary thyroid cancer (PTC) is a histological type of thyroid cancer, and CD8T is important for the immune response. The single-cell RNA data were acquired from Gene Expression Omnibus. SingleR package was used for cluster identification, and CellChat was exploited to evaluate the interaction among several cell types. Bulk RNA data obtained from the cancer genome atlas were used for determination of prognosis using Kaplan-Meier and Receiver Operating Characteristic curve. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were applied for assessment of function enrichment. The drug sensitivity was calculated in Gene Set Cancer Analysis. The regulatory network was constructed by STRING and Cytoscape. We identified 23 cell clusters and 10 cell types. Cell communication results showed CD8T cell was vital among all immune cell types. Enrichment analysis found the marker genes of CD8T cell was enriched in some signal pathways related to tumor development. Overall, FAM107B and TUBA4A were considered as hub genes and used to construct a risk model. Most immune checkpoint expressions were upregulated in tumor group. Tumor mutation burden results indicated that prognosis of PTC was not related to the mutation of hub genes. Drug sensitivity analysis showed some drugs could be effectively used for the treatment of PTC, and regulatory network identified some targets for the immunotherapy. A 2-gene model of PTC was developed based on the single-cell RNA and bulk RNA data. Besides, we found CD8T was essential for the immune response in PTC.

Local γδ T cells: translating promise to practice in cancer immunotherapy

Rapid bench-to-bedside translation of basic immunology to cancer immunotherapy has revolutionised the clinical practice of oncology over the last decade. Immune checkpoint inhibitors targeting αβ T cells now offer durable remissions and even cures for some patients with hitherto treatment-refractory metastatic cancers. Unfortunately, these treatments only benefit a minority of patients and efforts to improve efficacy through combination therapies utilising αβ T cells have seen diminishing returns. Alongside αβ T cells and B cells, γδ T cells are a third lineage of adaptive lymphocytes. Less is known about these cells, and they remain relatively untested in cancer immunotherapy. Whilst preclinical evidence supports their utility, the few early-phase trials involving γδ T cells have failed to demonstrate convincing efficacy in solid cancers. Here we review recent progress in our understanding of how these cells are regulated, especially locally within tissues, and the potential for translation. In particular, we focus on the latest advances in the field of butyrophilin (BTN) and BTN-like (BTNL) regulation of γδ T cells and speculate on how these advances may address the limitations of historical approaches in utilising these cells, as well as how they may inform novel approaches in deploying these cells for cancer immunotherapy.

Comparative Analysis of Cell Mixtures Deconvolution and Gene Signatures Generated for Blood, Immune and Cancer Cells

In the last two decades, many detailed full transcriptomic studies on complex biological samples have been published and included in large gene expression repositories. These studies primarily provide a bulk expression signal for each sample, including multiple cell-types mixed within the global signal. The cellular heterogeneity in these mixtures does not allow the activity of specific genes in specific cell types to be identified. Therefore, inferring relative cellular composition is a very powerful tool to achieve a more accurate molecular profiling of complex biological samples. In recent decades, computational techniques have been developed to solve this problem by applying deconvolution methods, designed to decompose cell mixtures into their cellular components and calculate the relative proportions of these elements. Some of them only calculate the cell proportions (supervised methods), while other deconvolution algorithms can also identify the gene signatures specific for each cell type (unsupervised methods). In these work, five deconvolution methods (CIBERSORT, FARDEEP, DECONICA, LINSEED and ABIS) were implemented and used to analyze blood and immune cells, and also cancer cells, in complex mixture samples (using three bulk expression datasets). Our study provides three analytical tools (corrplots, cell-signature plots and bar-mixture plots) that allow a thorough comparative analysis of the cell mixture data. The work indicates that CIBERSORT is a robust method optimized for the identification of immune cell-types, but not as efficient in the identification of cancer cells. We also found that LINSEED is a very powerful unsupervised method that provides precise and specific gene signatures for each of the main immune cell types tested: neutrophils and monocytes (of the myeloid lineage), B-cells, NK cells and T-cells (of the lymphoid lineage), and also for cancer cells.

Hypoxia induced responses are reflected in the stromal proteome of breast cancer

Cancers are often associated with hypoxia and metabolic reprogramming, resulting in enhanced tumor progression. Here, we aim to study breast cancer hypoxia responses, focusing on secreted proteins from low-grade (luminal-like) and high-grade (basal-like) cell lines before and after hypoxia. We examine the overlap between proteomics data from secretome analysis and laser microdissected human breast cancer stroma, and we identify a 33-protein stromal-based hypoxia profile (33P) capturing differences between luminal-like and basal-like tumors. The 33P signature is associated with metabolic differences and other adaptations following hypoxia. We observe that mRNA values for 33P predict patient survival independently of molecular subtypes and basic prognostic factors, also among low-grade luminal-like tumors. We find a significant prognostic interaction between 33P and radiation therapy.

Identification of Hub Genes and Biological Mechanisms Associated with Non-Alcoholic Fatty Liver Disease and Triple-Negative Breast Cancer

The relationship between non-alcoholic fatty liver disease (NAFLD) and triple-negative breast cancer (TNBC) has been widely recognized, but the underlying mechanisms are still unknown. The objective of this study was to identify the hub genes associated with NAFLD and TNBC, and to explore the potential co-pathogenesis and prognostic linkage of these two diseases. We used GEO, TCGA, STRING, ssGSEA, and Rstudio to investigate the common differentially expressed genes (DEGs), conduct functional and signaling pathway enrichment analyses, and determine prognostic value between TNBC and NAFLD. GO and KEGG enrichment analyses of the common DEGs showed that they were enriched in leukocyte aggregation, migration and adhesion, apoptosis regulation, and the PPAR signaling pathway. Fourteen candidate hub genes most likely to mediate NAFLD and TNBC occurrence were identified and validation results in a new cohort showed that ITGB2, RAC2, ITGAM, and CYBA were upregulated in both diseases. A univariate Cox analysis suggested that high expression levels of ITGB2, RAC2, ITGAM, and CXCL10 were associated with a good prognosis in TNBC. Immune infiltration analysis of TNBC samples showed that NCF2, ICAM1, and CXCL10 were significantly associated with activated CD8 T cells and activated CD4 T cells. NCF2, CXCL10, and CYBB were correlated with regulatory T cells and myeloid-derived suppressor cells. This study demonstrated that the redox reactions regulated by the NADPH oxidase (NOX) subunit genes and the transport and activation of immune cells regulated by integrins may play a central role in the co-occurrence trend of NAFLD and TNBC. Additionally, ITGB2, RAC2, and ITGAM were upregulated in both diseases and were prognostic protective factors of TNBC; they may be potential therapeutic targets for treatment of TNBC patients with NAFLD, but further experimental studies are still needed.

Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction

Background

Increasing evidence suggests that people with Coronavirus Disease 2019 (COVID-19) have a much higher prevalence of Acute Myocardial Infarction (AMI) than the general population. However, the underlying mechanism is not yet comprehended. Therefore, our study aims to explore the potential secret behind this complication.

Materials and methods

The gene expression profiles of COVID-19 and AMI were acquired from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed genes (DEGs) shared by COVID-19 and AMI, we conducted a series of bioinformatics analytics to enhance our understanding of this issue.

Results

Overall, 61 common DEGs were filtered out, based on which we established a powerful diagnostic predictor through 20 mainstream machine-learning algorithms, by utilizing which we could estimate if there is any risk in a specific COVID-19 patient to develop AMI. Moreover, we explored their shared implications of immunology. Most remarkably, through the Bayesian network, we inferred the causal relationships of the essential biological processes through which the underlying mechanism of co-pathogenesis between COVID-19 and AMI was identified.

Conclusion

For the first time, the approach of causal relationship inferring was applied to analyzing shared pathomechanism between two relevant diseases, COVID-19 and AMI. Our findings showcase a novel mechanistic insight into COVID-19 and AMI, which may benefit future preventive, personalized, and precision medicine.

Graphical abstract

Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis

Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.

A novel DNA methylation signature to improve survival prediction of progression-free survival for testicular germ cell tumors

This study aimed to develop a nomogram for predicting the progression-free survival (PFS) of testicular germ cell tumors (TGCT) patients based on DNA methylation signature and clinicopathological characteristics. The DNA methylation profiles, transcriptome data, and clinical information of TGCT patients were obtained from the Cancer Genome Atlas (TCGA) database. Univariate Cox, lasso Cox, and stepwise multivariate Cox regression were applied to identify a prognostic CpG sites-derived risk signature. Differential expression analysis, functional enrichment analysis, immunoinfiltration analysis, chemotherapy sensitivity analysis, and clinical feature correlation analysis were performed to elucidate the differences among risk groups. A prognostic nomogram integrating CpG sites-derived risk signature and clinicopathological features was further established and evaluated likewise. A risk score model based on 7 CpG sites was developed and found to exhibit significant differences among different survival, staging, radiotherapy, and chemotherapy subgroups. There were 1452 differentially expressed genes between the high- and low-risk groups, with 666 being higher expressed and 786 being lower expressed. Genes highly expressed were significantly enriched in immune-related biological processes and related to T-cell differentiation pathways; meanwhile, down-regulated genes were significantly enriched in extracellular matrix tissue organization-related biological processes and involved in multiple signaling pathways such as PI3K-AKT. As compared with the low-risk group, patients in the high-risk group had decreased lymphocyte infiltration (including T-cell and B-cell) and increased macrophage infiltration (M2 macrophages). They also showed decreased sensitivity to etoposide and bleomycin chemotherapy. Three clusters were obtained by consensus clustering analysis based on the 7 CpG sites and showed distinct prognostic features, and the risk scores in each cluster were significantly different. Multivariate Cox regression analysis found that the risk scores, age, chemotherapy, and staging were independent prognostic factors of PFS of TGCT, and the results were used to formulate a nomogram model that was validated to have a C-index of 0.812. Decision curve analysis showed that the nomogram model was superior to other strategies in the prediction of PFS of TGCT. In this study, we successfully established CpG sites-derived risk signature, which might serve as a useful tool in the prediction of PFS, immunoinfiltration, and chemotherapy sensitivity for TGCT patients.

A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology

Since genes do not function individually, the gene module is considered an important tool for interpreting gene expression profiles. In order to consider both functional similarity and expression similarity in module identification, GMIGAGO, a functional Gene Module Identification algorithm based on Genetic Algorithm and Gene Ontology, was proposed in this work. GMIGAGO is an overlapping gene module identification algorithm, which mainly includes two stages: In the first stage (initial identification of gene modules), Improved Partitioning Around Medoids Based on Genetic Algorithm (PAM-GA) is used for the initial clustering on gene expression profiling, and traditional gene co-expression modules can be obtained. Only similarity of expression levels is considered at this stage. In the second stage (optimization of functional similarity within gene modules), Genetic Algorithm for Functional Similarity Optimization (FSO-GA) is used to optimize gene modules based on gene ontology, and functional similarity within gene modules can be improved. Without loss of generality, we compared GMIGAGO with state-of-the-art gene module identification methods on six gene expression datasets, and GMIGAGO identified the gene modules with the highest functional similarity (much higher than state-of-the-art algorithms). GMIGAGO was applied in BRCA, THCA, HNSC, COVID-19, Stem, and Radiation datasets, and it identified some interesting modules which performed important biological functions. The hub genes in these modules could be used as potential targets for diseases or radiation protection. In summary, GMIGAGO has excellent performance in mining molecular mechanisms, and it can also identify potential biomarkers for individual precision therapy.

Comprehensive Molecular Analyses of Notch Pathway-Related Genes to Predict Prognosis and Immunotherapy Response in Patients with Gastric Cancer

Gastric cancer (GC) is a highly molecular heterogeneous tumor with unfavorable outcomes. The Notch signaling pathway is an important regulator of immune cell differentiation and has been associated with autoimmune disorders, the development of tumors, and immunomodulation caused by tumors. In this study, by developing a gene signature based on genes relevant to the Notch pathway, we could improve our ability to predict the outcome of patients with GC. From the TCGA database, RNA sequencing data of GC tumors and associated normal tissues were obtained. Microarray data were collected from GEO datasets. The Molecular Signature Database (MSigDB) was accessed in order to retrieve sets of human Notch pathway-related genes (NPRGs). The LASSO analysis performed on the TCGA cohort was used to generate a multigene signature based on prognostic NPRGs. In order to validate the gene signature, the GEO cohort was utilized. Using the CIBERSORT method, we were able to determine the amounts of immune cell infiltration in the GC. In this study, a total of 21 differentially expressed NPRGs were obtained between GC specimens and nontumor specimens. The construction of a prognostic prediction model for patients with GC involved the identification and selection of three different NPRGs. According to the appropriate cutoff value, the patients with GC were divided into two groups: those with a low risk and those with a high risk. The time-dependent ROC curves demonstrated that the new model had satisfactory performance when it came to prognostic prediction. Multivariate assays confirmed that the risk score was an independent marker that may be used to predict the outcome of GC. In addition, the generated nomogram demonstrated a high level of predictive usefulness. Moreover, the scores of immunological infiltration of the majority of immune cells were distinctly different between the two groups, and the low-risk group responded to immunotherapy in a significantly greater degree. According to the results of a functional enrichment study of candidate genes, there are multiple pathways and processes associated with cancer. Taken together, a new gene model associated with the Notch pathway may be utilized for the purpose of predicting the prognosis of GC. One potential method of treatment for GC is to focus on NPRGs.

Triple negative breast cancer: Immunogenicity, tumor microenvironment, and immunotherapy

Triple negative breast cancer (TNBC) is a biologically diverse subtype of breast cancer characterized by genomic and transcriptional heterogeneity and exhibiting aggressive clinical behaviour and poor prognosis. In recent years, emphasis has been placed on the identification of mechanisms underlying the complex genomic and biological profile of TNBC, aiming to tailor treatment strategies. High immunogenicity, specific immune activation signatures, higher expression of immunosuppressive genes and higher levels of stromal Tumor Infiltrating Lymphocytes, constitute some of the key elements of the immune driven landscape associated with TNBC. The unprecedented response of TNBC to immunotherapy has undoubtedly changed the standard of care in this disease both in the early and the metastatic setting. However, the extent of interplay between immune infiltration and mutational signatures in TNBC is yet to be fully unravelled. In the present review, we present clinical evidence on the immunogenicity and tumour microenvironment influence on TNBC progression and the current treatment paradigms in TNBC based on immunotherapy.

Dysfunctional epigenetic protein-coding gene-related signature is associated with the prognosis of pancreatic cancer based on histone modification and transcriptome analysis

Emerging evidence suggests that epigenetic alterations are responsible for the oncogenesis and progression of cancer. However, the role of epigenetic reprogramming in pancreatic cancer is still not clear. In this study, we used the limma R package to identify differentially expressed protein-coding genes (PCGs) between pancreatic cancer tissues and normal control tissues. The cell-type identification by the estimating relative subsets of RNA transcripts (CIBERSORT) package was used to quantify relative cell fractions in tumors. Prognostic molecular clusters were constructed using ConsensusClusterPlus analysis. Furthermore, the least absolute shrinkage and selection operator and stepAIC methods were used to construct a risk model. We identified 2351 differentially expressed PCGs between pancreatic cancer and normal control tissues in The cancer genome atlas dataset. Combined with histone modification data, we identified 363 epigenetic PCGs (epi-PCGs) and 19,010 non-epi-PCGs. Based on the epi-PCGs, we constructed three molecular clusters characterized by different expression levels of chemokines and immune checkpoint genes and distinct abundances of various immune cells. Furthermore, we generated a 9-gene model based on dysfunctional epi-PCGs. Additionally, we found that patients with high risk scores showed poorer prognoses than patients with low risk scores (p < 0.0001). Further analysis showed that the risk score was significantly related to survival and was an independent risk factor for pancreatic cancer patients. In conclusion, we constructed a 9-gene prognostic risk model based on epi-PCGs that might serve as an effective classifier to predict overall survival and the response to immunotherapy in pancreatic cancer patients.

Role of Single Nucleotide Polymorphism-Related Genes in Tumour Immune Cell Infiltration and Prognosis of Cutaneous Melanoma

Background

Advances in cancer research have allowed for early diagnosis and improved treatment of cutaneous melanoma (CM). However, its invasiveness and recurrent metastasis, along with rising resistance to newer therapies, have lent urgency to the search for novel biomarkers and the underlying molecular mechanisms of CM.

Methods

Single nucleotide polymorphism- (SNP-) related genes were obtained from the sequencing data of 428 CM samples in The Cancer Genome Atlas. Functional enrichment of these genes was analysed in clusterProfiler. Additionally, a protein-protein interaction (PPI) network was constructed with the Search Tool for the Retrieval of Interacting Gene (STRING) database. Gene Expression Profiling Interactive Analysis (GEPIA) was used to identify the expression and prognostic value of mutated genes. Finally, the Tumour Immune Estimation Resource (TIMER) analysed the relationship between gene expression and immune cell infiltration.

Results

We constructed a PPI network from the top 60 SNP-related genes. Mutated genes were mainly involved in calcium and oxytocin signalling pathways, as well as circadian entrainment. In addition, three SNP-related genes, BRAF, FLG, and SORL1, were significantly associated with patient prognosis. BRAF and SORL1 were positively associated with infiltration abundance of B cells, CD8+ T cells, CD4+ T cells, neutrophils, and dendritic cells, whereas FLG expression was negatively associated. Furthermore, higher immune cell infiltration was positively correlated with good prognosis.

Conclusions

Our study provides vital bioinformatic data and a relevant theoretical basis to further explore the molecular pathogenesis of CM and improve patient prognosis.

Molecular subtypes based on metabolic genes are potential biomarkers for predicting prognosis and immune responses of clear cell renal cell carcinoma

Due to the existence of tumor molecular heterogeneity, even patients having similar clinicopathological features could have vastly different survival rates. Hence, we aimed to explore novel metabolism-associated genes (MAGs) related molecular subtypes for clear cell renal cell carcinoma (ccRCC) and their immune landscapes for predicting prognosis and immune responses. Gene matrices and clinical information were downloaded from TCGA and ICGC datasets. Consensus clustering was conducted by the R "ConsensusClusterPlus" package. ccRCC patients were successfully divided into three clusters (MC1, MC2, and MC3) based on MAGs in both TCGA and ICGC datasets. Our established three MAGs were significantly associated with chemokine/chemokine receptor, IFN, CYT, angiogenesis, immune checkpoint molecules, tumor-infiltrating immune cells, oncogenic pathways, pan-cancer immune subtypes, and tumor microenvironment (TME) scores or expressions. Moreover, these three metabolic ccRCC subtypes could predict immunotherapeutic responses. We further constructed a characteristic index (LDAscore) in three metabolic ccRCC subtypes and identified LDAscore-related modules by WGCNA. After deep data mining, 10 hub genes were obtained and seven genes (ATRX, BPTF, DHX9, EP300, POLR2B, SIN3A, UBE3A) were finally validated by qRT-PCR. Our results successfully established a novel ccRCC subtype based on MAGs, providing novel insights into metabolism-related ccRCC tumor heterogeneity and facilitating individualized therapy for future work.

STK24 Promotes Progression of LUAD and Modulates the Immune Microenvironment

Objective

Recent studies have shown that serine/threonine-protein kinase 24 (STK24) plays an important role in cancer development. However, the significance of STK24 in lung adenocarcinoma (LUAD) remains to be determined. This study is aimed at investigating the significance of STK24 in LUAD.

Methods

STK24 was silenced and overexpressed by siRNAs and lentivirus, respectively. Cellular function was assessed by CCK8, colony formation, transwell, apoptosis, and cell cycle. mRNA and protein abundance was checked by qRT-PCR and WB assay, respectively. Luciferase reporter activity was evaluated to examine the regulation of KLF5 on STK24. Various public databases and tools were applied to investigate the immune function and clinical significance of STK24 in LUAD.

Results

We found that STK24 was overexpressed in lung adenocarcinoma (LUAD) tissues. High expression of STK24 predicted poor survival of LUAD patients. In vitro, STK24 enhanced the proliferation and colony growth ability of A549 and H1299 cells. STK24 knockdown induced apoptosis and cell cycle arrest at G0/G1 phase. Furthermore, Krüppel-like factor 5 (KLF5) activated STK24 in lung cancer cells and tissues. Enhanced lung cancer cell growth and migration triggered by KLF5 could be reversed by silencing of STK24. Finally, the bioinformatics results showed that STK24 may be involved in the regulation of the immunoregulatory process of LUAD.

Conclusion

KLF5 upregulation of STK24 contributes to cell proliferation and migration in LUAD. Moreover, STK24 may participate in the immunomodulatory process of LUAD. Targeting KLF5/STK24 axis may be a potential therapeutic strategy for LUAD.

The exploration of new biomarkers for oral cancer through the ceRNA network and immune microenvironment analysis

The competitive endogenous RNA (ceRNA) and tumor-penetrating immune cells may be related to the prognosis of oral cancer. However, few studies have focused on the correlation between ceRNAs and immune cells. Thus, we developed a method based on a ceRNA network and tumor-infiltrating immune cells to elucidate the molecular pathways that may predict prognosis in patients with oral cancer. Download RNAseq expression data of oral cancer and control samples from the Cancer Genome Atlas (TCGA), obtain differentially expressed genes and establish a ceRNA network. The cox analysis and lasso regression analysis were used to screen key RNAs to establish a prognostic risk assessment model, and draw a 1.3.5-year forecast nomogram. Then the CIBERSORT algorithm was used to screen important tumor immune infiltrating cells associated with oral cancer. Another prognostic predictive model related to immune cells was established. Finally, co-expression analysis was applied to explore the relationship between key genes in the ceRNA network and important immune cells. Multiple external data sets are used to test the expression of key biomarkers. We constructed prognostic risk models of ceRNA and immune cells, which included 9 differentially expressed mRNAs and 2 types of immune cells. It was discovered from the co-expression analysis that a pair of important biomarkers were associated with the prognosis of oral cancer. T cells regulatory and CGNL1 (R = 0.39, P < .001) showed a significant positive correlation. External data set validation also supports this result. In this study, we found that some crucial ceRNAs (GGCT, TRPS1, CGNL1, HENMT1, LCE3A, S100A8, ZNF347, TMEM144, TMEM192) and immune cells (T cells regulatory and Eosinophils) may be related to the prognosis of oral cancer.

'Why is survival with triple negative breast cancer so low? insights and talking points from preclinical and clinical research'

INTRODUCTION

Triple negative breast cancer is typically related to poor prognosis, early metastasis, and high recurrence rate. Intrinsic and extrinsic biological features of TNBC and resistance mechanisms to conventional therapies can support its aggressive behavior, characterizing TNBC how extremely heterogeneous. Novel combination strategies are under investigation, including immunotherapeutic agents, anti-drug conjugates, PARP inhibitors, and various targeting agents, exploring, in the meanwhile, possible predictive biomarkers to correctly select patients for the optimal treatment for their specific subtype.

AREAS COVERED

This article examines the main malignity characteristics across different subtype, both histological and molecular, and the resistance mechanisms, both primary and acquired, to different drugs explored in the landscape of TNBC treatment, that lead TNBC to still has high mortality rate.

EXPERT OPINION

The complexity of TNBC is not only the main reason of its aggressivity, but its heterogeneity should be exploited in terms of therapeutics opportunities, combining agents with different mechanism of action, after a correct selection by biologic or molecular biomarkers. The main goal is to understand what TNBC really is and to act selectively on its characteristics, with a personalized anticancer treatment.

PDLIM2 is highly expressed in Breast Cancer tumour-associated macrophages and is required for M2 macrophage polarization

The PDZ-LIM domain-containing protein 2 (PDLIM2) regulates cell polarity and the protein stability of key transcription factors in epithelial and hemopoietic cells. We previously reported that PDLIM2 is more highly expressed in Triple Negative Breast Cancer (TNBC) than in other breast cancer types or normal breast tissue. In the course of the TNBC study, it was noted that PDLIM2 was highly expressed in the stroma of PDLIM2-expressing tumours. Here, we investigated the phenotype of these stromal cells and whether any infiltrating immune population was linked to PDLIM2 expression. We found that high PDLIM2 expression in breast tumours was associated with higher levels of infiltrating M2 macrophages, but was not associated with infiltrating T cell sub-populations. We then tested whether PDLIM2 contributes to macrophage differentiation or function by using cultures of bone marrow-derived macrophages from wildtype and Pdlim2 knockout mice. This demonstrated that PDLIM2 is required for naïve macrophage migration and for the full adoption of IL-4-induced M2 polarization, including expression of M2 phenotypic markers, cell adhesion and cell migration. TLR4-, TLR3- or IFNγ-induced M1 macrophage activity was less dependent on PDLIM2. Finally, analysis of publicly available breast cancer datasets showed that high PDLIM2 expression is associated with increased M2 macrophage infiltration. We conclude that PDLIM2 expression influences the tumour associated stroma and, in particular, M2 macrophage infiltration that may contribute to the progression of TNBC or other subsets of breast cancer.

In Silico Identification of Genes Associated with Breast Cancer Progression and Prognosis and Novel Therapeutic Targets

Molecular mechanisms underlying breast cancer (BC) progression are complex and remain unclear. In this study, we used bioinformatic tools to identify genes associated with tumor progression mechanisms and novel therapeutic targets in BC. We identified genes with stepwise upregulated expression overlapping between the T and N stages during BC progression using LinkedOmics. We compared the expression level of each gene in BC tissues with that in normal breast tissues and evaluated differences in expression in their intrinsic subtypes and their prognostic value using UALCAN and GEPIA2. We also investigated the dependency of BC cell lines on these genes and whether they are potential therapeutic targets using DepMap. SPDEF, TRIM3, ABCB9, HSPB1, RHBG, SPINT1, EPN3, LRFN2, and PRPH were found to be involved in BC progression. High expression of ABCB9 and SPINT1 was associated with a poor prognosis. SPDEF, TRIM3, ABCB9, RHBG, SPINT1, and PRPH were found to be essential for survival in some BC cell lines (gene effect score < −0.5). PRPH was newly discovered to be involved in the progression of BC and the growth and survival of BC cell lines. Hence, SPDEF, TRIM3, ABCB9, RHBG, SPINT1, and PRPH may serve as novel potential therapeutic targets in BC.

Deconvolution of bulk tumors into distinct immune cell states predicts colorectal cancer recurrence

Predicting colorectal cancer recurrence after tumor resection is crucial because it promotes the administration of proper subsequent treatment or management to improve the clinical outcomes of patients. Several clinical or molecular factors, including tumor stage, metastasis, and microsatellite instability status, have been used to assess the risk of recurrence, although their predictive ability is limited. Here, we predicted colorectal cancer recurrence based on cellular deconvolution of bulk tumors into two distinct immune cell states: cancer-associated (tumor-infiltrating immune cell-like) and noncancer-associated (peripheral blood mononuclear cell-like). Prediction model performed significantly better when immune cells were deconvoluted into two states rather than a single state, suggesting that the difference in cancer recurrence was better explained by distinct states of immune cells. It indicates the importance of distinguishing immune cell states using cellular deconvolution to improve the prediction of colorectal cancer recurrence.

Dynamic Changes in the NK-, Neutrophil-, and B-cell Immunophenotypes Relevant in High Metastatic Risk Post Neoadjuvant Chemotherapy-Resistant Early Breast Cancers

PURPOSE

To identify potential immune targets in post-neoadjuvant chemotherapy (NAC)-resistant triple-negative breast cancer (TNBC) and ER+HER2- breast cancer disease.

EXPERIMENTAL DESIGN

Following pathology review, 153 patients were identified as having residual cancer burden (RCB) II/III disease (TNBC n = 80; ER+HER2-n = 73). Baseline pre-NAC samples were available for evaluation for 32 of 80 TNBC and 36 of 73 ER+HER2- cases. Bright-field hematoxylin and eosin assessment allowed for tumor-infiltrating lymphocyte (TIL) evaluation in all cases. Multiplexed immunofluorescence was used to identify the abundance and distribution of immune cell subsets. Levels of checkpoints including PD-1/PD-L1 expression were also quantified. Findings were then validated using expression profiling of cancer and immune-related genes. Cytometry by time-of-flight characterized the dynamic changes in circulating immune cells with NAC.

RESULTS

RCB II/III TNBC and ER+HER2- breast cancer were immunologically "cold" at baseline and end of NAC. Although the distribution of immune cell subsets across subtypes was similar, the mRNA expression profiles were both subtype- and chemotherapy-specific. TNBC RCB II/III disease was enriched with genes related to neutrophil degranulation, and displayed strong interplay across immune and cancer pathways. We observed similarities in the dynamic changes in B-cell biology following NAC irrespective of subtype. However, NAC induced changes in the local and circulating tumor immune microenvironment (TIME) that varied by subtype and response. Specifically, in TNBC residual disease, we observed downregulation of stimulatory (CD40/OX40L) and inhibitory (PD-L1/PD-1) receptor expression and an increase in NK cell populations (especially non-cytolytic, exhausted CD56dimCD16-) within both the local TIME and peripheral white cell populations.

CONCLUSIONS

This study identifies several potential immunologic pathways in residual disease, which may be targeted to benefit high-risk patients.

Molecular classification of human papillomavirus-positive cervical cancers based on immune signature enrichment

Background

Human papillomavirus-positive (HPV+) cervical cancers are highly heterogeneous in clinical and molecular characteristics. Thus, an investigation into their heterogeneous immunological profiles is meaningful in providing both biological and clinical insights into this disease.

Methods

Based on the enrichment of 29 immune signatures, we discovered immune subtypes of HPV+ cervical cancers by hierarchical clustering. To explore whether this subtyping method is reproducible, we analyzed three bulk and one single cell transcriptomic datasets. We also compared clinical and molecular characteristics between the immune subtypes.

Results

Clustering analysis identified two immune subtypes of HPV+ cervical cancers: Immunity-H and Immunity-L, consistent in the four datasets. In comparisons with Immunity-L, Immunity-H displayed stronger immunity, more stromal contents, lower tumor purity, proliferation potential, intratumor heterogeneity and stemness, higher tumor mutation burden, more neoantigens, lower levels of copy number alterations, lower DNA repair activity, as well as better overall survival prognosis. Certain genes, such as MUC17, PCLO, and GOLGB1, showed significantly higher mutation rates in Immunity-L than in Immunity-H. 16 proteins were significantly upregulated in Immunity-H vs. Immunity-L, including Caspase-7, PREX1, Lck, C-Raf, PI3K-p85, Syk, 14-3-3_epsilon, STAT5-α, GATA3, Src_pY416, NDRG1_pT346, Notch1, PDK1_pS241, Bim, NF-kB-p65_pS536, and p53. Pathway analysis identified numerous immune-related pathways more highly enriched in Immunity-H vs. Immunity-L, including cytokine-cytokine receptor interaction, natural killer cell-mediated cytotoxicity, antigen processing and presentation, T/B cell receptor signaling, chemokine signaling, supporting the stronger antitumor immunity in Immunity-H vs. Immunity-L.

Conclusion

HPV+ cervical cancers are divided into two subgroups based on their immune signatures' enrichment. Both subgroups have markedly different tumor immunity, progression phenotypes, genomic features, and clinical outcomes. Our data offer novel perception in the tumor biology as well as clinical implications for HPV+ cervical cancer.

Immunologically "cold" triple negative breast cancers engraft at a higher rate in patient derived xenografts

TNBC is a heterogeneous subtype of breast cancer, and only a subset of TNBC can be established as PDXs. Here, we show that there is an engraftment bias toward TNBC with low levels of immune cell infiltration. Additionally, TNBC that failed to engraft show gene expression consistent with a cancer-promoting immunological state, leading us to hypothesize that the immunological state of the tumor and possibly the state of the immune system of the host may be essential for engraftment.

Clinical interpretation of whole-genome and whole-transcriptome sequencing for precision oncology

Whole-genome sequencing either alone or in combination with whole-transcriptome sequencing has started to be used to analyze clinical tumor samples to improve diagnosis, provide risk stratification, and select patient-specific therapies. Compared with current genomic testing strategies, largely focused on small number of genes tested individually or targeted panels, whole-genome and transcriptome sequencing (WGTS) provides novel opportunities to identify and report a potentially much larger number of actionable alterations with diagnostic, prognostic, and/or predictive impact. Such alterations include point mutations, indels, copy- number aberrations and structural variants, but also germline variants, fusion genes, noncoding alterations and mutational signatures. Nevertheless, these comprehensive tests are accompanied by many challenges ranging from the extent and diversity of sequence alterations detected by these methods to the complexity and limited existing standardization in interpreting them. We describe the challenges of WGTS interpretation and the opportunities with comprehensive genomic testing.

Precision Breast Cancer Medicine: Early Stage Triple Negative Breast Cancer-A Review of Molecular Characterisation, Therapeutic Targets and Future Trends

Personalised approaches to the management of all solid tumours are increasing rapidly, along with wider accessibility for clinicians. Advances in tumour characterisation and targeted therapies have placed triple-negative breast cancers (TNBC) at the forefront of this approach. TNBC is a highly heterogeneous disease with various histopathological features and is driven by distinct molecular alterations. The ability to tailor individualised and effective treatments for each patient is of particular importance in this group due to the high risk of distant recurrence and death. The mainstay of treatment across all subtypes of TNBC has historically been cytotoxic chemotherapy, which is often associated with off-target tissue toxicity and drug resistance. Neoadjuvant chemotherapy is commonly used as it allows close monitoring of early treatment response and provides valuable prognostic information. Patients who achieve a complete pathological response after neoadjuvant chemotherapy are known to have significantly improved long-term outcomes. Conversely, poor responders face a higher risk of relapse and death. The identification of those subgroups that are more likely to benefit from breakthroughs in the personalised approach is a challenge of the current era where several targeted therapies are available. This review presents an overview of contemporary practice, and promising future trends in the management of early TNBC. Platinum chemotherapy, DNA damage response (DDR) inhibitors, immune checkpoint inhibitors, inhibitors of the PI3K-AKT-mTOR, and androgen receptor (AR) pathways are some of the increasingly studied therapies which will be reviewed. We will also discuss the growing evidence for less-developed agents and predictive biomarkers that are likely to contribute to the forthcoming advances in this field. Finally, we will propose a framework for the personalised management of TNBC based upon the integration of clinico-pathological and molecular features to ensure that long-term outcomes are optimised.