Prognostication of a 13-immune-related-gene signature in patients with early triple-negative breast cancer

KRAS/PI3K axis driven GTF3C6 expression and promotes LUAD via FAK pathway

INTRODUCTION

Effective targeting drugs for KRAS mutation-mediated Lung Adenocarcinoma (LUAD) are currently are limited.

OBJECTIVES

Investigating and intervening in the downstream key target genes of KRAS is crucial for clinically managing KRAS mutant-driven LUAD. GTF3C6, a newly identified member of the general transcription factor III (GTF3) family, plays a role in the transcription of RNA polymerase III (pol III)-dependent genes. However, its involvement in cancer remains unexplored.

METHODS

This study examined the expression, roles, and potential molecular mechanisms of GTF3C6 in LUAD tissues, LSL-KrasG12D/+;LSL-p53-/- LUAD mouse models, and LUAD patients-derived organoid using Western blot, qRT-PCR, immunofluorescence, immunohistochemistry, and gene manipulation assays.

RESULTS

We present the first evidence that GTF3C6 is highly expressed in LUAD tissues, LSL-KrasG12D/+;LSL-p53-/- LUAD mouse models, and LUAD organoids, correlating with poor clinical prognosis. Furthermore, GTF3C6 was found to promote anchorage-independent proliferation, migration, and invasion of LUAD cells. Mechanistically, KRAS mutation drives GTF3C6 expression through the PI3K pathway, and GTF3C6 knockdown reverses the malignant phenotype of KRAS mutation-driven LUAD cells. Additionally, the FAK pathway emerged as a crucial downstream signaling pathway through which GTF3C6 mediates the malignant phenotype of LUAD. Finally, GTF3C6 knockdown suppresses LUAD organoid formation and inhibits tumor growth in vivo.

CONCLUSION

Our findings demonstrate that GTF3C6, driven by KRAS mutation, promotes LUAD development by regulating FAK phosphorylation, suggesting its potential as a biomarker and therapeutic target in KRAS mutant-driven LUAD.

Differential whole-genome doubling based signatures for improvement on clinical outcomes and drug response in patients with breast cancer

Whole genome doublings (WGD), a hallmark of human cancer, is pervasive in breast cancer patients. However, the molecular mechanism of the complete impact of WGD on survival and treatment response in breast cancer remains unclear. To address this, we performed a comprehensive and systematic analysis of WGD, aiming to identify distinct genetic alterations linked to WGD and highlight its improvement on clinical outcomes and treatment response for breast cancer. A linear regression model along with weighted gene co-expression network analysis (WGCNA) was applied on The Cancer Genome Atlas (TCGA) dataset to identify critical genes related to WGD. Further Cox regression models with random selection were used to optimize the most useful prognostic markers in the TCGA dataset. The clinical implication of the risk model was further assessed through prognostic impact evaluation, tumor stratification, functional analysis, genomic feature difference analysis, drug response analysis, and multiple independent datasets for validation. Our findings revealed a high aneuploidy burden, chromosomal instability (CIN), copy number variation (CNV), and mutation burden in breast tumors exhibiting WGD events. Moreover, 247 key genes associated with WGD were identified from the distinct genomic patterns in the TCGA dataset. A risk model consisting of 22 genes was optimized from the key genes. High-risk breast cancer patients were more prone to WGD and exhibited greater genomic diversity compared to low-risk patients. Some oncogenic signaling pathways were enriched in the high-risk group, while primary immune deficiency pathways were enriched in the low-risk group. We also identified a risk gene, ANLN (anillin), which displayed a strong positive correlation with two crucial WGD genes, KIF18A and CCNE2. Tumors with high expression of ANLN were more prone to WGD events and displayed worse clinical survival outcomes. Furthermore, the expression levels of these risk genes were significantly associated with the sensitivities of BRCA cell lines to multiple drugs, providing valuable insights for targeted therapies. These findings will be helpful for further improvement on clinical outcomes and contribution to drug development in breast cancer.

Blood leukocytes as a non-invasive diagnostic tool for thyroid nodules: a prospective cohort study

BACKGROUND

Thyroid nodule (TN) patients in China are subject to overdiagnosis and overtreatment. The implementation of existing technologies such as thyroid ultrasonography has indeed contributed to the improved diagnostic accuracy of TNs. However, a significant issue persists, where many patients undergo unnecessary biopsies, and patients with malignant thyroid nodules (MTNs) are advised to undergo surgery therapy.

METHODS

This study included a total of 293 patients diagnosed with TNs. Differential methylation haplotype blocks (MHBs) in blood leukocytes between MTNs and benign thyroid nodules (BTNs) were detected using reduced representation bisulfite sequencing (RRBS). Subsequently, an artificial intelligence blood leukocyte DNA methylation (BLDM) model was designed to optimize the management and treatment of patients with TNs for more effective outcomes.

RESULTS

The DNA methylation profiles of peripheral blood leukocytes exhibited distinctions between MTNs and BTNs. The BLDM model we developed for diagnosing TNs achieved an area under the curve (AUC) of 0.858 in the validation cohort and 0.863 in the independent test cohort. Its specificity reached 90.91% and 88.68% in the validation and independent test cohorts, respectively, outperforming the specificity of ultrasonography (43.64% in the validation cohort and 47.17% in the independent test cohort), albeit with a slightly lower sensitivity (83.33% in the validation cohort and 82.86% in the independent test cohort) compared to ultrasonography (97.62% in the validation cohort and 100.00% in the independent test cohort). The BLDM model could correctly identify 89.83% patients whose nodules were suspected malignant by ultrasonography but finally histological benign. In micronodules, the model displayed higher specificity (93.33% in the validation cohort and 92.00% in the independent test cohort) and accuracy (88.24% in the validation cohort and 87.50% in the independent test cohort) for diagnosing TNs. This performance surpassed the specificity and accuracy observed with ultrasonography. A TN diagnostic and treatment framework that prioritizes patients is provided, with fine-needle aspiration (FNA) biopsy performed only on patients with indications of MTNs in both BLDM and ultrasonography results, thus avoiding unnecessary biopsies.

CONCLUSIONS

This is the first study to demonstrate the potential of non-invasive blood leukocytes in diagnosing TNs, thereby making TN diagnosis and treatment more efficient in China.

Integrated co-expression analysis of regulatory elements (miRNA, lncRNA, and TFs) in bovine monocytes induced by Str. uberis

Non-coding RNAs, including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), together with transcription factors, are critical pre-, co-, and post-transcriptional regulators. In addition to their criteria as ideal biomarkers, they have great potential in disease prognosis, diagnosis, and treatment of complex diseases. Investigation of regulatory mechanisms in the context of bovine mastitis, as most common and economic disease in the dairy industry, to identify elements influencing the expression of candidate genes as key regulators of the mammary immune response is not yet fully understood. Transcriptome profiles (50 RNA-Seq and 50 miRNA-Seq samples) of bovine monocytes induced by Str. uberis were used for co-expression module detection and preservation analysis using the weighted gene co-expression network analysis (WGCNA) approach. Assigned mi-, lnc-, and m-modules used to construct the integrated regulatory networks and miRNA-lncRNA-mRNA regulatory sub-networks. Remarkably, we have identified 18 miRNAs, five lncRNAs, and seven TFs as key regulators of str. uberis-induced mastitis. Most of the genes introduced here, mainly involved in immune response, inflammation, and apoptosis, were new to mastitis. These findings may help to further elucidate the underlying mechanisms of bovine mastitis, and the discovered genes may serve as signatures for early diagnosis and treatment of the disease.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels T cell-related prognostic risk model and tumor immune microenvironment modulation in triple-negative breast cancer

BACKGROUND

Triple negative breast cancer (TNBC) is an aggressive and fatal malignancy. The current success of tumor immunotherapy has focused attention on intermediate T-cell subsets and the tumor microenvironment, which are essential for activation of the anti-tumor response. Therefore, both areas require further research to accelerate progress in developing tailored immunotherapeutic approaches for patients with TNBC.

METHODS

We obtained scRNA-seq data of TNBC from the GEO database. A multiplex strategy was used to analyze and identify the T-cell heterogeneity of TNBC. By combining the METABRIC and GEO databases, a prognostic risk model for T-cell marker genes was constructed and validated. In addition, the immune-infiltrating cells of TNBC was analyzed using CIBERSORT, and the association between the risk model and response to immunotherapy was investigated.

RESULTS

Based on scRNA-seq data, 25,932 cells were identified for multiple analyzes. T cells were studied with a focus on 2 subtypes, including CD8⁺ and CD4⁺. There were also communication relationships between T cells and multiple cell types. The results of the enrichment analysis showed that the T-cell marker genes were focused in pathways related to the immune system. In addition, OPTN, TMEM176A, PKM and HES1 deserve attention as prognostic markers in TNBC. The immune infiltration results showed that the high-risk group had significant immune cell infiltration and immunosuppression status.

CONCLUSION

This study provides a resource for understanding T-cell heterogeneity and the associated prognostic risk model for TNBC. The results show that the model helps predict prognosis and response to treatment in breast cancer.

Tissue proteome analysis revealed an association between cancer, immune system response, and the idiopathic granulomatous mastitis

Idiopathic Granulomatous Mastitis (IGM) is a disease that clinically mimics breast cancers with symptoms of pain, edema, erythema, nipple discharge, nipple retraction, and fistula. Although IGM is considered to be formed by autoimmune responses or infections, the molecular mechanism behind formation and progress is unknown. Therefore, in this study, we aimed to investigate molecular mechanisms underlying IGM formation, progress, and recurrence by monitoring the changes at the proteome level. Protein extracts prepared from IGM (n = 15) and within-control tissues (n = 15) were subjected to nHPLC followed by LC-MS/MS proteomic analysis. Label-free quantitation analysis revealed that sixty differentially regulated between the two groups. Those proteins were classified based on their role in metabolic pathways using bioinformatics tools. Based on DAVID analysis, 16 of the differently regulated proteins were associated with the immune system, while 17 proteins were involved in cancer metabolism. STRING analysis showed that five of the differentially regulated proteins were associated with combined immune deficiency which were PNP, TAP1, ITGAL, PRKDC, and PTPRC while the other proteins were involved in insulin response and neutrophil degranulation. This study is one of the very few studies that investigated the changes in protein expressions of IGM tissues compared to controls. For the first time, we have shown the relationship of IGM with the immune system at the protein level and also underlined the cancer-like behavior of the disease. Furthermore, the proteins that were pointed out as combined immune deficiency-related proteins may have value as diagnostic markers for idiopathic granulomatous mastitis although further studies are needed to shed more light on the pathogenesis of the disease.

The Multi-Dimensional Biomarker Landscape in Cancer Immunotherapy

The field of immuno-oncology is now at the forefront of cancer care and is rapidly evolving. The immune checkpoint blockade has been demonstrated to restore antitumor responses in several cancer types. However, durable responses can be observed only in a subset of patients, highlighting the importance of investigating the tumor microenvironment (TME) and cellular heterogeneity to define the phenotypes that contribute to resistance as opposed to those that confer susceptibility to immune surveillance and immunotherapy. In this review, we summarize how some of the most widely used conventional technologies and biomarkers may be useful for the purpose of predicting immunotherapy outcomes in patients, and discuss their shortcomings. We also provide an overview of how emerging single-cell spatial omics may be applied to further advance our understanding of the interactions within the TME, and how these technologies help to deliver important new insights into biomarker discovery to improve the prediction of patient response.

Synergistic anti-proliferative activity of JQ1 and GSK2801 in triple-negative breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC) constitutes 10-20% of breast cancers and is challenging to treat due to a lack of effective targeted therapies. Previous studies in TNBC cell lines showed in vitro growth inhibition when JQ1 or GSK2801 were administered alone, and enhanced activity when co-administered. Given their respective mechanisms of actions, we hypothesized the combinatorial effect could be due to the target genes affected. Hence the target genes were characterized for their expression in the TNBC cell lines to prove the combinatorial effect of JQ1 and GSK2801.

METHODS

RNASeq data sets of TNBC cell lines (MDA-MB-231, HCC-1806 and SUM-159) were analyzed to identify the differentially expressed genes in single and combined treatments. The topmost downregulated genes were characterized for their downregulated expression in the TNBC cell lines treated with JQ1 and GSK2801 under different dose concentrations and combinations. The optimal lethal doses were determined by cytotoxicity assays. The inhibitory activity of the drugs was further characterized by molecular modelling studies.

RESULTS

Global expression profiling of TNBC cell lines using RNASeq revealed different expression patterns when JQ1 and GSK2801 were co-administered. Functional enrichment analyses identified several metabolic pathways (i.e., systemic lupus erythematosus, PI3K-Akt, TNF, JAK-STAT, IL-17, MAPK, Rap1 and signaling pathways) enriched with upregulated and downregulated genes when combined JQ1 and GSK2801 treatment was administered. RNASeq identified downregulation of PTPRC, MUC19, RNA5-8S5, KCNB1, RMRP, KISS1 and TAGLN (validated by RT-qPCR) and upregulation of GPR146, SCARA5, HIST2H4A, CDRT4, AQP3, MSH5-SAPCD1, SENP3-EIF4A1, CTAGE4 and RNASEK-C17orf49 when cells received both drugs. In addition to differential gene regulation, molecular modelling predicted binding of JQ1 and GSK2801 with PTPRC, MUC19, KCNB1, TAGLN and KISS1 proteins, adding another mechanism by which JQ1 and GSK2801 could elicit changes in metabolism and proliferation.

CONCLUSION

JQ1-GSK2801 synergistically inhibits proliferation and results in selective gene regulation. Besides suggesting that combinatorial use could be useful therapeutics for the treatment of TNBC, the findings provide a glimpse into potential mechanisms of action for this combination therapy approach.

Improved Prediction of Survival Outcomes Using Residual Cancer Burden in Combination With Ki-67 in Breast Cancer Patients Underwent Neoadjuvant Chemotherapy

We developed a model for improving the prediction of survival outcome using postoperative Ki-67 value in combination with residual cancer burden (RCB) in patients with breast cancer (BC) who underwent neoadjuvant chemotherapy (NAC). We analyzed the data from BC patients who underwent NAC between 2010 and 2019 at Samsung Medical Center and developed our residual proliferative cancer burden (RPCB) model using semi-quantitative Ki-67 value and RCB class. The Cox proportional hazard model was used to develop our RPCB model according to disease free survival (DFS) and overall survival (OS). In total, 1,959 patients were included in this analysis. Of 1,959 patients, 905 patients were excluded due to RCB class 0, and 32 were due to a lack of Ki-67 data. Finally, an RPCB model was developed using data from 1,022 patients. The RPCB score was calculated for DFS and OS outcomes, respectively (RPCB-DFS and RPCB-OS). For further survival analysis, we divided the population into 3 classes according to the RPCB score. In the prediction of DFS, C-indices were 0.751 vs 0.670 and time-dependent areas under the receiver operating characteristic curves (AUCs) at 3-year were 0.740 vs 0.669 for RPCB-DFS and RCB models, respectively. In the prediction of OS, C-indices were 0.819 vs 0.720 and time-dependent AUCs at 3-year were 0.875 vs 0.747 for RPCB-OS and RCB models, respectively. The RPCB model developed using RCB class and semi-quantitative Ki-67 had superior predictive value for DFS and OS compared with that of RCB class. This prediction model could provide the basis to decide risk-stratified treatment plan for BC patients who had residual disease after NAC.

Construction and Validation of a Prognostic Risk Model for Triple-Negative Breast Cancer Based on Autophagy-Related Genes

Background

Autophagy plays an important role in triple-negative breast cancer (TNBC). However, the prognostic value of autophagy-related genes (ARGs) in TNBC remains unknown. In this study, we established a survival model to evaluate the prognosis of TNBC patients using ARGs signature.

Methods

A total of 222 autophagy-related genes were downloaded from The Human Autophagy Database. The RNA-sequencing data and corresponding clinical data of TNBC were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed autophagy-related genes (DE-ARGs) between normal samples and TNBC samples were determined by the DESeq2 package. Then, univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were performed. According to the LASSO regression results based on univariate Cox, we identified a prognostic signature for overall survival (OS), which was further validated by using the Gene Expression Omnibus (GEO) cohort. We also found an independent prognostic marker that can predict the clinicopathological features of TNBC. Furthermore, a nomogram was drawn to predict the survival probability of TNBC patients, which could help in clinical decision for TNBC treatment. Finally, we validated the requirement of an ARG in our model for TNBC cell survival and metastasis.

Results

There are 43 DE-ARGs identified between normal and tumor samples. A risk model for OS using CDKN1A, CTSD, CTSL, EIF4EBP1, TMEM74, and VAMP3 was established based on univariate Cox regression and LASSO regression analysis. Overall survival of TNBC patients was significantly shorter in the high-risk group than in the low-risk group for both the training and validation cohorts. Using the Kaplan–Meier curves and receiver operating characteristic (ROC) curves, we demonstrated the accuracy of the prognostic model. Multivariate Cox regression analysis was used to verify risk score as an independent predictor. Subsequently, a nomogram was proposed to predict 1-, 3-, and 5-year survival for TNBC patients. The calibration curves showed great accuracy of the model for survival prediction. Finally, we found that depletion of EIF4EBP1, one of the ARGs in our model, significantly reduced cell proliferation and metastasis of TNBC cells.

Conclusion

Based on six ARGs (CDKN1A, CTSD, CTSL, EIF4EBP1, TMEM74, and VAMP3), we developed a risk prediction model that can help clinical doctors effectively predict the survival status of TNBC patients. Our data suggested that EIF4EBP1 might promote the proliferation and migration in TNBC cell lines. These findings provided a novel insight into the vital role of the autophagy-related genes in TNBC and may provide new therapeutic targets for TNBC.

Developing an immune signature for triple-negative breast cancer to predict prognosis and immune checkpoint inhibitor response

Aim: We aimed to develop a new signature based on immune-related genes to predict prognosis and response to immune checkpoint inhibitors in patients with triple-negative breast cancer (TNBC). Materials & methods: Single-sample gene set enrichment was used to develop an immune-based prognostic signature (IPRS) for TNBC patients. We conducted multivariate Cox analysis to evaluate the prognosis value of the IPRS. Result: An IPRS based on 66 prognostic genes was developed. Multivariate Cox analysis indicated that the IPRS was an independent factor for prognosis. PD-1, PD-L1, PD-L2 and CTLA4 gene expression was higher in the low-risk group, suggesting IPRS could predict the response to immune checkpoint inhibitors. Conclusion: The IPRS might be a reliable signature to predict TNBC patients' prognosis and response to immune checkpoint inhibitors, but needs prospective validation.

Exploration of genes and tumor infiltrating lymphocytes in female lung adenocarcinoma microenvironment that predicted prognosis

The tumor microenvironment has an important impact on tumor growth, invasion, metastasis, anti-tumor immune tolerance, and prognosis. The present study aimed to explore female lung adenocarcinoma microenvironment-associated tumor infiltrating lymphocytes (TILs) and genes that predict prognosis in The Cancer Genome Atlas (TCGA) database. Gene expression profiles of female patients with lung adenocarcinoma were downloaded from TCGA. Base on the CIBERSORT algorithm, we determined the fractions of TILs. By applying the ESTIMATE algorithm, immune scores and stromal scores were derived. According to the immune and stromal scores, we categorized the female patients with lung adenocarcinoma into high and low score groups. We also identified the fractions of TILs and differentially expressed genes (DEGs) that were significantly related with prognosis. The proportion of M1 macrophages was significantly negatively related to overall survival in female patients with lung adenocarcinoma. There were 269 upregulated genes and 35 downregulated genes both in immune scores and stromal scores. PTPRC (protein tyrosine phosphatase receptor type C) and GIMAP6 (GTPase, IMAP family member 6) were not only hub genes, but also were significantly related to overall survival in the Kaplan-Meier Plotter online and TCGA databases. In summary, our study provided new insight into the tumor microenvironment-related cellular and molecular mechanisms of women with lung adenocarcinoma. The results will be useful for future clinical studies.

A Novel Seven Gene Signature-Based Prognostic Model to Predict Distant Metastasis of Lymph Node-Negative Triple-Negative Breast Cancer

Background

The prognosis of lymph node-negative triple-negative breast cancer (TNBC) is still worse than that of other subtypes despite adjuvant chemotherapy. Reliable prognostic biomarkers are required to identify lymph node-negative TNBC patients at a high risk of distant metastasis and optimize individual treatment.

Methods

We analyzed the RNA sequencing data of primary tumor tissue and the clinicopathological data of 202 lymph node-negative TNBC patients. The cohort was randomly divided into training and validation sets. Least absolute shrinkage and selection operator Cox regression and multivariate Cox regression were used to construct the prognostic model.

Results

A clinical prognostic model, seven-gene signature, and combined model were constructed using the training set and validated using the validation set. The seven-gene signature was established based on the genomic variables associated with distant metastasis after shrinkage correction. The difference in the risk of distant metastasis between the low- and high-risk groups was statistically significant using the seven-gene signature (training set: P < 0.001; validation set: P = 0.039). The combined model showed significance in the training set (P < 0.001) and trended toward significance in the validation set (P = 0.071). The seven-gene signature showed improved prognostic accuracy relative to the clinical signature in the training data (AUC value of 4-year ROC, 0.879 vs. 0.699, P = 0.046). Moreover, the composite clinical and gene signature also showed improved prognostic accuracy relative to the clinical signature (AUC value of 4-year ROC: 0.888 vs. 0.699, P = 0.029; AUC value of 5-year ROC: 0.882 vs. 0.693, P = 0.038). A nomogram model was constructed with the seven-gene signature, patient age, and tumor size.

Conclusions

The proposed signature may improve the risk stratification of lymph node-negative TNBC patients. High-risk lymph node-negative TNBC patients may benefit from treatment escalation.

Integrated Analysis of Transcriptome and Differential Methylation of Neurofibromatosis Type 2 Vestibular Schwannomas

BACKGROUND

Vestibular schwannoma is the third most common benign intracranial tumor that can occur sporadically or be associated with neurofibromatosis type 2 (neurofibromatosis type 2 vestibular schwannoma [NF2-VS]). The aim of this study is to provide a comprehensive bioinformatic analysis of methylated-differentially expressed genes (MDEGs) in NF2-VS.

METHODS

Transcriptional sequencing datasets (GSE141801 and GSE108524) and gene methylation microarrays (GSE56598) from the Gene Expression Omnibus database were used to identify and analyze MDEGs in NF2-VS. A protein-protein interaction (PPI) network was built, and the hub genes and modules were identified. Finally, potential pharmacotherapy targeting MDEGs were extracted for NF2-VS.

RESULTS

A total of 57 hypermethylation-low expression genes and 88 hypomethylation-high expression genes were identified. Pathways associated with aberrantly MDEGs included P13K-AKT, MAPK, and Ras, which were also involved in NF2-VS. Six hub genes (EGFR, CCND1, CD53, CSF1R, PLAU, and FGFR1) were identified from the PPI network. Modification of the aforementioned genes altered cell-to-cell communication, response to stimulus, cellular regulation, and membrane and protein bindings. Thirty drugs targeting these pathways were selected based on the hub genes.

CONCLUSIONS

Analysis of MDEGs may enrich the understanding of the molecular mechanisms of NF2-VS pathogenesis and lay the groundwork for potential biomarkers and therapeutic targets for NF2-VS.

Refining patient selection for breast cancer immunotherapy: beyond PD-L1

Therapies that modulate immune response to cancer, such as immune checkpoint inhibitors, began an intense development a few years ago; however, in breast cancer (BC), the results have been relatively disappointing so far. Finding biomarkers for better selection of BC patients for various immunotherapies remains a significant unmet medical need. At present, only tumour tissue programmed death-ligand 1 (PD-L1) and mismatch repair deficiency status are approved as theranostic biomarkers for programmed cell death-1 (PD-1)/PD-L1 inhibitors in BC. However, due to the complexity of tumour microenvironment (TME) and cancer response to immunomodulators, none of them is a perfect selector. Therefore, an intense quest is ongoing for complementary tumour- or host-related predictive biomarkers in breast immuno-oncology. Among the upcoming biomarkers, quantity, immunophenotype and spatial distribution of tumour-infiltrating lymphocytes and other TME cells as well as immune gene signatures emerge as most promising and are being increasingly tested in clinical trials. Biomarkers or strategies allowing dynamic assessment of BC response to immunotherapy, such as circulating/exosomal PD-L1, quantity of white/immune blood cell subpopulations and molecular imaging are particularly suitable for immunotreatment monitoring. Finally, host-related factors, such as microbiome and lifestyle, should also be taken into account when planning integration of immunomodulating therapies into BC management. As none of the biomarkers taken separately is accurate enough, the solution could come from composite biomarkers, which would combine clinical, molecular and immunological features of the disease, possibly powered by artificial intelligence.

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At present, immune checkpoint inhibitors (ICIs) are the only approved immunotherapy drugs in BC.

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Tumour PD-L1 and microsatellite status are current companion biomarkers for ICIs in BC; however, these need improvement.

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Evaluation of tumour immune contexture and the dynamics of circulating immune cell counts are promising novel approaches.

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Development of noninvasive monitoring and composite biomarkers will facilitate cancer immunotherapy, including in BC.

Single-cell RNA sequencing profiling in a patient with discordant primary cutaneous B-cell and T-cell lymphoma reveals micromilieu-driven immune skewing

BACKGROUND

Primary cutaneous lymphomas comprise a heterogeneous group of B-cell and T-cell malignancies which often show an indolent course, but can progress to aggressive disease in a subset of patients. Diagnosis is often delayed owing to clinical and histopathological similarities with benign inflammatory conditions. Especially during early disease, cancer cells are present at relatively low percentages compared with the inflammatory infiltrate, an interplay that is currently only insufficiently understood.

OBJECTIVES

To improve diagnostics and perform molecular characterization of a complex type of primary cutaneous lymphoma.

METHODS

Single-cell RNA sequencing (scRNA-seq) was performed and combined with T-cell and B-cell receptor sequencing.

RESULTS

We were able to diagnose a patient with concurrent mycosis fungoides (MF) and primary cutaneous follicle centre lymphoma (PCFCL), appearing in mutually exclusive skin lesions. Profiling of tumour cells and the tissue microenvironment revealed a type-2 immune skewing in MF, most likely guided by the expanded clone that also harboured upregulation of numerous pro-oncogenic genes. By contrast, PCFCL lesions exhibited a more type-1 immune phenotype, consistent with its indolent behaviour.

CONCLUSIONS

These data not only illustrate the diagnostic potential of scRNA-seq, but also allow the characterization of specific clonal populations that shape the unique tissue microenvironment in clinically distinct types of lymphoma skin lesions.

Comprehensive Analysis of Prognostic and Genetic Signatures for General Transcription Factor III (GTF3) in Clinical Colorectal Cancer Patients Using Bioinformatics Approaches

Colorectal cancer (CRC) has the fourth-highest incidence of all cancer types, and its incidence has steadily increased in the last decade. The general transcription factor III (GTF3) family, comprising GTF3A, GTF3B, GTF3C1, and GTFC2, were stated to be linked with the expansion of different types of cancers; however, their messenger (m)RNA expressions and prognostic values in colorectal cancer need to be further investigated. To study the transcriptomic expression levels of GTF3 gene members in colorectal cancer in both cancerous tissues and cell lines, we first performed high-throughput screening using the Oncomine, GEPIA, and CCLE databases. We then applied the Prognoscan database to query correlations of their mRNA expressions with the disease-specific survival (DSS), overall survival (OS), and disease-free survival (DFS) status of the colorectal cancer patient. Furthermore, proteomics expressions of GTF3 family members in clinical colorectal cancer specimens were also examined using the Human Protein Atlas. Finally, genomic alterations of GTF3 family gene expressions in colorectal cancer and their signal transduction pathways were studied using cBioPortal, ClueGO, CluePedia, and MetaCore platform. Our findings revealed that GTF3 family members' expressions were significantly correlated with the cell cycle, oxidative stress, WNT/β-catenin signaling, Rho GTPases, and G-protein-coupled receptors (GPCRs). Clinically, high GTF3A and GTF3B expressions were significantly correlated with poor prognoses in colorectal cancer patients. Collectively, our study declares that GTF3A was overexpressed in cancer tissues and cell lines, particularly colorectal cancer, and it could possibly step in as a potential prognostic biomarker.

Identification of TYROBP and FCER1G as Key Genes with Prognostic Value in Clear Cell Renal Cell Carcinoma by Bioinformatics Analysis

The involvement of aberrantly expressed genes in the pathogenesis and progression of various human malignancies has been widely reported, including clear cell renal cell carcinoma (ccRCC). This study aimed to identify potential crucial genes in ccRCC and further investigate the role of these genes in ccRCC prognosis. Three gene expression profiles (GSE3, GSE6344 and GSE53000) were downloaded from GEO database. GEO2R was performed to identify the differentially expressed genes (DEGs) between ccRCC and normal samples. GO analysis and KEGG pathway enrichment analysis were applied for the function analysis. The DEGs were mapped into the PPI network, then the hub genes were identified and verified using the ONCOMINE database. Kaplan-Meier plotter was used to evaluate of the prognostic value of the identified hub genes. A total of 113 DEGs were identified from the three gene expression profiles, including 64 up-regulated genes and 69 down-regulated genes. DEGs were observed to be enriched in biological processes related to the progress and pathogenesis of human cancers. According to PPI network, 5 hub genes were collected, including TYROBP, C1QB, ITGB2, CD53 and FCER1G. Among them, CD53 was newly identified, and Kaplan-Meier survival curves suggested that high expression of CD53 was significantly associated with poor survival in ccRCC patients (log-rank P < 0.01). The present results may provide new insight into the understanding of molecular mechanisms and the clinical prognosis of ccRCC.

Construction of a Prognostic Gene Signature Associated with Immune Infiltration in Glioma: A Comprehensive Analysis Based on the CGGA

Background

Tumor microenvironment (TME) is closely related to the progression of glioma and the therapeutic effect of drugs on this cancer. The aim of this study was to develop a signature associated with the tumor immune microenvironment using machine learning.

Methods

We downloaded the transcriptomic and clinical data of glioma patients from the Chinese Glioma Genome Atlas (CGGA) databases (mRNAseq_693). The single-sample Gene Set Enrichment Analysis (ssGSEA) database was used to quantify the relative abundance of immune cells. We divided patients into two different infiltration groups via unsupervised clustering analysis of immune cells and then selected differentially expressed genes (DEGs) between the two groups. Survival-related genes were determined using Cox regression analysis. We next randomly divided patients into a training set and a testing set at a ratio of 7 : 3. By integrating the DEGs into least absolute shrinkage and selection operator (LASSO) regression analysis in the training set, we were able to construct a 15-gene signature, which was validated in the testing and total sets. We further validated the signature in the mRNAseq_325 dataset of CGGA.

Results

We identified 74 DEGs associated with tumor immune infiltration, 70 of which were significantly associated with overall survival (OS). An immune-related gene signature was established, consisting of 15 key genes: adenosine triphosphate (ATP)-binding cassette subfamily C member 3 (ABCC3), collagen type IV alpha 1 chain (COL4A1), podoplanin (PDPN), annexin A1 (ANXA1), COL4A2, insulin-like growth factor binding protein 2 (IGFBP2), serpin family A member 3 (SERPINA3), CXXC-type zinc finger protein 11 (CXXC11), junctophilin 3 (JPH3), secretogranin III (SCG3), secreted protein acidic and rich in cysteine (SPARC)-related modular calcium-binding protein 1 (SMOC1), Cluster of Differentiation 14 (CD14), COL1A1, S100 calcium-binding protein A4 (S100A4), and transforming growth factor beta 1 (TGF-β1). The OS of patients in the high-risk group was worse than that of patients in the low-risk group. GSEA showed that interleukin-6 (IL-6)/Janus kinase (JAK)/signal transducer and activator of transcription (STAT3) signaling, interferon gamma (IFN-γ) response, angiogenesis, and coagulation were more highly enriched in the high-risk group and that oxidative phosphorylation was more highly enriched in the low-risk group.

Conclusion

We constructed a stable gene signature associated with immune infiltration to predict the survival rates of glioma patients.