The balance between breast cancer and the immune system: Challenges for prognosis and clinical benefit from immunotherapies

Non-viral vector-based genome editing for cancer immunotherapy

Despite the exciting promise of cancer immunotherapy in the clinic, immune checkpoint blockade therapy and T cell-based therapies are often associated with low response rates, intrinsic and adaptive immune resistance, and systemic side effects. CRISPR-Cas-based genome editing appears to be an effective strategy to overcome these unmet clinical needs. As a safer delivery platform for the CRISPR-Cas system, non-viral nanoformulations have been recently explored to target tumor cells and immune cells, aiming to improve cancer immunotherapy on a gene level. In this review, we summarized the efforts of non-viral vector-based CRISPR-Cas-mediated genome editing in tumor cells and immune cells for cancer immunotherapy. Their design rationale and specific applications were highlighted.

Guardians and Mediators of Metastasis: Exploring T Lymphocytes, Myeloid-Derived Suppressor Cells, and Tumor-Associated Macrophages in the Breast Cancer Microenvironment

Breast cancers (BCs) are solid tumors composed of heterogeneous tissues consisting of cancer cells and an ever-changing tumor microenvironment (TME). The TME includes, among other non-cancer cell types, immune cells influencing the immune context of cancer tissues. In particular, the cross talk of immune cells and their interactions with cancer cells dramatically influence BC dissemination, immunoediting, and the outcomes of cancer therapies. Tumor-infiltrating lymphocytes (TILs), tumor-associated macrophages (TAMs), and myeloid-derived suppressor cells (MDSCs) represent prominent immune cell populations of breast TMEs, and they have important roles in cancer immunoescape and dissemination. Therefore, in this article we review the features of TILs, TAMs, and MDSCs in BCs. Moreover, we highlight the mechanisms by which these immune cells remodel the immune TME and lead to breast cancer metastasis.

Integration of multiomics analyses reveals unique insights into CD24-mediated immunosuppressive tumor microenvironment of breast cancer

BACKGROUND

Tumor immunotherapy brings new light and vitality to breast cancer patients, but low response rate and limitations of therapeutic targets become major obstacles to its clinical application. Recent studies have shown that CD24 is involved in an important process of tumor immune regulation in breast cancer and is a promising target for immunotherapy.

METHODS

In this study, singleR was used to annotate each cell subpopulation after t-distributed stochastic neighbor embedding (t-SNE) methods. Pseudo-time trace analysis and cell communication were analyzed by Monocle2 package and CellChat, respectively. A prognostic model based on CD24-related genes was constructed using several machine learning methods. Multiple quantitative immunofluorescence (MQIF) was used to evaluate the spatial relationship between CD24+PANCK+cells and exhausted CD8+T cells.

RESULTS

Based on the scRNA-seq analysis, 1488 CD24-related differential genes were identified, and a risk model consisting of 15 prognostic characteristic genes was constructed by combining the bulk RNA-seq data. Patients were divided into high- and low-risk groups based on the median risk score. Immune landscape analysis showed that the low-risk group showed higher infiltration of immune-promoting cells and stronger immune reactivity. The results of cell communication demonstrated a strong interaction between CD24+epithelial cells and CD8+T cells. Subsequent MQIF demonstrated a strong interaction between CD24+PANCK+ and exhausted CD8+T cells with FOXP3+ in breast cancer. Additionally, CD24+PANCK+ and CD8+FOXP3+T cells were positively associated with lower survival rates.

CONCLUSION

This study highlights the importance of CD24+breast cancer cells in clinical prognosis and immunosuppressive microenvironment, which may provide a new direction for improving patient outcomes.

Assessment of stromal tumor-infiltrating lymphocytes in neoadjuvant chemotherapy for invasive breast carcinoma: Predictive insights across molecular subtypes

BACKGROUND AND AIMS

Breast cancer, a leading cause of female mortality, has prompted the widespread adoption of Neoadjuvant chemotherapy (NAC) for its potential to minimize metastasis risk and downstaging tumors. Tumor Infiltrating Lymphocytes (TILs) have emerged as key immunological biomarkers, particularly in breast cancer research. This study focuses on evaluating Stromal TILs (sTILs) in pre-NAC core needle biopsies of Invasive Breast Carcinoma, No Special Type (IBC, NST) and correlating it with NAC response.

MATERIALS AND METHODS

A retrospective study spanning three years (October 2020 to September 2023) was conducted in a tertiary care hospital, involving 73 patients meeting specific inclusion criteria. Pathological assessments, including hormone receptor status, molecular subtyping, and TILs evaluation, were performed. Logistic regression and statistical analyses were conducted to determine associations between TILs, clinicopathological parameters, and complete response.

RESULTS

The study demonstrated excellent discriminatory power of TILs (>10 %) in predicting complete response. Univariate and multivariate logistic regression underscored the independent predictive value of TILs, emphasizing their significance across diverse molecular subtypes.

CONCLUSION

This study provides crucial insights into immune response assessment, particularly sTILs, in optimizing breast cancer treatment strategies and patient outcomes during NAC, contributing to the evolving landscape of personalized emphasising oncology.

Gut microbiota-derived cholic acid mediates neonatal brain immaturity and white matter injury under chronic hypoxia

Chronic hypoxia, common in neonates, disrupts gut microbiota balance, which is crucial for brain development. This study utilized cyanotic congenital heart disease (CCHD) patients and a neonatal hypoxic rat model to explore the association. Both hypoxic rats and CCHD infants exhibited brain immaturity, white matter injury (WMI), brain inflammation, and motor/learning deficits. Through 16s rRNA sequencing and metabolomic analysis, a reduction in B. thetaiotaomicron and P. distasonis was identified, leading to cholic acid accumulation. This accumulation triggered M1 microglial activation and inflammation-induced WMI. Administration of these bacteria rescued cholic acid-induced WMI in hypoxic rats. These findings suggest that gut microbiota-derived cholic acid mediates neonatal WMI and brain inflammation, contributing to brain immaturity under chronic hypoxia. Therapeutic targeting of these bacteria provides a non-invasive intervention for chronic hypoxia patients.

Ferroptosis heterogeneity within the tumor microenvironment revealed a genetic blueprint of breast cancer

The tumor microenvironment (TME) significantly influences disease progression through immune infiltration, while ferroptosis, a recently discovered cell death mechanism, plays a crucial role in tumor suppression. However, its role in breast cancer is not clear. In this study, we analyzed bulk RNA and single-cell RNA sequencing data from 1217 samples, including 1104 breast cancer patients and 113 controls, to identify ferroptosis-related genes (FRGs) and construct a prognostic model. Using univariate cox regression, LASSO regression, and multivariate cox regression analysis, we discovered 21 FRGs and 3 TME-related immune cell types with prognostic value. Dimensionality reduction clustering and visualization were performed using the UMAP method, while the immune infiltration process was calculated with the TIP online tool. We employed GSEA enrichment analysis, WGCNA clustering analysis, and correlation analysis to examine functional differences, and the mutation analysis of the best and worst prognosis groups was conducted using the maftools package. Our findings revealed that knocking down the expression of the hub gene SLC39A7 significantly impacted cancer cell apoptosis and combining ferroptosis and TME scores yielded high prognostic power. Epithelial cells and B cells exhibited higher ferroptosis scores, which were independently associated with immune checkpoint blockade (ICB) response and ICB gene expression. This study provides a foundation for further exploration of the relationship between ferroptosis and ICB response in breast cancer. In conclusion, we developed a prognostic model based on ferroptosis and infiltrated immune cells that effectively stratified breast cancer patients and demonstrated the role of SLC39A7 in breast cancer pathogenesis through the regulation of apoptosis.

Exploring the Immunological Profile in Breast Cancer: Recent Advances in Diagnosis and Prognosis through Circulating Tumor Cells

This review offers a comprehensive exploration of the intricate immunological landscape of breast cancer (BC), focusing on recent advances in diagnosis and prognosis through the analysis of circulating tumor cells (CTCs). Positioned within the broader context of BC research, it underscores the pivotal role of the immune system in shaping the disease's progression. The primary objective of this investigation is to synthesize current knowledge on the immunological aspects of BC, with a particular emphasis on the diagnostic and prognostic potential offered by CTCs. This review adopts a thorough examination of the relevant literature, incorporating recent breakthroughs in the field. The methodology section succinctly outlines the approach, with a specific focus on CTC analysis and its implications for BC diagnosis and prognosis. Through this review, insights into the dynamic interplay between the immune system and BC are highlighted, with a specific emphasis on the role of CTCs in advancing diagnostic methodologies and refining prognostic assessments. Furthermore, this review presents objective and substantiated results, contributing to a deeper understanding of the immunological complexity in BC. In conclusion, this investigation underscores the significance of exploring the immunological profile of BC patients, providing valuable insights into novel advances in diagnosis and prognosis through the utilization of CTCs. The objective presentation of findings emphasizes the crucial role of the immune system in BC dynamics, thereby opening avenues for enhanced clinical management strategies.

Prognostic value and distribution pattern of tumor infiltrating lymphocytes and their subsets in distant metastases of advanced breast cancer

BACKGROUND

There are significant correlations between the levels of tumor infiltrating lymphocytes (TILs) and the prognosis of primary breast cancer. While little is known about immunological mechanisms in the distant metastasis of advanced breast cancer.

PATIENTS AND METHODS

A total of 106 patients with advanced metastatic breast cancer were enrolled in this study between 2016 and 2022. Hematoxylin and eosin staining and immunohistochemistry were used to assess the densities of stromal TILs (sTILs), intratumoral TILs (iTILs) and invasive marginal TILs (imTILs) and CD4+, CD8+, CD20+, FOXP3+ TILs in the primary tumor and metastasis (bone, lung, liver, and distant lymph node) of advanced breast cancer.

RESULTS

Higher levels of sTILs at metastatic sites were associated with better progression-free survival (PFS), postmetastasis survival (PMS) and overall survival (OS) (p = .026, .001 and .005, respectively). The levels of iTILs were significantly lower than those of sTILs and imTILs in both primary tumor (p< .001, both) and metastasis (p< .001, both). The level of CD4+ T cells was higher than those of CD8+ T cells and CD20+ B cells in both primary tumor (p < .001) and metastasis (p < .001). The levels of sTILs (p=0. 001) and imTILs (p< .001) in the primary tumor were generally higher than those in the metastasis.

CONCLUSION

The levels of TILs and their subsets can predict the survival and prognosis of patients with advanced breast cancer. The distributions of TILs and their subsets are similar between the primary tumor and metastasis. The metastases have a lower degree of lymphocytes infiltration than its corresponding primary tumor.

PIK3CA mutation-driven immune signature as a prognostic marker for evaluating the tumor immune microenvironment and therapeutic response in breast cancer

PURPOSE

Gene mutations drive tumor immune microenvironment (TIME) heterogeneity, in turn affecting prognosis and immunotherapy efficacy. PIK3CA is the most frequently mutated gene in breast cancer (BC), yet its relevance to BC prognosis remains controversial. Herein, we sought to determine the impact of PIK3CA mutation-driven immune genes (PDIGs) on BC prognosis in relation to TIME heterogeneity.

METHODS

PIK3CA mutation characteristics were compared and verified between the TCGA-BRCA dataset and a patient cohort from our hospital. PIK3CA mutation-driven differentially expressed genes were identified for consensus clustering and weighted gene co-expression network analysis to select the modules most relevant to the immune subtype. Thereafter, the two were intersected to obtain PDIGs. Univariate Cox, LASSO, and multivariate Cox regression analyses were sequentially performed on PDIGs to obtain a PIK3CA mutation-driven immune signature (PDIS), which was then validated using the Gene Expression Omnibus (GEO) database. Differences in functional enrichment, mutation landscape, immune infiltration, checkpoint gene expression, and drug response were compared between different risk groups.

RESULTS

PIK3CA mutation frequencies in the TCGA and validation cohorts were 34.49% and 40.83%, respectively. PIK3CA mutants were significantly associated with ER, PR, and molecular BC subtypes in our hospital cohort. The PDIS allowed for effective risk stratification and exhibited prognostic power in TCGA and GEO sets. The low-risk patients exhibited greater immune infiltration, higher expression of common immune checkpoint factors, and lower scores for tumor immune dysfunction and exclusion.

CONCLUSION

The PDIS can be used as an effective prognostic model for predicting immunotherapy response to guide clinical decision-making.

The Relationship Between Gut Microbiome Estrobolome and Breast Cancer: A Systematic Review of Current Evidences

Breast cancer is the most frequent kind of cancer and the second leading cause of mortality worldwide, behind heart disease. Next-generation sequencing technologies enables for unprecedented enumeration of human resident gut microorganisms, conferring novel insights into the role of the microbiota in health and individuals with breast cancer. A growing body of research on microbial dysbiosis seems to indicate an elevated risk of health complications including cancer. Although several dysbiosis indices have been proposed, their underlying methodology, as well as the cohorts and conditions of breast cancer patients are significantly different. To date, these indices have not yet been thoroughly reviewed especially when it comes to researching the estrogen-gut microbiota axis. Instead of providing a thorough rating of the most effective diversity measurements, the current work aims to be used to assess the relevance of each study's findings across the demographic data, different subtypes, and stages of breast cancer, and tie them to the estrobolome, which controls the amount of oestrogen that circulates through humans. This review will cover 11 studies which will go into a detailed discussion for the microbiome results of the mentioned studies, leaving to the user the final choice of the most suited indices as well as highlight the observed bacteria found to be related to the estrobolome in hopes of giving the reader a better understanding for the biological cross-talk between gut microbiome and breast cancer progression.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12088-023-01135-z.

Predictive value of stromal tumor-infiltrating lymphocytes in patients with breast cancer treated with neoadjuvant chemotherapy: A meta-analysis

BACKGROUND

The predictive value of tumor-infiltrating lymphocytes (TILs) in response to neoadjuvant chemotherapy (NAC) for breast cancer (BC) has received increasing attention. Here, a meta-analysis was conducted to evaluate the correlation between the expression of stromal TILs and pathological complete response (pCR) after NAC in BC patients.

METHODS

The PubMed, Embase, Cochrane Library, and Web of Science databases were searched online by using a combination of keywords and free words to screen literature on the expression of stromal TILs and pCR after NAC in patients with BC. The data were extracted and evaluated for quality. Relative risk (RR) was used to evaluate the relationship between the expression of stromal TILs before NAC and pCR in BC patients. Meta-analysis was performed with Review Manager 5.3 and STATA 14.0 software.

RESULTS

Eleven studies involving 6039 BC patients were included in the meta-analysis. The results showed a generally high expression of stromal TILs in BC patients, and the pCR rate after NAC in BC patients with a high expression of stromal TILs was significantly higher than that in BC patients with a low expression of stromal TILs [RR = 1.83, 95% confidence interval (CI): 1.69-1.97]. Subgroup analysis based on the molecular subtypes of BC showed that the pCR rate was significantly higher in patients with a high expression of stromal TILs in hormone receptor (HR)-positive BC [RR = 3.23, 95% CI: 2.43-4.30], human epidermal growth factor receptor 2 (HER-2)-positive BC [RR = 1.41, 95% CI: 1.25-1.60], and triple-negative BC [RR = 1.70, 95% CI: 1.53-1.90] than in those with a low expression of stromal TILs. Subgroup analysis based on expression threshold showed that the pCR rate was higher in patients with a high expression of stromal TILs than in patients with a low expression of stromal TILs at different expression thresholds (10% [RR = 1.99, 95% CI: 1.55-2.55], 20%/30% [RR = 1.57, 95% CI: 1.37-1.81], 50%/60% [RR = 1.91, 95% CI: 1.73-2.11].

CONCLUSION

TILs can be used as a predictor of pCR after NAC in patients with BC, and the appropriate high expression threshold of stromal TILs should be selected as the predictive value according to the molecular subtype of BC.

Noninvasive radiomic biomarkers for predicting pseudoprogression and hyperprogression in patients with non-small cell lung cancer treated with immune checkpoint inhibition

This study aimed to develop a computed tomography (CT)-based radiomics model capable of precisely predicting hyperprogression and pseudoprogression (PP) in patients with non-small cell lung cancer (NSCLC) treated with immunotherapy. We retrospectively analyzed 105 patients with NSCLC, from three institutions, treated with immune checkpoint inhibitors (ICIs) and categorized them into training and independent testing set. Subsequently, we processed CT scans with a series of image-preprocessing techniques, and 6008 radiomic features capturing intra- and peritumoral texture patterns were extracted. We used the least absolute shrinkage and selection operator logistic regression model to select radiomic features and construct machine learning models. To further differentiate between progressive disease (PD) and hyperprogressive disease (HPD), we developed a new radiomics model. The logistic regression (LR) model showed optimal performance in distinguishing PP from HPD, with areas under the receiver operating characteristic curve (AUC) of 0.95 (95% confidence interval [CI]: 0.91-0.99) and 0.88 (95% CI: 0.66-1) in the training and testing sets, respectively. Additionally, the support vector machine model showed optimal performance in distinguishing PD from HPD, with AUC of 0.97 (95% CI: 0.93-1) and 0.87 (95% CI: 0.72-1) in the training and testing sets, respectively. Kaplan‒Meier survival curves showed clear stratification between PP predicted by the radiomics model and true progression (HPD and PD) (hazard ratio = 0.337, 95% CI: 0.200-0.568, p < 0.01) in overall survival. Our study demonstrates that radiomic features extracted from baseline CT scans are effective in predicting PP and HPD in patients with NSCLC treated with ICIs.

Non-invasive decision support for clinical treatment of non-small cell lung cancer using a multiscale radiomics approach

BACKGROUND

Selecting therapeutic strategies for cancer patients is typically based on key target-molecule biomarkers that play an important role in cancer onset, progression, and prognosis. Thus, there is a pressing need for novel biomarkers that can be utilized longitudinally to guide treatment selection.

METHODS

Using data from 508 non-small cell lung cancer (NSCLC) patients across three institutions, we developed and validated a comprehensive predictive biomarker that distinguishes six genotypes and infiltrative immune phenotypes. These features were analyzed to establish the association between radiological phenotypes and tumor genotypes/immune phenotypes and to create a radiological interpretation of molecular features. In addition, we assessed the sensitivity of the models by evaluating their performance at five different voxel intervals, resulting in improved generalizability of the proposed approach.

FINDINGS

The radiomics model we developed, which integrates clinical factors and multi-regional features, outperformed the conventional model that only uses clinical and intratumoral features. Our combined model showed significant performance for EGFR, KRAS, ALK, TP53, PIK3CA, and ROS1 mutation status with AUCs of 0.866, 0.874, 0.902, 0.850, 0.860, and 0.900, respectively. Additionally, the predictive performance for PD-1/PD-L1 was 0.852. Although the performance of all models decreased to different degrees at five different voxel space resolutions, the performance advantage of the combined model did not change.

CONCLUSIONS

We validated multiscale radiomic signatures across tumor genotypes and immunophenotypes in a multi-institutional cohort. This imaging-based biomarker offers a non-invasive approach to select patients with NSCLC who are sensitive to targeted therapies or immunotherapy, which is promising for developing personalized treatment strategies during therapy.

SAMHD1 expression is a surrogate marker of immune infiltration and determines prognosis after neoadjuvant chemotherapy in early breast cancer

PURPOSE

The lack of validated surrogate biomarkers is still an unmet clinical need in the management of early breast cancer cases that do not achieve complete pathological response after neoadjuvant chemotherapy (NACT). Here, we describe and validate the use of SAMHD1 expression as a prognostic biomarker in residual disease in vivo and in vitro.

METHODS

SAMHD1 expression was evaluated in a clinical cohort of early breast cancer patients with stage II-III treated with NACT. Heterotypic 3D cultures including tumor and immune cells were used to investigate the molecular mechanisms responsible of SAMHD1 depletion through whole transcriptomic profiling, immune infiltration capacity and subsequent delineation of dysregulated immune signaling pathways.

RESULTS

SAMHD1 expression was associated to increased risk of recurrence and higher Ki67 levels in post-NACT tumor biopsies of breast cancer patients with residual disease. Survival analysis showed that SAMHD1-expressing tumors presented shorter time-to-progression and overall survival than SAMHD1 negative cases, suggesting that SAMHD1 expression is a relevant prognostic factor in breast cancer. Whole-transcriptomic profiling of SAMHD1-depleted tumors identified downregulation of IL-12 signaling pathway as the molecular mechanism determining breast cancer prognosis. The reduced interleukin signaling upon SAMHD1 depletion induced changes in immune cell infiltration capacity in 3D heterotypic in vitro culture models, confirming the role of the SAMHD1 as a regulator of breast cancer prognosis through the induction of changes in immune response and tumor microenvironment.

CONCLUSION

SAMHD1 expression is a novel prognostic biomarker in early breast cancer that impacts immune-mediated signaling and differentially regulates inflammatory intra-tumoral response.

Radiation-targeted immunotherapy: A new perspective in cancer radiotherapy

In contemporary oncology, radiation therapy and immunotherapy stand as critical treatments, each with distinct mechanisms and outcomes. Radiation therapy, a key player in cancer management, targets cancer cells by damaging their DNA with ionizing radiation. Its effectiveness is heightened when used alongside other treatments like surgery and chemotherapy. Employing varied radiation types like X-rays, gamma rays, and proton beams, this approach aims to minimize damage to healthy tissue. However, it is not without risks, including potential damage to surrounding normal cells and side effects ranging from skin inflammation to serious long-term complications. Conversely, immunotherapy marks a revolutionary step in cancer treatment, leveraging the body's immune system to target and destroy cancer cells. It manipulates the immune system's specificity and memory, offering a versatile approach either alone or in combination with other treatments. Immunotherapy is known for its targeted action, long-lasting responses, and fewer side effects compared to traditional therapies. The interaction between radiation therapy and immunotherapy is intricate, with potential for both synergistic and antagonistic effects. Their combined use can be more effective than either treatment alone, but careful consideration of timing and sequence is essential. This review explores the impact of various radiation therapy regimens on immunotherapy, focusing on changes in the immune microenvironment, immune protein expression, and epigenetic factors, emphasizing the need for personalized treatment strategies and ongoing research to enhance the efficacy of these combined therapies in cancer care.

Identification of breast cancer subgroups and immune characterization based on glutamine metabolism-related genes

Immunotherapy is a promising treatment for breast cancer (BC). However, due to individual differences and tumor heterogeneity, immunotherapy is only applicable to some BC patients. Glutamine metabolism plays a role in inhibiting immunotherapy, but its role in BC is limitedly studied. Therefore, we aimed to identify different BC subgroups based on glutamine metabolism and characterize the features of different subgroups to provide guidance for personalized immunotherapy for BC patients. Using unsupervised clustering analysis, we classified BC patients in The Cancer Genome Atlas (TCGA) with glutamine metabolism-related genes and obtained low-risk (LR) and high-risk (HR) subgroups. Survival analysis revealed that prognosis of LR subgroup was notably better than HR subgroup. Through ssGSEA and CIBERSORT methods, we disclosed that infiltration levels of B cells, Mast cells, T helper cells, and Th2 cells, and Type II IFN Response immune function were notably higher in LR subgroup than in HR subgroup. The Wilcox algorithm comparison denoted that DEPTH of LR subgroup was significantly lower than HR subgroup. The TIDE of LR subgroup was significantly higher than HR subgroup. Functional annotation of differentially expressed genes revealed that channel activity and the Estrogen signaling pathway may be related to BC prognosis. Ten hub genes were selected between the subgroups through the STRING database and Cytoscape, and their correlation with drugs was predicted on the CellMiner website. This study analyzed the immune characteristics of BC subgroups based on glutamine metabolism and provided reference for prognosis prediction and personalized immunotherapy.

Integrating single-cell and bulk transcriptomic analyses to develop a cancer-associated fibroblast-derived biomarker for predicting prognosis and therapeutic response in breast cancer

Background

Cancer-associated fibroblasts (CAFs) contribute to the progression and treatment of breast cancer (BRCA); however, risk signatures and molecular targets based on CAFs are limited. This study aims to identify novel CAF-related biomarkers to develop a risk signature for predicting the prognosis and therapeutic response of patients with BRCA.

Methods

CAF-related genes (CAFRGs) and a risk signature based on these genes were comprehensively analyzed using publicly available bulk and single-cell transcriptomic datasets. Modular genes identified from bulk sequencing data were intersected with CAF marker genes identified from single-cell analysis to obtain reliable CAFRGs. Signature CAFRGs were screened via Cox regression and least absolute shrinkage and selection operator (LASSO) analyses. Multiple patient cohorts were used to validate the prognosis and therapeutic responsiveness of high-risk patients stratified based on the CAFRG-based signature. In addition, the relationship between the CAFRG-based signature and clinicopathological factors, tumor immune landscape, functional pathways, chemotherapy sensitivity and immunotherapy sensitivity was examined. External datasets were used and sample experiments were performed to examine the expression pattern of MFAP4, a key CAFRG, in BRCA.

Results

Integrated analyses of single-cell and bulk transcriptomic data as well as prognostic screening revealed a total of 43 prognostic CAFRGs; of which, 14 genes (TLN2, SGCE, SDC1, SAV1, RUNX1, PDLIM4, OSMR, NT5E, MFAP4, IGFBP6, CTSO, COL12A1, CCDC8 and C1S) were identified as signature CAFRGs. The CAFRG-based risk signature exhibited favorable efficiency and accuracy in predicting survival outcomes and clinicopathological progression in multiple BRCA cohorts. Functional enrichment analysis suggested the involvement of the immune system, and the immune infiltration landscape significantly differed between the risk groups. Patients with high CAF-related risk scores (CAFRSs) exhibited tumor immunosuppression, enhanced cancer hallmarks and hyposensitivity to chemotherapy and immunotherapy. Five compounds were identified as promising therapeutic agents for high-CAFRS BRCA. External datasets and sample experiments validated the downregulation of MFAP4 and its strong correlation with CAFs in BRCA.

Conclusions

A novel CAF-derived gene signature with favorable predictive performance was developed in this study. This signature may be used to assess prognosis and guide individualized treatment for patients with BRCA.

Immune cells within tertiary lymphoid structures are associated with progression-free survival in patients with locoregional recurrent breast cancer

INTRODUCTION

Locoregional recurrent breast cancers have a poor prognosis. Little is known about the prognostic impact of immune microenvironment, and tertiary lymphoid structures (TLSs) in particular have not been reported. Thus, we aimed to characterize the immune microenvironment in locoregional recurrent breast tumors and to investigate its relationship with prognosis.

METHODS

We retrospectively included 112 patients with locoregional recurrent breast cancer, and hematoxylin-eosin staining and immunohistochemical staining (CD3, CD4, CD8, CD19, CD38, and CD68) were performed on locoregional recurrent tumor samples. The association of immune cells and TLSs with progression-free survival (PFS) were analyzed by survival analysis.

RESULTS

We found more immune cells in the peritumor than stroma. After grouping according to estrogen receptor (ER) status, a low level of peritumoral CD3+ cells in ER+ subgroup (p = 0.015) and a low level of stromal CD68+ cells in ER- subgroup (p = 0.047) were both associated with longer PFS. TLSs were present in 68% of recurrent tumors, and CD68+ cells within TLSs were significantly associated with PFS as an independent prognostic factor (p = 0.035). TLSs and immune cells (CD3, CD38, and CD68) within TLSs were associated with longer PFS in ER- recurrent tumors (p = 0.044, p = 0.012, p = 0.050, p < 0.001, respectively), whereas CD38+ cells within TLSs were associated with shorter PFS in ER+ recurrent tumors (p = 0.037).

CONCLUSION

Our study proposes potential predictors for the clinical prognosis of patients with locoregional recurrent breast cancer, emphasizing the prognostic value of immune cells within TLSs, especially CD68+ cells.

PEX13 is a potential immunotherapeutic indicator and prognostic biomarker for various tumors including PAAD

The peroxisome serves a significant role in the occurrence and development of cancers. Specifically, the peroxisomal biogenesis factor 13 (PEX13) is crucial to the occurrence of peroxisomes. However, the biological function of PEX13 in cancers remains unclear. To address this, various portals and databases such as The Cancer Genome Atlas Program, The Genotype-Tissue Expression project, the Gene Expression Profiling Interactive Analysis 2, cBioPortal, the Genomic Identification of Significant Targets In Cancer 2.0, Tumor Immune Estimation Resource 2, SangerBox, LinkedOmics, DAVID and STRING were applied to extract and analyze PEX13 data in tumors. The correlations between PEX13 and prognosis, genetic alterations, PEX13-related gene enrichment analysis, weighted gene co-expression network analysis (WGCNA), protein interaction, long non-coding (lnc)RNA/circular (circ)RNA-micro (mi)RNA network and tumor immunity were explored in various tumors. The lncRNA-miRNA-PEX13 and circRNA-miRNA-PEX13 regulatory networks were identified via miRabel, miRDB, TargetScan and ENCORI portals and Cytoscape tool. In vitro assays were applied to verify the biological functions of PEX13 in pancreatic adenocarcinoma (PAAD) cells. The findings revealed that PEX13 is upregulated in various tumors and high PEX13 mRNA expression is associated with poor prognosis in patients with multiple cancers. Genetic alterations in PEX13 such as amplification, mutation and deep deletion have been found in multiple cancers. PEX13-related genes were associated with T cell receptor, signaling pathway and hippo signaling pathway through 'biological process' subontology of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. Through WGCNA analysis, it was discovered that PEX13 hub genes were mainly enriched in the Rap1, ErbB and AMPK signaling pathways in PAAD. Immune analysis showed that PEX13 was significantly related to tumor infiltration immune cells, immune checkpoint genes, microsatellite instability, TMB and tumor purity in a variety of tumors. Cell Counting Kit-8, wound healing, Transwell and colony formation assays displayed that PEX13 knockdown could suppress PAAD cell proliferation, migration, invasion, and colony formation in vitro, respectively. Overall, PEX13 is a potential predictor of immunotherapeutic and prognostic biomarkers in various malignant tumors, including ACC, KICH, LGG, LIHC and PAAD.

Targeting the Tumor Microenvironment in Breast Cancer: Prognostic and Predictive Significance and Therapeutic Opportunities

Breast cancer is one of the most prevalent tumors among women. Its prognosis and treatment outcomes depend on factors related to tumor cell biology. However, recent studies have revealed the critical role of the tumor microenvironment (TME) in the development, progression, and treatment response of breast cancer. In this review, we explore the different components of the TME and their relevance as prognostic and predictive biomarkers in breast cancer. In addition, techniques for assessing the tumor microenvironment, such as immunohistochemistry or gene expression profiling, and their clinical utility in therapeutic decision-making are examined. Finally, therapeutic strategies targeting the TME are reviewed, highlighting their potential clinical benefits. Overall, this review emphasizes the importance of the TME in breast cancer and its potential as a clinical tool for better patient stratification and the design of personalized therapies.

A novel model associated with tumor microenvironment on predicting prognosis and immunotherapy in triple negative breast cancer

Triple negative breast cancer (TNBC) is the most aggressive and malignant subtype in breast cancer. Immunotherapy is a currently promising and effective treatment for TNBC, while not all patients are responsive. Therefore, it is necessary to explore novel biomarkers to screen sensitive populations for immunotherapy. All mRNA expression profiles of TNBC from The Cancer Genome Atlas (TCGA) database were clustered into two subgroups by analyzing tumor immune microenvironment (TIME) with single sample gene set enrichment analysis (ssGSEA). A risk score model was constructed based on differently expressed genes (DEGs) identified from two subgroups using Cox and Least Absolute Shrinkage and Selector Operation (LASSO) regression model. And it was validated by Kaplan-Meier analysis and Receiver Operating Characteristic (ROC) analysis in Gene Expression Omnibus (GEO) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases. Multiplex immunofluorescence (mIF) and Immunohistochemical (IHC) staining were performed on clinical TNBC tissue samples. The relationship between risk score and immune checkpoint blockades (ICB) related signatures was further investigated, as well as the biological processes were performed by gene set enrichment analysis (GSEA). We obtained three DEGs positively related to prognosis and infiltrating immune cells in TNBC. Our risk score model could be an independent prognostic factor and the low risk group exhibited a prolonged overall survival (OS). Patients in low risk group were more likely to present a higher immune infiltration and stronger response to immunotherapy. GSEA revealed the model was associated with immune-related pathways. We constructed and validated a novel model based on three prognostic genes related to TIME in TNBC. The model contributed a robust signature that could predict the prognosis in TNBC, especially for the efficacy of immunotherapy.

Unveiling the Immune Microenvironment's Role in Breast Cancer: A Glimpse into Promising Frontiers

Breast cancer (BC), one of the most widespread and devastating diseases affecting women worldwide, presents a significant public health challenge. This review explores the emerging frontiers of research focused on deciphering the intricate interplay between BC cells and the immune microenvironment. Understanding the role of the immune system in BC is critical as it holds promise for novel therapeutic approaches and precision medicine strategies. This review delves into the current literature regarding the immune microenvironment's contribution to BC initiation, progression, and metastasis. It examines the complex mechanisms by which BC cells interact with various immune cell populations, including tumor-infiltrating lymphocytes (TILs) and tumor-associated macrophages (TAMs). Furthermore, this review highlights the impact of immune-related factors, such as cytokines and immune checkpoint molecules. Additionally, this comprehensive analysis sheds light on the potential biomarkers associated with the immune response in BC, enabling early diagnosis and prognostic assessment. The therapeutic implications of targeting the immune microenvironment are also explored, encompassing immunotherapeutic strategies and combination therapies to enhance treatment efficacy. The significance of this review lies in its potential to pave the way for novel therapeutic interventions, providing clinicians and researchers with essential knowledge to design targeted and personalized treatment regimens for BC patients.

Introduced the ITGB1-DT as a novel biomarker associated with five potential drugs using bioinformatics analysis of breast cancer proteomics data and RT-PCR

BACKGROUND

Breast cancer (BC) has been identified as a significant contributor to the rising number of female cancer deaths. As, it has become clear that breast cancer development depends on the interplay of several biological factors against a single molecule. This research aimed to use proteomics to gain a regulatory and metabolic understanding of BC pathophysiology.

METHOD

For the study, a breast cancer proteomics dataset was downloaded from ProteomeXchange and then analyzed by employing MaxQuant and Perseus. Functional enrichment analysis through Metascape and Cytoscape software showed DEPs related biomedical phenomena with potential abruption. The expression of selected lncRNA in terms of the highest connectivity parameters was then quantitatively assessed through RT-PCR in 30 tumor tissues of breast cancer patients, as compared to the adjacent healthy ones.

RESULT

The results indicated that among the 3048 identified proteins, 1149 were differentially expressed, which could be mainly enriched in several key terms. Furthermore, the obtained findings revealed that ITGB1-DT was significantly overexpressed in tumor tissues. Moreover, we found five potential compounds that could be attributed to ITGB1-DT targets (ATN-161, Firategrast, SB-683698, dabigatran-etexilate, and tranexamic-acid).

CONCLUSION

These analyses proposed that ITGB1-DT could be employed as a differentiated factor to identify breast tumor tissues in healthy samples. Besides this, Firategrast could be introduced as a potential remedial agent for breast cancer patients. Overall, from the analysis of a proteomics dataset, an integrative map was generated, and a novel biomarker that may have been implicated in the early detection of BC was introduced.

The Possible Role of Anti- and Protumor-Infiltrating Lymphocytes in Pathologic Complete Response in Early Breast Cancer Patients Treated with Neoadjuvant Systemic Therapy

The tumor microenvironment, composed of pro- and antitumor immune cells, affects cancer cell behavior. We aimed to evaluate whether tumor-infiltrating lymphocyte (TIL) density and TIL subtypes in core biopsies at the diagnosis of breast cancer patients could predict a pathologic complete response (pCR; ypT0/is ypN0) from neoadjuvant systemic therapy (NST). The TIL subtypes were determined based on the proportions of presumably antitumor (CD8+, CXCL13+) and protumor (PD-1+, FOXP3+) immune cells. A prospective, noninterventional study, including 171 participants undergoing NST, was performed. The median TIL density for the entire cohort was 10% (IQR: 3.5-23.8), and 59 (35%) patients achieved pCR. TIL density was positively associated with pCR (univariately and multivariably). In the multivariable logistic regression model, TIL density was an independent predictor of pCR (p = 0.012, OR 1.27; 95% CI 1.05-1.54) when controlled for age (p = 0.232), Ki-67 (p = 0.001), node-negative status (p = 0.024), and HER2+/triple negative vs. luminal B-like subtype (p < 0.001). In our sample, higher proportions of PD-1+ TILs and FOXP3+ TILs were associated with a higher probability of pCR but the association was not statistically significant and we could not make any conclusions on the direction of associations in the model with all four biomarkers. In the exploratory multivariable analysis, we showed that only higher CD8+ TILs were associated with pCR. In conclusion, TIL density and its subtypes are associated with pCR.

Ginsenosides: changing the basic hallmarks of cancer cells to achieve the purpose of treating breast cancer

In 2021, breast cancer accounted for a substantial proportion of cancer cases and represented the second leading cause of cancer deaths among women worldwide. Although tumor cells originate from normal cells in the human body, they possess distinct biological characteristics resulting from changes in gene structure and function of cancer cells in contrast with normal cells. These distinguishing features, known as hallmarks of cancer cells, differ from those of normal cells. The hallmarks primarily include high metabolic activity, mitochondrial dysfunction, and resistance to cell death. Current evidence suggests that the fundamental hallmarks of tumor cells affect the tissue structure, function, and metabolism of tumor cells and their internal and external environment. Therefore, these fundamental hallmarks of tumor cells enable tumor cells to proliferate, invade and avoid apoptosis. Modifying these hallmarks of tumor cells represents a new and potentially promising approach to tumor treatment. The key to breast cancer treatment lies in identifying the optimal therapeutic agent with minimal toxicity to normal cells, considering the specific types of tumor cells in patients. Some herbal medicines contain active ingredients which can precisely achieve this purpose. In this review, we introduce Ginsenoside's mechanism and research significance in achieving the therapeutic effect of breast cancer by changing the functional hallmarks of tumor cells, providing a new perspective for the potential application of Ginsenoside as a therapeutic drug for breast cancer.

Crosstalk of RNA methylation writers defines tumor microenvironment and alisertib resistance in breast cancer

Background

The five major RNA methylation modifications (m6A, m1A, m6Am, m5C, and m7G) exert biological roles in tumorigenicity and immune response, mediated mainly by "writer" enzymes. Here, the prognostic values of the "writer" enzymes and the TCP1 role in drug resistance in breast cancer (BC) were explored for further therapeutic strategies.

Methods

We comprehensively characterized clinical, molecular, and genetic features of subtypes by consensus clustering. RNA methylation modification "Writers" and related genes_risk (RMW_risk) model for BC was constructed via a machine learning approach. Moreover, we performed a systematical analysis for characteristics of the tumor microenvironment (TME), alisertib sensitivity, and immunotherapy response. A series of experiments in vitro were carried out to assess the association of TCP1 with drug resistance.

Results

One "writer" (RBM15B) and two related genes (TCP1 and ANKRD36) were identified for prognostic model construction, validated by GSE1456, GSE7390, and GSE20685 cohorts and our follow-up data. Based on the patterns of the genes related to prognosis, patients were classified into RMW_risk-high and RMW_risk-low subtypes. Lower RMW_Score was associated with better overall survival and the infiltration of immune cells such as memory B cells. Further analysis revealed that RMW_Score presented potential values in predicting drug sensitivity and response for chemo- and immunotherapy. In addition, TCP1 was confirmed to promote BC alisertib-resistant cell proliferation and migration in vitro.

Conclusion

RMW_Score could function as a robust biomarker for predicting BC patient survival and therapeutic benefits. This research revealed a potential TCP1 role regarding alisertib resistance in BC, providing new sights into more effective therapeutic plans.

GPR81-mediated reprogramming of glucose metabolism contributes to the immune landscape in breast cancer

BACKGROUND

Local tumor microenvironment (TME) plays a crucial role in immunotherapy for breast cancer (BC). Whereas, the molecular mechanism responsible for the crosstalk between BC cells and surrounding immune cells remains unclear. The present study aimed to determine the interplay between GPR81-mediated glucometabolic reprogramming of BC and the immune landscape in TME.

MATERIALS AND METHODS

Immunohistochemistry (IHC) assay was first performed to evaluate the association between GPR81 and the immune landscape. Then, several stable BC cell lines with down-regulated GPR81 expression were established to directly identify the role of GPR81 in glucometabolic reprogramming, and western blotting assay was used to detect the underlying molecular mechanism. Finally, a transwell co-culture system confirmed the crosstalk between glucometabolic regulation mediated by GPR81 in BC and induced immune attenuation.

RESULTS

IHC analysis demonstrated that the representation of infiltrating CD8⁺ T cells and FOXP3⁺ T cells were dramatically higher in BC with a triple negative (TN) subtype in comparison with that with a non-TN subtype (P < 0.001). Additionally, the ratio of infiltrating CD8⁺ to FOXP3⁺ T cells was significantly negatively associated with GPR81 expression in BC with a TN subtype (P < 0.001). Furthermore, GPR81 was found to be substantially correlated with the glycolytic capability (P < 0.001) of BC cells depending on a Hippo-YAP signaling pathway (P < 0.001). In the transwell co-culture system, GPR81-mediated reprogramming of glucose metabolism in BC significantly contributed to a decreased proportion of CD8⁺ T (P < 0.001) and an increased percentage of FOXP3⁺ T (P < 0.001) in the co-cultured lymphocytes.

CONCLUSION

Glucometabolic reprogramming through a GPR81-mediated Hippo-YAP signaling pathway was responsible for the distinct immune landscape in BC. GPR81 was a potential biomarker to stratify patients before immunotherapy to improve BC's clinical prospect.

Identifying the oncogenic roles of FAP in human cancers based on systematic analysis

BACKGROUND

Fibroblast activation protein-α (FAP) is a specific marker of cancer-associated fibroblasts (CAFs) and plays a crucial role in tumor development. However, the biological processes underlying FAP expression in tumor progression and tumor immunity have not been fully elucidated.

METHODS

We utilized RNA-seq data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) to perform differential analysis of FAP expression in tumor tissues and matched-normal tissues. The relationship between FAP expression and clinical prognosis, DNA methylation, and tumor-infiltrating immune cells in pan-cancer was assessed using R Studio (version 4.2.1). Additionally, we employed gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) to investigate the biological functions and pathways associated with FAP expression.

RESULTS

FAP exhibits high expression in most malignancies, albeit to a lesser extent in CESC, KICH, UCEC, SKCM, THCA, and UCS. Furthermore, FAP is either positively or negatively associated with the prognosis of several malignancies. In seven types of cancer, FAP expression is positively correlated with DNA methylation. CIBERSORT analysis revealed an inverse correlation between FAP expression and T cells, B cells, monocytes, and NK cells, while it exhibited a positive correlation with M0, M1, and M2 macrophages. Enrichment analysis further demonstrated that FAP modulates the cell cycle, epithelial-mesenchymal transition (EMT) process, angiogenesis, and immune-related functions and pathways.

CONCLUSION

Our findings indicate a close relationship between FAP expression and tumorigenesis as well as tumor immunity. FAP has the potential to serve as a diagnostic, prognostic, and immunotherapy marker.

Advancements in clinical aspects of targeted therapy and immunotherapy in breast cancer

Breast cancer is the second leading cause of death for women worldwide. The heterogeneity of this disease presents a big challenge in its therapeutic management. However, recent advances in molecular biology and immunology enable to develop highly targeted therapies for many forms of breast cancer. The primary objective of targeted therapy is to inhibit a specific target/molecule that supports tumor progression. Ak strain transforming, cyclin-dependent kinases, poly (ADP-ribose) polymerase, and different growth factors have emerged as potential therapeutic targets for specific breast cancer subtypes. Many targeted drugs are currently undergoing clinical trials, and some have already received the FDA approval as monotherapy or in combination with other drugs for the treatment of different forms of breast cancer. However, the targeted drugs have yet to achieve therapeutic promise against triple-negative breast cancer (TNBC). In this aspect, immune therapy has come up as a promising therapeutic approach specifically for TNBC patients. Different immunotherapeutic modalities including immune-checkpoint blockade, vaccination, and adoptive cell transfer have been extensively studied in the clinical setting of breast cancer, especially in TNBC patients. The FDA has already approved some immune-checkpoint blockers in combination with chemotherapeutic drugs to treat TNBC and several trials are ongoing. This review provides an overview of clinical developments and recent advancements in targeted therapies and immunotherapies for breast cancer treatment. The successes, challenges, and prospects were critically discussed to portray their profound prospects.

Metabolomic Signatures of Scarff-Bloom-Richardson (SBR) Grade in Non-Metastatic Breast Cancer

PURPOSE

Identification of metabolomic biomarkers of high SBR grade in non-metastatic breast cancer.

METHODS

This retrospective bicentric metabolomic analysis included a training set (n = 51) and a validation set (n = 49) of breast cancer tumors, all classified as high-grade (grade III) or low-grade (grade I-II). Metabolomes of tissue samples were studied by liquid chromatography coupled with mass spectrometry.

RESULTS

A molecular signature of the top 12 metabolites was identified from a database of 602 frequently predicted metabolites. Partial least squares discriminant analyses showed that accuracies were 0.81 and 0.82, the R2 scores were 0.57 and 0.55, and the Q2 scores were 0.44431 and 0.40147 for the training set and validation set, respectively; areas under the curve for the Receiver Operating Characteristic Curve were 0.882 and 0.886. The most relevant metabolite was diacetylspermine. Metabolite set enrichment analyses and metabolic pathway analyses highlighted the tryptophan metabolism pathway, but the concentration of individual metabolites varied between tumor samples.

CONCLUSIONS

This study indicates that high-grade invasive tumors are related to diacetylspermine and tryptophan metabolism, both involved in the inhibition of the immune response. Targeting these pathways could restore anti-tumor immunity and have a synergistic effect with immunotherapy. Recent studies could not demonstrate the effectiveness of this strategy, but the use of theragnostic metabolomic signatures should allow better selection of patients.

The construction and analysis of a prognostic assessment model based on P53-related multi-genes in breast carcinoma

BACKGROUND

Breast cancer ranks second in female tumor mortality, with an estimation of 2 million new cases diagnosed each year worldwide.

METHODS

In our current study, we screened 13 genes highly distributed on the P53 phenotype which were significantly expressed and had a strong correlation with survival in the Cancer Genome Atlas breast cancer dataset. Least absolute shrinkage and selection operator Cox regression was conducted to construct the risk assessment model. Based on bioinformatics and statistical methods, we confirmed the credibility and validity of the model by training set and testing set.

RESULTS

The result of comparing the other two previous hypoxia models was also satisfying. We also verified the model on one of the Gene Expression Omnibus datasets-GSE20685. Using clinical data from patients in the Cancer Genome Atlas, we acknowledged the risk score as an independent influence on breast cancer survival prognosis, and strong relevance was suggested between risk signature and age, lymphatic metastasis, tumor size and clinical stage by performing univariate and multivariate analysis. Immunology analysis demonstrated that the macrophages subset was positively associated with a risk score and other immune cell types had a negative effect with the risk score increases. The risk score was also emerging as a valuable prognostic factor for the prediction of chemotherapy drug curative effect because Gemcitabine, vinorelbine, paclitaxel and cisplatin known as a generic drug for breast cancer had more pleasing sensitivity in high-scored patients than low-scored patients.

CONCLUSION

The P53-related risk assessment model is promising to be a potential predictor for the prognosis of patients with breast cancer and a powerful guide for the selection of therapeutic strategies.

The impact of coding germline variants on contralateral breast cancer risk and survival

Evidence linking coding germline variants in breast cancer (BC)-susceptibility genes other than BRCA1, BRCA2, and CHEK2 with contralateral breast cancer (CBC) risk and breast cancer-specific survival (BCSS) is scarce. The aim of this study was to assess the association of protein-truncating variants (PTVs) and rare missense variants (MSVs) in nine known (ATM, BARD1, BRCA1, BRCA2, CHEK2, PALB2, RAD51C, RAD51D, and TP53) and 25 suspected BC-susceptibility genes with CBC risk and BCSS. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated with Cox regression models. Analyses included 34,401 women of European ancestry diagnosed with BC, including 676 CBCs and 3,449 BC deaths; the median follow-up was 10.9 years. Subtype analyses were based on estrogen receptor (ER) status of the first BC. Combined PTVs and pathogenic/likely pathogenic MSVs in BRCA1, BRCA2, and TP53 and PTVs in CHEK2 and PALB2 were associated with increased CBC risk [HRs (95% CIs): 2.88 (1.70-4.87), 2.31 (1.39-3.85), 8.29 (2.53-27.21), 2.25 (1.55-3.27), and 2.67 (1.33-5.35), respectively]. The strongest evidence of association with BCSS was for PTVs and pathogenic/likely pathogenic MSVs in BRCA2 (ER-positive BC) and TP53 and PTVs in CHEK2 [HRs (95% CIs): 1.53 (1.13-2.07), 2.08 (0.95-4.57), and 1.39 (1.13-1.72), respectively, after adjusting for tumor characteristics and treatment]. HRs were essentially unchanged when censoring for CBC, suggesting that these associations are not completely explained by increased CBC risk, tumor characteristics, or treatment. There was limited evidence of associations of PTVs and/or rare MSVs with CBC risk or BCSS for the 25 suspected BC genes. The CBC findings are relevant to treatment decisions, follow-up, and screening after BC diagnosis.

Comprehensive analysis of LILR family genes expression and tumour-infiltrating immune cells in early-stage pancreatic ductal adenocarcinoma

Leucocyte immunoglobulin-like receptors (LILRs) are closely related to tumourigenesis, but their clinical value in early-stage pancreatic ductal adenocarcinoma (PDAC) after pancreaticoduodenectomy remains unknown. Kaplan-Meier and Cox proportional hazards regression models is used to investigate the association between LILR expression and prognosis in tumour biopsies and peripheral blood mononuclear cells. Risk score was calculated for each patient based on the prognostic model. DAVID, STRING, GeneMANIA, and GSEA were used to conduct pathway and functional analyses. The CIBERSORT algorithm is used to analyse tumour-infiltrating immune cells. Survival analysis showed that high levels of LILRA4 (p = 0.006) and LILRB4 (p = 0.04) were significantly associated with better overall survival. High levels of LILRA2 (p = 0.008) and LILRB4 (p = 0.038) were significantly associated with better relapse-free survival. JAK-STAT signalling pathway, regulation of T cell activation, regulation of the immune effector process, and tumour necrosis factor superfamily cytokine production were involved in molecular mechanisms that affected poor prognoses in the high-risk group in GSEA. CIBERSORT demonstrated that the high-risk group had significantly higher infiltrating fraction of memory-activated CD4 T cells and activated NK cells and lower fraction of resting dendritic cells and neutrophils. LILRB4 plays crucial roles in affecting the clinical outcomes of early-stage PDAC.

Risk stratification based on DNA damage-repair-related signature reflects the microenvironmental feature, metabolic status and therapeutic response of breast cancer

DNA damage-repair machinery participates in maintaining genomic integrity and affects tumorigenesis. Molecular signatures based on DNA damage-repair-related genes (DRGs) capable of comprehensively indicating the prognosis, tumor immunometabolic profile and therapeutic responsiveness of breast cancer (BRCA) patients are still lacking. Integrating public datasets and bioinformatics algorithms, we developed a robust prognostic signature based on 27 DRGs. Multiple patient cohorts identified significant differences in various types of survival between high- and low-risk patients stratified by the signature. The signature correlated well with clinicopathological factors and could serve as an independent prognostic indicator for BRCA patients. Furthermore, low-risk tumors were characterized by more infiltrated CD8⁺ T cells, follicular helper T cells, M1 macrophages, activated NK cells and resting dendritic cells, and fewer M0 and M2 macrophages. The favorable immune infiltration patterns of low-risk tumors were also accompanied by specific metabolic profiles, decreased DNA replication, and enhanced antitumor immunity. Low-risk patients may respond better to immunotherapy, and experience improved outcomes with conventional chemotherapy or targeted medicine. Real-world immunotherapy and chemotherapy cohorts verified the predictive results. Additionally, four small molecule compounds promising to target high-risk tumors were predicted. In vitro experiments confirmed the high expression of GNPNAT1 and MORF4L2 in BRCA tissues and their association with immune cells, and the knockdown of these two DRGs suppressed the proliferation of human BRCA cells. In summary, this DNA damage-repair-related signature performed well in predicting patient prognosis, immunometabolic profiles and therapeutic sensitivity, hopefully contributing to precision medicine and new target discovery of BRCA.

Targeting interleukin-13 receptor α2 (IL-13Rα2) for glioblastoma therapy with surface functionalized nanocarriers

Despite surgical and therapeutic advances, glioblastoma multiforme (GBM) is among the most fatal primary brain tumor that is aggressive in nature. Patients with GBM have a median lifespan of just 15 months when treated with the current standard of therapy, which includes surgical resection and concomitant chemo-radiotherapy. In recent years, nanotechnology has shown considerable promise in treating a variety of illnesses, and certain nanomaterials have been proven to pass the blood-brain barrier (BBB) and stay in glioblastoma tissues. Recent preclinical research suggests that the diagnosis and treatment of brain tumor is significantly explored through the intervention of nanomaterials that has showed enhanced effect. In order to elicit an antitumor response, it is necessary to retain the therapeutic candidates within glioblastoma tissues and this job is effectively carried out by nanocarrier particularly functionalized nanocarriers. In the arena of neoplastic diseases including GBM have achieved great attention in recent decades. Furthermore, interleukin-13 receptor α chain variant 2 (IL13Rα2) is a highly expressed and studied target in GBM that is lacked by the surrounding environment. The absence of IL13Rα2 in surrounding normal tissues has made it a suitable target in glioblastoma therapy. In this review article, we highlighted the role of IL13Rα2 as a potential target in GBM along with design and fabrication of efficient targeting strategies for IL13Rα2 through surface functionalized nanocarriers.

The cross-talk of cancer-associated fibroblasts assist in prognosis and immunotherapy in patients with breast carcinoma

The association between cancer-associated fibroblasts (CAFs) and tumor microenvironment (TME) is a key factor in promoting tumor progression. However, the correlation between CAFs and TME in breast carcinoma has not been elucidated. Thus, further study about the cross-effect between CAFs and TME can provide novel strategies for breast carcinoma treatment, particularly targeted immunotherapy. First, we systematically analyzed cell communication in a single-cell dataset and identified the interacted genes between CAFs and TME components. Then, a robust fibroblast-related score (FRS) model was developed using the LASSO algorithm. The FRS can be a reliable adverse prognostic factor in three cohorts with breast carcinoma. Functional enrichment analysis and single-sample Gene Set Enrichment Analysis showed that patients with a high FRS had cold tumors with active proliferation and immunosuppression. Patients with a low FRS presented with hot tumors with active immune and cell-killing functions. Genomic variation analysis revealed that patients with a low FRS had a higher somatic mutation load and copy number variation burden. Finally, patients with a low FRS were more sensitive to chemotherapy and immunotherapy, particularly anti-PD-1 therapy. In conclusion, a reliable FRS model was constructed not only reliable for predicting prognosis but also competent to estimate clinical immunotherapy and chemotherapy response for patients with BRCA, which might provide significant clinical implications for guiding clinical decision-making for patients with BRCA.

Prognosis stratification in breast cancer and characterization of immunosuppressive microenvironment through a pyrimidine metabolism-related signature

Pyrimidine metabolism is a hallmark of cancer and will soon become an essential part of cancer therapy. In the tumor microenvironment, cells reprogram pyrimidine metabolism intrinsically and extracellularly, thereby promoting tumorigenesis. Metabolites in pyrimidine metabolism have a significant impact on promoting cancer advancement and modulating immune system responses. In preclinical studies and practical clinical applications, critical targets in pyrimidine metabolism are acted upon by drugs to exert promising therapeutic effects on tumors. However, the pyrimidine metabolism in breast cancer (BC) is still largely underexplored. In this study, 163 credible pyrimidine metabolism-related genes (PMGs) were retrieved, and their somatic mutations and expression levels were determined. In addition, by using The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases, 12 PMGs related to the overall survival (OS) were determined using the univariate Cox regression analysis. Subsequently, by performing the LASSO Cox hazards regression analysis in the 12 PMGs in TCGA-BRCA dataset, we developed a prognosis nomogram using eight OS-related PMGs and then verified the same in the METABRIC, GSE96058, GSE20685, GSE42568 and GSE86166 data. Moreover, we validated relationships between the pyrimidine metabolism index (PMI) and the survival probability of patients, essential clinical parameters, including the TNM stage and the PAM50 subtypes. Next, we verified the predictive capability of the optimum model, including the signature, the PAM50 subtype, and age, using ROC analysis and calibration curve, and compared it with other single clinical factors for the predictive power of benefit using decision curve analysis. Finally, we investigated the potential effects of pyrimidine metabolism on immune checkpoints, tumor-infiltrating immune cells, and cytokine levels and determined the potential implications of pyrimidine metabolism in BC immunotherapy. In conclusion, our findings suggest that pyrimidine metabolism has underlying prognostic significance in BC and can facilitate a new management approach for patients with different prognoses and more precise immunotherapy.

A Novel Inflammatory Response-Related Gene Signature Predicts Immune Status and Prognosis of Breast Cancer

PURPOSE

Breast cancer is the most common type of cancer and the leading cause of cancer-related death in women worldwide. In this study, we aimed to construct an inflammatory response-related gene model for predicting the immune status and prognosis of breast cancer patients.

METHODS

We obtained the inflammatory response-related genes from the Molecular Signatures Database. Furthermore, we used univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) regression analysis, and multivariate Cox regression to construct an inflammatory response-related gene signature (IRGS) model based on dataset obtained from The Cancer Genome Atlas (TCGA). Patients were consequently categorized into high-risk and low-risk groups. Kaplan-Meier analysis was used to compare the overall survival (OS) of high-risk and low-risk groups. Following that, we validated the model using a dataset (GSE96058) acquired from Gene Expression Omnibus (GEO) database. Univariate and multivariate Cox analyses were used to determine the independent prognostic value of the IRGS in the TCGA and GSE96058 cohorts. A nomogram was constructed to predict the OS in the TCGA cohort. Further, we used Gene Set Enrichment Analysis (GSEA), CIBERSORT, and single-sample Gene Set Enrichment Analysis (ssGSEA) to evaluate the associations of IRGS with immune-associated pathways and immune infiltration. Finally, the relationship between the expression of the signature genes and drug sensitivity was conducted using Pearson correlation analysis.

RESULTS

We established an IRGS to stratify breast cancer patients into the low-risk and high-risk groups. In both the training and validation sets, patients in the high-risk group had significantly shorter OS than those in the low-risk group. The risk score was significantly correlated with the clinical characteristics and could be used as a tool to predict the prognosis of breast cancer. Moreover, we found that the IRGS risk score was an independent predictor of OS in breast cancer patients, and a nomogram model based on IRGS risk score and other clinical factors could effectively predict the prognosis of breast cancer patients. Furthermore, the IRGS risk score was correlated with immune characteristics and was inversely associated with the abundance of immune cell infiltration. Patients with a low IRGS risk score had higher expression levels of immune checkpoint genes, suggesting that IRGS can be used as a potential indicator for immunotherapy. Finally, we found that the expression levels of prognostic genes were significantly correlated with tumor cell sensitivity to chemotherapeutic drugs.

CONCLUSION

Overall, these findings suggest that the IRGS can be used to predict the prognosis and immune status of breast cancer patients and provide new therapeutic targets for the treatment of these patients.

Immunogenic cell death-related classifications in breast cancer identify precise immunotherapy biomarkers and enable prognostic stratification

Background: Immunogenic cell death (ICD) remodels the tumor immune microenvironment, plays an inherent role in tumor cell apoptosis, and promotes durable protective antitumor immunity. Currently, appropriate biomarker-based ICD immunotherapy for breast cancer (BC) is under active exploration. Methods: To determine the potential link between ICD genes and the clinical risk of BC, TCGA-BC was used as the training set and GSE58812 was used as the validation set. Gene expression, consistent clustering, enrichment analysis, and mutation omics analyses were performed to analyze the potential biological pathways of ICD genes involved in BC. Furthermore, a risk and prognosis model of ICD was constructed to evaluate the correlation between risk grade and immune infiltration, clinical stage, and survival prognosis. Results: We identified two ICD-related subtypes by consistent clustering and found that the C2 subtype was associated with good survival prognosis, abundant immune cell infiltration, and high activity of immune biological processes. Based on this, we constructed and validated an ICD risk and prognosis model of BC, including ATG5, HSP90AA1, PIK3CA, EIF2AK3, MYD88, IL1R1, and CD8A. This model can effectively predict the survival rate of patients with BC and is negatively correlated with the immune microenvironment and clinical stage. Conclusion: This study provides new insights into the role of ICD in BC. The novel classification risk model based on ICD in BC established in this study can aid in estimating the potential prognosis of patients with BC and the clinical outcomes of immunotherapy and postulates targets that are more useful in comprehensive treatment strategies.

Combining precision oncology and immunotherapy by targeting the MALT1 protease

An innovative strategy for cancer therapy is to combine the inhibition of cancer cell-intrinsic oncogenic signaling with cancer cell-extrinsic immunological activation of the tumor microenvironment (TME). In general, such approaches will focus on two or more distinct molecular targets in the malignant cells and in cells of the surrounding TME. In contrast, the protease Mucosa-associated lymphoid tissue protein 1 (MALT1) represents a candidate to enable such a dual approach by engaging only a single target. Originally identified and now in clinical trials as a lymphoma drug target based on its role in the survival and proliferation of malignant lymphomas addicted to chronic B cell receptor signaling, MALT1 proteolytic activity has recently gained additional attention through reports describing its tumor-promoting roles in several types of non-hematological solid cancer, such as breast cancer and glioblastoma. Besides cancer cells, regulatory T (Treg) cells in the TME are particularly dependent on MALT1 to sustain their immune-suppressive functions, and MALT1 inhibition can selectively reprogram tumor-infiltrating Treg cells into Foxp3-expressing proinflammatory antitumor effector cells. Thereby, MALT1 inhibition induces local inflammation in the TME and synergizes with anti-PD-1 checkpoint blockade to induce antitumor immunity and facilitate tumor control or rejection. This new concept of boosting tumor immunotherapy in solid cancer by MALT1 precision targeting in the TME has now entered clinical evaluation. The dual effects of MALT1 inhibitors on cancer cells and immune cells therefore offer a unique opportunity for combining precision oncology and immunotherapy to simultaneously impair cancer cell growth and neutralize immunosuppression in the TME. Further, MALT1 targeting may provide a proof of concept that modulation of Treg cell function in the TME represents a feasible strategy to augment the efficacy of cancer immunotherapy. Here, we review the role of MALT1 protease in physiological and oncogenic signaling, summarize the landscape of tumor indications for which MALT1 is emerging as a therapeutic target, and consider strategies to increase the chances for safe and successful use of MALT1 inhibitors in cancer therapy.

Identification of CD161 expression as a novel prognostic biomarker in breast cancer correlated with immune infiltration

Background:CD161 has been identified as a prognostic biomarker in many neoplasms, but its role in breast cancer (BC) has not been fully explained. We aimed to investigate the molecular mechanism and prognostic value of CD161 in BC.

Methods:CD161 expression profile was extracted from TIMER, Oncomine, UALCAN databases, and verified by the Gene Expression Omnibus (GEO) database and quantitative real-time polymerase chain reaction (qRT-PCR). The prognostic value of CD161 was assessed via GEPIA, Kaplan–Meier plotter and PrognoScan databases. The Cox regression and nomogram analyses were conducted to further validate the association between CD161 expression and survival. Gene set enrichment analysis (GSEA), Gene Ontology (GO) analysis, and KEGG pathway enrichment analysis were performed to probe the tumor-associated annotations of CD161. CIBERSORT and ssGSEA were employed to investigate the correlation between CD161 expression and immune cell infiltration in BC, and the result was verified by TIMER and TISIDB.

Results: Multiple BC cohorts showed that CD161 expression was decreased in BC, and a high CD161 expression was associated with a preferable prognosis. Therefore, we identified the combined model including CD161, age and PR status to predict the survival (C index = 0.78) of BC patients. Functional enrichment analysis indicated that CD161 and its co-expressed genes were closely related to several cancerous and immune signaling pathways, suggesting its involvement in immune response during cancer development. Moreover, immune infiltration analysis revealed that CD161 expression was correlated with immune infiltration.

Conclusion: Collectively, our findings revealed that CD161 may serve as a potential biomarker for favorable prognosis and a promising immune therapeutic target in BC.

Identification of tumor antigens and immune subtypes in breast cancer for mRNA vaccine development

Background

Poor prognosis, resistance to chemotherapy, insensitivity to radiotherapy, and a high prevalence of adverse drug reactions remain urgent issues for breast cancer (BC) patients. Increased knowledge of tumor immunobiology and vaccine development suggests the possibility of cancer vaccination. Here, we investigated potential BC-associated antigens for the development of an anti-BC mRNA vaccine and populations suitable for mRNA vaccination.

Methods

Gene expression and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). The single-cell sequencing data were obtained from the Single Cell Portal platform. cBioPortal was used to visualize and compare genetic alterations. Correlations between immune cell infiltration and antigen expression were visualized with the Tumor Immune Estimation Resource (TIMER). Immune subtypes were identified by consensus clustering and analysis of immune infiltration. Biomarkers for the assessment of mRNA vaccination suitability were investigated.

Results

Three tumor-associated antigens, CD74, IRF1, and PSME2, that showed overexpression, amplification, and mutation and were linked with prognosis and immune cell infiltration, were identified. Single-cell sequencing analysis showed the expression of the three tumor-associated antigens in different cells of BC. Three immune subtypes were identified among BC patients, with Cluster B patients having a tumor microenvironment conducive to immunotherapy. These subtypes also showed different expression patterns of immune checkpoints, immune cell death-promoting genes, and response to immune checkpoint inhibitor (ICI) therapy. Thus, we identified five biomarkers that could be applied for assessing vaccination suitability and predicted drugs that would be appropriate for patients unsuited for vaccination.

Conclusions

Our findings suggest new directions for the development of mRNA vaccines against breast cancer.

Differential distribution and prognostic value of CD4+ T cell subsets before and after radioactive iodine therapy in differentiated thyroid cancer with varied curative outcomes

Differentiated thyroid cancer is the most frequently diagnosed endocrine tumor. While differentiated thyroid cancers often respond to initial treatment, little is known about the differences in circulating immune cells amongst patients who respond differently. A prospective study of 39 patients with differentiated thyroid cancer was conducted. Serum thyroglobulin levels and thyroid and immunological functions were tested before and after radioactive iodine treatment (RAIT). Efficacy assessments were performed 6 to 12 months after radioactive iodine treatment. Most patients showed an excellent response to radioactive iodine treatment. Before radioactive iodine treatment, the excellent response group had considerably fewer circulating CD4⁺ T cell subsets than the non-excellent response group. Both the excellent response and non-excellent response groups had considerably lower circulating CD4⁺ T lymphocyte subsets 30 days after radioactive iodine treatment, but those of the excellent response group were still lower than those of the non-excellent response group. All circulating CD4⁺ T cell subsets in the excellent response group rose by varying degrees by the 90th day, but only Treg cell amounts increased in the non-excellent response group. Interestingly, in the non-excellent response group, we noticed a steady drop in Th1 cells. However, the bulk of circulating CD4⁺ T cell subsets between the two groups did not differ appreciably by the 90th day. Finally, we discovered that CD4⁺ T cell subsets had strong predictive potential, and we thus developed high-predictive-performance models that deliver more dependable prognostic information. In conclusion, in individuals with differentiated thyroid cancer, there is great variation in circulating immune cells, resulting in distinct treatment outcomes. Low absolute CD4⁺ T cell counts is linked to improved clinical outcomes as well as stronger adaptive and resilience capacities.

Construction of a prognostic model related to copper dependence in breast cancer by single-cell sequencing analysis

Purpose: To explore the clinical significance of copper-dependent-related genes (CDRG) in female breast cancer (BC).

Methods: CDRG were obtained by single-cell analysis of the GSE168410 dataset in the Gene Expression Omnibus (GEO) database. According to a 1:1 ratio, the Cancer Genome Atlas (TCGA) cohort was separated into a training and a test cohort randomly. Based on the training cohort, the prognostic model was built using COX and Lasso regression. The test cohort was used to validate the model. The GSE20685 dataset and GSE20711 dataset were used as two external validation cohorts to further validate the prognostic model. According to the median risk score, patients were classified as high-risk or low-risk. Survival analysis, immune microenvironment analysis, drug sensitivity analysis, and nomogram analysis were used to evaluate the clinical importance of this prognostic model.

Results: 384 CDRG were obtained by single-cell analysis. According to the prognostic model, patients were classified as high-risk or low-risk in both cohorts. The high-risk group had a significantly worse prognosis. The area under the curve (AUC) of the model was around 0.7 in the four cohorts. The immunological microenvironment was examined for a possible link between risk score and immune cell infiltration. Veliparib, Selumetinib, Entinostat, and Palbociclib were found to be more sensitive medications for the high-risk group after drug sensitivity analysis.

Conclusion: Our CDRG-based prognostic model can aid in the prediction of prognosis and treatment of BC patients.

A novel ferroptosis-related long noncoding RNA signature for relapse free survival prediction in patients with breast cancer

Ferroptosis is the process of cell death dependent on iron. Growing evidence suggests that ferroptosis plays vital roles in the biological process of many cancers. However, just a small number of ferroptosis-related lncRNAs have been explored in depth. Ferroptosis-related lncRNAs in breast cancer (BC) were identified by co-expression analysis based on The Cancer Genome Atlas database (TCGA). The whole set was divided into a training set and a test set with a 1:1 ratio. Univariate Cox regression and LASSO analyses were performed to establish a signature in the 3 sets. Kaplan-Meier analysis and receiver operating characteristic (ROC) for the 3 sets validated the effectiveness and robustness of the signature. Besides, we also explore the relationship between this and clinical characteristics, immune cell infiltration and tumor microenvironment. Meanwhile, the nomogram was drawn by screening indicators of independent recurrent prediction. Finally, we evaluated the relationships between the signature and tumor microenvironment. We identified 391 ferroptosis-related lncRNAs and constructed a 5 lncRNAs-based signature in the training, test, and whole sets, stratifying patients into high-risk and low-risk groups. According to survival analysis, patients in the high-risk groups had worse relapse free survival (RFS) compared to the low risk-groups. The ROC curves indicated that the recurrent signature had a promising predictive capability for BC patients. Moreover, an independent factors-based nomogram model could offer the quantitative prediction and net benefit for the recurrence of BC patients. Finally, the microenvironment, including tumor mutational burden (TMB), immune cell functions and immune checkpoints, showed big differences between the 2 groups. The 5 ferroptosis-related lncRNAs and their signature might be novel promising biomarkers and immunotherapy targets for patients with BC.

Oncogenic signaling pathway-related long non-coding RNAs for predicting prognosis and immunotherapy response in breast cancer

Background

The clinical outcomes of breast cancer (BC) are unpredictable due to the high level of heterogeneity and complex immune status of the tumor microenvironment (TME). When set up, multiple long non-coding RNA (lncRNA) signatures tended to be employed to appraise the prognosis of BC. Nevertheless, predicting immunotherapy responses in BC is still essential. LncRNAs play pivotal roles in cancer development through diverse oncogenic signal pathways. Hence, we attempted to construct an oncogenic signal pathway–based lncRNA signature for forecasting prognosis and immunotherapy response by providing reliable signatures.

Methods

We preliminarily retrieved RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) database and extracted lncRNA profiles by matching them with GENCODE. Following this, Gene Set Variation Analysis (GSVA) was used to identify the lncRNAs closely associated with 10 oncogenic signaling pathways from the TCGA-BRCA (breast-invasive carcinoma) cohort and was further screened by the least absolute shrinkage and selection operator Cox regression model. Next, an lncRNA signature (OncoSig) was established through the expression level of the final 29 selected lncRNAs. To examine survival differences in the stratification described by the OncoSig, the Kaplan–Meier (KM) survival curve with the log-rank test was operated on four independent cohorts (n = 936). Subsequently, multiple Cox regression was used to investigate the independence of the OncoSig as a prognostic factor. With the concordance index (C-index), the time-dependent receiver operating characteristic was employed to assess the performance of the OncoSig compared to other publicly available lncRNA signatures for BC. In addition, biological differences between the high- and low-risk groups, as portrayed by the OncoSig, were analyzed on the basis of statistical tests. Immune cell infiltration was investigated using gene set enrichment analysis (GSEA) and deconvolution tools (including CIBERSORT and ESTIMATE). The combined effect of the Oncosig and immune checkpoint genes on prognosis and immunotherapy was elucidated through the KM survival curve. Ultimately, a pan-cancer analysis was conducted to attest to the prevalence of the OncoSig.

Results

The OncoSig score stratified BC patients into high- and low-risk groups, where the latter manifested a significantly higher survival rate and immune cell infiltration when compared to the former. A multivariate analysis suggested that OncoSig is an independent prognosis predictor for BC patients. In addition, compared to the other four publicly available lncRNA signatures, OncoSig exhibited superior predictive performance (AUC = 0.787, mean C-index = 0.714). The analyses of the OncoSig and immune checkpoint genes clarified that a lower OncoSig score meant significantly longer survival and improved response to immunotherapy. In addition to BC, a high OncoSig score in several other cancers was negatively correlated with survival and immune cell infiltration.

Conclusions

Our study established a trustworthy and discriminable prognostic signature for BC patients with similar clinical profiles, thus providing a new perspective in the evaluation of immunotherapy responses. More importantly, this finding can be generalized to be applicable to the vast majority of human cancers.

Function and Application of Flavonoids in the Breast Cancer

Breast cancer is one of the top causes of death, particularly among women, and it affects many women. Cancer can also be caused by various factors, including acquiring genetic alteration. Doctors use radiation to detect and treat breast cancer. As a result, breast cancer becomes radiation-resistant, necessitating a new strategy for its treatment. The approach discovered by the researchers is a flavonoid, which is being researched to see if it might help treat radiation-resistant breast cancer more safely than an approved medicine already being used in the field. As a result, this study focuses on the role of flavonoids in breast cancer suppression, breast cancer gene anomalies, and the resulting apoptotic mechanism.

High Expression of TACC3 Is Associated with the Poor Prognosis and Immune Infiltration in Lung Adenocarcinoma Patients

Background

Lung adenocarcinoma (LUAD) has been recognized as one of the commonest aggressive malignant tumors occurring in humans. The transforming acidic coiled-coil-containing protein 3 (TACC3) seems to be a probable prognostic marker and treatment target for non-small-cell lung cancer (NSCLC). Nevertheless, there exist no reports on the association between TACC3 and immunotherapy or other therapeutic interventions in LUAD.

Methods

Premised on the data accessed from The Cancer Genome Atlas- (TCGA-) LUAD, we carried out bioinformatics analysis. The TACC3 expression in LUAD was analyzed utilizing the GEPIA. A survival module was constructed to evaluate the effect of TACC3 on the survival of patients with LUAD. Logistic regression was undertaken to examine the relationship between TACC3 expression and clinical factors. Protein-protein interaction analysis was performed in the GeneMANIA database, and enrichment analysis and identification of predicted signaling pathways were performed using Gene Ontology and Kyoto Encyclopedia of Genes. Additionally, the Cox regression was used to assess the clinicopathologic features linked to the overall survival in TCGA patients. Lastly, we investigated the link between TACC3 and tumor-infiltrating immune cells (TIICs) through CIBERSORT and the “Correlation” module of GEPIA. The association between TACC3 gene expression and drug response was analyzed using the CellMiner database to predict drug sensitivity.

Results

The outcomes illustrated that TACC3 was upregulated and considerably correlated with dismal prognosis in LUAD patients. Moreover, the multivariate Cox regression analysis depicted TACC3 as an independent prognostic marker in LUAD patients. It was also revealed that the expression of TACC3 was related to clinical stage (P = 0.014), age (P = 0.002), and T classification (P ≤ 0.018). Moreover, we discovered that the expression of TACC3 was considerably linked to a wide range of TIICs, especially the T cells and NK cells. Single-cell results found that TACC3 was mainly expressed in the immune cells (especially tprolif cells) and malignant cells. TACC3 gene expression was positively correlated with TMB and MSI, and TACC3 may provide a prediction of the efficacy of immunotherapy. Moreover, the correlation analysis between TACC3 gene expression and immune checkpoint gene expression revealed that TACC3 may coordinate the activities of these ICP genes in different signal transduction pathways. TACC3 is related to biological progress (BP), cellular component (CC), and molecular function (MF). The pathways involved in the interaction network involving TACC3 include nonhomologous end-joining, RNA transport, pantothenate and CoA biosynthesis, homologous recombination, and nucleotide excision repair. Furthermore, we investigated the association between the expression of TACC3 and the use of antitumor drugs, and TACC3 was positively correlated with response to most drugs.

Conclusion

The findings from this research offer robust proof that the expression of TACC3 could be a prognostic marker correlated with TIICs in LUAD. TACC3 can also provide new ideas for immunotherapy as a potential therapeutic target.

Modulation of Lymphocyte Functions in the Microenvironment by Tumor Oncogenic Pathways

Despite the broad application of different immunotherapeutic strategies for the treatment of solid as well as hematopoietic cancers, the efficacy of these therapies is still limited, with only a minority of patients having a long-term benefit resulting in an improved survival rate. In order to increase the response rates of patients to the currently available immunotherapies, a better understanding of the molecular mechanisms underlying the intrinsic and/or extrinsic resistance to treatment is required. There exist increasing evidences that activation of different oncogenic pathways as well as inactivation of tumor suppressor genes (TSG) in tumor cells inhibit the immune cell recognition and influegnce the composition of the tumor microenvironment (TME), thus leading to an impaired anti-tumoral immune response. A deeper understanding of the link between the tumor milieu and genomic alterations of TSGs and oncogenes is indispensable for the optimization of immunotherapies and to predict the patients’ response to these treatments. This review summarizes the role of different cancer-related, oncogene- and TSG-controlled pathways in the context of anti-tumoral immunity and response to different immunotherapies.

Development of a Novel Immune-Related Gene Prognostic Index for Breast Cancer

Objective

To construct an immune-related gene prognostic index (IRGPI) for breast cancer (BC) and investigate its prognostic specificity and the molecular and immune characteristics.

Methods

BC hub genes were identified from The Cancer Genome Atlas and immune-related databases using weighted gene co-expression network analysis (WGCNA). IRGPI was constructed using univariate, LASSO, and multivariate regression analyses, and was validated with GSE58812 and GSE97342 in the Gene Expression Omnibus database (GEO). At the same time, we evaluated the predictive ability of IRGPI for different BC subtypes. Subsequently, the molecular and immune characteristics, clinical relevance, and benefits of immune checkpoint inhibitor treatment were analyzed for different IRGPI subgroups.

Results

IRGPI consisted of six genes: SOCS3, TCF7L2, TSLP NPR3, ANO6, and HMGB3. The IRGPI 1-, 5-, and 10-years area under curve (AUC) values were 0.635, 0.752, and 0.753, respectively, indicating that IRGPI has good potential in predicting the long-term survival of BC patients, consistent with the results in the GEO cohort. IRGPI showed good predictive power in four different breast cancer subtypes: ER positive, PR positive, HER2 positive and triple-negative (P<0.01). Compared with the low-IRGPI group, the high-IRGPI group had a worse prognosis and a lower degree of immune infiltrating cells (p < 0.05). IRGPI showed specificity in distinguishing age, TNM stage, ER, and HER2 statuses, and our study found that the high-IRGPI group had low tumor immune dysfunction and exclusion (TIDE), microsatellite instability (MSI), and T cell dysfunction scores (p < 0.05). In addition, compared with the TIDE and TIS models, showed that the AUCs of IRGPI were better during the 5-year follow-up.

Conclusion

IRGPI can be used as an independent prognostic indicator of breast cancer, providing a method for monitoring the long-term treatment of BC.