Construction and Validation of an Immune Infiltration-Related Gene Signature for the Prediction of Prognosis and Therapeutic Response in Breast Cancer

GLDADec: marker-gene guided LDA modeling for bulk gene expression deconvolution

Inferring cell type proportions from bulk transcriptome data is crucial in immunology and oncology. Here, we introduce guided LDA deconvolution (GLDADec), a bulk deconvolution method that guides topics using cell type-specific marker gene names to estimate topic distributions for each sample. Through benchmarking using blood-derived datasets, we demonstrate its high estimation performance and robustness. Moreover, we apply GLDADec to heterogeneous tissue bulk data and perform comprehensive cell type analysis in a data-driven manner. We show that GLDADec outperforms existing methods in estimation performance and evaluate its biological interpretability by examining enrichment of biological processes for topics. Finally, we apply GLDADec to The Cancer Genome Atlas tumor samples, enabling subtype stratification and survival analysis based on estimated cell type proportions, thus proving its practical utility in clinical settings. This approach, utilizing marker gene names as partial prior information, can be applied to various scenarios for bulk data deconvolution. GLDADec is available as an open-source Python package at https://github.com/mizuno-group/GLDADec.

GBP2 enhances paclitaxel sensitivity in triple‑negative breast cancer by promoting autophagy in combination with ATG2 and inhibiting the PI3K/AKT/mTOR pathway

Chemoresistance is a major challenge in treating triple‑negative breast cancer (TNBC); chemotherapy remains the primary approach. The present study aimed to elucidate the role of guanylate‑binding protein 2 (GBP2) in activating autophagy in TNBC and its impact on the sensitivity of TNBC cells to paclitaxel (PTX). Transfection with lentivirus was performed to establish TNBC cell lines with stable, high GBP2 expression. The mRNA and protein levels of GBP2 expression were evaluated utilizing reverse transcription‑quantitative PCR and western blotting, respectively. Autophagy in TNBC cells was evaluated using immunoblotting, transmission electron microscopy and fluorescence microscopy. The PI3K/AKT/mTOR pathway proteins and their phosphorylation were detected by immunoblotting, and fluorescence co‑localization analysis was performed to evaluate the association between GBP2 and autophagy‑related protein 2 (ATG2). BALB/c NUDE mice were subcutaneously injected with GBP2 wild‑type/overexpressing MDA‑MB‑231 cells. Low GBP2 expression was detected in TNBC, which was associated with a poor prognosis. Overexpression of GBP2 suppressed cell growth, and especially enhanced autophagy in TNBC. Forced expression of GBP2 significantly increased the PTX sensitivity of TNBC cells, and the addition of autophagy inhibitors reversed this effect. GBP2 serves as a prognostic marker and exerts a notable inhibitory impact on TNBC. It functions as a critical regulator of activated autophagy by co‑acting with ATG2 and inhibiting the PI3K/AKT/mTOR pathway, which contributes to increasing sensitivity of TNBC cells to PTX. Therefore, GBP2 is a promising therapeutic target for enhancing TNBC treatment.

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.

LncRNA HOTAIR as a ceRNA is related to breast cancer risk and prognosis

PURPOSE

Breast cancer (BC) is one of the biggest threats to women's health. LncRNA HOTAIR is related to the recurrence and metastasis of BC. Whether HOTAIR can serve as an effective biomarker to distinguish BC patients with different prognosis need to be further studied.

METHODS

The miRNA and mRNA expression profile data of BC patients were downloaded from TCGA database. Univariate Cox regression was used to screen differential expression genes (DEGs). The miRcode database and miRWalk database were used to predict miRNA binding to HOTAIR and binding sites of miRNAs, respectively. Kaplan-Meier (KM) analysis was used to estimate the overall survival rate of BC patients. Finally, qRT-PCR and western blot were applied to evaluate the expression level of HOTAIR and mRNAs between BC cells and normal mammary cells.

RESULTS

The patients with high HOTAIR expression had poor prognosis in BC. Totally 10 genes correlated with BC prognosis were identified from 170 DEGs, among which PAX7, IYD, ZIC2, MS4A1, TPRXL, CD24, LHX1 were positively correlated with HOTAIR, while CHAD, NPY1R, TPRG1 were opposite. The levels of IYD, ZIC2, CD24 mRNA and protein were increased in BC tissues and BC cells. In BC cells, the levels of IYD, ZIC2 and CD24 mRNA and protein were significantly increased in HOTAIR overexpressed group. HOTAIR had the strongest interaction with hsa-miR-129-5p, followed by hsa-miR-107.

CONCLUSION

HOTAIR regulated the expression of downstream genes by interacting with 8 miRNAs and ultimately affected the prognosis of BC patients.

An original cuproptosis-related genes signature effectively influences the prognosis and immune status of head and neck squamous cell carcinoma

Background: Recently, a non-apoptotic cell death pathway that is dependent on the presence of copper ions was proposed, named as cuproptosis. Cuproptosis have been found to have a strong association with the clinical progression and prognosis of several cancers. Head and neck squamous cell carcinoma (HNSC) are among the most common malignant tumors, with a 5-year relative survival rate ranging between 40% and 50%. The underlying mechanisms and clinical significance of cuproptosis-related genes (CRGs) in HNSC progression have not been clarified. Methods: In this study, expression pattern, biological functions, Immunohistochemistry (IHC), gene variants and immune status were analyzed to investigate the effects of CRGs on HNSC progression. Moreover, a 12-CRGs signature and nomogram were also constructed for prognosis prediction of HNSC. Results: The results revealed that some CRGs were dysregulated, had somatic mutations, and CNV in HNSC tissues. Among them, ISCA2 was found to be upregulated in HNSC and was strongly correlated with the overall survival (OS) of HNSC patients (HR = 1.13 [1.01-1.26], p-value = 0.0331). Functionally, CRGs was mainly associated with the TCA cycle, cell cycle, iron-sulfur cluster assembly, p53 signaling pathway, chemical carcinogenesis, and carbon metabolism in cancer. A 12-CRGs signature for predicting the OS was constructed which included, CAT, MTFR1L, OXA1L, POLE, NTHL1, DNA2, ATP7B, ISCA2, GLRX5, NDUFA1, and NDUFB2. This signature showed good prediction performance on the OS (HR = 5.3 [3.4-8.2], p-value = 3.4e-13) and disease-specific survival (HR = 6.4 [3.6-11], p-value = 2.4e-10). Furthermore, 12-CRGs signature significantly suppressed the activation of CD4⁺ T cells and antigen processing and presentation. Finally, a nomogram based on a 12-CRGs signature and clinical features was constructed which showed a significantly adverse effect on OS (HR = 1.061 [1.042-1.081], p-value = 1.6e-10) of HNSC patients. Conclusion: This study reveals the association of CRGs with the progression of HNSC based on multi-omics analysis. The study of CRGs is expected to improve clinical diagnosis, immunotherapeutic responsiveness and prognosis prediction of HNSC.

Research progress on immunotherapy in triple‑negative breast cancer (Review)

Triple-negative breast cancer (TNBC) is a highly heterogeneous and aggressive malignancy. Due to the absence of estrogen receptors and progesterone receptors and the lack of overexpression of human epidermal growth factor receptor 2, TNBC responds poorly to endocrine and targeted therapies. As a neoadjuvant therapy, chemotherapy is usually the only option for TNBC; however, chemotherapy may induce tumor resistance. The emergence of immunotherapy as an adjuvant therapy is expected to make up for the deficiency of chemotherapy. Most of the research on immunotherapies has been performed on advanced metastatic TNBC, which has provided significant clinical benefits. In the present review, possible immunotherapy targets and ongoing immunotherapy strategies were discussed. In addition, progress in research on immune checkpoint inhibitors in early TNBC was outlined.

Breast Cancer Prognosis Prediction and Immune Pathway Molecular Analysis Based on Mitochondria-Related Genes

Background

Mitochondria play an important role in breast cancer (BRCA). We aimed to build a prognostic model based on mitochondria-related genes.

Method

Univariate Cox regression analysis, random forest, and the LASSO method were performed in sequence on pretreated TCGA BRCA datasets to screen out genes from a Gene Set Enrichment Analysis, Gene Ontology: biological process gene set to build a prognosis risk score model. Survival analyses and ROC curves were performed to verify the model by using the GSE103091 dataset. The BRCA datasets were equally divided into high- and low-risk score groups. Comparisons between clinical features and immune infiltration related to different risk scores and gene mutation analysis and drug sensitivity prediction were performed for different groups.

Result

Four genes, MRPL36, FEZ1, BMF, and AFG1L, were screened to construct our risk score model in which the higher the risk score, the poorer the prognosis. Univariate and multivariate analyses showed that the risk score was significantly associated with age, M stage, and N stage. The gene mutation probability in the high-risk score group was significantly higher than that in the low-risk score group. Patients with higher risk scores were more likely to die. Drug sensitivity prediction in different groups indicated that PF-562271 and AS601245 might be new inhibitors of BRCA.

Conclusion

We developed a new workable risk score model based on mitochondria-related genes for BRCA prognosis and identified new targets and drugs for BRCA research.

Development and Validation of a Four Adenosine-to-Inosine RNA Editing Site-Relevant Prognostic Signature for Assessing Survival in Breast Cancer Patients

Background

Adenosine-to-inosine RNA editing (ATIRE) is increasingly being used to characterize cancer. However, no studies have been conducted to identify an ATIRE signature for predicting cancer survival.

Methods

Breast cancer (BRCA) samples with ATIRE profiles from The Cancer Genome Atlas were divided into training (n = 452) and internal validation cohorts (n = 311), and 197 additional BRCA patients were recruited as an external validation cohort. The ATIRE signature for BRCA overall survival (OS) and disease-free survival (DFS) were identified using forest algorithm analysis and experimentally verified by direct sequencing. An ATIRE-based risk score (AIRS) was established with these selected ATIRE sites. Significantly prognostic factors were incorporated to generate a nomogram that was evaluated using Harrell’s C-index and calibration plot for all cohorts.

Results

Seven ATIRE sites were revealed to be associated with both BRCA OS and DFS, of which four sites were experimentally confirmed. Patients with high AIRS displayed a higher risk of death than those with low AIRS in the training (hazard ratio (HR) = 3.142, 95%CI = 1.932–5.111), internal validation (HR = 2.097, 95%CI = 1.123–3.914), and external validation cohorts (HR = 2.680, 95%CI = 1.000–7.194). A similar hazard effect of high AIRS on DFS was also observed. The nomogram yielded Harrell’s C-indexes of 0.816 (95%CI = 0.784–0.847), 0.742 (95%CI = 0.684–0.799), and 0.869 (95%CI = 0.835–0.902) for predicting OS and 0.767 (95%CI = 0.708–0.826), 0.684 (95%CI = 0.605–0.763), and 0.635 (95%CI = 0.566–0.705) for predicting DFS in the three cohorts.

Conclusion

AIRS nomogram could help to predict OS and DFS of patients with BRCA.

Development and Validation of a 7-Gene Inflammatory Signature Forecasts Prognosis and Diverse Immune Landscape in Lung Adenocarcinoma

Background: Inflammatory responses are strongly linked with tumorigenesis and cancer development. This research aimed to construct and validate a novel inflammation response–related risk predictive signature for forecasting the prognosis of patients with LUAD.

Methods: Differential expression analysis, univariate Cox, LASSO, and multivariate Cox regression analyses of 200 inflammatory response–related genes (IRRG) were performed to establish a risk predictive model in the TCGA training cohort. The performance of the IRRG model was verified in eight GEO datasets. GSEA analysis, ESTIMATE algorithms, and ssGSEA analysis were applied to elucidate the possible mechanisms. Furthermore, the relationship analysis between risk score, model genes, and chemosensitivity was performed. Last, we verified the protein expression of seven model genes by immunohistochemical staining or Western blotting.

Results: We constructed a novel inflammatory response–related 7-gene signature (MMP14, BTG2, LAMP3, CCL20, TLR2, IL7R, and PCDH7). Patients in the high-risk group presented markedly decreased survival time in the TCGA cohort and eight GEO cohorts than the low-risk group. Interestingly, multiple pathways related to immune response were suppressed in high-risk groups. The low infiltration levels of B cell, dendritic cell, natural killer cell, and eosinophil can significantly affect the unsatisfactory prognosis of the high-risk group in LUAD. Moreover, the tumor cells’ sensitivity to anticancer drugs was markedly related to risk scores and model genes. The protein expression of seven model genes was consistent with the mRNA expression.

Conclusion: Our IRRG prognostic model can effectively forecast LUAD prognosis and is tightly related to immune infiltration.

A Clinical–Radiomics Model for Predicting Axillary Pathologic Complete Response in Breast Cancer With Axillary Lymph Node Metastases

Purpose

To develop a clinical–radiomics model based on radiomics features extracted from MRI and clinicopathologic factors for predicting the axillary pathologic complete response (apCR) in breast cancer (BC) patients with axillary lymph node (ALN) metastases.

Materials and Methods

The MR images and clinicopathologic data of 248 eligible invasive BC patients at the Peking University First Hospital from January 2013 to December 2020 were included in this study. All patients received neoadjuvant chemotherapy (NAC), and the presence of ALN metastases was confirmed through cytology pre-NAC. The data from January 2013 to December 2018 were randomly divided into the training and validation sets in a ratio of 7:3, and the data from January 2019 to December 2020 served as the independent testing set. The following three types of prediction models were investigated in this study. 1) A clinical model: the model was built by independently predicting clinicopathologic factors through logistic regression. 2) Radiomics models: we used an automatic segmentation model based on deep learning to segment the axillary areas, visible ALNs, and breast tumors on post-NAC dynamic contrast-enhanced MRI. Radiomics features were then extracted from the region of interest (ROI). Radiomics models were built based on different ROIs or their combination. 3) A clinical–radiomics model: it was built by integrating radiomics signature and independent predictive clinical factors by logistic regression. All models were assessed using a receiver operating characteristic curve analysis and by calculating the area under the curve (AUC).

Results

The clinical model yielded AUC values of 0.759, 0.787, and 0.771 in the training, validation, and testing sets, respectively. The radiomics model based on the combination of MRI features of breast tumors and visible ALNs yielded the best AUC values of 0.894, 0.811, and 0.806 in the training, validation, and testing sets, respectively. The clinical–radiomics model yielded AUC values of 0.924, 0.851, and 0.878 in the training, validation, and testing sets, respectively, for predicting apCR.

Conclusion

We developed a clinical–radiomics model by integrating radiomics signature and clinical factors to predict apCR in BC patients with ALN metastases post-NAC. It may help the clinicians to screen out apCR patients to avoid lymph node dissection.

Integrated Analysis of Copy Number Variation, Microsatellite Instability, and Tumor Mutation Burden Identifies an 11-Gene Signature Predicting Survival in Breast Cancer

Genetic variants such as copy number variation (CNV), microsatellite instability (MSI), and tumor mutation burden (TMB) have been reported to associate with the immune microenvironment and prognosis of patients with breast cancer. In this study, we performed an integrated analysis of CNV, MSI, and TMB data obtained from The Cancer Genome Atlas, thereby generating two genetic variants-related subgroups. We characterized the differences between the two subgroups in terms of prognosis, MSI burden, TMB, CNV, mutation landscape, and immune landscape. We found that cluster 2 was marked by a worse prognosis and lower TMB. According to these groupings, we identified 130 differentially expressed genes, which were subjected to univariate and least absolute shrinkage and selection operator-penalized multivariate modeling. Consequently, we constructed an 11-gene signature risk model called the genomic variation-related prognostic risk model (GVRM). Using ROC analysis and a calibration plot, we estimated the prognostic prediction of this GVRM. We confirmed the predictive efficiency of this GVRM by validating it in another independent International Cancer Genome Consortium cohort. Our results conclude that an 11-gene signature developed by integrated analysis of CNV, MSI, and TMB has a high potential to predict breast cancer prognosis, which provided a strong rationale for further investigating molecular mechanisms and guiding clinical decision-making in breast cancer.