A prognostic eight-gene expression signature for patients with breast cancer receiving adjuvant chemotherapy

Downregulated SPESP1-driven fibroblast senescence decreases wound healing in aged mice

BACKGROUND

Human dermal fibroblasts (HDFs) are essential in the processes of skin ageing and wound healing. However, the underlying mechanism of HDFs in skin healing of the elderly has not been well defined. This study aims to elucidate the mechanisms of HDFs senescence and how senescent HDFs affect wound healing in aged skin.

METHODS

The expression and function of sperm equatorial segment protein 1 (SPESP1) in skin ageing were evaluated via in vivo and in vitro experiments. To delve into the potential molecular mechanisms by which SPESP1 influences skin ageing, a combination of techniques was employed, including proteomics, RNA sequencing, immunoprecipitation, chromatin immunoprecipitation and liquid chromatography-mass spectrometry analyses. Clearance of senescent cells by dasatinib plus quercetin (D+Q) was investigated to explore the role of SPESP1-induced senescent HDFs in wound healing.

RESULTS

Here, we define the critical role of SPESP1 in ameliorating HDFs senescence and retarding the skin ageing process. Mechanistic studies demonstrate that SPESP1 directly binds to methyl-binding protein, leading to Decorin demethylation and subsequently upregulation of its expression. Moreover, SPESP1 knockdown delays wound healing in young mice and SPESP1 overexpression induces wound healing in old mice. Notably, pharmacogenetic clearance of senescent cells by D+Q improved wound healing in SPESP1 knockdown skin.

CONCLUSIONS

Taken together, these findings reveal the critical role of SPESP1 in skin ageing and wound healing, expecting to facilitate the development of anti-ageing strategies and improve wound healing in the elderly.

MIRS: An AI scoring system for predicting the prognosis and therapy of breast cancer

Tumor-infiltrating immune cells (TIICs) and metastasis are crucial characteristics for tumorigenesis. However, the potential role of their combination in breast cancer (BRCA) remains elusive. Herein, on the basis of quantifying TIICs and tumor metastasis together, we established a precise prognostic scoring system named metastatic and immunogenomic risk score (MIRS) using a neural network model. MIRS showed better performance when compared with other published signatures. MIRS stratifies patients into a high risk subtype (MIRShigh) and a low risk subtype (MIRSlow). The MIRShigh patients exhibit significantly lower survival rate compared with MIRSlow patients (P < 0.0001 ), higher response to chemotherapy, but lower response to immunotherapy. Conversely, higher infiltration level of TIICs and significantly prolonged survival (P = 0.029 ) are observed in MIRSlow patients, indicating sensitive response in immunotherapy. This work presents a promising indicator to guide treatment options of the BRCA population and provides a predicted webtool that is almost universally applicable to BRCA patients.

Analysis of the regulatory mechanisms of prognostic immune factors in thyroid cancer

Objective

To explore the regulatory mechanism of immune prognostic factors in thyroid cancer.

Methods

Based on the TCGA database and GEO database, this study used bioinformatics methods to study the potential regulatory mechanism of thyroid cancer prognosis, analyzed the differentially expressed genes and differential miRNAs between thyroid cancer and normal paracancerous tissues by R software, and constructed lasso risk factors. The immune prognostic factors of thyroid cancer were obtained from the model, and the miRDB website was used to predict the possibility of differential miRNA target binding of the immune prognostic factors and correlation analysis was performed, and finally verified by cell experiments.

Results

There were 1413 differentially expressed genes between thyroid cancer and normal paracancerous tissues, among which 21 immune-related genes were prognostic factors with significant differences in expression; lasso risk model obtained AKAP12, APOC1, TIMP3, ADAMTS9, ANK2, HTRA3, SYNDIG1 , ADAMTS5 and DACT1 were nine prognostic factors. A total of 58 differential miRNAs were found in thyroid cancer tissues and non-cancerous tissues. The possibility of differential miRNA targeting and binding of immune prognostic factors on the miRDB website and cell experiments was analyzed.

Conclusions

The potential miRNA regulatory mechanism of immune prognostic factors in thyroid cancer has been explored.

C2CD4A/B variants in the predisposition of lung cancer in the Chinese Han population

BACKGROUND

The mRNA levels of C2CD4A and C2CD4B were dysregulation in lung cancer (LC). We aimed to evaluate the role of C2CD4A/B variants in LC susceptibility.

METHODS

There were 710 cases with LC and 710 healthy controls enrolled in the study. The genotyping of twelve variants in C2CD4A/B was carried out by Agena MassARRAY system. Odds ratios (ORs) were calculated by logistic regression analysis to assess the relationship between these variants and LC predisposition.

RESULTS

Rs8037894 (OR = 0.81, p = 0.005), rs7172432 (OR = 0.83, p = 0.013), rs11856307 (OR = 0.86, p = 0.043), and rs1436953 (OR = 0.79, p = 0.002) were related to the reduced risk of LC. Besides, the relation of rs7172432 with LC risk in subjects aged > 60 years was observed. Rs4502156 conferred to the increased LC risk, while rs1436953 was associated with the lower susceptibility to LC among males. Rs731820, rs4502156, rs11071657, rs7172432, and rs11856307 contributed to the predisposition of LC among subjects with BMI > 24 kg/m², while rs7495253 was associated with an increased risk of LC in subjects with BMI ≤ 24 kg/m². The increased LC risk was found in rs4502156, while the protective risk effect of rs8037894, rs7172432, rs11856307, and rs1436953 on the occurrence of LC was observed in smokers and non-drinkers. Moreover, rs7495253 and rs7495931 had a higher risk of lymphatic metastasis. Rs1436953 was related to the reduced risk of lung adenocarcinoma, while rs4502156, rs8037894, rs7172432, rs11856307, and rs1436953 were related to the risk of small cell carcinoma.

CONCLUSIONS

Our results first display that C2CD4A/B polymorphisms served as protective factors for LC predisposition in a Chinese Han population. These findings could provide new biological insight into the understanding of C2CD4A/B genes on LC pathogenesis.

Identification and Prognostic Value Exploration of Cyclophosphamide (Cytoxan)-Centered Chemotherapy Response-Associated Genes in Breast Cancer

In this study, we aimed to explore cyclophosphamide (Cytoxan) response-associated genes and constructed a model to predict the prognosis of breast cancer (BRCA) patients. Samples obtained from TCGA and GEO databases were subjected to Weighted Gene Coexpression Network Analysis (WGCNA) and univariate Cox and LASSO Cox regression analysis to identify and validate the Cytoxan response-related prognostic signature. Moreover, multivariate Cox regression analysis was performed to analyze the independence of factors, and the nomogram model was constructed by including all the independent factors. WGCNA revealed that 159 genes are significantly correlated with Cytoxan response in BRCA samples, and the samples with a different prognosis could be effectively distinguished based on the expression of those 159 genes. Ten genes were further selected to be related to the prognosis of BRCA patients, including PCDHB2, GRIK2, FRMD7, CCSER1, PCDHGA1, PCDHA1, LRRC37A6P, PCDHGA12, ZNF486, and PCDHGB5, based on the Risk Score model. Among them, PCDHA1 expression was validated in cells and patient samples. Multivariate Cox regression analysis confirmed that the Risk Score is an independent factor. Furthermore, the nomogram model showed that the predicted survival probability is closely related to the actual survival probability. In conclusion, we identified 159 genes potentially correlated with the Cytoxan response of BRCA patients, which had prognostic value in BRCA.

Identification and Validation of a Five-Gene Signature Associated With Overall Survival in Breast Cancer Patients

BACKGROUND

Recent years, the global prevalence of breast cancer (BC) was still high and the underlying molecular mechanisms remained largely unknown. The investigation of prognosis-related biomarkers had become an urgent demand.

RESULTS

In this study, gene expression profiles and clinical information of breast cancer patients were downloaded from the TCGA database. The differentially expressed genes (DEGs) were estimated by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. A risk score formula involving five novel prognostic associated biomarkers (EDN2, CLEC3B, SV2C, WT1, and MUC2) were then constructed by LASSO. The prognostic value of the risk model was further confirmed in the TCGA entire cohort and an independent external validation cohort. To explore the biological functions of the selected genes, in vitro assays were performed, indicating that these novel biomarkers could markedly influence breast cancer progression.

CONCLUSIONS

We established a predictive five-gene signature, which could be helpful for a personalized management in breast cancer patients.

Precision treatment exploration of breast cancer based on heterogeneity analysis of lncRNAs at the single-cell level

BACKGROUND

Breast cancer (BC) is a complex disease with high heterogeneity, which often leads to great differences in treatment results. Current common molecular typing method is PAM50, which shows positive results for precision medicine; however, room for improvement still remains because of the different prognoses of subtypes. Therefore, in this article, we used lncRNAs, which are more tissue-specific and developmental stage-specific than other RNAs, as typing markers and combined single-cell expression profiles to retype BC, to provide a new method for BC classification and explore new precise therapeutic strategies based on this method.

METHODS

Based on lncRNA expression profiles of 317 single cells from 11 BC patients, SC3 was used to retype BC, and differential expression analysis and enrichment analysis were performed to identify biological characteristics of new subtypes. The results were validated for survival analysis using data from TCGA. Then, the downstream regulatory genes of lncRNA markers of each subtype were searched by expression correlation analysis, and these genes were used as targets to screen therapeutic drugs, thus proposing new precision treatment strategies according to the different subtype compositions of patients.

RESULTS

Seven lncRNA subtypes and their specific biological characteristics are obtained. Then, 57 targets and 210 drugs of 7 subtypes were acquired. New precision medicine strategies were proposed according to the different compositions of patient subtypes.

CONCLUSIONS

For patients with different subtype compositions, we propose a strategy to select different drugs for different patients, which means using drugs targeting multi subtype or combinations of drugs targeting a single subtype to simultaneously kill different cancer cells by personalized treatment, thus reducing the possibility of drug resistance and even recurrence.

An autophagic gene-based signature to predict the survival of patients with low-grade gliomas

Background

Since autophagy remains an important topic of investigation, the RNA‐sequence profiles of autophagy‐related genes (ARGs) can provide insights into predicting low‐grade gliomas (LGG) prognosis.

Methods

The RNA‐seq profiles of autophagic genes and prognosis data of LGG were integrated from the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA). Univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) regression model were carried out to identify the differentially expressed prognostic autophagy‐related genes. Then, the autophagic‐gene signature was formed and verified in TCGA test set and external CGGA cohorts. Time‐dependent receiver operating characteristic (ROC) was examined to test the accuracy of this signature feature. A nomogram was conducted to meet the needs of clinicians. Sankey diagrams were performed to visualize the relationship between the multigene signatures and clinic‐pathological features.

Results

Twenty‐four ARGs were finally identified most relevant to LGG prognosis. According to the specific prediction index formula, the patients were classified into low‐risk or high‐risk groups. Prognostic accuracy was proved by time‐dependent ROC analysis, with AUC 0.9, 0.93, and 0.876 at the survival time of 2‐, 3‐, and 5‐year, respectively, which was superior to the AUC of the isocitrate dehydrogenase (IDH) mutation. The result was confirmed while validated in the TCGA test set and external validation CGGA cohort. A nomogram was constructed to meet individual needs. With a visualization approach, Sankey diagrams show the relationship of the histological type, IDH status, and predict index. In TCGA and CGGA cohorts, both low‐risk groups displayed better survival rate in LGG while histological type and IDH status did not show consistency results.

Conclusions

24‐ARGs may play crucial roles in the progression of LGG, and LGG patients were effectively divided into low‐risk and high‐risk groups according to prognostic prediction. Overall, our study will provide novel strategies for clinical applications.

An immune-related gene signature for determining Ewing sarcoma prognosis based on machine learning

PURPOSE

Ewing sarcoma (ES) is one of the most common malignant bone tumors in children and adolescents. The immune microenvironment plays an important role in the development of ES. Here, we developed an optimal signature for determining ES patient prognosis based on immune-related genes (IRGs).

METHODS

We analyzed the ES gene expression profile dataset, GSE17679, from the GEO database and extracted differential expressed IRGs (DEIRGs). Then, we conducted functional correlation and protein-protein interaction (PPI) analyses of the DEIRGs and used the machine learning algorithm-iterative Lasso Cox regression analysis to build an optimal DEIRG signature. In addition, we applied ES samples from the ICGC database to test the optimal gene signature. We performed univariate and multivariate Cox regressions on clinicopathological characteristics and optimal gene signature to evaluate whether signature is an important prognostic factor. Finally, we calculated the infiltration of 24 immune cells in ES using the ssGSEA algorithm, and analyzed the correlation between the DEIRGs in the optimal gene signature and immune cells.

RESULTS

A total of 249 DEIRGs were screened and an 11-gene signature with the strongest correlation with patient prognoses was analyzed using a machine learning algorithm. The 11-gene signature also had a high prognostic value in the ES external verification set. Univariate and multivariate Cox regression analyses showed that 11-gene signature is an independent prognostic factor. We found that macrophages and cytotoxic, CD8 T, NK, mast, B, NK CD56bright, TEM, TCM, and Th2 cells were significantly related to patient prognoses; the infiltration of cytotoxic and CD8 T cells in ES was significantly different. By correlating prognostic biomarkers with immune cell infiltration, we found that FABP4 and macrophages, and NDRG1 and Th2 cells had the strongest correlation.

CONCLUSION

Overall, the IRG-related 11-gene signature can be used as a reliable ES prognostic biomarker and can provide guidance for personalized ES therapy.

A DNA Methylation-Based Panel for the Prognosis and Dagnosis of Patients With Breast Cancer and Its Mechanisms

Objective

To identify DNA methylation related biomarkers in patients with breast cancer (BC).

Materials and Methods

A total of seven BC methylation studies including 1,438 BC patients or breast tissues were included in this study. An elastic net regularized Cox proportional hazards regression (CPH) model was used to build a multi-5′-C-phosphate-G-3′ methylation panel. The diagnosis and prognosis power of the panel was evaluated and validated using a Kaplan–Meier curve, univariate and multivariable CPH, subgroup analysis. A nomogram containing the panel was developed. The relationships between the panel-based methylation risk and the immune landscape and genomic metrics were investigated.

Results

Sixty-eight CpG sites were significantly correlated with the overall survival (OS) of BC patients, and based on the result of penalized CPH, a 28-CpG site based multi CpG methylation panel was found. The prognosis and diagnosis role of the panel was validated in the discovery set, validation set, and six independent cohorts, which indicated that higher methylation risk was associated with poor OS, and the panel outperformed currently available biomarkers and remained an independent factor after adjusting for other clinical features. The methylation risk was negatively correlated with innated and adaptive immune cells, and positively correlated with total mutation load, SCNA, and MATH.

Conclusions

We validated a multi CpG methylation panel that could independently predict the OS of BC patients. The Th2-mediated tumor promotion effect—suppression of innate and adaptive immunity—participated in the progression of high-risk BC. Patients with high methylation risk were associated with tumor heterogeneity and poor survival.