Turning up a new pattern: Identification of cancer-associated fibroblast-related clusters in TNBC

EMT-related gene classifications predict the prognosis, immune infiltration, and therapeutic response of osteosarcoma

Background

Osteosarcoma (OS), a bone tumor with high ability of invasion and metastasis, has seriously affected the health of children and adolescents. Many studies have suggested a connection between OS and the epithelial-mesenchymal transition (EMT). We aimed to integrate EMT-Related genes (EMT-RGs) to predict the prognosis, immune infiltration, and therapeutic response of patients with OS.

Methods

We used consensus clustering to identify potential EMT-Related OS molecular subtypes. Somatic mutation, tumor immune microenvironment, and functional enrichment analyses were performed for each subtype. We next constructed an EMT-Related risk signature and evaluated it by Kaplan-Meier (K-M) analysis survival and receiver operating characteristic (ROC) curves. Moreover, we constructed a nomogram to more accurately predict OS patients' clinical outcomes. Response effects of immunotherapy in OS patients was analyzed by Tumor Immune Dysfunction and Exclusion (TIDE) analysis, while sensitivity for chemotherapeutic agents was analyzed using oncoPredict. Finally, the expression patterns of hub genes were investigated by single-cell RNA sequencing (scRNA-seq) data analysis.

Results

A total of 53 EMT-RDGs related to prognosis were identified, separating OS samples into two separate subgroups. The EMT-high subgroup showed favourable overall survival and more active immune response. Significant correlations were found between EMT-Related DEGs and functions as well as pathways linked to the development of OS. Additionally, a risk signature was established and OS patients were divided into two categories based on the risk scores. The signature presented a good predictive performance and could be recognized as an independent predictive factor for OS. Furthermore, patients with higher risk scores exhibited better sensitivity for five drugs, while no significant difference existed in immunotherapy response between the two risk subgroups. scRNA-seq data analysis displayed different expression patterns of the hub genes.

Conclusion

We developed a novel EMT-Related risk signature that can be considered as an independent predictor for OS, which may help improve clinical outcome prediction and guide personalized treatments for patients with OS.

Non-invasive prediction for pathologic complete response to neoadjuvant chemoimmunotherapy in lung cancer using CT-based deep learning: a multicenter study

Neoadjuvant chemoimmunotherapy has revolutionized the therapeutic strategy for non-small cell lung cancer (NSCLC), and identifying candidates likely responding to this advanced treatment is of important clinical significance. The current multi-institutional study aims to develop a deep learning model to predict pathologic complete response (pCR) to neoadjuvant immunotherapy in NSCLC based on computed tomography (CT) imaging and further prob the biologic foundation of the proposed deep learning signature. A total of 248 participants administrated with neoadjuvant immunotherapy followed by surgery for NSCLC at Ruijin Hospital, Ningbo Hwamei Hospital, and Affiliated Hospital of Zunyi Medical University from January 2019 to September 2023 were enrolled. The imaging data within 2 weeks prior to neoadjuvant chemoimmunotherapy were retrospectively extracted. Patients from Ruijin Hospital were grouped as the training set (n = 104) and the validation set (n = 69) at the 6:4 ratio, and other participants from Ningbo Hwamei Hospital and Affiliated Hospital of Zunyi Medical University served as an external cohort (n = 75). For the entire population, pCR was obtained in 29.4% (n = 73) of cases. The areas under the curve (AUCs) of our deep learning signature for pCR prediction were 0.775 (95% confidence interval [CI]: 0.649 - 0.901) and 0.743 (95% CI: 0.618 - 0.869) in the validation set and the external cohort, significantly superior than 0.579 (95% CI: 0.468 - 0.689) and 0.569 (95% CI: 0.454 - 0.683) of the clinical model. Furthermore, higher deep learning scores correlated to the upregulation for pathways of cell metabolism and more antitumor immune infiltration in microenvironment. Our developed deep learning model is capable of predicting pCR to neoadjuvant chemoimmunotherapy in patients with NSCLC.

Innate immune cells: Key players of orchestra in modulating tumor microenvironment (TME)

The tumor microenvironment (TME) with vital role in cancer progression is composed of various cells such as endothelial cells, immune cells, and mesenchymal stem cells. In particular, innate immune cells such as macrophages, dendritic cells, myeloid-derived suppressor cells, neutrophils, innate lymphoid cells, γδT lymphocytes, and natural killer cells can either promote or suppress tumor progression when present in the TME. An increase in research on the cross-talk between the TME and innate immune cells will lead to new approaches for anti-tumoral therapeutic interventions. This review primarily focuses on the biology of innate immune cells and their main functions in the TME. In addition, it summarizes several innate immune-based immunotherapies that are currently tested in clinical trials.

Pan-cancer analysis reveals potential immunological and prognostic roles of METTL7A in human cancers

Methyltransferase-like protein 7A (METTL7A) is an m6A RNA methyltransferase that has been linked to cancer prognosis and drug resistance. However, a comprehensive analysis of METTL7A is lacking. The expression of METTL7A, prognostic performance, correlation with microsatellite instability (MSI), tumor mutational burden (TMB), and immune infiltration was investigated in The Cancer Genome Atlas (TCGA). Immunohistochemistry staining was applied to detect METTL7A in 6 tumors. METTL7A was significantly decreased in 19 cancers in TCGA including LUAD. Alterations of METTL7A include amplification and mutation, and epigenetic alterations revealed increased promoter methylation may result in down-regulation of METTL7A in LUAD. We also found that METTL7A was linked to both TMB and MSI in LUAD. METTL7A was increasingly correlated with invasive immune cells, while being negatively associated with Macrophages M0, Mast cells activated, activated memory CD4 T cells, CD8 T cells, and follicular helper T cells in several tumors. Additionally, METTL7A showed similar correlation with immune therapy-related genes across cancers. Our biological validation found that the protein levels of METTL7A were down-regulated in breast cancer (BRCA), endometrioid cancer (UCEC), colon cancer (COAD), prostate cancer (PRAD), and kidney clear cell carcinoma (KIRC), as detected by immunohistochemistry staining. Overall, our work indicates that METTL7A may serve as promising diagnostic and prognostic indicator of LUAD, and our work sheds light on the potential immunological and prognostic roles of METTL7A in human cancers.

A Novel Oncogenic Role of Disulfidptosis-related Gene SLC7A11 in Anti-tumor Immunotherapy Response to Human Cancers

BACKGROUND

The protein Solute Carrier Family 7 Member 11 (SLC7A11) plays a pivotal role in cellular redox homeostasis by suppressing disulfidptosis, which restricts tumor growth. Yet, its relevance in prognosis, immunity, and cancer treatment efficacy is not well understood.

METHODS

We conducted a comprehensive analysis of the expression of SLC7A11 across 33 cancer types, employing datasets from public databases. Methods, such as Cox regression and survival analyses assessed its prognostic significance, while functional enrichment explored the biological processes tied to SLC7A11. The association between SLC7A11 expression, immune cell infiltration, and immune-related gene expression was also scrutinized.

RESULTS

Notably, SLC7A11 expression was more pronounced in cancerous compared to normal samples and correlated with higher tumor grades. Increased SLC7A11 expression was linked to poor outcomes, particularly in liver hepatocellular carcinoma (LIHC). This protein's expression also showcased significant relationships with diverse molecular and immune subtypes. Additionally, a prognostic nomogram was devised, integrating SLC7A11 expression and clinical variables. High SLC7A11 levels corresponded with cell growth and senescence pathways in various cancers and with lipid and cholesterol metabolism in LIHC. Furthermore, potential therapeutic compounds for LIHC with high SLC7A11 were identified. Real-time PCR (qPCR) and Western blot were conducted to explore the expression of SLC7A11 in tumor tissues and cancer cell lines.

CONCLUSION

In summation, this study emphasizes the prognostic and immunological importance of SLC7A11, spotlighting its potential as a therapeutic target in LIHC.

Homologous recombination deficiency reflects the heterogeneity and monitoring treatment response for patients with breast cancer

BACKGROUND

In breast cancer (BC), homologous recombination defect (HRD) is a common carcinogenic mechanism. It is meaningful to classify BC according to HRD biomarkers and to develop a platform for identifying BC molecular features, pathological features and therapeutic responses.

METHODS

In total, 109 HRD genes were collected and screened by univariate Cox regression analysis to determine the prognostic genes, which were used to construct a consensus matrix to identify BC subtype. Differentially expressed genes (DEGs) were filtered by the Limma package and screened by random forest analysis to build a model to analyze the immunotherapy response and sensitivity and prognosis of patients suffering from BC to different drugs.

RESULTS

Thirteen out of 109 HRD genes were prognostic genes of BC, and BC was classified into two subgroups based on their expression. Cluster 1 had a significantly backward survival outcome and a significantly higher adaptive immunity score relative to cluster 2. Six genes were identified by random forest analysis as factors for developing the model. The model provided a prediction called risk score, which showed a significant stratification effect on BC prognosis, immunotherapy response and IC50 values of 62 drugs.

CONCLUSIONS

In the present study, two HRD subtypes of BC were successfully identified, for which mutation and immunological features were determined. A model based on differential genes of HRD subtypes was established, which was a potential predictor of prognosis, immunotherapy response and drug sensitivity of BC.

Identification of UBFD1 as a prognostic biomarker and molecular target among estrogen receptor-positive breast cancer

Estrogen receptor (ER)-positive breast cancer (BRCA) is the most commonly diagnosed molecular subtype of BRCA. It is routinely treated with endocrine therapy; however, some patients relapse after therapy and develop drug resistance, resulting in treatment failure. In the present study, we identified markers of ER-positive BRCA and evaluated their putative function in immune infiltration as well as their clinicopathological significance. The ubiquitin family domain containing 1 (UBFD1) protein was associated with the prognosis of ER-positive BRCA patients. Its expression was higher in ER-positive BRCA tissues compared with adjacent nontumor tissues. Patients with higher UBFD1 expression had a poorer prognosis. UBFD1 is an independent risk factor for ER-positive BRCA patients and its function was primarily associated with hormone activity and inflammation. Taken together, UBFD1 is a potential prognostic biomarker and candidate target of ER-positive BRCA.

Integration of single-cell and bulk RNA sequencing to establish a prognostic signature based on tumor-associated macrophages in colorectal cancer

Several studies have shown significant involvement of tumor-associated macrophages (TAMs) in the tumor microenvironment and cancer progression. However, no data on reliable TAM-related biomarkers are available for predicting the prognosis of patients with colorectal cancer (CRC). We analyzed the clinical data and gene expression profiles of patients with CRC from databases. The single-cell transcriptomic data was applied to identify M2-like TAM-related differentially expressed genes. Univariate Cox and least absolute shrinkage and selection operator regression analyses were used to determine the prognostic signature genes. Then, seven key genes were screened to develop the prognostic signature. In the training and external validation cohorts, the overall survival (OS) of patients in the high-risk group was significantly shorter compared to the low-risk group. Consequently, we created a nomogram that could accurately and reliably predict the prognosis of patient with CRC. A significant correlation was observed between the patient's prognosis, clinical features, sensitivity to anticancer drugs, TME, and risk scores. Moreover, risk score was strongly related to the response to immunotherapy in patients from GSE91061, GSE78220, and GSE60331 cohorts. Finally, high expression of HSPA1A, SERPINA1, CXCL1, and low expression of DNASE1L3 were found in human CRC tissue and normal tissue by using qRT-PCR. In conclusion, the M2-like TAM-related prognostic signature could predict the survival, prognosis, and response of patients with CRC to immunotherapy, which sheds light on the role of TAMs in CRCs and enhances our understanding of TAMs.

Multi-omics analyses unravel DNA damage repair-related clusters in breast cancer with experimental validation

Background

As one of the most common malignancies worldwide, breast cancer (BC) exhibits high heterogeneity of molecular phenotypes. The evolving view regarding DNA damage repair (DDR) is that it is context-specific and heterogeneous, but its role in BC remains unclear.

Methods

Multi-dimensional data of transcriptomics, genomics, and single-cell transcriptome profiling were obtained to characterize the DDR-related features of BC. We collected 276 DDR-related genes based on the Molecular Signature Database (MSigDB) database and previous studies. We acquired public datasets included the SCAN-B dataset (GEO: GSE96058), METABRIC database, and TCGA-BRCA database. Corresponding repositories such as transcriptomics, genomics, and clinical information were also downloaded. We selected scRNA-seq data from GEO: GSE176078, GSE114727, GSE161529, and GSE158724. Bulk RNA-seq data from GEO: GSE176078, GSE18728, GSE5462, GSE20181, and GSE130788 were extracted for independent analyses.

Results

The DDR classification was constructed in the SCAN-B dataset (GEO: GSE96058) and METABRIC database, Among BC patients, there were two clusters with distinct clinical and molecular characteristics: the DDR-suppressed cluster and the DDR-active cluster. A superior survival rate is found for tumors in the DDR-suppressed cluster, while those with the DDR-activated cluster tend to have inferior prognoses and clinically aggressive behavior. The DDR classification was validated in the TCGA-BRCA cohort and shown similar results. We also found that two clusters have different pathway activities at the genomic level. Based on the intersection of the different expressed genes among these cohorts, we found that PRAME might play a vital role in DDR. The DDR classification was then enabled by establishing a DDR score, which was verified through multilayer cohort analysis. Furthermore, our results revealed that malignant cells contributed more to the DDR score at the single-cell level than nonmalignant cells. Particularly, immune cells with immunosuppressive properties (such as FOXP3+ CD4+ T cells) displayed higher DDR scores among those with distinguishable characteristics.

Conclusion

Collectively, this study performs general analyses of DDR heterogeneity in BC and provides insight into the understanding of individualized molecular and clinicopathological mechanisms underlying unique DDR profiles.

Identification of Niche-Specific Gene Signatures between Malignant Tumor Microenvironments by Integrating Single Cell and Spatial Transcriptomics Data

The tumor microenvironment significantly affects the transcriptomic states of tumor cells. Single-cell RNA sequencing (scRNA-seq) helps elucidate the transcriptomes of individual cancer cells and their neighboring cells. However, cell dissociation results in the loss of information on neighboring cells. To address this challenge and comprehensively assess the gene activity in tissue samples, it is imperative to integrate scRNA-seq with spatial transcriptomics. In our previous study on physically interacting cell sequencing (PIC-seq), we demonstrated that gene expression in single cells is affected by neighboring cell information. In the present study, we proposed a strategy to identify niche-specific gene signatures by harmonizing scRNA-seq and spatial transcriptomic data. This approach was applied to the paired or matched scRNA-seq and Visium platform data of five cancer types: breast cancer, gastrointestinal stromal tumor, liver hepatocellular carcinoma, uterine corpus endometrial carcinoma, and ovarian cancer. We observed distinct gene signatures specific to cellular niches and their neighboring counterparts. Intriguingly, these niche-specific genes display considerable dissimilarity to cell type markers and exhibit unique functional attributes independent of the cancer types. Collectively, these results demonstrate the potential of this integrative approach for identifying novel marker genes and their spatial relationships.

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.

Reprogramming of Treg cells in the inflammatory microenvironment during immunotherapy: a literature review

Regulatory T cells (Treg), as members of CD4+ T cells, have garnered extensive attention in the research of tumor progression. Treg cells have the function of inhibiting the immune effector cells, preventing tissue damage, and suppressing inflammation. Under the stimulation of the tumor inflammatory microenvironment (IM), the reprogramming of Treg cells enhances their suppression of immune responses, ultimately promoting tumor immune escape or tumor progression. Reducing the number of Treg cells in the IM or lowering the activity of Treg cells while preventing their reprogramming, can help promote the body's anti-tumor immune responses. This review introduces a reprogramming mechanism of Treg cells in the IM; and discusses the regulation of Treg cells on tumor progression. The control of Treg cells and the response to Treg inflammatory reprogramming in tumor immunotherapy are analyzed and countermeasures are proposed. This work will provide a foundation for downregulating the immunosuppressive role of Treg in the inflammatory environment in future tumor immunotherapy.

Reassessing Breast Cancer-Associated Fibroblasts (CAFs) Interactions with Other Stromal Components and Clinico-Pathologic Parameters by Using Immunohistochemistry and Digital Image Analysis (DIA)

BACKGROUND

Breast cancer (BC) stroma has CD34- and αSMA-positive cancer-associated fibroblasts (CAFs) differently distributed. During malignant transformation, CD34-positive fibroblasts decrease while αSMA-positive CAFs increase. The prevalence of αSMA-positive CAFs in BC stroma makes microscopic examination difficult without digital image analysis processing (DIA). DIA was used to compare CD34- and αSMA-positive CAFs among breast cancer molecular subgroups. DIA-derived data were linked to age, survival, tumor stroma vessels, tertiary lymphoid structures (TLS), invasion, and recurrence.

METHODS

Double immunostaining for CD34 and αSMA showed different CAF distribution patterns in normal and BC tissues. Single CD34 immunohistochemistry on supplemental slides quantified tumor stroma CD34_CAFs. Digital image analysis (DIA) data on CAF density, intensity, stromal score, and H-score were correlated with clinico-pathologic factors.

RESULTS

CD34/αSMA CAF proportion was significantly related to age in Luminal A (LA), Luminal B (LB), and HER2 subtypes. CD34_CAF influence on survival, invasion, and recurrence of LA, LB-HER2, and TNBC subtypes was found to be significant. The CD34/αSMA-expressing CAFs exhibited a heterogeneous impact on stromal vasculature and TLS.

CONCLUSION

BC stromal CD34_CAFs/αSMA_CAFs have an impact on survival, invasion, and recurrence differently between BC molecular subtypes. The tumor stroma DIA assessment may have predictive potential to prognosis and long-term follow-up of patients with breast cancer.

Identification and validation of a novel HOX-related classifier signature for predicting prognosis and immune microenvironment in pediatric gliomas

Background: Pediatric gliomas (PGs) are highly aggressive and predominantly occur in young children. In pediatric gliomas, abnormal expression of Homeobox (HOX) family genes (HFGs) has been observed and is associated with the development and progression of the disease. Studies have found that overexpression or underexpression of certain HOX genes is linked to the occurrence and prognosis of gliomas. This aberrant expression may contribute to the dysregulation of important pathological processes such as cell proliferation, differentiation, and metastasis. This study aimed to propose a novel HOX-related signature to predict patients' prognosis and immune infiltrate characteristics in PGs. Methods: The data of PGs obtained from publicly available databases were utilized to reveal the relationship among abnormal expression of HOX family genes (HFGs), prognosis, tumor immune infiltration, clinical features, and genomic features in PGs. The HFGs were utilized to identify heterogeneous subtypes using consensus clustering. Then random forest-supervised classification algorithm and nearest shrunken centroid algorithm were performed to develop a prognostic signature in the training set. Finally, the signature was validated in an internal testing set and an external independent cohort. Results: Firstly, we identified HFGs significantly differentially expressed in PGs compared to normal tissues. The individuals with PGs were then divided into two heterogeneous subtypes (HOX-SI and HOX-SII) based on HFGs expression profiles. HOX-SII showed higher total mutation counts, lower immune infiltration, and worse prognosis than HOX-SI. Then, we constructed a HOX-related gene signature (including HOXA6, HOXC4, HOXC5, HOXC6, and HOXA-AS3) based on the cluster for subtype prediction utilizing random forest supervised classification and nearest shrunken centroid algorithm. The signature was revealed to be an independent prognostic factor for patients with PGs by multivariable Cox regression analysis. Conclusion: Our study provides a novel method for the prognosis classification of PGs. The findings also suggest that the HOX-related signature is a new biomarker for the diagnosis and prognosis of patients with PGs, allowing for more accurate survival prediction.

Identification and in vitro and in vivo validation of the key role of GSDME in pyroptosis-related genes signature in hepatocellular carcinoma

We used pyroptosis-related genes to establish a risk-score model for prognostic prediction of liver hepatocellular carcinoma (LIHC) patients. A total of 52 pyroptosis-associated genes were identified. Then, data for 374 LIHC patients and 50 normal individuals were acquired from the TCGA database. Through gene expression analyses, differentially expressed genes (DEGs) were determined. The 13 pyroptosis-related genes (PRGs) confirmed as potential prognostic factors through univariate Cox regression analysis were entered into Lasso and multivariate Cox regression to build a PRGs prognostic signature, containing four PRGs (BAK1, GSDME, NLRP6, and NOD2) determined as independent prognostic factors. mRNA levels were evaluated by qRT-PCR, while overall survival (OS) rates were assessed by the Kaplan-Meier method. Enrichment analyses were done to establish the mechanisms associated with differential survival status of LIHC patients from a tumor immunology perspective. Additionally, a risk score determined by the prognostic model could divide LIHC patients into low- and high-risk groups using median risk score as cut-off. A prognostic nomogram, derived from the prognostic model and integrating clinical characteristics of patients, was constructed. The prognostic function of the model was also validated using GEO, ICGC cohorts, and online databases Kaplan-Meier Plotter. Small interfering RNA-mediated knockdown of GSDME, as well as lentivirus-mediated GSDME knockdown, were performed to validate that knockdown of GSDME markedly suppressed growth of HCC cells both in vivo and in vitro. Collectively, our study demonstrated a PRGs prognostic signature that had great clinical value in prognosis assessment.

Melatonin blunts the tumor-promoting effect of cancer-associated fibroblasts by reducing IL-8 expression and reversing epithelial-mesenchymal transition

BACKGROUND

Most studies on melatonin have focused on tumor cells but have ignored the tumor microenvironment (TME), especially one of its important components, the cancer-associated fibroblasts (CAFs). Therefore, we attempted to explore the role of melatonin in TME.

METHODS

We investigated the regulatory role of melatonin in the tumor-promoting effect of CAFs and its underlying mechanism by using cell and animal models.

RESULTS

CAFs promoted tumor progression, but melatonin weakened the tumor-promoting effect of CAFs. Compared with tumor cells, IL-8 was mainly expressed in CAFs. CAFs-overexpressing IL-8 induced the epithelial-mesenchymal transition (EMT) of tumor cells, and a positive crosstalk was observed between CAFs and tumor cells undergoing EMT, thereby further promoting the IL-8 expression. Melatonin suppressed this crosstalk by inhibiting the NF-κB pathway, thereby impeding the IL-8 expression from CAFs. Importantly, melatonin reversed CAFs-derived IL-8-mediated EMT by inhibiting the AKT pathway. Melatonin was found to directly and indirectly inhibit tumor progression.

CONCLUSION

Our research reveals the potential action mechanism of melatonin in regulating the CAF-tumor cell interaction and suggests the potential of melatonin as an adjuvant of tumor therapy.

PARM1 Drives Smooth Muscle Cell Proliferation in Pulmonary Arterial Hypertension via AKT/FOXO3A Axis

Pulmonary arterial hypertension (PAH) is a group of severe, progressive, and debilitating diseases with limited therapeutic options. This study aimed to explore novel therapeutic targets in PAH through bioinformatics and experiments. Weighted gene co-expression network analysis (WGCNA) was applied to detect gene modules related to PAH, based on the GSE15197, GSE113439, and GSE117261. GSE53408 was applied as validation set. Subsequently, the validated most differentially regulated hub gene was selected for further ex vivo and in vitro assays. PARM1, TSHZ2, and CCDC80 were analyzed as potential intervention targets for PAH. Consistently with the bioinformatic results, our ex vivo and in vitro data indicated that PARM1 expression increased significantly in the lung tissue and/or pulmonary artery of the MCT-induced PAH rats and hypoxia-induced PAH mice in comparison with the respective controls. Besides, a similar expression pattern of PARM1 was found in the hypoxia- and PDGF--treated isolated rat primary pulmonary arterial smooth muscle cells (PASMCs). In addition, hypoxia/PDGF--induced PARM1 protein expression could promote the elevation of phosphorylation of AKT, phosphorylation of FOXO3A and PCNA, and finally the proliferation of PASMCs in vitro, whereas PARM1 siRNA treatment inhibited it. Mechanistically, PARM1 promoted PAH via AKT/FOXO3A/PCNA signaling pathway-induced PASMC proliferation.

A degradome-based prognostic signature that correlates with immune infiltration and tumor mutation burden in breast cancer

Introduction

Female breast cancer is the most common malignancy worldwide, with a high disease burden. The degradome is the most abundant class of cellular enzymes that play an essential role in regulating cellular activity. Dysregulation of the degradome may disrupt cellular homeostasis and trigger carcinogenesis. Thus we attempted to understand the prognostic role of degradome in breast cancer by means of establishing a prognostic signature based on degradome-related genes (DRGs) and assessed its clinical utility in multiple dimensions.

Methods

A total of 625 DRGs were obtained for analysis. Transcriptome data and clinical information of patients with breast cancer from TCGA-BRCA, METABRIC and GSE96058 were collected. NetworkAnalyst and cBioPortal were also utilized for analysis. LASSO regression analysis was employed to construct the degradome signature. Investigations of the degradome signature concerning clinical association, functional characterization, mutation landscape, immune infiltration, immune checkpoint expression and drug priority were orchestrated. Cell phenotype assays including colony formation, CCK8, transwell and wound healing were conducted in MCF-7 and MDA-MB-435S breast cancer cell lines, respectively.

Results

A 10-gene signature was developed and verified as an independent prognostic predictor combined with other clinicopathological parameters in breast cancer. The prognostic nomogram based on risk score (calculated based on the degradome signature) showed favourable capability in survival prediction and advantage in clinical benefit. High risk scores were associated with a higher degree of clinicopathological events (T4 stage and HER2-positive) and mutation frequency. Regulation of toll-like receptors and several cell cycle promoting activities were upregulated in the high-risk group. PIK3CA and TP53 mutations were dominant in the low- and high-risk groups, respectively. A significantly positive correlation was observed between the risk score and tumor mutation burden. The infiltration levels of immune cells and the expressions of immune checkpoints were significantly influenced by the risk score. Additionally, the degradome signature adequately predicted the survival of patients undergoing endocrinotherapy or radiotherapy. Patients in the low-risk group may achieve complete response after the first round of chemotherapy with cyclophosphamide and docetaxel, whereas patients in the high-risk group may benefit from 5-flfluorouracil. Several regulators of the PI3K/AKT/mTOR signaling pathway and the CDK family/PARP family were identified as potential molecular targets in the low- and high-risk groups, respectively. In vitro experiments further revealed that the knockdown of ABHD12 and USP41 significantly inhibit the proliferation, invasion and migration of breast cancer cells.

Conclusion

Multidimensional evaluation verified the clinical utility of the degradome signature in predicting prognosis, risk stratification and guiding treatment for patients with breast cancer.

Development and Verification of a Prognostic Stemness-Related Gene Signature in Triple-Negative Breast Cancer

Background. It is well known that cancer stem cells can induce cancer metastasis, which causes the majority of cancer-related death, especially in triple-negative breast cancer (TNBC). TNBC features a high metastatic rate and low metastasis-free survival and is regarded as the most malignant subtype of breast cancer. The purpose of this study is to explore prognostic biomarkers that can predict metastasis of triple-negative breast cancer. Methods. The human triple-negative breast cancerSUM149PT cells were used for the study. The cancer stem cell spheres (sum149-Stem) and paired adherent cancer cells (sum149-Tumor) were collected to extract total RNAs. RNA-seq was used to analysis the mRNA expression of cancer stem cells and paired adherent cancer cells. Two different gene expression omnibus datasets (https://www.ncbi.nlm.nih.gov/gds), GSE58812 and GSE33926, were used to explore the mechanism of different expression genes between stem cells and adherent cancer cells. Seven genes showed prognostic function in all datasets. The STITCH database (https://www.stitchdata.com/) was used to explore the possible metastasis-inhibiting drugs that can target the seven genes. Each gene expression was compared by Pearson analysis. The receiver operating characteristic curve (ROC) and Kaplan–Meier survival curve were performed to assess the metastasis prognostic ability of the seven-gene modeling two different GEO datasets. Results. A subset of 7 stemness-related genes (SRGs) containing UCN, ST3GAL5, FDPS, HK2, MALL, LMTK3, and CRHR2 were identified in three independent cohorts. Univariate Cox analysis showed that ST3GAL5 plays an antitumor role in TNBC metastasis, and the other 6 genes promote the metastatic progression of TNBC. The ability of the 7-SRGs gene Cox model to predict TNBC metastasis was constructed with the GSE58812 dataset. Most of the genes showed significant expression in patients with different risk levels. Additionally, the model showed predictive value in another GEO dataset of TNBC patients. ROC curves indicated that the seven-gene model has a significant predictive value of TNBC metastasis. Conclusions. Expression analysis of the 7-SRGs signature model at diagnosis has predictive value for metastasis in TNBC patients.

Exploring the role of sphingolipid-related genes in clinical outcomes of breast cancer

Background

Despite tremendous advances in cancer research, breast cancer (BC) remains a major health concern and is the most common cancer affecting women worldwide. Breast cancer is a highly heterogeneous cancer with potentially aggressive and complex biology, and precision treatment for specific subtypes may improve survival in breast cancer patients. Sphingolipids are important components of lipids that play a key role in the growth and death of tumor cells and are increasingly the subject of new anti-cancer therapies. Key enzymes and intermediates of sphingolipid metabolism (SM) play an important role in regulating tumor cells and further influencing clinical prognosis.

Methods

We downloaded BC data from the TCGA database and GEO database, on which we performed in depth single-cell sequencing analysis (scRNA-seq), weighted co-expression network analysis, and transcriptome differential expression analysis. Then seven sphingolipid-related genes (SRGs) were identified using Cox regression, least absolute shrinkage, and selection operator (Lasso) regression analysis to construct a prognostic model for BC patients. Finally, the expression and function of the key gene PGK1 in the model were verified by in vitro experiments.

Results

This prognostic model allows for the classification of BC patients into high-risk and low-risk groups, with a statistically significant difference in survival time between the two groups. The model is also able to show high prediction accuracy in both internal and external validation sets. After further analysis of the immune microenvironment and immunotherapy, it was found that this risk grouping could be used as a guide for the immunotherapy of BC. The proliferation, migration, and invasive ability of MDA-MB-231 and MCF-7 cell lines were dramatically reduced after knocking down the key gene PGK1 in the model through cellular experiments.

Conclusion

This study suggests that prognostic features based on genes related to SM are associated with clinical outcomes, tumor progression, and immune alterations in BC patients. Our findings may provide insights for the development of new strategies for early intervention and prognostic prediction in BC.

Dissecting the role of cancer-associated fibroblast-derived biglycan as a potential therapeutic target in immunotherapy resistance: A tumor bulk and single-cell transcriptomic study

INTRODUCTION

Cancer-associated fibroblasts (CAFs) are correlated with the immunotherapy response. However, the culprits that link CAFs to immunotherapy resistance are still rarely investigated in real-world studies.

OBJECTIVES

This study aims to systematically assess the landscape of fibroblasts in cancer patients by combining single-cell and bulk profiling data from pan-cancer cohorts. We further sought to decipher the expression, survival predictive value and association with immunotherapy response of biglycan (BGN), a proteoglycan in the extracellular matrix, in multiple cohorts.

METHODS

Pan-cancer tumor bulks and 27 single-cell RNA sequencing cohorts were enrolled to investigate the correlations and crosstalk between CAFs and tumor or immune cells. Specific secreting factors of CAFs were then identified by expression profiling at tissue microdissection, isolated primary fibroblasts and single-cell level. The role of BGN was further dissected in additional three bulk and five single-cell profiling datasets from immunotherapy cohorts and validated in real-world patients who have received PD-1 blockade using immunohistochemistry and immunofluorescence.

RESULTS

CAFs were closely correlated with immune components. Frequent crosstalk between CAFs and other cells was revealed by the CellChat analysis. Single-cell regulatory network inference and clustering identified common and distinct regulators for CAFs across cancers. The BGN was determined to be a specific secreting factor of CAFs. The BGN served as an unfavourable indicator for overall survival and immunotherapy response. In the real-world immunotherapy cohort, patients with high BGN levels presented a higher proportion of poor response compared with those with low BGN (46.7% vs. 11.8%) and a lower level of infiltrating CD8+ T cells was also observed.

CONCLUSIONS

We highlighted the importance of CAFs in the tumor microenvironment and revealed that the BGN, which is mainly derived from CAFs, may be applicable in clinical practice and serve as a therapeutic target in immunotherapy resistance.

Mechanism of immune infiltration in synovial tissue of osteoarthritis: a gene expression-based study

BACKGROUND

Osteoarthritis is a chronic degenerative joint disease, and increasing evidences suggest that the pathogenic mechanism involves immune system and inflammation.

AIMS

The aim of current study was to uncover hub genes linked to immune infiltration in osteoarthritis synovial tissue using comprehensive bioinformatics analysis and experimental confirmation.

METHODS

Multiple microarray datasets (GSE55457, GSE55235, GSE12021 and GSE1919) for osteoarthritis in Gene Expression Omnibus database were downloaded for analysis. Differentially expressed genes (DEGs) were identified using Limma package in R software, and immune infiltration was evaluated by CIBERSORT algorithm. Then weighted gene co-expression network analysis (WGCNA) was performed to uncover immune infiltration-associated gene modules. Protein-protein interaction (PPI) network was constructed to select the hub genes, and the tissue distribution of these genes was analyzed using BioGPS database. Finally, the expression pattern of these genes was confirmed by RT-qPCR using clinical samples.

RESULTS

Totally 181 DEGs between osteoarthritis and normal control were screened. Macrophages, mast cells, memory CD4 T cells and B cells accounted for the majority of immune cell composition in synovial tissue. Osteoarthritis synovial showed high abundance of infiltrating resting mast cells, B cells memory and plasma cells. WGCNA screened 93 DEGs related to osteoarthritis immune infiltration. These genes were involved in TNF signaling pathway, IL-17 signaling pathway, response to steroid hormone, glucocorticoid and corticosteroid. Ten hub genes including MYC, JUN, DUSP1, NFKBIA, VEGFA, ATF3, IL-6, PTGS2, IL1B and SOCS3 were selected by using PPI network. Among them, four genes (MYC, JUN, DUSP1 and NFKBIA) specifically expressed in immune system were identified and clinical samples revealed consistent change of these four genes in synovial tissue retrieved from patients with osteoarthritis.

CONCLUSION

A 4-gene-based diagnostic model was developed, which had well predictive performance in osteoarthritis. MYC, JUN, DUSP1 and NFKBIA might be biomarkers and potential therapeutic targets in osteoarthritis.

The significant clinical correlation of the intratumor oral microbiome in oral squamous cell carcinoma based on tissue-derived sequencing

Background: The microbiota is a critical component of the complex human microenvironment, impacting various physiological processes and disease development via the microbe-host interaction. In particular, the oral microbiota profoundly affects tumor development and progression. There is increasing evidence that oral microbiota is associated with the development of oral cancer, especially oral squamous cell carcinoma (OSCC). Methods: We comprehensively analyzed the oral microbiota in 133 OSCC samples worldwide. Subsequently, we evaluated the microbial compositions between OSCC patients and healthy people and their correlation with clinical parameters. The value of the oral microbiota as a diagnostic and prognostic biomarker was also determined. Results: This study found differences in critical oral microbiota between OSCC and normal controls. The most notable differences are present in p_Firmicutes, p_Actinobacteria, c_Fusobacteriia, o_Fusobacteriales, f_Fusobacteriaceae, and g_Fusobacterium. All six-level oral microorganisms were also associated with the clinical characteristics of OSCC, particularly with the clinical outcomes (survival time and status). We developed a predictive model based on this. We found that five different oral microorganisms have high confidence and can be used for clinical diagnosis and prognostic prediction, except for p_Actinobacteria. Conclusion: This study revealed that the intratumor oral microbiota of OSCC patients worldwide and the microbial signatures of OSCC patients possess similar properties in different regions, further refining the shortcomings of the current research field. We revealed that the oral microbiota could be used as a biomarker to reflect human health and disease progression status. This will provide new directions for tumor microbiome research. This means we can develop strategies through diet, probiotics, and antibiotics for cancer prevention and treatment.

Tumor Suppressive Role of MicroRNAs in Triple Negative Breast Cancer

Triple-negative breast cancers are highly aggressive, a heterogeneous form of breast cancer with a high re-occurrence rate that further lacks an efficient treatment strategy and prognostic marker. The tumor microenvironment of the disease comprises cancer-associated fibroblasts, cancer stem cells, immunological molecules, epithelial-mesenchymal transition, and a metastatic microenvironment that contributes to disease progression and metastasis to distant sites. Emerging evidence indicated that miRNA clusters would be of clinical utility as they exert an oncogenic or tumor suppressor role in TNBC. The present review article aims to highlight the therapeutic significance of miRNA in targeting the above-mentioned signaling cascades and modulating the intracellular crosstalk in the tumor microenvironment of TNBC. Prognostic implications of miRNAs to depict disease-free survival, distant metastasis-free survival, relapse-free survival, and overall survival outcome were also unveiled.

NCAPG2 could be an immunological and prognostic biomarker: From pan-cancer analysis to pancreatic cancer validation

More recently, NCAPG2 has emerged as an intrinsically essential participant of the condensin II complex involved in the process of chromosome cohesion and stabilization in mitosis, and its position in particular tumours is now being highlighted. Simultaneously, the genetic properties of NCAPG2 hint that it might have enormous potential to interpret the malignant progression of tumors in a broader perspective, that is, in pan-cancer. Yet, at present, this recognition remains merely superficial and there is a lack of more detailed studies to explore the underlying pathogenesis. To meet this need, the current study was undertaken to comprehensively elucidate the potential functions of NCAPG2 in pan-cancer, based on a combination of existing databases like TCGA and GTEx. NCAPG2 was identified to be overexpressed in almost every tumor and to exhibit significant prognostic and diagnostic efficacy. Furthermore, the correlation between NCAPG2 and selected immune features, namely immune cell infiltration, immune checkpoint genes, TMB, MSI, etc. also indicates that NCAPG2 could potentially be applied in guidance of immunotherapy. Subsequently, in pancreatic cancer, this study further clarified the utility of NCAPG2 that downregulation of its expression could result in reduced proliferation, invasion and metastasis of pancreatic cancer cells, among such phenotypical changes, the epithelial-mesenchymal transition disruption could be at least one of the possible mechanisms raising or enhancing tumorigenesis. Taken above, NCAPG2, as a member of pan-oncogenes, would serve as a biomarker and potential therapeutic target for a range of malignancies, sharing new insights into precision medicine.

Lactate in the tumor microenvironment: A rising star for targeted tumor therapy

Metabolic reprogramming is one of fourteen hallmarks of tumor cells, among which aerobic glycolysis, often known as the "Warburg effect," is essential to the fast proliferation and aggressive metastasis of tumor cells. Lactate, on the other hand, as a ubiquitous molecule in the tumor microenvironment (TME), is generated primarily by tumor cells undergoing glycolysis. To prevent intracellular acidification, malignant cells often remove lactate along with H⁺, yet the acidification of TME is inevitable. Not only does the highly concentrated lactate within the TME serve as a substrate to supply energy to the malignant cells, but it also works as a signal to activate multiple pathways that enhance tumor metastasis and invasion, intratumoral angiogenesis, as well as immune escape. In this review, we aim to discuss the latest findings on lactate metabolism in tumor cells, particularly the capacity of extracellular lactate to influence cells in the tumor microenvironment. In addition, we examine current treatment techniques employing existing medications that target and interfere with lactate generation and transport in cancer therapy. New research shows that targeting lactate metabolism, lactate-regulated cells, and lactate action pathways are viable cancer therapy strategies.

A Novel Molecular Signature of Cancer-Associated Fibroblasts Predicts Prognosis and Immunotherapy Response in Pancreatic Cancer

Cancer-associated fibroblasts (CAFs), a prominent population of stromal cells, play a crucial role in tumor progression, prognosis, and treatment response. However, the relationship among CAF-based molecular signatures, clinical outcomes, and tumor microenvironment infiltration remains largely elusive in pancreatic cancer (PC). Here, we collected multicenter PC data and performed integrated analysis to investigate the role of CAF-related genes (CRGs) in PC. Firstly, we demonstrated that α-SMA⁺ CAFs were the most prominent stromal components and correlated with the poor survival rates of PC patients in our tissue microarrays. Then, we discriminated two diverse molecular subtypes (CAF clusters A and B) and revealed the significant differences in the tumor immune microenvironment (TME), four reported CAF subpopulations, clinical characteristics, and prognosis in PC samples. Furthermore, we analyzed their association with the immunotherapy response of PC patients. Lastly, a CRG score was constructed to predict prognosis, immunotherapy responses, and chemosensitivity in pancreatic cancer patients. In summary, these findings provide insights into further research targeting CAFs and their TME, and they pave a new road for the prognosis evaluation and individualized treatment of PC patients.

The Pan-Cancer Multi-Omics Landscape of FOXO Family Relevant to Clinical Outcome and Drug Resistance

The forkhead box O (FOXO) transcription factors (TFs) family are frequently mutated, deleted, or amplified in various human cancers, making them attractive candidates for therapy. However, their roles in pan-cancer remain unclear. Here, we evaluated the expression, prognostic value, mutation, methylation, and clinical features of four FOXO family genes (FOXO1, FOXO3, FOXO4, and FOXO6) in 33 types of cancers based on the Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) databases. We used a single sample gene set enrichment analysis (ssGSEA) algorithm to establish a novel index called "FOXOs score". Moreover, we investigated the association between the FOXOs score and tumor microenvironment (TME), the responses to multiple treatments, along with drug resistance. We found that the FOXO family genes participated in tumor progression and were related to the prognosis in various types of cancer. We calculated the FOXOs score and found that it was significantly correlated with multiple malignant pathways in pan-cancer, including Wnt/beta-catenin signaling, TGF-beta signaling, and hedgehog signaling. In addition, the FOXOs score was also associated with multiple immune-related characteristics. Furthermore, the FOXOs score was sensitive for predicting the efficacy of diverse treatments in multiple cancers, especially immunotherapy. In conclusion, FOXO family genes were vital in pan-cancer and were strongly correlated with the TME. A high FOXOs score indicated an excellent immune-activated TME and sensitivity to multiple treatments. Hence, the FOXOs score might potentially be used as a biomarker in patients with a tumor.

Preoperative systemic immune‐inflammation index as a significant prognostic factor after TURBT in patients with non‐muscle‐invasive bladder cancer: A retrospective study based on propensity score matching analysis

OBJECTIVE

To investigate the association of the preoperative systemic immune-inflammation index (SII) with recurrence-free survival (RFS) after transurethral resection of the bladder tumor (TURBT) of non-muscle-invasive bladder cancer (NMIBC) using propensity score matching (PSM) analysis.

METHODS

The clinicopathological characteristics and follow-up data of NMIBC patients were collected retrospectively from two tertiary medical centers. A 1:1 PSM analysis was carried out using the nearest-neighbor method (caliper size: 0.02). Cox regression analysis was used to identify the risk factors associated with RFS.

RESULTS

A total of 416 NMIBC patients were included in this study. Before and after matching, patients with increased SII had worse RFS (p < 0.0001 and p = 0.027, respectively). Multivariate Cox analysis identified SII as an independent predictor of RFS before (HR [95% CI]: 1.789 [1.232, 2.599], p = 0.002) and after matching (HR [95% CI]: 1.646 [1.077, 2.515], p = 0.021). In the matched subgroup analysis, an elevated SII had a significant association with postoperative worse RFS in the T1 stage (p = 0.025), primary status (p = 0.049), high-grade (p = 0.0015), and multiple lesions (p = 0.043) subgroups.

CONCLUSION

SII could accurately stratify the prognosis of NMIBC patients before and after PSM analysis. An elevated SII was significantly associated with worse RFS in NMIBC patients.

Impact of cervical and global spine sagittal alignment on cervical curvature changes after posterior cervical laminoplasty

OBJECTIVE

To analyze the correlation between the changes in cervical curvature and the sagittal parameters of spino-pelvic and clinical efficacy after posterior laminoplasty (LP).

METHODS

The patients with cervical spondylosis treated with LP from June 2018 to December 2020 were reviewed. The preoperative and follow-up spine full-length films were measured. The measured data included C2-C7 Cobb angle, C2-7 sagittal vertical axis (SVA), T1 slope (T1S), pelvic incidence, sacral slope (SS), pelvic tilt (PT), lumbar lordosis (LL), thoracic kyphosis (TK), and C7-SVA. Japanese Orthopaedic Association (JOA) score and neck disability index (NDI) score were recorded before surgery and follow-up.

RESULTS

There were 56 patients in this study. There were no significant differences in spino-pelvic sagittal parameters before and after surgery; however, the JOA score significantly improved. The changes in postoperative cervical lordosis correlated with SS, PT, LL, T1S, and C7-SVA (P < 0.05). Regression analysis showed that T1S and C7-SVA were associated with reducing cervical lordosis (P = 0.021 and P = 0.001, respectively). Patients with larger T1S combined with larger C7-SVA had more cervical lordosis loss, poor JOA improvement, and high postoperative NDI scores (P < 0.001, P = 0.018, and P < 0.001, respectively).

CONCLUSION

Patients should be examined with full-length spine film before surgery to evaluate the cervical and spino-pelvic sagittal balance. T1S and C7-SVA correlated with changes in cervical sagittal alignment after LP.

LEVEL OF EVIDENCE

III.

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.