Multi-omics analysis reveals the interaction between the complement system and the coagulation cascade in the development of endometriosis

Proteomics approach to discovering non-invasive diagnostic biomarkers and understanding the pathogenesis of endometriosis: a systematic review and meta-analysis

BACKGROUND

Endometriosis is one of the most common gynaecological diseases, yet it lacks efficient biomarkers for early detection and unravels disease mechanisms. Proteomic profiling has revealed diverse patterns of protein changes in various clinical samples. Integrating and systematically analysing proteomics data can facilitate the development of biomarkers, expediting diagnosis and providing insights for potential clinical and therapeutic applications. Hence, this systematic review and meta-analysis aimed to explore potential non-invasive diagnostic biomarkers in various biological samples and therapeutic targets for endometriosis.

METHODS

Online databases, including Scopus, PubMed, Web of Science, MEDLINE, Embase via Ovid, and Google Scholar, were searched using MeSH terms. Two independent authors screened the articles, extracted the data, and assessed the methodological quality of the included studies. GO and KEGG analyses were performed to identify the pathways that were significantly enriched. Protein‑protein interaction and hub gene selection analyses were also conducted to identify biomarker networks for endometriosis.

RESULTS

Twenty-six observational studies with a total of 2,486 participants were included. A total of 644 differentially expressed proteins (180 upregulated and 464 downregulated) were identified from 9 studies. Proteins in peripheral blood exhibited a sensitivity and specificity of 38-100% and 59-99%, respectively, for detecting endometriosis, while proteins in urine had a sensitivity of 58-91% and specificity of 76-93%. Alpha-1-antitrypsin, albumin, and vitamin D binding proteins were significantly DEPs in both serum and urine. Complement C3 is commonly expressed in serum, menstrual blood, and cervical mucus. Additionally, S100-A8 is commonly expressed in both menstrual blood and cervical mucus. Haptoglobin is commonly detected in both serum and plasma, whereas cathepsin G is found in urine, serum, and plasma. GO and KEGG enrichment analyses revealed that proteoglycans in cancer pathways, which regulate cell-to-cell interactions, modulate the extracellular matrix, and promote the proliferation and invasion of endometrial cells, are commonly enriched in serum and urine.

CONCLUSION

This comprehensive study revealed potential proteomes that were significantly differentially expressed in women with endometriosis utilizing various non-invasive clinical samples. Exploring common differentially expressed proteins in various biological samples provides insights into the diagnosis and pathophysiology of endometriosis, as well as potential clinical and therapeutic applications.

Proangiogenic properties of complement protein C1q can contribute to endometriosis

Endometriosis (EM) is defined as the engraftment and proliferation of functional endometrial-like tissue outside the uterine cavity, leading to a chronic inflammatory condition. While the precise etiology of EM remains elusive, recent studies have highlighted the crucial involvement of a dysregulated immune system. The complement system is one of the predominantly altered immune pathways in EM. Owing to its involvement in the process of angiogenesis, here, we have examined the possible role of the first recognition molecule of the complement classical pathway, C1q. C1q plays seminal roles in several physiological and pathological processes independent of complement activation, including tumor growth, placentation, wound healing, and angiogenesis. Gene expression analysis using the publicly available data revealed that C1q is expressed at higher levels in EM lesions compared to their healthy counterparts. Immunohistochemical analysis confirmed the presence of C1q protein, being localized around the blood vessels in the EM lesions. CD68+ macrophages are the likely producer of C1q in the EM lesions since cultured EM cells did not produce C1q in vitro. To explore the underlying reasons for increased C1q expression in EM, we focused on its established pro-angiogenic role. Employing various angiogenesis assays on primary endothelial endometriotic cells, such as migration, proliferation, and tube formation assays, we observed a robust proangiogenic effect induced by C1q on endothelial cells in the context of EM. C1q promoted angiogenesis in endothelial cells isolated from EM lesions (as well as healthy ovary that is also rich in C1q). Interestingly, endothelial cells from EM lesions seem to overexpress the receptor for the globular heads of C1q (gC1qR), a putative C1q receptor. Experiments with siRNA to silence gC1qR resulted in diminished capacity of C1q to perform its angiogenic functions, suggesting that C1q is likely to engage gC1qR in the pathophysiology of EM. gC1qR can be a potential therapeutic target in EM patients that will disrupt C1q-mediated proangiogenic activities in EM.

Comparative Analysis of Shapley Values Enhances Transcriptomics Insights across Some Common Uterine Pathologies

Uterine pathologies pose a challenge to women's health on a global scale. Despite extensive research, the causes and origin of some of these common disorders are not well defined yet. This study presents a comprehensive analysis of transcriptome data from diverse datasets encompassing relevant uterine pathologies such as endometriosis, endometrial cancer and uterine leiomyomas. Leveraging the Comparative Analysis of Shapley values (CASh) technique, we demonstrate its efficacy in improving the outcomes of the classical differential expression analysis on transcriptomic data derived from microarray experiments. CASh integrates the microarray game algorithm with Bootstrap resampling, offering a robust statistical framework to mitigate the impact of potential outliers in the expression data. Our findings unveil novel insights into the molecular signatures underlying these gynecological disorders, highlighting CASh as a valuable tool for enhancing the precision of transcriptomics analyses in complex biological contexts. This research contributes to a deeper understanding of gene expression patterns and potential biomarkers associated with these pathologies, offering implications for future diagnostic and therapeutic strategies.

Artificial Intelligence in the Management of Women with Endometriosis and Adenomyosis: Can Machines Ever Be Worse Than Humans?

Artificial intelligence (AI) is experiencing advances and integration in all medical specializations, and this creates excitement but also concerns. This narrative review aims to critically assess the state of the art of AI in the field of endometriosis and adenomyosis. By enabling automation, AI may speed up some routine tasks, decreasing gynecologists' risk of burnout, as well as enabling them to spend more time interacting with their patients, increasing their efficiency and patients' perception of being taken care of. Surgery may also benefit from AI, especially through its integration with robotic surgery systems. This may improve the detection of anatomical structures and enhance surgical outcomes by combining intra-operative findings with pre-operative imaging. Not only that, but AI promises to improve the quality of care by facilitating clinical research. Through the introduction of decision-support tools, it can enhance diagnostic assessment; it can also predict treatment effectiveness and side effects, as well as reproductive prognosis and cancer risk. However, concerns exist regarding the fact that good quality data used in tool development and compliance with data sharing guidelines are crucial. Also, professionals are worried AI may render certain specialists obsolete. This said, AI is more likely to become a well-liked team member rather than a usurper.

Omics-based novel strategies in the diagnosis of endometriosis

Endometriosis, an enigmatic and chronic disorder, is considered a debilitating condition despite being benign. Globally, this gynecologic disorder affects up to 10% of females of reproductive age, impacting almost 190 million individuals. A variety of genetic and environmental factors are involved in endometriosis development, hence the pathophysiology and etiology of endometriosis remain unclear. The uncertainty of the etiology of the disease and its complexity along with nonspecific symptoms have led to misdiagnosis or lack of diagnosis of affected people. Biopsy and laparoscopy are referred to as the gold standard for endometriosis diagnosis. However, the invasiveness of the procedure, the unnecessary operation in disease-free women, and the dependence of the reliability of diagnosis on experience in this area are considered the most significant limitations. Therefore, continuous studies have attempted to offer a noninvasive and reliable approach. The recent advances in modern technologies have led to the generation of large-scale biological data sets, known as -omics data, resulting in the proceeding of the -omics century in biomedical sciences. Thereby, the present study critically reviews novel and noninvasive biomarkers that are based on -omics approaches from 2020 onward. The findings reveal that biomarkers identified based on genomics, epigenomics, transcriptomics, proteomics, and metabolomics are potentially able to diagnose endometriosis, predict prognosis, and stage patients, and potentially, in the near future, a multi-panel of these biomarkers will generate clinical benefits.

Potential shared pathogenic mechanisms between endometriosis and inflammatory bowel disease indicate a strong initial effect of immune factors

Introduction

Over the past decades, immune dysregulation has been consistently demonstrated being common charactoristics of endometriosis (EM) and Inflammatory Bowel Disease (IBD) in numerous studies. However, the underlying pathological mechanisms remain unknown. In this study, bioinformatics techniques were used to screen large-scale gene expression data for plausible correlations at the molecular level in order to identify common pathogenic pathways between EM and IBD.

Methods

Based on the EM transcriptomic datasets GSE7305 and GSE23339, as well as the IBD transcriptomic datasets GSE87466 and GSE126124, differential gene analysis was performed using the limma package in the R environment. Co-expressed differentially expressed genes were identified, and a protein-protein interaction (PPI) network for the differentially expressed genes was constructed using the 11.5 version of the STRING database. The MCODE tool in Cytoscape facilitated filtering out protein interaction subnetworks. Key genes in the PPI network were identified through two topological analysis algorithms (MCC and Degree) from the CytoHubba plugin. Upset was used for visualization of these key genes. The diagnostic value of gene expression levels for these key genes was assessed using the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) The CIBERSORT algorithm determined the infiltration status of 22 immune cell subtypes, exploring differences between EM and IBD patients in both control and disease groups. Finally, different gene expression trends shared by EM and IBD were input into CMap to identify small molecule compounds with potential therapeutic effects.

Results

113 differentially expressed genes (DEGs) that were co-expressed in EM and IBD have been identified, comprising 28 down-regulated genes and 86 up-regulated genes. The co-expression differential gene of EM and IBD in the functional enrichment analyses focused on immune response activation, circulating immunoglobulin-mediated humoral immune response and humoral immune response. Five hub genes (SERPING1、VCAM1、CLU、C3、CD55) were identified through the Protein-protein Interaction network and MCODE.High Area Under the Curve (AUC) values of Receiver Operating Characteristic (ROC) curves for 5hub genes indicate the predictive ability for disease occurrence.These hub genes could be used as potential biomarkers for the development of EM and IBD. Furthermore, the CMap database identified a total of 9 small molecule compounds (TTNPB、CAY-10577、PD-0325901 etc.) targeting therapeutic genes for EM and IBD.

Discussion

Our research revealed common pathogenic mechanisms between EM and IBD, particularly emphasizing immune regulation and cell signalling, indicating the significance of immune factors in the occurence and progression of both diseases. By elucidating shared mechanisms, our study provides novel avenues for the prevention and treatment of EM and IBD.

The effect of pentoxifylline and different types of exercise training on coagulation factors in a rat endometriosis model

Objectives

This study evaluated the effects of high-intensity interval training (HIIT), moderate-intensity continuous training (MICT), and pentoxifylline (PTX) on coagulation factors, including the amount and percentage of lymphocytes, PLC, PLR, aPTT, PT, PT.I.N. R in a model of rats with endometriosis.

Methods

Endometriosis was surgically induced in female Sprague-Dawley rats. The rats with confirmed endometrial implants were divided into control, MICT, pentoxifylline (D), HIIT+D, and MICT+D, HIIT groups. D and exercise interventions were performed for eight weeks. Then, the macroscopic size of endometriosis lesions was measured, and inflammatory factors (count and percentage of lymphocytes) and coagulation factors, including PLC, PLR, aPTT, PT, PT.I.N. R, and PLR in blood samples were evaluated.

Results

D significantly decreased the volume of lesions and significantly increased PT and PT.I.N. R in blood. HIIT decreased the volume of lesions and significantly increased PT. MICT did not cause significant effects on the target variables. MICT+D decreased the volume of lesions. HIIT+D significantly decreased the volume of lesions and PLC and increased aPTT as well as the count and percentage of lymphocytes, PT, and PT.I.N. R, and decreased PLR.

Conclusions

All interventions(except for MICT) especially HIIT+D and D by priority, induced a significant effect on reducing some indices of inflammation and coagulation.

Characterizing the Extracellular Matrix Transcriptome of Endometriosis

In recent years, the matrisome, a set of proteins that make up the extracellular matrix (ECM) or are closely involved in ECM behavior, has been shown to have great importance for characterizing and understanding disease pathogenesis and progression. The matrisome is especially critical for examining diseases characterized by extensive tissue remodeling. Endometriosis is characterized by the extrauterine growth of endometrial tissue, making it an ideal condition to study through the lens of matrisome gene expression. While large gene expression datasets have become more available and gene dysregulation in endometriosis has been the target of several studies, the gene expression profile of the matrisome specifically in endometriosis has not been well characterized. In our study, we explored four Gene Expression Omnibus (GEO) DNA microarray datasets containing eutopic endometrium of people with and without endometriosis. After batch correction, menstrual cycle phase accounted for 53% of variance and disease accounted for 23%; thus, the data were separated by menstrual cycle phase before performing differential expression analysis, statistical and machine learning modeling, and enrichment analysis. We established that matrisome gene expression alone can effectively differentiate endometriosis samples from healthy ones, demonstrating the potential of matrisome gene expression for diagnostic applications. Furthermore, we identified specific matrisome genes and gene networks whose expression can distinguish endometriosis stages I/II from III/IV. Taken together, these findings may aid in developing future in vitro models of disease, offer insights into novel treatment strategies, and advance diagnostic tools for this underserved patient population.

Identifying hub genes of sepsis-associated and hepatic encephalopathies based on bioinformatic analysis-focus on the two common encephalopathies of septic cirrhotic patients in ICU

BACKGROUND

In the ICU ward, septic cirrhotic patients are susceptible to suffering from sepsis-associated encephalopathy and/or hepatic encephalopathy, which are two common neurological complications in such patients. However, the mutual pathogenesis between sepsis-associated and hepatic encephalopathies remains unclear. We aimed to identify the mutual hub genes, explore effective diagnostic biomarkers and therapeutic targets for the two common encephalopathies and provide novel, promising insights into the clinical management of such septic cirrhotic patients.

METHODS

The precious human post-mortem cerebral tissues were deprived of the GSE135838, GSE57193, and GSE41919 datasets, downloaded from the Gene Expression Omnibus database. Furthermore, we identified differentially expressed genes and screened hub genes with weighted gene co-expression network analysis. The hub genes were then subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway functional enrichment analyses, and protein-protein interaction networks were constructed. Receiver operating characteristic curves and correlation analyses were set up for the hub genes. Finally, we explored principal and common signaling pathways by using Gene Set Enrichment Analysis and the association between the hub genes and immune cell subtype distribution by using CIBERSORT algorithm.

RESULTS

We identified seven hub genes-GPR4, SOCS3, BAG3, ZFP36, CDKN1A, ADAMTS9, and GADD45B-by using differentially expressed gene analysis and weighted gene co-expression network analysis method. The AUCs of these genes were all greater than 0.7 in the receiver operating characteristic curves analysis. The Gene Set Enrichment Analysis results demonstrated that mutual signaling pathways were mainly enriched in hypoxia and inflammatory response. CIBERSORT indicated that these seven hub genes were closely related to innate and adaptive immune cells.

CONCLUSIONS

We identified seven hub genes with promising diagnostic value and therapeutic targets in septic cirrhotic patients with sepsis-associated encephalopathy and/or hepatic encephalopathy. Hypoxia, inflammatory, and immunoreaction responses may share the common downstream pathways of the two common encephalopathies, for which earlier recognition and timely intervention are crucial for management of such septic cirrhotic patients in ICU.

Construction and evaluation of endometriosis diagnostic prediction model and immune infiltration based on efferocytosis-related genes

Background: Endometriosis (EM) is a long-lasting inflammatory disease that is difficult to treat and prevent. Existing research indicates the significance of immune infiltration in the progression of EM. Efferocytosis has an important immunomodulatory function. However, research on the identification and clinical significance of efferocytosis-related genes (EFRGs) in EM is sparse. Methods: The EFRDEGs (differentially expressed efferocytosis-related genes) linked to datasets associated with endometriosis were thoroughly examined utilizing the Gene Expression Omnibus (GEO) and GeneCards databases. The construction of the protein-protein interaction (PPI) and transcription factor (TF) regulatory network of EFRDEGs ensued. Subsequently, machine learning techniques including Univariate logistic regression, LASSO, and SVM classification were applied to filter and pinpoint diagnostic biomarkers. To establish and assess the diagnostic model, ROC analysis, multivariate regression analysis, nomogram, and calibration curve were employed. The CIBERSORT algorithm and single-cell RNA sequencing (scRNA-seq) were employed to explore immune cell infiltration, while the Comparative Toxicogenomics Database (CTD) was utilized for the identification of potential therapeutic drugs for endometriosis. Finally, immunohistochemistry (IHC) and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were utilized to quantify the expression levels of biomarkers in clinical samples of endometriosis. Results: Our findings revealed 13 EFRDEGs associated with EM, and the LASSO and SVM regression model identified six hub genes (ARG2, GAS6, C3, PROS1, CLU, and FGL2). Among these, ARG2, GAS6, and C3 were confirmed as diagnostic biomarkers through multivariate logistic regression analysis. The ROC curve analysis of GSE37837 (AUC = 0.627) and GSE6374 (AUC = 0.635), along with calibration and DCA curve assessments, demonstrated that the nomogram built on these three biomarkers exhibited a commendable predictive capacity for the disease. Notably, the ratio of nine immune cell types exhibited significant differences between eutopic and ectopic endometrial samples, with scRNA-seq highlighting M0 Macrophages, Fibroblasts, and CD8 Tex cells as the cell populations undergoing the most substantial changes in the three biomarkers. Additionally, our study predicted seven potential medications for EM. Finally, the expression levels of the three biomarkers in clinical samples were validated through RT-qPCR and IHC, consistently aligning with the results obtained from the public database. Conclusion: we identified three biomarkers and constructed a diagnostic model for EM in this study, these findings provide valuable insights for subsequent mechanistic research and clinical applications in the field of endometriosis.

ITRAQ-based proteomics analysis of human ectopic endometrial stromal cells treated by Maqian essential oil

BACKGROUND

Endometriosis is a common and complex syndrome characterized by the presence of endometrial-like tissue outside the uterus. Chinese medicine has been recently found to show good efficacy in treating endometriosis. Our previous results revealed that Maqian fruit essential oil (MQEO) could inhibit the proliferation and induce apoptosis of ectopic endometrial stromal cells (EESCs), but the mechanisms remain unclear. In this study, we aim to explore the molecular mechanism of MQEO's specific effects in EESCs.

METHODS

We conducted a quantitative proteomics analysis by iTRAQ on EESCs treated with MQEO or DMSO. Then deep analysis was performed based on differentially expressed proteins, including Gene Ontology enrichment analysis, pathway enrichment analysis and protein interaction analysis. Candidate protein targets were subsequently verified by western blotting.

RESULTS

Among 6575 identified proteins, 435 proteins exhibited altered expression levels in MQEO-treated EESCs. Of these proteins, most were distributed in signal transduction as well as immune system and the most significantly altered pathway was complement and coagulation cascades. Moreover, two differentially expressed proteins (Heme oxygenase 1 and Acyl-CoA 6-desaturase) were verified and they can be potential biomarkers for endometriosis treatment.

CONCLUSIONS

Our proteomic analysis revealed distinct protein expression patterns induced by MQEO treatment in EESCs, highlighting the potential of MQEO for endometriosis treatment and biomarker discovery.

Integrated bioinformatics and validation reveal SOX12 as potential biomarker in colon adenocarcinoma based on an immune infiltration-related ceRNA network

PURPOSE

The primary objective of this study was to construct competing endogenous RNA (ceRNA) networks and evaluate the prognostic significance of tumor-infiltrating immune cells (TIICs) and key biomarkers within the ceRNA networks in colon adenocarcinoma (COAD) patients.

METHODS

Comprehensive bioinformatics tools were used to screen differentially expressed genes (DEGs), miRNAs (DEMs), and lncRNAs (DELs) related to COAD, leading to the creation of ceRNA networks. The CIBERSORT technique was employed to assess the significance of TIICs in COAD, and an immune-related prognosis prediction model was subsequently developed. Co-expression analyses were conducted to determine the relationship between key genes in ceRNA networks and immunologically significant TIICs. The study also utilized 5 GEO datasets and web-based databases to externally validate the findings.

RESULTS

The study revealed a statistically significant relationship between key hub genes and immune cells, as determined through co-expression analysis. Two hub regulators (SOX12 and H19) demonstrated significant prognostic value in the ceRNA-related prognostic model, and their elevated expression levels were verified across multiple CRC cell lines. Additionally, the knockdown of SOX12 led to a suppression of proliferation, migration, and invasion in colon cancer cells.

CONCLUSION

Through the construction of ceRNA networks and evaluation of TIICs, the study successfully established two risk score models and nomograms. These models serve as valuable tools for understanding the molecular processes and predicting the prognosis of COAD patients. Further validation of hub regulators SOX12 and H19 substantiates their potential role as key biomarkers in COAD.

Multi-omics characteristics of tumor-associated macrophages in the tumor microenvironment of gastric cancer and their exploration of immunotherapy potential

The incidence and mortality rate of gastric cancer (GC) have remained high worldwide. Although some progress has been made in immunotargeted therapy, the treatment effect remains limited. With more attention has been paid to the immune potential of tumor-associated macrophages (TAMs), but the specific mechanisms of tumor immunity are still unclear. Thus, we screened marker genes in TAMs differentiation (MDMs) through single-cell RNA sequencing, and combined with GC transcriptome data from TCGA and GEO databases, the clinical and TME characteristics, prognostic differences, immune infiltration, and drug sensitivity among different subtypes of patients with GC in different data sets were analyzed. A prognostic model of GC was constructed to evaluate the prognosis and immunotherapy response of patients with GC. In this study, we extensively studied the mutations in MDMs such as CGN, S100A6, and C1QA, and found differences in the infiltration of immune cells and immune checkpoints including M2 TAMs, T cells, CD274, and CTLA4 in different GC subtypes. In the model, we constructed a predictive scoring system with high accuracy and screened out key MDMs-related genes associated with prognosis and M2 TAMs, among which VKORC1 may be involved in GC progression and iron death in tumor cells. Therefore, this study explores the therapeutic strategy of TAMs reprogramming in-depth, providing new ideas for the clinical diagnosis, treatment, and prognosis assessment of GC.

High expression of CCDC69 is correlated with immunotherapy response and protective effects on breast cancer

BACKGROUND

As a molecule controlling the assembly of central spindles and recruitment of midzone component, coiled-coil domain-containing protein 69 (CCDC69) plays an important role in multiple cancers. Currently, the relationships between CCDC69 and immune infiltration or immunotherapy in breast cancer remain unclear.

METHODS

The expression and prognostic significance of CCDC69 in breast cancer were comprehensively analyzed by quantitative real-time PCR, immunohistochemical staining and various databases. The data source of differentially expressed genes, gene set enrichment analysis, and immune cell infiltration analysis came from The Cancer Genome Atlas (TCGA) database. Single-cell analysis based on IMMUcan database was used. The protein-protein interaction network was developed applying STRING, Cytoscape, CytoHubba, and GeneMANIA. TISIDB was employed in analyzing the CCDC69 co-expressed immune related genes. The correlations between CCDC69 and immunotherapy or immune-related scores were analyzed by CAMOIP and TISMO. Ctr-db was also used to conduct drug sensitivity analysis.

RESULTS

The mRNA of CCDC69 was downregulated in breast cancer tissues compared with normal tissues. Higher CCDC69 expression was associated with a better breast cancer prognosis. Enrichment analysis showed that the co-expression genes of CCDC69 were mainly related to immune-related pathways. The expression of CCDC69 was found to be positively correlated with multiple tumor-suppression immune infiltration cells, especially T cells and dendritic cells. Meanwhile, high CCDC69 expression can predict better immunotherapy responses when compared with low CCDC69 expression. After the interferon-gamma treatment, the CCDC69 expression was elevated in vitro. CCDC69 expression was a reliable predictor for the response status of two therapeutic strategies in breast cancer.

CONCLUSIONS

Our research revealed the clinical significance of CCDC69 in breast cancer and validated the critical roles of CCDC69 in the tumor immune infiltration and immunotherapy responses.

ceRNA network-regulated COL1A2 high expression correlates with poor prognosis and immune infiltration in colon adenocarcinoma

Collagen type I α 2 (COL1A2) is a major component of collagen type I. Recently, abnormal COL1A2 expression has been reported in human cancers. However, the specific role and mechanism of COL1A2 in colon adenocarcinoma (COAD) remain unclear. We performed the pan-cancer analysis of COL1A2 expression in 33 types of human cancers from TIMER database and integrated data combined TCGA with GTEx. The prognostic values of COL1A2 for 17 cancer types of interest were estimated from GEPIA database. The results showed that COL1A2 was significantly upregulated in COAD tissues and that higher COL1A2 expression predicted unfavorable prognosis for patients with COAD. Next, COL1A2-related functional pathways in COAD were analyzed with TCGA data using R package. Additionally, we constructed a ceRNA network that LINC00638/hsa-miR-552-3p axis served as a potential regulatory pathway of COL1A2 in COAD. Furthermore, our findings showed that COL1A2 positively associated with immune infiltration and that tumor immune escape might be involved in COL1A2-mediated carcinogenesis in COAD. For the first time, we constructed a ceRNA prediction network of COL1A2 and explored the association of COL1A2 with tumor immune microenvironment remodeling. The findings may advance our understanding of the pathogenesis mechanism in COAD and paves the way for further cancer therapeutics.

Comprehensive analysis and experimental validation reveal elevated CLCN4 is a promising biomarker in endometrial cancer

Several studies have reported the role of CLCN4 in tumor progression. However, its mechanism remains to be thoroughly studied. The objective of this study was to explore the potential pathogenic role of CLCN4 in endometrial carcinoma (UCEC) with a better understanding of the pathological mechanisms involved. The potential roles of CLCN4 in different tumors were explored based on The Cancer Genome Atlas (TCGA), the expression difference, mutation, survival, pathological stage, Immunity subtypes, Immune infiltration, tumor microenvironment (TME), tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR) related to CLCN4 were analyzed. Then, the expression, prognosis, mutation, and functional enrichment of CLCN4 in UCEC were analyzed. Immunohistochemical experiment was used to verify the expression of CLCN4 in endometrial cancer tissues and normal tissues. In vitro, we knocked down of CLCN4 in HEC-1-A cells and performed CCK8, WB, RT-PCR, wound-healing, transwell assays to further validation of the molecular function. Results revealed that high expression of CLCN4 was observed in 20 cancer types of TCGA. CLCN4 expression correlates with poor survival in MESO, BLCA, THCA, especially UCEC tumors. CLCN4 expression was significantly associated with CD4+ T-cell infiltration, especially CD4+ Th1-cell. Immunohistochemical experiment reveals that CLCN4 is high expressed in endometrial tumors, in vitro experiment reveals that knockdown of CLCN4 inhibits the cells proliferation, migration and invasion. Our study is the first to offer a comprehensive understanding of the oncogenic roles of CLCN4 on different tumors. CLCN4 may become a potential biomarker in UCEC.

Causes of death after testicular cancer diagnosis: a US population-based analysis

BACKGROUND

After the introduction of cisplatin-based chemotherapy, the survival time of testicular cancer (TC) patients has improved dramatically. However, the overall risk of death in patients with TC remains significantly higher than in the general population. The aim of this study was to assess and quantify the causes of death after TC diagnosis.

METHOD

In total, 44,975 men with TC in the United States diagnosed and registered by the Surveillance, Epidemiology, and End Results (SEER) database during 2000 to 2018 were studied. In this study, standardized mortality rates (SMRs) were calculated for each cause of death in TC individuals and further analyzed in strata according to age and race.

RESULT

Of the included participants, 3,573 (7.94%) died during the follow-up period. The greatest proportion of deaths (38.20%) occurred within 1 to 5 years after diagnosis. Most deaths occurred from TC itself and other cancers. For non-malignant conditions, the most common causes of death within 1 years after diagnosis were accidents and adverse effects (53, 4.75%) followed by diseases of heart (45, 4.04%). However, > 1 years after diagnosis, the most common noncancer causes of death were heart diseases. Results of stratified analysis show that non-Hispanic White TC participants have a lower SMR (0.68, 95% CI, 33.39-38.67) from Cerebrovascular Diseases than the general U.S.

POPULATION

CONCLUSIONS

Although TC remains the most common cause of death after TC diagnosis, other non-TC causes of death represent a significant number of deaths among TC men. These findings help TC survivors understand the various health risks that may occur at different follow-up periods.

Development and validation of nomograms to predict survival of primary adrenal lymphoma: a population-based retrospective study

While it is known that accurate evaluation of overall survival (OS) and disease-specific survival (DSS) for patients with primary adrenal lymphoma (PAL) can affect their prognosis, no stable and effective prediction model exists. This study aimed to develop prediction models to evaluate survival. This study enrolled 5448 patients with adrenal masses from the SEER Program. The influencing factors were selected using the least absolute shrinkage and selection operator regression model (LASSO) and Fine and Gray model (FGM). In addition, nomograms were constructed. Receiver operating characteristic curves and bootstrap self-sampling methods were used to verify the discrimination and consistency of the nomograms. The independent influencing factors for PAL survival were selected by LASSO and FGM, and three models were built: the OS, DSS, and FGS (DSS analysis by FGM) model. The areas under the curve and decision curve analyses indicated that the models were valid. This study developed survival prediction models to predict OS and DSS of patients with PAL. The FGS model was more accurate than the DSS model in the short term. Above all, these models should offer benefits to patients with PAL in terms of the treatment modality choice and survival evaluation.

Pan-cancer analysis identifies SPEN mutation as a predictive biomarker with the efficacy of immunotherapy

The association between specific genetic mutations and immunotherapy benefits has been widely known, while such studies in pan-cancer are still limited. SPEN, mainly involved in X chromosome inactivation (XCI), plays an essential in tumorigenesis and sex differences in cancer. Thus, we firstly analyzed the potential role of SPEN in the TCGA pan-cancer cohort and clinical samples. Bioinformatics analysis and immunohistochemistry (IHC) staining confirm that the expression of SPEN is significantly different in various cancers and may involve RNA splicing and processing via enrichment analysis. Then, our data further revealed that those patients with SPEN mutation could predict a better prognosis in pan-cancer and had distinct immune signatures, higher tumor mutation burden (TMB), and microsatellite instability (MSI) in common cancer types. Finally, the cancer patients from 9 studies treated with immune checkpoint inhibitors were included to investigate the efficacy of immunotherapy. The results further showed that SPEN mutation was associated with better clinical outcomes (HR, 0.74; 95%CI, 0.59-0.93, P = 0.01), and this association remained existed in female patients (HR, 0.60; 95%CI, 0.38-0.94 P = 0.024), but not in male patients (HR, 0.82; 95%CI, 0.62-1.08 P = 0.150). Our findings demonstrated that SPEN mutation might strongly predict immunotherapy efficacy in pan-cancer.

Identification of a two metastasis-related prognostic signature in the process of predicting the survival of laryngeal squamous cell carcinoma

Metastasis is a major cause of treatment failure and poor outcomes in cancer patients. The data used in the current study was downloaded from TCGA and GEO databases. Differentially expressed metastasis-related genes were identified and the biological functions were implemented. Kaplan-Meier analysis univariate, and, multivariate Cox regression analyses were performed to identify robust prognostic biomarkers, followed by construction of the risk model and nomogram. Gene set enrichment analysis was performed to identify pathways enriched in low- and high-risk groups. POLR2J3 and MYH11 were treated as prognostic biomarkers in LSCC and the risk model was constructed. Receiver operating characteristic curves revealed the good performance of the risk model. A nomogram with high accuracy was constructed, as evidenced by calibration and decision curves. Moreover, we found that the expressions of POLR2J3 and MYH11 was significantly higher in metastasis tissues compared with those in non-metastasis tissues by RT-qPCR and IHC. Our study identified novel metastasis-related prognostic biomarkers in LSCC and constructed a unique nomogram for predicting the prognosis of LSCC patients. Moreover, we explored the related mechanisms of metastasis-related genes in regulating LSCC.

Molecular Mechanisms of Endometriosis Revealed Using Omics Data

Endometriosis is a gynecological disorder prevalent in women of reproductive age. The primary symptoms include dysmenorrhea, irregular menstruation, and infertility. However, the pathogenesis of endometriosis remains unclear. With the advent of high-throughput technologies, various omics experiments have been conducted to identify genes related to the pathophysiology of endometriosis. This review highlights the molecular mechanisms underlying endometriosis using omics. When genes identified in omics experiments were compared with endometriosis disease genes identified in independent studies, the number of overlapping genes was moderate. However, the characteristics of these genes were found to be equivalent when functional gene set enrichment analysis was performed using gene ontology and biological pathway information. These findings indicate that omics technology provides invaluable information regarding the pathophysiology of endometriosis. Moreover, the functional characteristics revealed using enrichment analysis provide important clues for discovering endometriosis disease genes in future research.

ADNP is associated with immune infiltration and radiosensitivity in hepatocellular carcinoma for predicting the prognosis

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most lethal diseases due to its high faculty of invasiveness and metastasis. Activity-dependent neuroprotective protein (ADNP) has been regarded as an oncogene in bladder cancer and ovarian cancer. However, the role of ADNP in the regulation of tumor immune response, development, and treatment resistance in HCC remains unknown and is worth exploring.

METHODS

The correlation between ADNP and prognosis, immune cell infiltration, immune checkpoints, chemokines, tumor mutation burden, microsatellite instability, and genomic mutation of pan-cancer cohorts in The Cancer Genome Atlas was analyzed. ADNP expression in HCC cell lines, HCC and the adjacent normal tissues was measured by western blotting and immunochemistry. Nomogram was constructed to predict the survival of patients with HCC based on the ADNP expression and significant clinical characteristics. The potential biological functions and impacts on radiotherapy of ADNP in HCC cell lines were verified by vitro experiments.

RESULTS

ADNP was upregulated in most cancers and patients with elevated ADNP expression were related to poor survival in several types of cancers including HCC. Functional enrichment analysis showed ADNP participated in the pathways correlated with coagulation cascades and DNA double strand break repair. Further, ADNP exhibited a negative correlation with the immune score, stromal score, estimated score, and chemokines, and a positive correlation with cancer-associated fibroblasts, myeloid-derived suppressor cells, neutrophils, regulatory T cells, and endothelial cells. Immunochemistry and western blotting results demonstrated ADNP was up-regulated in HCC. Vitro experiments verified that suppressing the ADNP expression significantly inhibited the proliferation, invasion and migration and elevated the radiosensitivity via decreasing DNA damage repair in HCC.

CONCLUSION

ADNP might play an oncogene and immunosuppression role in tumor immune infiltration and response, thus influencing the prognosis. Its downregulation could attenuate the proliferation, invasion, migration, radioresistance of HCC. Our results indicated the potential of ADNP as a promising biomarker to predict the survival of HCC patients, providing a theoretical basis for novel integrative strategies.

Integrative analysis illustrates the role of PCDH7 in lung cancer development, cisplatin resistance, and immunotherapy resistance: an underlying target

Background: Cisplatin resistance is a common clinical problem in lung cancer. However, the underlying mechanisms have not yet been fully elucidated, highlighting the importance of searching for biological targets. Methods: Bioinformatics analysis is completed through downloaded public data (GSE21656, GSE108214, and TCGA) and specific R packages. The evaluation of cell proliferation ability is completed through CCK8 assay, colony formation, and EdU assay. The evaluation of cell invasion and migration ability is completed through transwell and wound-healing assays. In addition, we evaluated cell cisplatin sensitivity by calculating IC50. Results: Here, we found that PCDH7 may be involved in cisplatin resistance in lung cancer through public database analysis (GSE21656 and GSE108214). Then, a series of in vitro experiments was performed, which verified the cancer-promoting role of PCDH7 in NSCLC. Moreover, the results of IC50 detection showed that PCDH7 might be associated with cisplatin resistance of NSCLC. Next, we investigated the single-cell pattern, biological function, and immune analysis of PCDH7. Importantly, we noticed PCDH7 may regulate epithelial-mesenchymal transition activity, and the local infiltration of CD8+ T and activated NK cells. Furthermore, we noticed that patients with high PCDH7 expression might be more sensitive to bortezomib, docetaxel, and gemcitabine, and resistant to immunotherapy. Finally, a prognosis model based on three PCDH7-derived genes (GPX8, BCAR3, and TNS4) was constructed through a machine learning algorithm, which has good prediction ability on NSCLC patients' survival. Conclusion: Our research has improved the regulatory framework for cisplatin resistance in NSCLC and can provide direction for subsequent related research, especially regarding PCDH7.

A pan-cancer analysis for the oncogenic role of cyclin-dependent kinase inhibitor 1B in human cancers

BACKGROUND

Human health and life are threatened by cancer with high morbidity and mortality worldwide. In many experiments, CDKN1B level is associated with cancer risk, Nevertheless, no pan-cancer analysis has been conducted on CDKN1B in human cancers.

METHODS

With the help of bioinformatics, a pan-cancer analysis was conducted on the expression levels of CDKN1B in cancer tissues and adjacent tissues from the TCGA, CPTAC and GEO databases. The CDKN1B expression levels in tumor patients was further validated using immunohistochemistry (IHC) and quantitative real-time PCR.

RESULTS

In the study, we first investigated the cancer-related roles of CDKN1B's in 40 tumors with malignancy. The CDKN1B gene encodes the p27^Kip1 protein, which can block the production cyclin-dependent kinase (CDK), which is obviously related to the function and survival of cancer cells and alters the prognosis of cancer patients. Furthermore, CDKN1B function requires both protein processing and RNA metabolism. Additionally, the elevated expression of the CDKN1B gene and protein was validated in several cancer tissues from the patients.

CONCLUSIONS

These results showed that the levels of CDKN1B were considerably different in a number of cancer tissues, offering a potential future target for cancer therapy.

An integrative bioinformatics investigation and experimental validation of chromobox family in diffuse large B-cell lymphoma

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is one of the most aggressive malignant tumors. Chromobox (CBX) family plays the role of oncogenes in various malignancies.

METHODS

The transcriptional and protein levels of CBX family were confirmed by GEPIA, Oncomine, CCLE, and HPA database. Screening of co-expressed genes and gene function enrichment analysis were performed by GeneMANIA and DAVID 6.8. The prognostic value, immune cell infiltration and drug sensitivity analysis of CBX family in DLBCL were performed by Genomicscape, TIMER2.0, and GSCALite database. Confirmatory Tests of CBX family protein expression in DLBCL were performed by immunohistochemistry.

RESULTS

The mRNA and protein expressions of CBX1/2/3/5/6 were higher in DLBCL tissues than control groups. Enrichment analysis showed that the functions of CBX family were mainly related to chromatin remodeling, methylation-dependent protein binding, and VEGF signaling pathway. The high mRNA expressions of CBX2/3/5/6 were identified to be associated with short overall survival (OS) in DLBCL patients. Multivariate COX regression indicated that CBX3 was independent prognostic marker. Immune infiltration analysis revealed that the mRNA expressions of CBX family (especially CBX1, CBX5, and CBX6) in DLBCL were significantly correlated with the infiltration of most immune cells (including B cells, CD8 + T cells, CD4 + T cells, neutrophils, monocytes, macrophages, and Treg cells). Meanwhile, there was a strong correlation between the expression levels of CBX1/5/6 and surface markers of immune cells, such as the widely studied PVR-like protein receptor/ligand and PDL-1 immune checkpoint. Notably, our study found that DLBCL cells with CBX1 over-expression were resistant to the common anti-tumor drugs, but CBX2/5 had two polarities. Finally, we confirmed the higher expressions of CBX1/2/3/5/6 in DLBCL tissues compared with control groups by immunohistochemistry.

CONCLUSION

We provided a detailed analysis of the relationship between the CBX family and the prognosis of DLBCL. Distinguished from other studies, We found that high mRNA expressions of CBX2/3/5/6 were associated with poor prognosis in DLBCL patients, and Multivariate COX regression indicated that CBX3 was independent prognostic marker. Besides, our study also found an association between the CBX family and anti-tumour drug resistance, and provided a relationship between CBX family expression and immune cell infiltration.

Identification of STEAP3-based molecular subtype and risk model in ovarian cancer

BACKGROUND

Ovarian cancer (OC) is one of the most common malignancies in women. It has a poor prognosis owing to its recurrence and metastasis. Unfortunately, reliable markers for early diagnosis and prognosis of OC are lacking. Our research aimed to investigate the value of the six-transmembrane epithelial antigen of prostate family member 3 (STEAP3) as a prognostic predictor and therapeutic target in OC using bioinformatics analysis.

METHODS

STEAP3 expression and clinical data were acquired from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO). Unsupervised clustering was used to identify molecular subtypes. Prognosis, tumor immune microenvironment (TIME), stemness indexes, and functional enrichment analysis were compared between two definite clusters. Through the least absolute shrinkage and selection operator (LASSO) regression analysis, a STEAP3-based risk model was developed, and the predictive effectiveness of this signature was confirmed using GEO datasets. A nomogram was used to predict the survival possibility of patients. Additionally, TIME, tumor immune dysfunction and exclusion (TIDE), stemness indexes, somatic mutations, and drug sensitivity were evaluated in different risk groups with OC. STEAP3 protein expression was detected using immunohistochemistry (IHC).

RESULTS

STEAP3 displayed marked overexpression in OC. STEAP3 is an independent risk factor for OC. Based on the mRNA levels of STEAP3-related genes (SRGs), two distinct clusters were identified. Patients in the cluster 2 (C2) subgroup had a considerably worse prognosis, higher immune cell infiltration, and lower stemness scores. Pathways involved in tumorigenesis and immunity were highly enriched in the C2 subgroup. A prognostic model based on 13 SRGs was further developed. Kaplan-Meier analysis indicated that the overall survival (OS) of high-risk patients was poor. The risk score was significantly associated with TIME, TIDE, stemness indexes, tumor mutation burden (TMB), immunotherapy response, and drug sensitivity. Finally, IHC revealed that STEAP3 protein expression was noticeably elevated in OC, and overexpression of STEAP3 predicted poor OS and relapse-free survival (RFS) of patients.

CONCLUSION

In summary, this study revealed that STEAP3 reliably predicts patient prognosis and provides novel ideas for OC immunotherapy.

Identification and immunological characterization of cuproptosis-related molecular clusters in ulcerative colitis

BACKGROUND

Ulcerative colitis is one of the two main forms of inflammatory bowel disease. Cuproptosis is reported to be a novel mode of cell death.

METHODS

We examined clusters of cuproptosis related genes and immune cell infiltration molecules in 86 ulcerative colitis samples from the GSE179285 dataset. We identified the differentially expressed genes according to the clustering method, and the performance of the SVM model, the random forest model, the generalized linear model, and the limit gradient enhancement model were compared, and then the optimal machine model was selected. To assess the accuracy of the learning predictions, the nomogram and the calibration curve and decision curve analyses showed that the subtypes of ulcerative colitis have been accurately predicted.

RESULTS

Significant cuproptosis-related genes and immune response cells were detected between the ulcerative colitis and control groups. Two cuproptosis-associated molecular clusters were identified. Immune infiltration analysis indicated that different clusters exhibited significant heterogeneity. The immune scores for Cluster2 were elevated. Both the residual error and root mean square error of the random forest machine model had clinical significance. There was a clear correlation between the differentially expressed genes in cluster 2 and the response of immune cells. The nomogram and the calibration curve and decision curve analyses showed that the subtypes of ulcerative colitis had sufficient accuracy.

CONCLUSION

We examined the complex relationship between cuproptosis and ulcerative colitis in a systematic manner. To estimate the likelihood that each subtype of cuproptosis will occur in ulcerative colitis patients and their disease outcome, we developed a promising prediction model.

Development and validation of a nomogram to predict long-term cancer-specific survival for patients with osteosarcoma

The present work aimed to establish a new model to accurately estimate overall survival (OS) as well as cancer-specific survival (CSS) of osteosarcoma. Osteosarcoma cases were collected from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2017 and randomized as training or validation sets. Then, the OS- and CSS-related variables were discovered through multivariate Cox regression analysis to develop new nomograms to predict the 1-, 3- and 5-year OS and CSS. Besides, consistency index (C-index), decision curve analysis (DCA), along with calibration curve were adopted for assessing the predicting ability of our constructed nomograms after calibrating for 1-, 3- and 5-year OS and CSS. Altogether, 1727 osteosarcoma cases were enrolled in the present study and randomly divided as training (n = 1149, 70%) or validation (n = 576, 30%) set. As shown by univariate as well as multivariate Cox regression analyses, age, grade, T stage, M stage, surgery, chemotherapy, and histological type were identified to be the adverse factors to independently predict OS and CSS among the osteosarcoma cases. Besides, based on results of multivariate Cox regression analysis, we constructed the OS and CSS prediction nomograms. The C-index in training set was 0.806 (95% CI 0.769-0.836) for OS nomogram and 0.807 (95% CI 0.769-0.836) for CSS nomogram. In the meantime, C-index value in validation set was 0.818 (95% CI 0.789-0.847) for OS nomogram, while 0.804 (95% CI 0.773-0.835) for CSS nomogram. Besides, those calibration curves regarding the 3- and 5-year CSS of our constructed nomogram were highly consistent between the predicted values and the measurements in the training set as well as the external validation set. Our constructed nomogram outperformed the TNM stage in prediction. Our constructed nomogram is facile, creditable, and feasible; it efficiently predicts OS and CSS for osteosarcoma cases and can assist clinicians in assessing the prognosis for individuals and making decisions.

Analyzing molecular typing and clinical application of immunogenic cell death-related genes in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is considered one of the most common cancers, characterized by low early detection and high mortality rates, and is a global health challenge. Immunogenic cell death (ICD) is defined as a specific type of regulated cell death (RCD) capable of reshaping the tumor immune microenvironment by releasing danger signals that trigger immune responses, which would contribute to immunotherapy.

METHODS

The ICD gene sets were collected from the literature. We collected expression data and clinical information from public databases for the HCC samples in our study. Data processing and mapping were performed using R software to analyze the differences in biological characteristics between different subgroups. The expression of the ICD representative gene in clinical specimens was assessed by immunohistochemistry, and the role of the representative gene in HCC was evaluated by various in vitro assays, including qRT-PCR, colony formation, and CCK8 assay. Lasso-Cox regression was used to screen prognosis-related genes, and an ICD-related risk model (ICDRM) was constructed. To improve the clinical value of ICDRM, Nomograms and calibration curves were created to predict survival probabilities. Finally, the critical gene of ICDRM was further investigated through pan-cancer analysis and single-cell analysis.

RESULTS

We identified two ICD clusters that differed significantly in terms of survival, biological function, and immune infiltration. As well as assessing the immune microenvironment of tumors in HCC patients, we demonstrate that ICDRM can differentiate ICD clusters and predict the prognosis and effectiveness of therapy. High-risk subpopulations are characterized by high TMB, suppressed immunity, and poor survival and response to immunotherapy, whereas the opposite is true for low-risk subpopulations.

CONCLUSIONS

This study reveals the potential impact of ICDRM on the tumor microenvironment (TME), immune infiltration, and prognosis of HCC patients, but also a potential tool for predicting prognosis.

A multi-omics analysis reveals CLSPN is associated with prognosis, immune microenvironment and drug resistance in cancers

BACKGROUND

Immunotherapy is effective only in limited patients. It is urgent to discover a novel biomarker to predict immune cells infiltration status and immunotherapy response of different cancers. CLSPN has been reported to play a pivotal role in various biological processes. However, a comprehensive analysis of CLSPN in cancers has not been conducted.

METHODS

To show the whole picture of CLSPN in cancers, a pan-cancer analysis was conducted in 9125 tumor samples across 33 cancer types by integrating transcriptomic, epigenomic and pharmacogenomics data. Moreover, the role of CLSPN in cancer was validated by CCK-8, EDU, colony formation and flow cytometry in vitro and tumor cell derived xenograft model in vivo.

RESULTS

CLSPN expression was generally upregulated in most cancer types and was significantly associated with prognosis in different tumor samples. Moreover, elevated CLSPN expression was closely correlated with immune cells infiltration, TMB (tumor mutational burden), MSI (microsatellite instability), MMR (mismatch repair), DNA methylation and stemness score across 33 cancer types. Enrichment analysis of functional genes revealed that CLSPN participated in the regulation of numerous signaling pathways involved in cell cycle and inflammatory response. The expression of CLSPN in LUAD patients were further analyzed at the single-cell level. Knockdown CLSPN significantly inhibited cancer cell proliferation and cell cycle related cyclin-dependent kinase (CDK) family and Cyclin family expression in LUAD (lung adenocarcinoma) both in vitro and in vivo experiments. Finally, we conducted structure-based virtual screening by modelling the structure of CHK1 kinase domain and Claspin phosphopeptide complex. The top five hit compounds were screened and validated by molecular docking and Connectivity Map (CMap) analysis.

CONCLUSION

Our multi-omics analysis offers a systematic understanding of the roles of CLSPN in pan-cancer and provides a potential target for future cancer treatment.

The Comprehensive Analysis Illustrates the Role of CDCA5 in Breast Cancer: An Effective Diagnosis and Prognosis Biomarker

Background

Several studies have been conducted to investigate the role of cell division cycle-associated 5 (CDCA5) in cancer. Its role in breast cancer, however, remains unknown.

Methods

The Gene Expression Omnibus and Cancer Genome Atlas Program databases provided the open-access information needed for the research. The CCK8 and colony formation assays were used to measure cell proliferation. The capacity of breast cancer cells to invade and migrate was assessed using the transwell assay.

Results

In our study, CDCA5 was identified as the interested gene through a series of bioinformatics analysis. We found a higher CDCA5 expression level in tissue and cells of breast cancer. Meanwhile, CDCA5 has been linked to increased proliferation, invasion, and migration of breast cancer cells, which was also associated with worse clinical features. The biochemical pathways, in which CDCA5 was engaged, were identified using biological enrichment analysis. Immune infiltration research revealed that CDCA5 was linked to enhanced activity of several immune function terms. Meanwhile, DNA methylation might be responsible for the aberrant level of CDCA5 in tumor tissue. In addition, CDCA5 could significantly increase the paclitaxel and docetaxel sensitivity, indicating that it has the potential for clinical application. Also, we found that CDCA5 is mainly localized in cell nucleoplasm. Moreover, in the breast cancer microenvironment, we found that CDCA5 is mainly expressed in malignant cells, proliferation T cells, and neutrophils.

Conclusion

Overall, our findings suggest that CDCA5 is a potential prognostic indicator and target for breast cancer, which can indicate the direction of the relevant research.

Exploring the prognostic function of TMB-related prognostic signature in patients with colon cancer

Tumor mutation burden (TMB) level is identified as a useful predictor in multiple tumors including colon adenocarcinoma (COAD). However, the function of TMB related genes has not been explored previously. In this study, we obtained patients' expression and clinical data from The Cancer Genome Atlas (TCGA) and the National Center for Biotechnology Information (NCBI). TMB genes were screened and subjected to differential expression analysis. Univariate Cox and LASSO analyses were utilized to construct the prognostic signature. The efficiency of the signature was tested by using a receiver operating characteristic (ROC) curve. A nomogram was further plotted to assess the overall survival (OS) time of patients with COAD. In addition, we compared the predictive performance of our signature with other four published signatures. Functional analyses indicated that patients in the low-risk group have obviously different enrichment of tumor related pathways and tumor infiltrating immune cells from that of high-risk patients. Our findings suggested that the ten genes' prognostic signature could exert undeniable prognostic functions in patients with COAD, which might provide significant clues for the development of personalized management of these patients.

PANoptosis-related prognostic signature predicts overall survival of cutaneous melanoma and provides insights into immune infiltration landscape

Cutaneous melanoma (CM) is a highly malignant tumor originating from melanocytes, and its metastasis and recurrence are the major causes of death in CM patients. PANoptosis is a newly defined inflammatory programmed cell death that crosstalk pyroptosis, apoptosis, and necroptosis. PANoptosis participates in the regulation of tumor progression, especially the expression of PANoptosis related genes (PARGs). Although pyroptosis, apoptosis, and necroptosis have received attention in CM, respectively, the link between them remains elusive. Therefore, this study aimed to investigate the potential regulatory role of PANoptosis and PARGs in CM and the relationship among PANoptosis, PARGs and tumor immunity. We identified 3 PARGs associated with prognosis in CM patients by The Cancer Genome Atlas. Risk model and nomogram were established. Enrichment analysis of differentially expressed genes indicated that CM was immune-related. Subsequent analyses indicated that prognosis-related PARGs were associated with immune scores and infiltration of immune cells in CM patients. In addition, immunotherapy and drug sensitivity results indicated an association between prognosis-related PARGs and drug resistance in CM patients. In conclusion, PARGs play a key role in the progression of tumors in CM patients. PARGs can be used not only for risk assessment and OS prediction in CM patients, but also reflect the immune landscape of CM patients, which can provide a novel reference for individualized tumor treatment.

Cantharidin suppresses hepatocellular carcinoma development by regulating EZH2/H3K27me3-dependent cell cycle progression and antitumour immune response

BACKGROUND

Cantharidin (CTD) is a major ingredient of cantharis (Mylabris phalerata Pallas) and has been used extensively in traditional Chinese medicines. It has been shown to exhibit anticancer activity in multiple types of cancer, especially hepatocellular carcinoma (HCC). However, there is no systematic study on the relationships among the regulatory networks of its targets in HCC therapy. We focused on histone epigenetic regulation and the influence of CTD on the immune response in HCC.

METHODS

We performed a comprehensive analysis of novel CTD targets in HCC based on network pharmacology and RNA-seq approaches. The mRNA levels of target genes were analyzed by qRT-PCR, and the corresponding protein levels were confirmed using enzyme-linked immunosorbent assay (ELISA) and immunohistochemical staining (IHC). ChIP-seq data were visualized by IGV software. The associations of gene transcript levels with the cancer immune score and infiltration level were investigated using TIMER. In vivo, the H22 mouse model of hepatocellular carcinoma was established by treatment with CTD and 5-Fu. The immune cell proportions in the blood were elevated in model mice, as shown by flow cytometry.

RESULTS

We identified 58 targets of CTD, which were involved in various pathways in cancer, including apoptosis, the cell cycle, EMT and immune pathways. Moreover, we found that 100 EMT-related genes were differentially expressed after CTD treatment in HCC cells. Interestingly, our results confirmed that the EZH2/H3K27me3 -related cell cycle pathway is a therapeutic target of CTD in antitumour. In addition, we evaluated the influence of CTD on the immune response. Our data showed that the significantly enriched gene sets were positively correlated with the chemokine biosynthetic and chemokine metabolic modules. The proportions of CD4+/CD8 + T cells and B cells were increased, but the proportion of Tregs was decreased after treatment with CTD in vivo. Moreover, we found that the expression of the inflammatory factor and immune checkpoint genes PD-1/PD-L1 was significantly reduced in the mouse model.

CONCLUSION

We performed a novel integrated analysis of the potential role of CTD in HCC treatment. Our results provide innovative insight into the mechanism by which cantharidin exerts antitumour effects by regulating target genes expression to mediate apoptosis, EMT, cell cycle progression and the immune response in HCC. Based on the effect of CTD on the immune response, it can be used as a potential effective drug to activate antitumour immunity for the treatment of liver cancer.

ARHGAP39 is a prognostic biomarker involved in immune infiltration in breast cancer

BACKGROUND

Current studies on the role of ARHGAP39 mainly focused on its effect on neurodevelopment. However, there are few studies on the comprehensive analysis of ARHGAP39 in breast cancer.

METHODS

ARHGAP39 expression level was analyzed based on the Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression Project (GTEx), and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database and validated by qPCR in various cell lines and tumor tissues. The prognostic value was analyzed using Kaplan-Meier curve analysis. CCK-8 and transwell assays were conducted to identify the biological function of ARHGAP39 in tumorigenesis. Signaling pathways related to ARHGAP39 expression were identified by the GO and KEGG enrichment analysis and gene set enrichment analysis (GSEA). The correlations between ARHGAP39 and cancer immune infiltrates were investigated via TIMER, CIBERSORT, ESTIMATE and tumor-immune system interactions database (TISIDB).

RESULTS

ARHGAP39 was overexpressed in breast cancer and associated with poor survival outcomes. In vitro experiments revealed that ARHGAP39 could facilitate the proliferation, migration, and invasion capability of breast cancer cells. GSEA analysis showed that the main enrichment pathways of ARHGAP39 was immunity-related pathways. Considering the immune infiltration level, ARHGAP39 was negatively associated with infiltrating levels of CD8 + T cell and macrophage, and positively associated with CD4 + T cell. Furthermore, ARHGAP39 was significantly negatively correlated with immune score, stromal score, and ESTIMATE score.

CONCLUSIONS

Our findings suggested that ARHGAP39 can be used as a potential therapeutic target and prognostic biomarker in breast cancer. ARHGAP39 was indeed a determinant factor of immune infiltration.

CEP55: an immune-related predictive and prognostic molecular biomarker for multiple cancers

BACKGROUND

Centrosomal protein 55 (CEP55) plays a significant role in specific cancers. However, comprehensive research on CEP55 is lacking in pan-cancer.

METHODS

In-house and multi-center samples (n = 15,823) were used to analyze CEP55 in 33 cancers. The variance of CEP55 expression levels among tumor and control groups was evaluated by the Wilcoxon rank-sum test and standardized mean difference (SMD). The clinical value of CEP55 in cancers was assessed using receiver operating characteristic (ROC) curves, Cox regression analysis, and Kaplan-Meier curves. The correlations between CEP55 expression and the immune microenvironment were explored using Spearman's correlation coefficient.

RESULTS

The data of clustered regularly interspaced short palindromic repeats confirmed that CEP55 was essential for the survival of cancer cells in multiple cancer types. Elevated CEP55 mRNA expression was observed in 20 cancers, including glioblastoma multiforme (p < 0.05). CEP55 mRNA expression made it feasible to distinguish 21 cancer types between cancer specimens and their control samples (AUC = 0.97), indicating the potential of CEP55 for predicting cancer status. Overexpression of CEP55 was correlated with the prognosis of cancer individuals for 18 cancer types, exhibiting its prognostic value. CEP55 expression was relevant to tumor mutation burden, microsatellite instability, neoantigen counts, and the immune microenvironment in various cancers (p < 0.05). The expression level and clinical relevance of CEP55 in cancers were verified in lung squamous cell carcinoma using in-house and multi-center samples (SMD = 4.07; AUC > 0.95; p < 0.05).

CONCLUSION

CEP55 may be an immune-related predictive and prognostic marker for multiple cancers, including lung squamous cell carcinoma.

The m6A/m5C/m1A regulator genes signature reveals the prognosis and is related with immune microenvironment for hepatocellular carcinoma

BACKGROUND

RNA methylation is a crucial in many biological functions, and its aberrant regulation is associated with cancer progression. N6-Methyladenosine (m6A), 5-Methylcytosine (m5C), N1-methyladenosine (m1A) are common modifications of RNA methylation. However, the effect of methylation of m6A/m5C/m1A in hepatocellular carcinoma (HCC) remains unclear.

METHOD

The transcriptome datasets, clinic information, and mutational data of 48 m6A/m5C/m1A regulator genes were acquired from the TCGA database, and the prognostic hazard model was established by univariate and Least absolute shrinkage and selection operator (Lasso) regression. The multivariate regression was performed to determine whether the risk score was an independent prognostic indicator. Kaplan-Meier survival analysis and ROC curve analysis were used to evaluate the predictive ability of the risk model. Decision curve analysis（DCA）analysis was conducted to estimate the clinical utility of the risk model. We further analyzed the association between risk score and functional enrichment, tumor immune microenvironment, and somatic mutation.

RESULT

The four-gene (YTHDF1, YBX1, TRMT10C, TRMT61A) risk signature was constructed. The high-risk group had shorter overall survival (OS) than the low-risk group. Univariate and multivariate regression analysis indicated that risk score was an independent prognostic indicator. Risk scores in male group, T3 + T4 group and Stage III + IV group were higher in female group, T1 + T2 group and stage I + II group. The AUC values for 1-, 2-, and 3-year OS in the TCGA dataset were 0.764, 0.693, and 0.689, respectively. DCA analysis showed that the risk score had a higher clinical net benefit in 1- and 2-year OS than other clinical features.The risk score was positively related to some immune cell infiltration and most immune checkpoints.

CONCLUSION

We developed a novel m6A/m5C/m1A regulator genes' prognostic model, which could be applied as a latent prognostic tool for HCC and might guide the choice of immunotherapies.

A novel PD-1/PD-L1 pathway molecular typing-related signature for predicting prognosis and the tumor microenvironment in breast cancer

BACKGROUND

Currently, the development of breast cancer immunotherapy based on the PD-1/PD-L1 pathway is relatively slow, and the specific mechanism affecting the immunotherapy efficacy in breast cancer is still unclear.

METHODS

Weighted correlation network analysis (WGCNA) and the negative matrix factorization (NMF) were used to distinguish subtypes related to the PD-1/PD-L1 pathway in breast cancer. Then univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were used to construct the prognostic signature. A nomogram was established based on the signature. The relationship between the signature gene IFNG and breast cancer tumor microenvironment was analyzed.

RESULTS

Four PD-1/PD-L1 pathway-related subtypes were distinguished. A prognostic signature related to PD-1/PD-L1 pathway typing was constructed to evaluate breast cancer's clinical characteristics and tumor microenvironment. The nomogram based on the RiskScore could be used to accurately predict breast cancer patients' 1-year, 3-year, and 5-year survival probability. The expression of IFNG was positively correlated with CD8+ T cell infiltration in the breast cancer tumor microenvironment.

CONCLUSION

A prognostic signature is constructed based on the PD-1/PD-L1 pathway typing in breast cancer, which can guide the precise treatment of breast cancer. The signature gene IFNG is positively related to CD8+ T cell infiltration in breast cancer.

An innovative model based on N7-methylguanosine-related lncRNAs for forecasting prognosis and tumor immune landscape in bladder cancer

BACKGROUND

As a novel type of the prevalent post-transcriptional modifications, N7-methylguanosine (m7G) modification is essential in the tumorigenesis, progression, and invasion of many cancers, including bladder cancer (BCa). However, the integrated roles of m7G-related lncRNAs in BCa remain undiscovered. This study aims to develop a prognostic model based on the m7G-related lncRNAs and explore its predictive value of the prognosis and anti-cancer treatment sensitivity.

METHODS

We obtained RNA-seq data and corresponding clinicopathological information from the TCGA database and collected m7G-related genes from previous studies and GSEA. Based on LASSO and Cox regression analysis, we developed a m7G prognostic model. The Kaplan-Meier (K-M) survival analysis and ROC curves were performed to evaluate the predictive power of the model. Gene set enrichment analysis (GSEA) was conducted to explore the molecular mechanisms behind apparent discrepancies between the low- and high-risk groups. We also investigated immune cell infiltration, TIDE score, TMB, the sensitivity of common chemotherapy drugs, and the response to immunotherapy between the two risk groups. Finally, we validated the expression levels of these ten m7G-related lncRNAs in BCa cell lines by qRT-PCR.

RESULTS

We developed a m7G prognostic model (risk score) composed of 10 m7G-related lncRNAs that are significantly associated with the OS of BCa patients. The K-M survival curves revealed that the high-risk group patients had significantly worse OS than those in the low-risk group. The Cox regression analysis confirmed that the risk score was a significant independent prognostic factor for BCa patients. We found that the high-risk group had higher the immune scores and immune cell infiltration. Furthermore, the results of the sensitivity of common anti-BCa drugs showed that the high-risk group was more sensitive to neoadjuvant cisplatin-based chemotherapy and anti-PD1 immunotherapy. Finally, qRT-PCR revealed that AC006058.1, AC073133.2, LINC00677, and LINC01338 were significantly downregulated in BCa cell lines, while the expression levels of AC124312.2 and AL158209.1 were significantly upregulated in BCa cell lines compared with normal cell lines.

CONCLUSION

The m7G prognostic model can be applied to accurately predict the prognosis and provide robust directions for clinicians to develop better individual-based and precise treatment strategies for BCa patients.

Succinylation-associated lncRNA signature to predict the prognosis of colon cancer based on integrative bioinformatics analysis

Colon cancer (CC) has a poor 5-year survival rate though the treatment techniques and strategies have been improved. Succinylation and long noncoding RNAs (lncRNAs) have prognostic value for CC patients. We analyzed and obtained succinylation-related lncRNA by co-expression in CC. A novel succinylation-related lncRNA model was developed by univariate and Least absolute shrinkage and selection operator (Lasso) regression analysis and we used principal component analysis (PCA), functional enrichment annotation, tumor immune environment, drug sensitivity and nomogram to verify the model, respectively. Six succinylation-related lncRNAs in our model were finally confirmed to distinguish the survival status of CC and showed statistically significant differences in training set, testing set, and entire set. The prognosis of with this model was associated with age, gender, M0 stage, N2 stage, T3 + T4 stage and Stage III + IV. The high-risk group showed a higher mutation rate than the low-risk group. We constructed a model to predict overall survival for 1-, 3-, and 5-year with AUCs of 0.694, 0.729, and 0.802, respectively. The high-risk group was sensitive to Cisplatin and Temozolomide compounds. Our study provided novel insights into the value of the succinylation-related lncRNA signature as a predictor of prognosis, which had high clinical application value in the future.

Use of machine learning-based integration to develop an immune-related signature for improving prognosis in patients with gastric cancer

Gastric cancer is one of the most common malignancies. Although some patients benefit from immunotherapy, the majority of patients have unsatisfactory immunotherapy outcomes, and the clinical significance of immune-related genes in gastric cancer remains unknown. We used the single-sample gene set enrichment analysis (ssGSEA) method to evaluate the immune cell content of gastric cancer patients from TCGA and clustered patients based on immune cell scores. The Weighted Correlation Network Analysis (WGCNA) algorithm was used to identify immune subtype-related genes. The patients in TCGA were randomly divided into test 1 and test 2 in a 1:1 ratio, and a machine learning integration process was used to determine the best prognostic signatures in the total cohort. The signatures were then validated in the test 1 and the test 2 cohort. Based on a literature search, we selected 93 previously published prognostic signatures for gastric cancer and compared them with our prognostic signatures. At the single-cell level, the algorithms "Seurat," "SCEVAN", "scissor", and "Cellchat" were used to demonstrate the cell communication disturbance of high-risk cells. WGCNA and univariate Cox regression analysis identified 52 prognosis-related genes, which were subjected to 98 machine-learning integration processes. A prognostic signature consisting of 24 genes was identified using the StepCox[backward] and Enet[alpha = 0.7] machine learning algorithms. This signature demonstrated the best prognostic performance in the overall, test1 and test2 cohort, and outperformed 93 previously published prognostic signatures. Interaction perturbations in cellular communication of high-risk T cells were identified at the single-cell level, which may promote disease progression in patients with gastric cancer. We developed an immune-related prognostic signature with reliable validity and high accuracy for clinical use for predicting the prognosis of patients with gastric cancer.

The prognostic value and immunological role of CD44 in pan-cancer study

To investigate the correlation between cluster of differentiation-44 (CD44) expression and immunotherapy response and identify its possible predictive value in pan-cancer. Datasets of 33 cancer types from The Cancer Genome Atlas (TCGA) database were applied to investigate the relationship of CD44 expression with prognosis, tumor mutational burden (TMB), and microsatellite instability (MSI), and determine its potential prognostic value in pan-cancer. Patients were split into high-risk and low-risk cancer groups based on the survival outcomes of various cancer types. Additionally, the underlying mechanisms of CD44 in the tumor microenvironment (TME) were analyzed using ESTIMATE and CIBERSORT algorithms and Gene Set Enrichment Analysis (GSEA). Subsequently, the biological role of CD44 at single-cell level was investigated using CancerSEA database. Variable expression levels of CD44 between tumor and adjacent normal tissues were identified in pan-cancer datasets, further survival analysis revealed that CD44 expression was associated with multiple clinical annotations and survival indicators. Besides, the expression of CD44 was significantly associated with TMB and MSI in 10 types and 6 types of cancer, respectively, indicating it could be exploited as a potential biomarker predicting immunotherapy outcomes. Meanwhile, CD44 could influence several crucial immune cell-related pathways. and the results revealed by CancerSEA database denoted the correlation of CD44 with malignant phenotype and functional states, further indicating it can serve as a potential therapeutic target in cancer management. Our study demonstrated that CD44 shows great promise as a prognostic biomarker in numerous cancers, which will assist in developing new strategies in cancer management.

Apoptosis related genes mediated molecular subtypes depict the hallmarks of the tumor microenvironment and guide immunotherapy in bladder cancer

Apoptosis has been discovered as a mechanism of cell death. The purpose of this study is to identify the diagnostic signature factors related to bladder cancer (BLCA) through apoptosis related genes (ARGs). Clinicopathological parameters and transcriptomics data of 1,440 BLCA patients were obtained from 7 datasets (GSE13507, GSE31684, GSE32548, GSE32894, GSE48075, TCGA-BLCA, and IMvigor210). We first identified prognosis-related ARGs in BLCA and used them to construct two ARGs molecular subtypes by using consensus clustering algorithm. By using principal component analysis algorithms, a ARGscore was constructed to quantify the index of individualized patient. High ARGscore correlated with progressive malignancy and poor outcomes in BLCA patients. High ARGscore was associated with higher immune cell, higher estimate scores, higher stromal scores, higher immune scores, higher immune checkpoint, and lower tumor purity, which was consistent with the "immunity tidal model theory". Preclinically, BLCA immunotherapy cohorts confirmed patients with low ARGscore demonstrated significant therapeutic advantages and clinical benefits. These findings contribute to our understanding of ARGs and immunotherapy in BLCA. The ARGscore is a potentially useful tool to predict the prognosis and immunotherapy in BLCA.

A propensity score matching study on survival benefits of radiotherapy in patients with inoperable hepatocellular carcinoma

With the advancements in radiotherapy (RT) in recent years, several studies have shown that RT can significantly prolong the survival of patients with hepatocellular carcinoma (HCC). As a noninvasive treatment option, the application of RT for the treatment of HCC is garnering increasing attention. In this retrospective study, we included data from 13,878 patients with HCC from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2019 and 325 patients with HCC treated in three tertiary hospitals in China between 2015 and 2021. Patient data were divided into RT and non-RT groups based on whether the patients underwent RT. Propensity score matching analysis was performed to minimize the deviation between the RT and non-RT groups, and the Kaplan-Meier method, Cox proportional hazard model, and nomogram were used to assess the efficacy of undergoing RT. The median overall survival (mOS) of the RT group was significantly longer compared with that of the non-RT group for the SEER data (16 months versus 9 months, p < 0.01). Similarly, the survival benefit was more significant in the RT group than in the non-RT group at our hospitals (34.1 months versus 15.4 months, p < 0.01). Furthermore, multivariate Cox analysis revealed that factors, including tumor (T) stage, patient age, tumor grade, serum AFP level, and chemotherapy, also affected patient survival. Moreover, these factors were also used to construct a nomogram. Subgroup analysis of these factors showed that RT was effective in prolonging patient survival in different populations. RT significantly improves the survival time of patients with inoperable HCC, thereby providing a basis for selecting HCC patients who can benefit from RT.

Comprehensive research into prognostic and immune signatures of transcription factor family in breast cancer

BACKGROUND

Breast cancer (BRCA) is the most common malignancy with high morbidity and mortality in women, and transcription factor (TF) is closely related to the occurrence and development of BRCA. This study was designed to identify a prognostic gene signature based on TF family to reveal immune characteristics and prognostic survival of BRCA.

METHODS

In this study, RNA-sequence with corresponding clinical data were obtained from The Cancer Genome Atlas (TCGA) and GSE42568. Prognostic differentially expressed transcription factor family genes (TFDEGs) were screened to construct a risk score model, after which BRCA patients were stratified into low-risk and high-risk groups based on their corresponding risk scores. Kaplan-Meier (KM) analysis was applied to evaluate the prognostic implication of risk score model, and a nomogram model was developed and validated with the TCGA and GSE20685. Furthermore, the GSEA revealed pathological processes and signaling pathways enriched in the low-risk and high-risk groups. Finally, analyses regarding levels of immune infiltration, immune checkpoints and chemotactic factors were all completed to investigate the correlation between the risk score and tumor immune microenvironment (TIME).

RESULTS

A prognostic 9-gene signature based on TFDEGs was selected to establish a risk score model. According to KM analyses, high-risk group witnessed a significantly worse overall survival (OS) than low-risk group in both TCGA-BRCA and GSE20685. Furthermore, the nomogram model proved great possibility in predicting the OS of BRCA patients. As indicted in GSEA analysis, tumor-associated pathological processes and pathways were relatively enriched in high-risk group, and the risk score was negatively correlated with ESTIMATE score, infiltration levels of CD4+ and CD8+T cells, as well as expression levels of immune checkpoints and chemotactic factors.

CONCLUSIONS

The prognostic model based on TFDEGs could distinguish as a novel biomarker for predicting prognosis of BRCA patients; in addition, it may also be utilized to identify potential benefit population from immunotherapy in different TIME and predict potential drug targets.

Construction of an RNA modification-related gene predictive model associated with prognosis and immunity in gastric cancer

BACKGROUND

Gastric cancer (GC) is one of the most common causes of cancer-related fatalities worldwide, and its progression is associated with RNA modifications. Here, using RNA modification-related genes (RNAMRGs), we aimed to construct a prognostic model for patients with GC.

METHODS

Based on RNAMRGs, RNA modification scores (RNAMSs) were obtained for GC samples from The Cancer Genome Atlas and were divided into high- and low-RNAMS groups. Differential analysis and weighted correlation network analysis were performed for the differential expressed genes (DEGs) to obtain the key genes. Next, univariate Cox regression, least absolute shrinkage and selection operator, and multivariate Cox regression analyses were performed to obtain the model. According to the model risk score, samples were divided into high- and low-risk groups. Enrichment analysis and immunoassays were performed for the DEGs in these groups. Four external datasets from Gene Expression Omnibus data base were used to test the accuracy of the predictive model.

RESULTS

We identified SELP and CST2 as key DEGs, which were used to generate the predictive model. The high-risk group had a worse prognosis compared to the low-risk group (p < 0.05). Enrichment analysis and immunoassays revealed that 144 DEGs related to immune cell infiltration were associated with the Wnt signaling pathway and included hub genes such as ELN. Overall mutation levels, tumor mutation burden, and microsatellite instability were lower, but tumor immune dysfunction and exclusion scores were greater (p < 0.05) in the high-risk group than in the low-risk group. The validation results showed that the prediction model score can accurately predict the prognosis of GC patients. Finally, a nomogram was constructed using the risk score combined with the clinicopathological characteristics of patients with GC.

CONCLUSION

This risk score from the prediction model related to the tumor microenvironment and immunotherapy could accurately predict the overall survival of GC patients.

KIF2C is a prognostic biomarker associated with immune cell infiltration in breast cancer

BACKGROUND

The kinesin-13 family member 2C (KIF2C) is a versatile protein participating in many biological processes. KIF2C is frequently up-regulated in multiple types of cancer and is associated with cancer development. However, the role of KIF2C in immune cell infiltration of tumor microenvironment and immunotherapy in breast cancer remains unclear.

METHODS

The expression of KIF2C was analyzed using Tumor Immune Estimation Resource (TIMER) database and further verified by immunohistochemical staining in human breast cancer tissues. The correlation between KIF2C expression and clinical parameters, the impact of KIF2C on clinical prognosis and independent prognostic factors were analyzed by using TCGA database, the Kaplan-Meier plotter, and Univariate and multivariate Cox analyses, respectively. The nomograms were constructed according to independent prognostic factors and validated with C-index, calibration curves, ROC curves, and decision curve analysis. A gene set enrichment analysis (GSEA) was performed to explore the underlying molecular mechanisms of KIF2C. The degree of immune infiltration was assessed by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using the Expression (ESTIMATE) algorithm and the single sample GSEA (ssGSEA). The Tumor mutational burden and Tumor Immune Dysfunction and Rejection (TIDE) were used to analyze immunotherapeutic efficiency. Finally, the KIF2C-related competing endogenous RNA (ceRNA) network was constructed to predict the putative regulatory mechanisms of KIF2C.

RESULTS

KIF2C was remarkably up-regulated in 18 different types of cancers, including breast cancer. Kaplan-Meier survival analysis showed that high KIF2C expression was associated with poor overall survival (OS). KIF2C expression was associated with clinical parameters such as age, TMN stage, T status, and molecular subtypes. We identified age, stage, estrogen receptor (ER) and KIF2C expression as OS-related independent prognosis factors for breast cancer. An OS-related nomogram was developed based on these independent prognosis factors and displayed good predicting ability for OS of breast cancer patients. Finally, our results revealed that KIF2C was significantly related to immune cell infiltration, tumor mutational burden, and immunotherapy in patients with breast cancer.

CONCLUSION

KIF2C was overexpressed in breast cancer and was positively correlated with immune cell infiltration and immunotherapy response. Therefore, KIF2C can serve as a potential biomarker for prognosis and immunotherapy in breast cancer.

RNA Sequencing Reveals the Differentially Expressed circRNAs between Stable and Unstable Carotid Atherosclerotic Plaques

Objective. This study aimed to identify circular RNA profiles (circRNAs) via high-throughput RNA sequencing and distinguish the differentially expressed (DE) circRNAs between stable and unstable plaques. Methods. RNA sequencing was performed on unstable and stable carotid plaque samples obtained from patients with carotid artery stenosis. DE circRNAs were screened, and six DE circRNAs were verified using quantitative real-time PCR (qRT-PCR). Functional evaluation of the DE circRNAs was conducted via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Results. We screened 344 DE circRNAs in unstable plaques, consisting of 342 upregulated and 2 downregulated circRNAs. GO analysis showed that the host genes of the upregulated circRNAs were related to ER to Golgi transport vesicle membrane, endocytic vesicle membrane, and Ran GTPase binding. KEGG analysis revealed that the host genes of the upregulated circRNAs were primarily associated with protein processing in endoplasmic reticulum, lysine degradation, homologous recombination, epithelial cell signaling in Helicobacter pylori infection, and yersinia infection. The results of qRT-PCR verified three upregulated DE circRNAs and two downregulated DE circRNAs in unstable plaques. Conclusion. Hsa-circ-0001523, hsa-circ-0008950, hsa-circ-0000571, hsa-circ-0001946, and hsa-circ-0000745 may be involved in regulating the stability of atherosclerotic plaques and serves as a therapeutic target for unstable plaques.

Comprehensive Explorations of CCL28 in Lung Adenocarcinoma Immunotherapy and Experimental Validation

Background

Chemokines have been reported to play an important role in cancer immunotherapy. This study aimed to explore the chemokines involved in lung cancer immunotherapy.

Methods

All the public data were downloaded from The Cancer Genome Atlas Program database. Quantitative real time-PCR was used to detect the mRNA level of specific molecules and Western blot was used for the protein level. Other experiments used include luciferase reporter experiments, flow cytometric analysis, Chromatin immunoprecipitation assay, ELISA and co-cultured system.

Results

We found that the CCL7, CCL11, CCL14, CCL24, CCL25, CCL26, CCL28 had a higher level, while the CCL17, CCL23 had a lower level in immunotherapy non-responders. Also, we found that immunotherapy non-responders had a higher level of CD56dim NK cells, NK cells, Th1 cells, Th2 cells and Treg, yet a lower level of iDC and Th17 cells. Biological enrichment analysis indicated that in the patients with high Treg infiltration, the pathways of pancreas beta cells, KRAS signaling, coagulation, WNT BETA catenin signaling, bile acid metabolism, interferon alpha response, hedgehog signaling, PI3K/AKT/mTOR signaling, apical surface, myogenesis were significantly enriched in. CCL7, CCL11, CCL26 and CCL28 were selected for further analysis. Compared with the patients with high CCL7, CCL11, CCL26 and CCL28 expression, the patients with low CCL7, CCL11, CCL26 and CCL28 expression had a better performance of immunotherapy response and this effect might partly be due to Treg cells. Furthermore, biological exploration and clinical correlation of CCL7, CCL11, CCL26 and CCL28 were conducted, Finally, CCL28 was selected for validation. Experiments showed that under the hypoxia condition, HIF-1α was upregulated, which can directly bind to the promoter region of CCL28 and lead to its higher level. Also, CCL28 secreted by lung cancer cells could induce Tregs infiltration.

Conclusion

Our study provides a novel insight focused on the chemokines in lung cancer immunotherapy. Also, CCL28 was identified as an underlying biomarker for lung cancer immunotherapy.