Chemokines driven ovarian cancer progression, metastasis and chemoresistance: potential pharmacological targets for cancer therapy

Dual-Amplification CRISPR Biosensor for Ultrasensitive miRNA Detection: A Powerful Tool for Cancer and Immune Monitoring

MicroRNAs (miRNAs) are pivotal regulators of gene expression and emerging biomarkers for cancer diagnosis and inflammation monitoring. Sensitive and specific detection methods for miRNAs are crucial for advancing their clinical and research applications. In this study, we present a CRISPR-based biosensor driven by a dual-circle amplification cascade. This system combines rolling circle amplification (RCA) for initial signal amplification and a secondary amplification cycle within the RCA products, enabling precise miRNA expression detection across different cell types. The method is highly sensitive and specific and requires minimal sample input. Using miRNA-221 as a model, the system demonstrated a linear detection range from 15 fM to 1 nM and a detection limit of 1.35 fM. It also effectively differentiated miRNA expression profiles across various cell types, supporting cell classification based on miRNA signatures. Furthermore, the platform's versatility was confirmed with miRNA-155, highlighting its ability to monitor miRNA expression changes in macrophages upon immune stimulation. The system's modular design allows easy adaptation to other miRNA targets, making it a robust tool for miRNA research. This biosensor provides a powerful approach for miRNA profiling in complex biological samples, offering valuable insights into cancer and inflammation with potential for clinical diagnostics and therapeutic monitoring.

Integration of Bulk and Single-Cell RNA Sequencing to Identify RNA Modifications-Related Prognostic Signature in Ovarian Cancer

Background

Ovarian cancer (OC), a common fatal malignancy in women, has a poor prognosis. RNA modifications are associated with the development of OC. In this study, we aimed to identify and verify RNA modifications-related prognostic genes in OC by integrating bulk and single-cell RNA sequencing (scRNA-seq) data.

Methods

Transcriptome data came from public databases and RNA modifications-related genes (RMRGs) were obtained from literature. Candidate genes were identified by intersecting RMRGs with differentially expressed genes (DEGs) in OC patients. Prognostic genes were gained via machine learning techniques, particularly LASSO regression. A risk model was built to predict the prognosis. OC patients were divided into high-risk and low-risk groups according to risk score. Subsequent analyses covered enrichment analysis, immune microenvironment, mutation analysis, and chemotherapeutic drug sensitivity. In addition, scRNA-seq data was assessed for key cells and gene expression in them. Finally, RT-qPCR was applied to identify the expression of prognostic genes.

Results

LSM4, SNRPC, ZC3H13, LSM2, WTAP, DCP2, PUS7, and TUT1 were selected as prognostic genes. The risk model exhibited excellent predictive abilities. Seventeen pathways were enriched like calcium signaling pathway, 7 differential immune cells were identified like regulatory T cells and plasmacytoid dendritic cells, and TP53 had highest mutation rate. Half-maximal inhibitory concentrations (IC50) values of 47 drugs like paclitaxel differed between two risk groups. The prognostic genes were distributed mainly in fibroblast cells, epithelial cells and endothelial cells. During fibroblast cells differentiation, expression of prognostic genes fluctuated to varying degrees. The RT-qPCR demonstrated that the expression of LSM2, LSM4, PUS7, SNRPC, and TUT1 were upregulated in OC, while DCP2, WTAP, and ZC3H13 were downregulated.

Conclusion

We constructed an RNA modifications-related prognostic signature that can effectively predict clinical outcomes and therapeutic responses in patients with OC.

Reciprocal Modulation of Tumour and Immune Cell Motility: Uncovering Dynamic Interplays and Therapeutic Approaches

Dysregulated cell movement is a hallmark of cancer progression and metastasis, the leading cause of cancer-related mortality. The metastatic cascade involves tumour cell migration, invasion, intravasation, dissemination, and colonisation of distant organs. These processes are influenced by reciprocal interactions between cancer cells and the tumour microenvironment (TME), including immune cells, stromal components, and extracellular matrix proteins. The epithelial-mesenchymal transition (EMT) plays a crucial role in providing cancer cells with invasive and stem-like properties, promoting dissemination and resistance to apoptosis. Conversely, the mesenchymal-epithelial transition (MET) facilitates metastatic colonisation and tumour re-initiation. Immune cells within the TME contribute to either anti-tumour response or immune evasion. These cells secrete cytokines, chemokines, and growth factors that shape the immune landscape and influence responses to immunotherapy. Notably, immune checkpoint blockade (ICB) has transformed cancer treatment, yet its efficacy is often dictated by the immune composition of the tumour site. Elucidating the molecular cross-talk between immune and cancer cells, identifying predictive biomarkers for ICB response, and developing strategies to convert cold tumours into immune-active environments is critical to overcoming resistance to immunotherapy and improving patient survival.

GFPT2 promotes paclitaxel resistance in epithelial ovarian cancer cells via activating NF-κB signaling pathway

This study investigated the role of glutamine-fructose-6-phosphate transaminase 2 (GFPT2) in the response of epithelial ovarian cancer cells to paclitaxel, a standard chemotherapy drug. We analyzed GFPT2 expression across various EOC cell lines, including SKOV3, HEY, ES-2, A2780, and OVCR3. In HEY cell lines, we performed GFPT2 knockdown, while A2780 cells were engineered for GFPT2 overexpression. Following these manipulations, we assessed the cellular responses to paclitaxel treatment. Results demonstrated a correlation between GFPT2 levels and paclitaxel resistance; those with high GFPT2 (SKOV3 and HEY) expression were less sensitive compared to the cells with low GFPT2 expression (A2780). Downregulating GFPT2 enhanced drug sensitivity in HEY cells, whereas its overexpression impaired drug sensitivity in A2780 cells. Mechanistically, GFPT2's role in facilitating paclitaxel resistance was linked to the activation of the nuclear factor-κB (NF-κB) signaling pathway, possibly influenced by NK3 Homeobox 2. Our findings suggest that GFPT2 is a critical mediator of paclitaxel resistance through NF-κB pathway activation in EOC, providing potential targets for improving therapeutic efficacy against this challenging malignancy.

From Defense to Disease: How the Immune System Fuels Epithelial-Mesenchymal Transition in Ovarian Cancer

Ovarian cancer is one of the most deadly gynecological cancers, with over 300 thousand new cases per year, most of which are diagnosed in advanced stages. The limited availability of effective biomarkers and lack of characteristic symptoms make early diagnosis difficult, resulting in a five-year survival rate of 30-40%. Mutations in the BRCA1 and BRCA2 genes and abnormalities of signaling pathways such as PI3K/AKT and TP53 play a key role in the progression of ovarian cancer. The immune system, which can act against tumors, often supports tumor development in the ovarian cancer microenvironment through immunoevasion, which is influenced by cytokines such as IL-6, IL-10, and TGF-β. Epithelial-to-mesenchymal transition (EMT) allows cancer cells to acquire mesenchymal characteristics, increasing their invasiveness and metastatic capacity. Immunological factors, including pro-inflammatory cytokines and signals from the tumor microenvironment regulate the EMT process. This review aims to present the role of EMT in ovarian cancer progression, its interactions with the immune system, and potential biomarkers and therapeutic targets. Modulation of the immune response and inhibition of EMT may constitute the basis for personalized therapies, which opens new possibilities for improving the prognosis and efficacy of treatment in patients with ovarian cancer.

The role of macrophage polarization in ovarian cancer: from molecular mechanism to therapeutic potentials

Ovarian cancer (OC) remains the most lethal gynecological malignancy, primarily due to its late-stage diagnosis, frequent recurrence, and resistance to conventional chemotherapy. A critical factor contributing to OC's aggressiveness is the tumor microenvironment (TME), particularly the presence and polarization of tumor-associated macrophages (TAMs). TAMs, often skewed toward an immunosuppressive M2-like phenotype, facilitate tumor growth, angiogenesis, metastasis, and resistance to therapy. This comprehensive review delves into the multifaceted regulation of macrophage polarization in OC, highlighting key molecular pathways such as PTEN loss, Wnt/β-catenin signaling, NF-κB, Myc, STAT3, and JNK, among others. Additionally, it explores the role of chemokines, non-coding RNAs, and various proteins in modulating TAM phenotypes. Emerging evidence underscores the significance of extracellular vesicles (EVs) and ovarian cancer stem cells (CSCs) in promoting M2 polarization, thereby enhancing tumor progression and therapy resistance. The review also identifies critical biomarkers associated with macrophage polarization, including CD163, LILRB1, MUC2, and others, which hold prognostic and therapeutic potential. Therapeutic strategies targeting TAMs are extensively discussed, encompassing oncolytic viruses, engineered EVs, immunotherapies, nanoparticles, targeted therapies, and natural products. These approaches aim to reprogram TAMs from a pro-tumorigenic M2 state to an anti-tumorigenic M1 phenotype, thereby enhancing immune responses and overcoming resistance to treatments such as chemotherapy and immune checkpoint inhibitors. Furthermore, the review addresses the interplay between macrophage polarization and therapy resistance, emphasizing the need for novel interventions to modulate the TME effectively. By synthesizing current knowledge on macrophage polarization in ovarian cancer, this study underscores the potential of targeting TAMs to improve clinical outcomes and personalize treatment strategies for OC patients. Continued research in this domain is essential to develop robust therapeutic frameworks that can mitigate the immunosuppressive TME and enhance the efficacy of existing and novel cancer therapies.

Tumor microenvironment: recent advances in understanding and its role in modulating cancer therapies

Tumor microenvironment (TME) denotes the non-cancerous cells and components presented in the tumor, including molecules produced and released by them. Interactions between cancer cells, immune cells, stromal cells, and the extracellular matrix within the TME create a dynamic ecosystem that can either promote or hinder tumor growth and spread. The TME plays a pivotal role in either promoting or inhibiting tumor growth and dissemination, making it a critical factor to consider in the development of effective cancer therapies. Understanding the intricate interplay within the TME is crucial for devising effective cancer therapies. Combination therapies involving inhibitors of immune checkpoint blockade (ICB), and/or chemotherapy now offer new approaches for cancer therapy. However, it remains uncertain how to best utilize these strategies in the context of the complex tumor microenvironment. Oncogene-driven changes in tumor cell metabolism can impact the TME to limit immune responses and present barriers to cancer therapy. Cellular and acellular components in tumor microenvironment can reprogram tumor initiation, growth, invasion, metastasis, and response to therapies. Components in the TME can reprogram tumor behavior and influence responses to treatments, facilitating immune evasion, nutrient deprivation, and therapeutic resistance. Moreover, the TME can influence angiogenesis, promoting the formation of blood vessels that sustain tumor growth. Notably, the TME facilitates immune evasion, establishes a nutrient-deprived milieu, and induces therapeutic resistance, hindering treatment efficacy. A paradigm shift from a cancer-centric model to a TME-centric one has revolutionized cancer research and treatment. However, effectively targeting specific cells or pathways within the TME remains a challenge, as the complexity of the TME poses hurdles in designing precise and effective therapies. This review highlights challenges in targeting the tumor microenvironment to achieve therapeutic efficacy; explore new approaches and technologies to better decipher the tumor microenvironment; and discuss strategies to intervene in the tumor microenvironment and maximize therapeutic benefits.

Investigation of the mechanism by which FOXJ2 inhibits proliferation, metastasis and cell cycle progression of ovarian cancer cells through the PI3K/AKT signaling pathway

BACKGROUND

As one member of the Forkhead Box transcription factor, Forkhead Box J2 (FOXJ2) is involved in diverse cancers. At present, the specific role and mechanism of FOXJ2 in ovarian cancer (OC) have not been fully addressed, which allows us to fill the blank.

MATERIALS AND METHODS

Accordingly, the expression of FOXJ2 in OC cells and ovarian epithelial cells was quantified via real-time qPCR. Following the transfection, cell counting kit-8, Transwell, wound healing and flow cytometry assays were performed to measure the proliferation, metastasis, apoptosis and cell cycle of OC cells A2780 and HEY. Further, real-time qPCR and Western blotting were both employed for the quantification assays on the expression levels of FOXJ2 as well as phosphoinositide 3-kinase (PI3K) and protein kinase B (AKT) (in both unphosphorylated and phosphorylated forms).

RESULTS

Based on the results, FOXJ2 were highly-expressed in OC cells (P < 0.05). Silencing of FOXJ2 resulted in attenuated OC cell proliferation, reduced numbers of migrating and invading OC cells, decreased apoptotic capacity, and cell cycle arrest in G1/S phase (P < 0.05). In addition, the knockdown of FOXJ2 caused the downward trend on the phosphorylation level of both PI3K and AKT in OC cells (P < 0.05).

CONCLUSION

The silencing of FOXJ2 could repress the growth and metastasis potentials and cause the cell cycle G1/S arrest of OC cells in vitro, which was possibly achieved via targeting the PI3K/AKT pathway.

Harnessing the tumor microenvironment: targeted cancer therapies through modulation of epithelial-mesenchymal transition

The tumor microenvironment (TME) is integral to cancer progression, impacting metastasis and treatment response. It consists of diverse cell types, extracellular matrix components, and signaling molecules that interact to promote tumor growth and therapeutic resistance. Elucidating the intricate interactions between cancer cells and the TME is crucial in understanding cancer progression and therapeutic challenges. A critical process induced by TME signaling is the epithelial-mesenchymal transition (EMT), wherein epithelial cells acquire mesenchymal traits, which enhance their motility and invasiveness and promote metastasis and cancer progression. By targeting various components of the TME, novel investigational strategies aim to disrupt the TME's contribution to the EMT, thereby improving treatment efficacy, addressing therapeutic resistance, and offering a nuanced approach to cancer therapy. This review scrutinizes the key players in the TME and the TME's contribution to the EMT, emphasizing avenues to therapeutically disrupt the interactions between the various TME components. Moreover, the article discusses the TME's implications for resistance mechanisms and highlights the current therapeutic strategies toward TME modulation along with potential caveats.

Dual Inhibition of SYK and EGFR Overcomes Chemoresistance by Inhibiting CDC6 and Blocking DNA Replication

Targeting multiple signaling pathways has been proposed as a strategy to overcome resistance to single-pathway inhibition in cancer therapy. A previous study in epithelial ovarian cancers identified hyperactivity of spleen tyrosine kinase (SYK) and EGFR, which mutually phosphorylate and activate each other. Given the potential for pharmacologic inhibition of both kinases with clinically available agents, this study aimed to assess the antitumor efficacy of both pharmacologic and genetic SYK and EGFR coinhibition using a multifaceted approach. We assessed the coinactivation effects in chemoresistant ovarian cancer cell lines, patient-derived organoids, and xenograft models. Dual inhibition of SYK and EGFR in chemoresistant ovarian cancer cells elicited a synergistic antitumor effect. Notably, the combined inhibition activated the DNA damage response, induced G1 cell-cycle arrest, and promoted apoptosis. The phosphoproteomic analysis revealed that perturbation of SYK and EGFR signaling induced a significant reduction in both phosphorylated and total protein levels of cell division cycle 6, a crucial initiator of DNA replication. Together, this study provides preclinical evidence supporting dual inhibition of SYK and EGFR as a promising treatment for chemoresistant ovarian cancer by disrupting DNA synthesis and impairing formation of the prereplication complex. These findings warrant further clinical investigation to explore the potential of this combination therapy in overcoming drug resistance and improving patient outcomes. Significance: SYK and EGFR coinhibition exerts synergistic anticancer effects in chemoresistant ovarian cancer, providing a strategy to treat chemotherapy-resistant ovarian cancers using clinically available agents by targeting critical signaling pathways involved in DNA replication.

Association of the Single Nucleotide Polymorphisms rs11556218, rs4778889, rs4072111, and rs1131445 of the Interleukin-16 Gene with Ovarian Cancer

Single nucleotide polymorphisms (SNPs) of the IL-16 gene have been reported to influence the risk of several cancers, but their role in ovarian cancer (OC) has not been studied. Using the restriction fragment length polymorphism (PCR-RFLP) method, we examined four IL-16 SNPs: rs11556218 (T > G), rs4778889 (T > C), rs4072111 (C > T), and rs1131445 (T > C) in blood samples from 413 women of Central European descent, including 200 OC patients and 213 healthy controls. Among the patients, 62% were postmenopausal, 84.5% were diagnosed in late stages (FIGO IIb-IV), and 73.5% had high-grade serous OC (HGSOC). Minor allele frequencies in controls were 9.2% for rs11556218 (G allele), 13.7% for rs4778889 (C allele), 10.4% for rs4072111 (T allele), and 32.3% for rs1131445 (C allele). We found significant associations of rs11556218 (G vs. T allele: OR 2.76, 95% CI 1.84-4.14, p < 0.0001) with elevated OC risk in the whole cohort (p < 0.001) and in both premenopausal (p < 0.001) and postmenopausal (p = 0.001) subgroups. These associations remained significant across heterozygote (p < 0.001), dominant (p < 0.001), and overdominant (p < 0.001) models. IL-16 rs4778889 was associated with OC risk predominantly in premenopausal women (p < 0.0001 in almost all models). In the whole cohort, the C allele was associated with OC risk (OR 1.54, CI 95% 1.06-2.23, p = 0.024), and the association of rs4778889 was significant in dominant (p = 0.019), overdominant (p = 0.033), and heterozygote (p = 0.027) models. Furthermore, rs4778889 was linked with HGSOC (p = 0.036) and endometriosis-related OC subtypes (p = 0.002). No significant associations were found for rs4072111 or rs1131445 (p = 0.81 or 0.47, respectively). In conclusion, rs11556218 and rs4778889 SNPs are associated with OC risk, especially in premenopausal women.

A clinical prognostic model related to T cells based on machine learning for predicting the prognosis and immune response of ovarian cancer

Background

Ovarian cancer (OV) is regarded as one of the most lethal malignancies affecting the female reproductive system, with individuals diagnosed with OV often facing a dismal prognosis due to resistance to chemotherapy and the presence of an immunosuppressive environment. T cells serve as a crucial mediator for immune surveillance and cancer elimination. This study aims to analyze the mechanism of T cell-associated markers in OV and create a prognostic model for clinical use in enhancing outcomes for OV patients.

Methods

Based on the single-cell dataset GSE184880, this study used single-cell data analysis to identify characteristic T cell subsets. Analysis of high dimensional weighted gene co-expression network analysis (hdWGCNA) is utilized to identify crucial gene modules along with their corresponding hub genes. A grand total of 113 predictive models were formed utilizing ten distinct machine learning algorithms along with the combination of the cancer genome atlas (TCGA)-OV dataset and the GSE140082 dataset. The most dependable clinical prognostic model was created utilizing the leave one out cross validation (LOOCV) framework. The validation process for the models was achieved by conducting survival curve analysis and receiver operating characteristic (ROC) analysis. The relationship between risk scores and immune cells was explored through the utilization of the Cibersort algorithm. Additionally, an analysis of drug sensitivity was carried out to anticipate chemotherapy responses across various risk groups. The genes implicated in the model were authenticated utilizing qRT-PCR, cell viability experiments, and EdU assay.

Results

This study developed a clinical prognostic model that includes ten risk genes. The results obtained from the training set of the study indicate that patients classified in the low-risk group experience a significant survival advantage compared to those in the high-risk group. The ROC analysis demonstrates that the model holds significant clinical utility. These results were verified using an independent dataset, strengthening the model's precision and dependability. The risk assessment provided by the model also serves as an independent prognostic factor for OV patients. The study also unveiled a noteworthy relationship between the risk scores calculated by the model and various immune cells, suggesting that the model may potentially serve as a valuable tool in forecasting responses to both immune therapy and chemotherapy in ovarian cancer patients. Notably, experimental evidence suggests that PFN1, one of the genes included in the model, is upregulated in human OV cell lines and has the capacity to promote cancer progression in in vitro models.

Conclusion

We have created an accurate and dependable clinical prognostic model for OV capable of predicting clinical outcomes and categorizing patients. This model effectively forecasts responses to both immune therapy and chemotherapy. By regulating the immune microenvironment and targeting the key gene PFN1, it may improve the prognosis for high-risk patients.

Single-cell RNA sequencing in ovarian cancer: revealing new perspectives in the tumor microenvironment

Single-cell sequencing technology has emerged as a pivotal tool for unraveling the complexities of the ovarian tumor microenvironment (TME), which is characterized by its cellular heterogeneity and intricate cell-to-cell interactions. Ovarian cancer (OC), known for its high lethality among gynecologic malignancies, presents significant challenges in treatment and diagnosis, partly due to the complexity of its TME. The application of single-cell sequencing in ovarian cancer research has enabled the detailed characterization of gene expression profiles at the single-cell level, shedding light on the diverse cell populations within the TME, including cancer cells, stromal cells, and immune cells. This high-resolution mapping has been instrumental in understanding the roles of these cells in tumor progression, invasion, metastasis, and drug resistance. By providing insight into the signaling pathways and cell-to-cell communication mechanisms, single-cell sequencing facilitates the identification of novel therapeutic targets and the development of personalized medicine approaches. This review summarizes the advancement and application of single-cell sequencing in studying the stromal components and the broader TME in OC, highlighting its implications for improving diagnosis, treatment strategies, and understanding of the disease's underlying biology.

IL-6 regulates epithelial ovarian cancer EMT, invasion, and metastasis by modulating Let-7c and miR-200c through the STAT3/HIF-1α pathway

Interleukin-6 (IL-6) and hypoxia-inducible factor-1α (HIF-1α) play important roles in epithelial-mesenchymal transformation (EMT) and tumor development. Previous studies have demonstrated that IL-6 promotes EMT, invasion, and metastasis in epithelial ovarian cancer (EOC) cells by activating the STAT3/HIF-1α pathway. MicroRNA (miRNA) is non-coding small RNAs that also play an important role in tumor development. Notably, Let-7 and miR-200 families are prominently altered in EOC. However, whether IL-6 regulates the expression of Let-7 and miR-200 families through the STAT3/HIF-1α signaling to induce EMT in EOC remains poorly understood. In this study, we conducted in vitro and in vivo investigations using two EOC cell lines, SKOV3, and OVCAR3 cells. Our findings demonstrate that IL-6 down-regulates the mRNA levels of Let-7c and miR-200c while up-regulating their target genes HMGA2 and ZEB1 through the STAT3/HIF-1α signaling in EOC cells and in vivo. Additionally, to explore the regulatory role of HIF-1α on miRNAs, both exogenous HIF blockers YC-1 and endogenous high expression or inhibition of HIF-1α can be utilized. Both approaches can confirm that the downstream molecule HIF-1α inhibits the expression and function of Let-7c and miR-200c. Further mechanistic research revealed that the overexpression of Let-7c or miR-200c can reverse the malignant evolution of EOC cells induced by IL-6, including EMT, invasion, and metastasis. Consequently, our results suggest that IL-6 regulates the expression of Let-7c and miR-200c through the STAT3/HIF-1α pathway, thereby promoting EMT, invasion, and metastasis in EOC cells.

Knockdown of CENPM activates cGAS-STING pathway to inhibit ovarian cancer by promoting pyroptosis

OBJECTIVE

We aimed to screen novel gene signatures for ovarian cancer (OC) and explore the role of biomarkers in OC via regulating pyroptosis using bioinformatics analysis.

METHODS

Differentially expressed genes (DEGs) of OC were screened from GSE12470 and GSE16709 datasets. Hub genes were determined from protein-protein interaction networks after bioinformatics analysis. The role of Centromeric protein M (CENPM) in OC was assessed by subcutaneous tumor experiment using hematoxylin-eosin and immunohistochemical staining. Tumor metastasis was evaluated by detecting epithelial-mesenchymal transition-related proteins. The proliferation, migration, and invasion were determined using cell counting kit and transwell assay. Enzyme-linked immunosorbent assay was applied to measure inflammatory factors. The mRNA and protein expression were detected using real-time quantitative PCR and western blot.

RESULTS

We determined 9 hub genes (KIFC1, PCLAF, CDCA5, KNTC1, MCM3, OIP5, CENPM, KIF15, and ASF1B) with high prediction value for OC. In SKOV3 and A2780 cells, the expression levels of hub genes were significantly up-regulated, compared with normal ovarian cells. CENPM was selected as a key gene. Knockdown of CENPM suppressed proliferation, migration, and invasion of OC cells. Subcutaneous tumor experiment revealed that CENPM knockdown significantly suppressed tumor growth and metastasis. Additionally, pyroptosis was promoted in OC cells and xenograft tumors after CENPM knockdown. Furthermore, CENPM knockdown activated cGAS-STING pathway and the pathway inhibitor reversed the inhibitory effect of CENPM knockdown on viability, migration, and invasion of OC cells.

CONCLUSION

CENPM was a novel biomarker of OC, and knockdown of CENPM inhibited OC progression by promoting pyroptosis and activating cGAS-STING pathway.

Chemokines and Their Receptors: Predictors of Therapeutic Potential in Tumor Microenvironment on Esophageal Cancer

Esophageal carcinoma (ESCA) is an aggressive solid tumor. The 5-year survival rate for patients with ESCA is estimated to be less than 20%, mainly due to tumor invasion and metastasis. Therefore, it is urgent to improve early diagnostic tools and effective treatments for ESCA patients. Tumor microenvironment (TME) enhances the ability of tumor cells to proliferate, migrate, and escape from the immune system, thus promoting the occurrence and development of tumor. TME contains chemokines. Chemokines consist of four major families, which are mainly composed of CC and CXC families. The main purpose of this review is to understand the CC and CXC chemokines and their receptors in ESCA, to improve the understanding of tumorigenesis of ESCA and determine new biomarkers for the diagnosis and prognosis of ESCA. We reviewed the literature on CC and CXC chemokines and their receptors in ESCA identified by PubMed database. This article introduces the general structures and functions of CC, CXC chemokines and their receptors in TME, as well as their roles in the progress of ESCA. Chemokines are involved in the development of ESCA, such as cancer cell invasion, metastasis, angiogenesis, and radioresistance, and are key determinants of disease progression, which have a great impact on patient prognosis and treatment response. In addition, a full understanding of their mechanism of action is essential to further verify that these chemokines and their receptors may serve as biomarkers or therapeutic targets of ESCA.

Delivery of SIRT1 by cancer-associated adipocyte-derived extracellular vesicles regulates immune response and tumorigenesis of ovarian cancer cells

PURPOSE

This study intends to investigate the possible molecular mechanism of immune response and tumorigenesis in ovarian cancer cells, mediated by sirtuin 1 (SIRT1)-containing extracellular vesicles (EVs) derived from cancer-associated adipocytes (CAAs) (CAA-EVs).

METHODS

Differentially expressed genes in EVs from CAAs were screened by RNA transcriptome sequencing, and the downstream pathway was predicted in silico. The binding between SIRT1 and CD24 was investigated by luciferase activity and ChIP-PCR assays. EVs were extracted from human ovarian cancer tissue-isolated CAAs, and the internalization of CCA-EVs by ovarian cancer cells was characterized. The ovarian cancer cell line was injected into mice to establish an animal model. Flow cytometry was performed to analyze the proportions of M1 and M2 macrophages, CD8+ T, T-reg, and CD4+ T cells. TUNEL staining was used to detect cell apoptosis in the mouse tumor tissues. ELISA detection was performed on immune-related factors in the serum of mice.

RESULTS

CAA-EVs could deliver SIRT1 to ovarian cancer cells, thereby affecting the immune response of ovarian cancer cells in vitro and promoting tumorigenesis in vivo. SIRT1 could transcriptionally activate the expression of CD24, and CD24 could up-regulate Siglec-10 expression. CAA-EVs-SIRT1 activated the CD24/Siglec-10 axis and promoted CD8+ T cell apoptosis, thereby promoting tumorigenesis in mice.

CONCLUSION

CAA-EVs-mediated transfer of SIRT1 regulates the CD24/Siglec-10 axis to curb immune response and promote tumorigenesis of ovarian cancer cells.

Multifactor assessment of ovarian cancer reveals immunologically interpretable molecular subtypes with distinct prognoses

Background

Ovarian cancer (OC) is a highly heterogeneous and malignant gynecological cancer, thereby leading to poor clinical outcomes. The study aims to identify and characterize clinically relevant subtypes in OC and develop a diagnostic model that can precisely stratify OC patients, providing more diagnostic clues for OC patients to access focused therapeutic and preventative strategies.

Methods

Gene expression datasets of OC were retrieved from TCGA and GEO databases. To evaluate immune cell infiltration, the ESTIMATE algorithm was applied. A univariate Cox analysis and the two-sided log-rank test were used to screen OC risk factors. We adopted the ConsensusClusterPlus algorithm to determine OC subtypes. Enrichment analysis based on KEGG and GO was performed to determine enriched pathways of signature genes for each subtype. The machine learning algorithm, support vector machine (SVM) was used to select the feature gene and develop a diagnostic model. A ROC curve was depicted to evaluate the model performance.

Results

A total of 1,273 survival-related genes (SRGs) were firstly determined and used to clarify OC samples into different subtypes based on their different molecular pattern. SRGs were successfully stratified in OC patients into three robust subtypes, designated S-I (Immunoreactive and DNA Damage repair), S-II (Mixed), and S-III (Proliferative and Invasive). S-I had more favorable OS and DFS, whereas S-III had the worst prognosis and was enriched with OC patients at advanced stages. Meanwhile, comprehensive functional analysis highlighted differences in biological pathways: genes associated with immune function and DNA damage repair including CXCL9, CXCL10, CXCL11, APEX, APEX2, and RBX1 were enriched in S-I; S-II combined multiple gene signatures including genes associated with metabolism and transcription; and the gene signature of S-III was extensively involved in pathways reflecting malignancies, including many core kinases and transcription factors involved in cancer such as CDK6, ERBB2, JAK1, DAPK1, FOXO1, and RXRA. The SVM model showed superior diagnostic performance with AUC values of 0.922 and 0.901, respectively. Furthermore, a new dataset of the independent cohort could be automatically analyzed by this innovative pipeline and yield similar results.

Conclusion

This study exploited an innovative approach to construct previously unexplored robust subtypes significantly related to different clinical and molecular features for OC and a diagnostic model using SVM to aid in clinical diagnosis and treatment. This investigation also illustrated the importance of targeting innate immune suppression together with DNA damage in OC, offering novel insights for further experimental exploration and clinical trial.

Mutations in the DNA polymerase binding pathway affect the immune microenvironment of patients with small-cell lung cancer and enhance the efficacy of platinum-based chemotherapy

Background

Small-cell lung cancer (SCLC) is characterized by its high malignancy and is associated with a poor prognosis. In the early stages of the disease, platinum-based chemotherapy is the recommended first-line treatment and has demonstrated efficacy. However, SCLC is prone to recurrence and is generally resistant to chemotherapy in its later stages.

Methods

Here, we collected samples from SCLC patients who received platinum-based chemotherapy, performed genomic and transcriptomic analyses, and validated our results with publicly available data.

Results

SCLC patients with DNA polymerase binding pathway mutations had an improved prognosis after platinum chemotherapy compared with patients without such mutations. Patients in the mutant (MT) group had higher infiltration of T cells, B cells, and M1 macrophages compared with patients without DNA polymerase binding pathway mutations.

Conclusions

DNA polymerase binding pathway mutations can be used as prognostic markers for platinum-based chemotherapy in SCLC.

Engineering strategies to optimise adoptive cell therapy in ovarian cancer

Ovarian cancer is amongst the ten most common cancer types in women, and it is one of the leading causes of death. Despite the promising results of targeted therapies, including anti-angiogenic agents and poly (ADP-ribose) polymerase inhibitors (PARPi), the majority of patients will relapse and develop treatment resistance, implying that novel therapeutic strategies are required. Adoptive cell therapy (ACT) refers to the process by which autologous immune cells are used to eliminate cancer. Examples include tumour infiltrating lymphocytes (TILs), T cells genetically engineered with T cell receptors (TCR), or chimeric antigen receptor (CAR)-T cells. Recently, ACT has revealed promising results in the treatment of haematological malignancies, however, its application to solid tumours is still limited due to lack of functionality and persistence of T cells, prevalence of an exhausted phenotype and impaired trafficking towards the tumour microenvironment (TME). In this review we explore the potential of ACT for the treatment of ovarian cancer and strategies to overcome its principal limitations.

The roles of FXYD family members in ovarian cancer: an integrated analysis by mining TCGA and GEO databases and functional validations

BACKGROUND

The FXYD family of ion transport regulators have emerged as important modulators of cancer progression and metastasis. However, their expression and roles in ovarian cancer (OCa) have not been systematically investigated.

METHODS

The expression of FXYD genes in OCa was analyzed using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), as well as independent clinical samples. The prognostic values of FXYD genes were evaluated by Kaplan-Meier and Cox regression analysis. To explore potential mechanisms, bioinformatics approaches including Gene Ontology, KEGG pathway analysis, GSEA and drug sensitivity correlation analysis were performed. OCa cell lines overexpressing FXYD1, FXYD5 or FXYD7 were also generated and their impacts on proliferation, migration and invasion were assessed.

RESULTS

FXYD1 and FXYD6 were significantly downregulated while FXYD3, FXYD4 and FXYD5 were upregulated in OCa tissues compared to normal tissues. FXYD1, FXYD5 and FXYD7 were independent adverse prognostic factors for OCa patients. Pathway and drug correlation analysis revealed that FXYD1, FXYD5 and FXYD7 genes regulated diverse oncogenic signaling cascades and modulated the response to various chemotherapeutic agents. Overexpression of FXYD1, FXYD5 or FXYD7 enhanced OCa cell motility and invasiveness in vitro.

CONCLUSION

Our results demonstrate aberrant expression patterns, prognostic values, and oncogenic activities of FXYD genes in OCa. FXYD1, FXYD5 and FXYD7 may serve as biomarkers and therapeutic targets for this disease. Targeting FXYD-mediated signaling represents a promising therapeutic strategy against OCa.

Chemokines and receptors in the development and progression of malignant tumors

Cancer cells, endothelial cells, inflammatory cells and various cytokines form a part of the tumor microenvironment (TME). Chemokines constitute the largest family of cytokines, and are mainly secreted by tumor cells and inflammatory cells in the TME. They play an important role in tumor development and progression by promoting tumor growth and metastasis, angiogenesis, and targeting the chemoattraction of inflammatory cells. Currently, some chemokine receptor antagonists are being used in clinical trials as targeted anti-tumor drugs. In this article, we review the roles of chemokines in the development and progression of malignant tumors based on recently published papers, taking into consideration of the new anti-tumor therapeutic strategies targeting chemokines and receptors.

The Value of CXCL1, CXCL2, CXCL3, and CXCL8 as Potential Prognosis Markers in Cervical Cancer: Evidence of E6/E7 from HPV16 and 18 in Chemokines Regulation

Cervical cancer (CC) is a serious global health issue, and it is well-known that HPV infection is the main etiological factor that triggers carcinogenesis. In cancer, chemokine ligands and receptors are involved in tumor cell growth, metastasis, leukocyte infiltration, and angiogenesis; however, information on the role played by E6/E7 of HPV16/18 in the modulation of chemokines is very limited. Therefore, this study aimed to determine whether chemokines are differentially expressed in CC-derived cell lines; if E6/E7 oncoproteins from HPV16 and 18 are capable of mediating chemokine expression, what is the expression profile of chemokines in tissues derived from CC and what is their impact on the overall survival of patients with this pathology? For this purpose, RNA sequencing and real-time PCR were performed on SiHa, HeLa, and C33A tumorigenic cell lines, on the non-tumorigenic HaCaT cells, and the E6/E7 HPV-transduced HaCaT cell models. Furthermore, chemokine expression and survival analysis were executed on 304 CC and 22 normal tissue samples from The Cancer Genome Atlas (TCGA) repository. The results demonstrate that CXCL1, CXCL2, CXCL3, and CXCL8 are regulated by E6/E7 of HPV16 and 18, are overexpressed in CC biopsies, and that their higher expression is related to a worse prognostic survival.

Catecholamines Promote Ovarian Cancer Progression through Secretion of CXC-Chemokines

Considerable evidence has accumulated in the last decade supporting the notion that chronic stress is closely related to the growth, metastasis, and angiogenesis of ovarian cancer. In this study, we analyzed the conditioned media in SKOV3 ovarian cancer cell lines treated with catecholamines to identify secreted proteins responding to chronic stress. Here, we observed that epinephrine and norepinephrine enhanced the secretion and mRNA expression of CXC-chemokines (CXCL1, 2, 3, and 8). Neutralizing antibodies to CXCL8 and CXCL8 receptor (CXCR2) inhibitors significantly reduced catecholamine-mediated invasion of SKOV3 cells. Finally, we found that the concentration of CXCL1 and CXCL8 in the plasma of ovarian cancer patients increased with stage progression. Taken together, these findings suggest that stress-related catecholamines may influence ovarian cancer progression through the secretion of CXC-chemokines.

Comprehensive analyses of fatty acid metabolism-related lncRNA for ovarian cancer patients

Ovarian cancer (OC) is a disease with difficult early diagnosis and treatment and poor prognosis. OC data profiles were downloaded from The Cancer Genome Atlas. Eight key fatty acid metabolism-related long non-coding RNAs (lncRNAs) were finally screened for building a risk scoring model by univariate/ multifactor and least absolute shrinkage and selection operator (LASSO) Cox regression. To make this risk scoring model more applicable to clinical work, we established a nomogram containing the clinical characteristics of OC patients after confirming that the model has good reliability and validity and the ability to distinguish patient prognosis. To further explore how these key lncRNAs are involved in OC progression, we explored their relationship with LUAD immune signatures and tumor drug resistance. The structure shows that the risk scoring model established based on these 8 fatty acid metabolism-related lncRNAs has good reliability and validity and can better predict the prognosis of patients with different risks of OC, and LINC00861in these key RNAs may be a hub gene that affects the progression of OC and closely related to the sensitivity of current OC chemotherapy drugs. In addition, combined with immune signature analysis, we found that patients in the high-risk group are in a state of immunosuppression, and Tfh cells may play an important role in it. We innovatively established a prognostic prediction model with excellent reliability and validity from the perspective of OC fatty acid metabolism reprogramming and lncRNA regulation and found new molecular/cellular targets for future OC treatment.

Single-cell transcriptome analysis uncovers underlying mechanisms of acute liver injury induced by tripterygium glycosides tablet in mice

Tripterygium glycosides tablet (TGT), the classical commercial drug of Tripterygium wilfordii Hook. F. has been effectively used in the treatment of rheumatoid arthritis, nephrotic syndrome, leprosy, Behcet's syndrome, leprosy reaction and autoimmune hepatitis. However, due to its narrow and limited treatment window, TGT-induced organ toxicity (among which liver injury accounts for about 40% of clinical reports) has gained increasing attention. The present study aimed to clarify the cellular and molecular events underlying TGT-induced acute liver injury using single-cell RNA sequencing (scRNA-seq) technology. The TGT-induced acute liver injury mouse model was constructed through short-term TGT exposure and further verified by hematoxylin-eosin staining and liver function-related serum indicators, including alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase and total bilirubin. Using the mouse model, we identified 15 specific subtypes of cells in the liver tissue, including endothelial cells, hepatocytes, cholangiocytes, and hepatic stellate cells. Further analysis indicated that TGT caused a significant inflammatory response in liver endothelial cells at different spatial locations; led to marked inflammatory response, apoptosis and fatty acid metabolism dysfunction in hepatocytes; activated hepatic stellate cells; brought about the activation, inflammation, and phagocytosis of liver capsular macrophages cells; resulted in immune dysfunction of liver lymphocytes; disturbed the intercellular crosstalk in liver microenvironment by regulating various signaling pathways. Thus, these findings elaborate the mechanism underlying TGT-induced acute liver injury, provide new insights into the safe and rational applications in the clinic, and complement the identification of new biomarkers and therapeutic targets for liver protection.

Neo-vascularization-based therapeutic perspectives in advanced ovarian cancer

The process of angiogenesis is well described for its potential role in the development of normal ovaries, and physiological functions as well as in the initiation, progression, and metastasis of ovarian cancer (OC). In advanced stages of OC, cancer cells spread outside the ovary to the pelvic, abdomen, lung, or multiple secondary sites. This seriously limits the efficacy of therapeutic options contributing to fatal clinical outcomes. Notably, a variety of angiogenic effectors are produced by the tumor cells to initiate angiogenic processes leading to the development of new blood vessels, which provide essential resources for tumor survival, dissemination, and dormant micro-metastasis of tumor cells. Multiple proangiogenic effectors and their signaling axis have been discovered and functionally characterized for potential clinical utility in OC. In this review, we have provided the current updates on classical and emerging proangiogenic effectors, their signaling axis, and the immune microenvironment contributing to the pathogenesis of OC. Moreover, we have comprehensively reviewed and discussed the significance of the preclinical strategies, drug repurposing, and clinical trials targeting the angiogenic processes that hold promising perspectives for the better management of patients with OC.

Upregulated X-C motif chemokine ligand 2 (XCL2) is associated with poor prognosis and increased immune infiltration in clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is one of the most popular malignant carcinomas in the genitourinary system. As a novel tumor-related gene, X-C Motif Chemokine Ligand 2 (XCL2) was up-regulated in ccRCC. The current study aims to reveal the functional activity of XCL2 in ccRCC.

METHODS

The transcriptome profiling, clinical parameters, and simple nucleotide variation profiles of ccRCC samples were obtained from the Cancer Genome Atlas (TCGA) database. The survival analysis, multivariate/univariate Cox analysis, correlation analysis, gene set enrichment analysis (GSEA), and tumor mutation burden (TMB) analysis were performed. Next, immune cell infiltration and immune functions were analyzed. Finally, the functions of XCL2 were investigated in Caki-1 and 786-O cells.

RESULTS

Upregulated XCL2 was associated with worse overall survival of ccRCC and correlated to age, grade, stage, and T stage. Age, grade, and XCL2 were independent prognostic factors. Significant enrichment in apoptosis, DNA replication, and immune response was demonstrated by GSEA. XCL2 was not only tightly associated with immune cell infiltration, but also significantly linked with several immune functions. Moreover, patients, who had higher XCL2 expression, owned higher levels of TMB. Interestingly, XCL2 was positively correlated with common immune checkpoints. In vitro, XCL2 could inhibit apoptosis, and promote proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) of Caki-1 and 786-O cells.

CONCLUSIONS

In general, the current study suggested that XCL2 may participate in the progression of ccRCC. Importantly, XCL2 may be a potential new target of immunotherapy.

lncRNA DARS-AS1 Modulates TSPAN1-Mediated ITGA2 Hypomethylation by Interaction with miR-194-5p Thus Promoting Ovarian Cancer Progression

Objective

Ovarian cancer (OC) is usually called the "silent killer" due to its asymptomatic characteristics until advanced stages, thus being a significant threat to female health worldwide. In this work, we characterized an oncogenic DARS-AS1 role in OC.

Methods

The aggressiveness behaviors of the OC cell model were examined by CCK-8 assay, transwell invasion assay, flow cytometry, and immunoblotting analysis of apoptosis-related proteins. Interactions of miR-194-5p with lncRNA DARS-AS1 or TSPAN1 and of TSPAN1 with ITGA2 were validated by using a luciferase activity assay and chromatin immunoprecipitation (ChIP) assay.

Results

The OC cell model exhibited overexpressed lncRNA DARS-AS1 compared to normal cells. lncRNA DARS-AS1 knockdown led to reduced OC cell growth and metastasis while inducing the apoptosis in the OC cell model. lncRNA DARS-AS1 positively regulated TSPAN1 expression by binding with miR-194-5p and TSPAN1-mediated ITGA2 hypomethylation in OC cells. Further rescue function studies demonstrated that lncRNA DARS-AS1 affected OC cell viability, migration, invasion, and apoptosis ability by modulating miR-194-5p and TSPAN1 expressions.

Conclusion

Our work demonstrates that lncRNA DARS-AS1 promotes OC progression by modulating TSPAN1 and ITGA2 hypomethylation by binding with miR-194-5p.

Identification of a Prognostic Model Based on Immune Cell Signatures in Clear Cell Renal Cell Carcinoma

Background. Accumulating evidence substantiated that the immune cells were intricately intertwined with the prognosis and therapy of clear cell renal cell carcinoma (ccRCC). We aimed to construct an immune cell signatures (ICS) score model to predict the prognosis of ccRCC patients and furnish guidance for finding appropriate treatment strategies. Methods. Based on The Cancer Genome Atlas (TCGA) database, the normalized enrichment score (NES) of 184 ICSf was calculated using single-sample gene set enrichment analysis (ssGSEA). An ICS score model was generated in light of univariate Cox regression and Least absolute shrinkage and selection operator (Lasso)-Cox regression, which was independently validated in ArrayExpress database. In addition, we appraised the predictive power of the model via Kaplan-Meier (K-M) curves and time-dependent receiver operating characteristic (ROC) curves. Eventually, immune infiltration, genomic alterations and immunotherapy were analyzed between high and low ICS score groups. Results. Initially, we screened 11 ICS with prognostic impact based on 515 ccRCC patients. K-M curves presented that the high ICS score group experienced a poorer prognosis ( $P<0.001$ ). In parallel, ROC curves revealed a satisfactory reliability of model to predict individual survival at 1, 3, and 5 years, with area under the curves (AUCs) of 0.744, 0.713, and 0.742, respectively. In addition, we revealed that the high ICS score group was characterized by increased infiltration of immune cells, strengthened BAP1 mutation frequency, and enhanced expression of immune checkpoint genes. Conclusion. The ICS score model has higher predictive power for patients’ prognosis and can instruct ccRCC patients in seeking suitable treatment.