Integrated clinical characteristics and omics analysis identifies a ferroptosis and iron-metabolism-related lncRNA signature for predicting prognosis and therapeutic responses in ovarian cancer

Prognostic and immunological implications of paraptosis-related genes in lung adenocarcinoma: Comprehensive analysis and functional verification of hub gene

BACKGROUND

Lung adenocarcinoma (LUAD) poses significant clinical challenges due to its inherent heterogeneity and variable response to treatment. Recent research has specifically focused on elucidating the role of Paraptosis-related genes (PRGs) in the progression of cancer and the prognosis of patients.

METHODS

We conducted a comprehensive analysis of the differential expression of PRGs in LUAD. Additionally, univariate Cox regression analysis was utilized to determine the prognostic significance of these genes. Furthermore, consensus clustering was employed to differentiate molecular subtypes within LUAD, while immune heterogeneity was assessed. To evaluate treatment outcomes, the expression of immune checkpoint inhibitors was examined, and the sensitivity of LUAD patients to chemotherapy drugs was assessed. Moreover, machine learning algorithms were employed to construct a Paraptosis-related risk score with prognostic and immunological indicators. Finally, to validate the findings, in vitro experiments were performed to verify the regulatory effect of key PRGs on Paraptosis.

RESULTS

Our analysis identified 24 PRGs that exhibited differential expression, with CDKN3, TP53, and PHB emerging as the most prominently upregulated genes in tumor tissues. Among these genes, seven were identified as prognostic markers, with HSPB8 being the sole protective factor. Notably, our analysis also revealed the existence of two distinct molecular subtypes within LUAD, each characterized by unique prognoses and immune responses. Specifically, Subtype B displayed a poorer prognosis but demonstrated increased sensitivity to both chemotherapy and immunotherapy. In addition, our development of a Paraptosis-Associated Risk Score yielded a significant prognostic value in predicting patient outcomes. Furthermore, we found regulatory effect of CDKN3 on Paraptosis in two cell lines.

CONCLUSIONS

Our study highlights the importance of PRGs in LUAD, particularly in prognosis and treatment response. The identified molecular subtypes and Paraptosis-Associated Risk Score offer valuable insights for personalized treatment strategies.

RETRACTED: Refining molecular subtypes and risk stratification of ovarian cancer through multi-omics consensus portfolio and machine learning

Ovarian cancer (OC), known for its pronounced heterogeneity, has long evaded a unified classification system despite extensive research efforts. This study integrated five distinct multi-omics datasets from eight multicentric cohorts, applying a combination of ten clustering algorithms and ninety-nine machine learning models. This methodology has enabled us to refine the molecular subtyping of OC, leading to the development of a novel Consensus Machine Learning-driven Signature (CMLS). Our analysis delineated two prognostically significant cancer subtypes (CS), each marked by unique genetic and immunological signatures. Notably, CS1 is associated with an adverse prognosis. Leveraging a subtype classifier, we identified five key genes (CTHRC1, SPEF1, SCGB3A1, FOXJ1, and C1orf194) instrumental in constructing the CMLS. Patients classified within the high CMLS group exhibited a poorer prognosis and were characterized by a "cold tumor" phenotype, indicative of an immunosuppressive microenvironment rich in MDSCs, CAFs, and Tregs. Intriguingly, this group also presented higher levels of tumor mutation burden (TMB) and tumor neoantigen burden (TNB), factors that correlated with a more favorable response to immunotherapy compared to their low CMLS counterparts. In contrast, the low CMLS group, despite also displaying a "cold tumor" phenotype, showed a favorable prognosis and a heightened responsiveness to chemotherapy. This study's findings underscore the potential of targeting immune-suppressive cells, particularly in patients with high CMLS, as a strategic approach to enhance OC prognosis. Furthermore, the redefined molecular subtypes and risk stratification, achieved through sophisticated multi-omics analysis, provide a framework for the selection of therapeutic agents.

Multi‑omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machine learning study

BACKGROUND

Predictive, preventive, and personalized medicine (PPPM/3PM) is a strategy aimed at improving the prognosis of cancer, and programmed cell death (PCD) is increasingly recognized as a potential target in cancer therapy and prognosis. However, a PCD-based predictive model for serous ovarian carcinoma (SOC) is lacking. In the present study, we aimed to establish a cell death index (CDI)-based model using PCD-related genes.

METHODS

We included 1254 genes from 12 PCD patterns in our analysis. Differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were screened. Subsequently, 14 PCD-related genes were included in the PCD-gene-based CDI model. Genomics, single-cell transcriptomes, bulk transcriptomes, spatial transcriptomes, and clinical information from TCGA-OV, GSE26193, GSE63885, and GSE140082 were collected and analyzed to verify the prediction model.

RESULTS

The CDI was recognized as an independent prognostic risk factor for patients with SOC. Patients with SOC and a high CDI had lower survival rates and poorer prognoses than those with a low CDI. Specific clinical parameters and the CDI were combined to establish a nomogram that accurately assessed patient survival. We used the PCD-genes model to observe differences between high and low CDI groups. The results showed that patients with SOC and a high CDI showed immunosuppression and hardly benefited from immunotherapy; therefore, trametinib_1372 and BMS-754807 may be potential therapeutic agents for these patients.

CONCLUSIONS

The CDI-based model, which was established using 14 PCD-related genes, accurately predicted the tumor microenvironment, immunotherapy response, and drug sensitivity of patients with SOC. Thus this model may help improve the diagnostic and therapeutic efficacy of PPPM.

Causal relationship between iron status and preeclampsia-eclampsia: a Mendelian randomization analysis

BACKGROUND

Preeclampsia/eclampsia is a severe pregnancy-related disorder associated with hypertension and organ damage. While observational studies have suggested a link between maternal iron status and preeclampsia/eclampsia, the causal relationship remains unclear. The aim of this study was to investigate the genetic causality between iron status and preeclampsia/eclampsia using large-scale genome-wide association study (GWAS) summary data and Mendelian randomization (MR) analysis.

METHODS

Summary data for the GWAS on preeclampsia/eclampsia and genetic markers related to iron status were obtained from the FinnGen Consortium and the IEU genetic databases. The "TwoSampleMR" software package in R was employed to test the genetic causality between these markers and preeclampsia/eclampsia. The inverse variance weighted (IVW) method was primarily used for MR analysis. Heterogeneity, horizontal pleiotropy, and potential outliers were evaluated for the MR analysis results.

RESULTS

The random-effects IVW results showed that ferritin (OR = 1.11, 95% CI: .89-1.38, p = .341), serum iron (OR = .90, 95% CI: .75-1.09, p = .275), TIBC (OR = .98, 95% CI: .89-1.07, p = .613), and TSAT (OR = .94, 95% CI: .83-1.07, p = .354) have no genetic causal relationship with preeclampsia/eclampsia. There was no evidence of heterogeneity, horizontal pleiotropy, or possible outliers in our MR analysis (p > .05).

CONCLUSIONS

Our study did not detect a genetic causal relationship between iron status and preeclampsia/eclampsia. Nonetheless, this does not rule out a relationship between the two at other mechanistic levels.

Ferroptosis contributes to the progression of female-specific neoplasms, from breast cancer to gynecological malignancies in a manner regulated by non-coding RNAs: Mechanistic implications

Ferroptosis, a recently identified type of non-apoptotic cell death, triggers the elimination of cells in the presence of lipid peroxidation and in an iron-dependent manner. Indeed, ferroptosis-stimulating factors have the ability of suppressing antioxidant capacity, leading to the accumulation of reactive oxygen species (ROS) and the subsequent oxidative death of the cells. Ferroptosis is involved in the pathophysiological basis of different maladies, such as multiple cancers, among which female-oriented malignancies have attracted much attention in recent years. In this context, it has also been unveiled that non-coding RNA transcripts, including microRNAs, long non-coding RNAs, and circular RNAs have regulatory interconnections with the ferroptotic flux, which controls the pathogenic development of diseases. Furthermore, the potential of employing these RNA transcripts as therapeutic targets during the onset of female-specific neoplasms to modulate ferroptosis has become a research hotspot; however, the molecular mechanisms and functional alterations of ferroptosis still require further investigation. The current review comprehensively highlights ferroptosis and its association with non-coding RNAs with a focus on how this crosstalk affects the pathogenesis of female-oriented malignancies, from breast cancer to ovarian, cervical, and endometrial neoplasms, suggesting novel therapeutic targets to decelerate and even block the expansion and development of these tumors.

Orientin promotes diabetic wounds healing by suppressing ferroptosis via activation of the Nrf2/GPX4 pathway

Diabetic patients often experience delayed wound healing due to impaired functioning of human umbilical vein endothelial cells (HUVECs) under high glucose (HG) conditions. This is because HG conditions trigger uncontrolled lipid peroxidation, leading to iron-dependent ferroptosis, which is caused by glucolipotoxicity. However, natural flavonoid compound Orientin (Ori) possesses anti-inflammatory bioactive properties and is a promising treatment for a range of diseases. The current study aimed to investigate the function and mechanism of Ori in HG-mediated ferroptosis. A diabetic wound model was established in mice by intraperitoneal injection of streptozotocin (STZ), and HUVECs were cultured under HG to create an in vitro diabetic environment. The results demonstrated that Ori inhibited HG-mediated ferroptosis, reducing levels of malondialdehyde (MDA), lipid peroxidation, and mitochondrial reactive oxygen species (mtROS), while increasing decreased levels of malondialdehyde, lipid peroxidation, and mitochondrial reactive oxygen species, as well as increased levels of glutathione (GSH). Ori treatment also improved the wound expression of glutathione peroxidase 4 (GPX4) and angiogenesis markers, reversing the delayed wound healing caused by diabetes mellitus (DM). Additional investigations into the mechanism revealed that Ori may stimulate the nuclear factor-erythroid 2-related factor 2 (Nrf2)/GPX4 signaling pathway. Silencing Nrf2 in HG-cultured HUVECs negated the beneficial impact mediated by Ori. By stimulating the Nrf2/GPX4 signaling pathway, Ori may expedite diabetic wound healing by decreasing ferroptosis.

Machine learning-based cell death marker for predicting prognosis and identifying tumor immune microenvironment in prostate cancer

Background

Prostate cancer (PCa) incidence and mortality rates are rising, necessitating precise prognostic tools to guide personalized treatment. Dysregulation of programmed cell death pathways in tumor suppression and cancer development has garnered increasing attention, providing a new research direction for identifying biomarkers and potential therapeutic targets.

Methods

Integrating multiple database resources, we constructed and optimized a prognostic signature based on the expression of programmed cell death-related genes (PCDRG) using ten machine learning algorithms. Model performance and prognostic effects were further evaluated. We analyzed the relationships between signature and clinicopathological features, somatic mutations, drug sensitivity, and the tumor immune microenvironment, and constructed a nomogram. The expression level of PCDRGs were evaluated and compared.

Results

Of 1560 PCDRGs, 149 were differentially expressed in PCa, with 34 associated with biochemical recurrence. The PCDRG-derived index (PCDI), constructed using the random forest algorithm, exhibited optimal prognostic performance, successfully stratifying PCa patients into two groups with significant prognostic differences. Patients with high PCDI scores exhibited poorer survival and lower immunotherapy benefit. PCDI was closely associated with the infiltration of specific immune cells, particularly positive correlations with macrophages and T helper cells, and negative correlations with neutrophils, suggesting that PCDI may influence the tumor immune microenvironment, thereby affecting patient prognosis and treatment response. PCDI was associated with age, pathological stage, somatic mutations, and drug sensitivity. The PCDI-based nomogram demonstrated excellent performance in predicting biochemical recurrence in PCa patients. Finally, the differential expression of these PCDRGs was verified based on cell lines and PCa patient expression profile data.

Conclusion

This study developed an effective prognostic indicator for prostate cancer, PCDI, using machine learning approaches. PCDI reflects the link between aberrant programmed cell death pathways and disease progression and treatment response.

Senescence-Related LncRNAs: Pioneering Indicators for Ovarian Cancer Outcomes

In gynecological oncology, ovarian cancer (OC) remains the most lethal, highlighting its significance in public health. Our research focused on the role of long non-coding RNA (lncRNA) in OC, particularly senescence-related lncRNAs (SnRlncRNAs), crucial for OC prognosis. Utilizing data from the genotype-tissue expression (GTEx) and cancer genome Atlas (TCGA), SnRlncRNAs were discerned and subsequently, a risk signature was sculpted using co-expression and differential expression analyses, Cox regression, and least absolute shrinkage and selection operator (LASSO). This signature's robustness was validated through time-dependent receiver operating characteristics (ROC), and multivariate Cox regression, with further validation in the international cancer genome consortium (ICGC). Gene set enrichment analyses (GSEA) unveiled pathways intertwined with risk groups. The ROC, alongside the nomogram and calibration outcomes, attested to the model's robust predictive accuracy. Of particular significance, our model has demonstrated superiority over several commonly utilized clinical indicators, such as stage and grade. Patients in the low-risk group demonstrated greater immune infiltration and varied drug sensitivities compared to other groups. Moreover, consensus clustering classified OC patients into four distinct groups based on the expression of 17 SnRlncRNAs, showing diverse survival rates. In conclusion, these findings underscored the robustness and reliability of our model and highlighted its potential for facilitating improved decision-making in the context of risk assessment, and demonstrated that these markers potentially served as robust, efficacious biomarkers and prognostic tools, offering insights into predicting OC response to anticancer therapeutics.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-024-00163-z.

Redox-Regulated Iron Metabolism and Ferroptosis in Ovarian Cancer: Molecular Insights and Therapeutic Opportunities

Ovarian cancer (OC), known for its lethality and resistance to chemotherapy, is closely associated with iron metabolism and ferroptosis-an iron-dependent cell death process, distinct from both autophagy and apoptosis. Emerging evidence suggests that dysregulation of iron metabolism could play a crucial role in OC by inducing an imbalance in the redox system, which leads to ferroptosis, offering a novel therapeutic approach. This review examines how disruptions in iron metabolism, which affect redox balance, impact OC progression, focusing on its essential cellular functions and potential as a therapeutic target. It highlights the molecular interplay, including the role of non-coding RNAs (ncRNAs), between iron metabolism and ferroptosis, and explores their interactions with key immune cells such as macrophages and T cells, as well as inflammation within the tumor microenvironment. The review also discusses how glycolysis-related iron metabolism influences ferroptosis via reactive oxygen species. Targeting these pathways, especially through agents that modulate iron metabolism and ferroptosis, presents promising therapeutic prospects. The review emphasizes the need for deeper insights into iron metabolism and ferroptosis within the redox-regulated system to enhance OC therapy and advocates for continued research into these mechanisms as potential strategies to combat OC.

Portulaca oleracea L. polysaccharide inhibits ovarian cancer via inducing ACSL4-dependent ferroptosis

The antitumor effect of Portulaca oleracea L. polysaccharide (POL) has been demonstrated, but whether it curbs the development of ovarian cancer has not been reported. Here, we treated ovarian cancer cells with different concentrations of POL, detected cell activity by CCK-8 assay, and apoptosis rate by flow cytometry. The results showed that SKOV3 and Hey cell survival decreased with increasing POL concentration in a dose-dependent manner. POL significantly inhibited ovarian cancer cell migration and increased cell death compared with the control group. Ferroptosis inhibitors, but not apoptosis, necrosis, and autophagy inhibitors, reversed POL-induced cell death. Further studies revealed that POL promoted the accumulation of lipid reactive oxygen species (ROS), Fe2+, malondialdehyde (MDA), and decreased glutathione (GSH) production. Moreover, POL significantly increased the mortality of ovarian cancer cells. In vivo studies confirmed that POL reduced the volume and weight of tumors and increased the levels of Fe2+ and MDA in mice in vivo. Western blot assay revealed that POL increased the expression of ACSL4 in ovarian cancer cells as well as in tumors in mice in vivo. More importantly, the POL-mediated increase in lipid ROS, Fe2+, MDA, and decrease in GSH were significantly reversed after knocking down ACSL4 in ovarian cancer cells. Thus, POL can effectively inhibit ovarian cancer development, which may be achieved by increasing ACSL4-mediated ferroptosis. These results suggest that POL has the potential to be a potential drug for targeted treatment of ovarian cancer.

An integrated machine learning-based model for joint diagnosis of ovarian cancer with multiple test indicators

OBJECTIVE

To construct a machine learning diagnostic model integrating feature dimensionality reduction techniques and artificial neural network classifiers to develop the value of clinical routine blood indexes for the auxiliary diagnosis of ovarian cancer.

METHODS

Patients with ovarian cancer clearly diagnosed in our hospital were collected as a case group (n = 185), and three groups of patients with other malignant otolaryngology tumors (n = 138), patients with benign otolaryngology diseases (n = 339) and those with normal physical examination (n = 92) were used as an overall control group. In this paper, a fully automated segmentation network for magnetic resonance images of ovarian cancer is proposed to improve the reproducibility of tumor segmentation results while effectively reducing the burden on radiologists. A pre-trained Res Net50 is used to the three edge output modules are fused to obtain the final segmentation results. The segmentation results of the proposed network architecture are compared with the segmentation results of the U-net based network architecture and the effect of different loss functions and region of interest sizes on the segmentation performance of the proposed network is analyzed.

RESULTS

The average Dice similarity coefficient, average sensitivity, average specificity (specificity) and average hausdorff distance of the proposed network segmentation results reached 83.62%, 89.11%, 96.37% and 8.50, respectively, which were better than the U-net based segmentation method. For ROIs containing tumor tissue, the smaller the size, the better the segmentation effect. Several loss functions do not differ much. The area under the ROC curve of the machine learning diagnostic model reached 0.948, with a sensitivity of 91.9% and a specificity of 86.9%, and its diagnostic efficacy was significantly better than that of the traditional way of detecting CA125 alone. The model was able to accurately diagnose ovarian cancer of different disease stages and showed certain discriminative ability for ovarian cancer in all three control subgroups.

CONCLUSION

Using machine learning to integrate multiple conventional test indicators can effectively improve the diagnostic efficacy of ovarian cancer, which provides a new idea for the intelligent auxiliary diagnosis of ovarian cancer.

Integrative analysis from multi-center studies identifies a weighted gene co-expression network analysis-based Tregs signature in ovarian cancer

Ovarian cancer (OC) is a malignancy associated with poor prognosis and has been linked to regulatory T cells (Tregs) in the immune microenvironment. Nevertheless, the association between Tregs-related genes (TRGs) and OC prognosis remains incompletely understood. The xCell algorithm was used to analyze Tregs scores across multiple cohorts. Weighted gene co-expression network analysis (WGCNA) was utilized to identify potential TRGs and molecular subtypes. Furthermore, we used nine machine learning algorithms to create risk models with prognostic indicators for patients. Reverse transcription-quantitative polymerase chain reaction and immunofluorescence staining were used to demonstrate the immunosuppressive ability of Tregs and the expression of key TRGs in clinical samples. Our study found that higher Tregs scores were significantly correlated with poorer overall survival. Recurrent patients exhibited increased Tregs infiltration and reduced CD8+ T cell. Moreover, molecular subtyping using seven key TRGs revealed that subtype B exhibited higher enrichment of multiple oncogenic pathways and had a worse prognosis. Notably, subtype B exhibited high Tregs levels, suggesting immune suppression. In addition, we validated machine learning-derived prognostic models across multiple platform cohorts to better distinguish patient survival and predict immunotherapy efficacy. Finally, the differential expression of key TRGs was validated using clinical samples. Our study provides novel insights into the role of Tregs in the immune microenvironment of OC. We identified potential therapeutic targets derived from Tregs (CD24, FHL2, GPM6A, HOXD8, NAP1L5, REN, and TOX3) for personalized treatment and created a machining learning-based prognostic model for OC patients, which could be useful in clinical practice.

TGF-β Score based on Silico Analysis can Robustly Predict Prognosis and Immunological Characteristics in Lower-grade Glioma: The Evidence from Multicenter Studies

INTRODUCTION

Nowadays, mounting evidence shows that variations in TGF-β signaling pathway-related components influence tumor development. Current research has patents describing the use of anti-TGF-β antibodies and checkpoint inhibitors for the treatment of proliferative diseases. Importantly, TGF-β signaling pathway is significant for lower-grade glioma (LGG) to evade host immunity. Loss of particular tumor antigens and shutdown of professional antigenpresenting cell activity may render the anti-tumor response ineffective in LGG patients. However, the prognostic significance of TGF-β related genes in LGG is still unknown.

METHODS

We collected RNA-seq data from the GTEx database (normal cortical tissues), the Cancer Genome Atlas database (TCGA-LGG), and the Chinese Glioma Genome Atlas database (CGGA-693 and CGGA-325) for conducting our investigation.

RESULTS

In addition, previous publications were explored for the 223 regulators of the TGF-β signaling pathway, and 30 regulators with abnormal expression in TCGA and GTEx database were identified. In order to identify hub prognostic regulators, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were used to screen from differentially expressed genes (DEGs). On the basis of 11 genes from LASSO-Cox regression analysis (NEDD8, CHRD, TGFBR1, TP53, BMP2, LRRC32, THBS2, ID1, NOG, TNF, and SERPINE1), TGF-β score was calculated. Multiple statistical approaches verified the predictive value of the TGF-β score for the training cohort and two external validation cohorts. Considering the importance of the TGF-β signaling pathway in immune regulation, we evaluated the prediction of the TGF-β score for immunological characteristics and the possible application of the immunotherapeutic response using six algorithms (TIMER, CIBERSORT, QUANTISEQ, MCP-counter, XCELL and EPIC) and three immunotherapy cohorts (GSE78820, Imvigor-210 and PRJEB23709). Notably, we compared our risk signature with the signature in ten publications in the meta-cohort (TCGA-LGG, CGGA-693 and CGGA-325), and the TGF-β score had the best predictive efficiency (C-index =0.812).

CONCLUSION

In conclusion, our findings suggest that TGF-β signaling pathway-related signatures are prognostic biomarkers in LGG and provide a novel tool for tumor microenvironment (TME) assessment.

Personalization of Therapy in High-Grade Serous Tubo-Ovarian Cancer-The Possibility or the Necessity?

High-grade serous tubo-ovarian cancer (HGSTOC) is the most lethal tumor of the female genital tract. The foregoing therapy consists of cytoreduction followed by standard platinum/taxane chemotherapy; alternatively, for primary unresectable tumors, neo-adjuvant platinum/taxane chemotherapy followed by delayed interval cytoreduction. In patients with suboptimal surgery or advanced disease, different forms of targeted therapy have been accepted or tested in clinical trials. Studies on HGSTOC discovered its genetic and proteomic heterogeneity, epigenetic regulation, and the role of the tumor microenvironment. These findings turned attention to the fact that there are several distinct primary tumor subtypes of HGSTOC and the unique biology of primary, metastatic, and recurrent tumors may result in a differential drug response. This results in both chemo-refractoriness of some primary tumors and, what is significantly more frequent and destructive, secondary chemo-resistance of metastatic and recurrent HGSTOC tumors. Treatment possibilities for platinum-resistant disease include several chemotherapeutics with moderate activity and different targeted drugs with difficult tolerable effects. Therefore, the question appears as to why different subtypes of ovarian cancer are predominantly treated based on the same therapeutic schemes and not in an individualized way, adjusted to the biology of a specific tumor subtype and temporal moment of the disease. The paper reviews the genomic, mutational, and epigenetic signatures of HGSTOC subtypes and the tumor microenvironment. The clinical trials on personalized therapy and the overall results of a new, comprehensive approach to personalized therapy for ovarian cancer have been presented and discussed.

Ferroptosis in epithelial ovarian cancer: a burgeoning target with extraordinary therapeutic potential

Epithelial ovarian cancer (EOC) is universally acknowledged as a terrifying women killer for its high mortality. Recent research advances support that ferroptosis, an emerging iron-dependent type of regulated cell death (RCD) triggered by the excessive accumulation of lipid peroxides probably possesses extraordinary therapeutic potential in EOC therapy. Herein, we firstly provide a very concise introduction of ferroptosis. Special emphasis will be put on the ferroptosis's vital role in EOC, primarily covering its role in tumorigenesis and progression of EOC, the capability of reversing chemotherapy resistance, and the research and development of related therapeutic strategies. Furthermore, the construction of ferroptosis-related prognostic prediction systems, and mechanisms of ferroptosis resistance in EOC are also discussed. Finally, we propose and highlight several important yet unanswered problems and some future research directions in this field.

The recent advancements of ferroptosis in the diagnosis, treatment and prognosis of ovarian cancer

Ovarian cancer affects the female reproductive system and is the primary cause of cancer related mortality globally. The imprecise and non-specific nature of ovarian cancer symptoms often results in patients being diagnosed at an advanced stage, with metastatic lesions extending beyond the ovary. This presents a significant clinical challenge and imposes a substantial economic burden on both patients and society. Despite advancements in surgery, chemotherapy, and immunotherapy, the prognosis for most patients with ovarian cancer remains unsatisfactory. Therefore, the development of novel treatment strategies is imperative. Ferroptosis, a distinct form of regulated cell death, characterized by iron-dependent lipid peroxidation, differs from autophagy, apoptosis, and necrosis, and may hold promise as a novel cell death. Numerous studies have demonstrated the involvement of ferroptosis in various conventional signaling pathways and biological processes. Recent investigations have revealed the significant contribution of ferroptosis in the initiation, progression, and metastasis of diverse malignant tumors, including ovarian cancer. Moreover, ferroptosis exhibits a synergistic effect with chemotherapy, radiotherapy, and immunotherapy in restraining the proliferation of ovarian cancer cells. The aforementioned implies that ferroptosis holds considerable importance in the management of ovarian cancer and has the potential to serve as a novel therapeutic target. The present review provides a comprehensive overview of the salient features of ferroptosis, encompassing its underlying mechanisms and functional role in ovarian cancer, along with the associated signaling pathways and genes. Furthermore, the review highlights the prospective utility of ferroptosis in the treatment of ovarian cancer.

Integrated machine learning survival framework develops a prognostic model based on inter-crosstalk definition of mitochondrial function and cell death patterns in a large multicenter cohort for lower-grade glioma

BACKGROUND

Lower-grade glioma (LGG) is a highly heterogeneous disease that presents challenges in accurately predicting patient prognosis. Mitochondria play a central role in the energy metabolism of eukaryotic cells and can influence cell death mechanisms, which are critical in tumorigenesis and progression. However, the prognostic significance of the interplay between mitochondrial function and cell death in LGG requires further investigation.

METHODS

We employed a robust computational framework to investigate the relationship between mitochondrial function and 18 cell death patterns in a cohort of 1467 LGG patients from six multicenter cohorts worldwide. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations. Ultimately, we devised the mitochondria-associated programmed cell death index (mtPCDI) using machine learning models that exhibited optimal performance.

RESULTS

The mtPCDI, generated by combining 18 highly influential genes, demonstrated strong predictive performance for prognosis in LGG patients. Biologically, mtPCDI exhibited a significant correlation with immune and metabolic signatures. The high mtPCDI group exhibited enriched metabolic pathways and a heightened immune activity profile. Of particular importance, our mtPCDI maintains its status as the most potent prognostic indicator even following adjustment for potential confounding factors, surpassing established clinical models in predictive strength.

CONCLUSION

Our utilization of a robust machine learning framework highlights the significant potential of mtPCDI in providing personalized risk assessment and tailored recommendations for metabolic and immunotherapy interventions for individuals diagnosed with LGG. Of particular significance, the signature features highly influential genes that present further prospects for future investigations into the role of PCD within mitochondrial function.

Identification of lncRNAs Deregulated in Epithelial Ovarian Cancer Based on a Gene Expression Profiling Meta-Analysis

Epithelial ovarian cancer (EOC) is one of the deadliest gynecological cancers worldwide, mainly because of its initially asymptomatic nature and consequently late diagnosis. Long non-coding RNAs (lncRNA) are non-coding transcripts of more than 200 nucleotides, whose deregulation is involved in pathologies such as EOC, and are therefore envisaged as future biomarkers. We present a meta-analysis of available gene expression profiling (microarray and RNA sequencing) studies from EOC patients to identify lncRNA genes with diagnostic and prognostic value. In this meta-analysis, we include 46 independent cohorts, along with available expression profiling data from EOC cell lines. Differential expression analyses were conducted to identify those lncRNAs that are deregulated in (i) EOC versus healthy ovary tissue, (ii) unfavorable versus more favorable prognosis, (iii) metastatic versus primary tumors, (iv) chemoresistant versus chemosensitive EOC, and (v) correlation to specific histological subtypes of EOC. From the results of this meta-analysis, we established a panel of lncRNAs that are highly correlated with EOC. The panel includes several lncRNAs that are already known and even functionally characterized in EOC, but also lncRNAs that have not been previously correlated with this cancer, and which are discussed in relation to their putative role in EOC and their potential use as clinically relevant tools.

Development of a machine learning-based signature utilizing inflammatory response genes for predicting prognosis and immune microenvironment in ovarian cancer

Ovarian cancer (OC) represents a significant health challenge, characterized by a particularly unfavorable prognosis for affected women. Accumulating evidence supports the notion that inflammation-related factors impacting the normal ovarian epithelium may contribute to the development of OC. However, the precise role of inflammatory response-related genes (IRRGs) in OC remains largely unknown. To address this gap, we performed an integration of mRNA expression profiles from 7 cohorts and conducted univariate Cox regression analysis to screen 26 IRRGs. By utilizing these IRRGs, we categorized patients into subtypes exhibiting diverse inflammatory responses, with subtype B displaying the most prominent immune infiltration. Notably, the elevated abundance of Treg cells within subtype B contributed to immune suppression, resulting in an unfavorable prognosis for these patients. Furthermore, we validated the distribution ratios of stromal cells, inflammatory cells, and tumor cells using whole-slide digitized histological slides. We also elucidated differences in the activation of biological pathways among subtypes. In addition, machine learning algorithms were employed to predict the likelihood of survival in OC patients based on the expression of prognostic IRRGs. Through rigorous testing of over 100 combinations, we identified CXCL10 as a crucial IRRG. Single-cell analysis and vitro experiments further confirmed the potential secretion of CXCL10 by macrophages and its involvement in lymphangiogenesis within the tumor microenvironment. Overall, the study provides new insights into the role of IRRGs in OC and may have important implications for the development of novel therapeutic approaches.

Machine learning-based mRNA signature in early acute myocardial infarction patients: the perspective toward immunological, predictive, and personalized

Patients diagnosed with stable coronary artery disease (CAD) are at continued risk of experiencing acute myocardial infarction (AMI). This study aims to unravel the pivotal biomarkers and dynamic immune cell changes, from an immunological, predictive, and personalized viewpoint, by implementing a machine-learning approach and a composite bioinformatics strategy. Peripheral blood mRNA data from different datasets were analyzed, and CIBERSORT was used for deconvoluting human immune cell subtype expression matrices. Weighted gene co-expression network analysis (WGCNA) in single-cell and bulk transcriptome levels was conducted to explore possible biomarkers for AMI, with a particular emphasis on examining monocytes and their involvement in cell-cell communication. Unsupervised cluster analysis was performed to categorize AMI patients into different subtypes, and machine learning methods were employed to construct a comprehensive diagnostic model to predict the occurrence of early AMI. Finally, RT-qPCR on peripheral blood samples collected from patients validated the clinical utility of the machine learning-based mRNA signature and hub biomarkers. The study identified potential biomarkers for early AMI, including CLEC2D, TCN2, and CCR1, and found that monocytes may play a vital role in AMI samples. Differential analysis revealed that CCR1 and TCN2 exhibited elevated expression levels in early AMI compared to stable CAD. Machine learning methods showed that the glmBoost+Enet [alpha=0.9] model achieved high predictive accuracy in the training set, external validation sets, and clinical samples in our hospital. The study provided comprehensive insights into potential biomarkers and immune cell populations involved in the pathogenesis of early AMI. The identified biomarkers and the constructed comprehensive diagnostic model hold great promise for predicting the occurrence of early AMI and can serve as auxiliary diagnostic or predictive biomarkers.

Comprehensive analysis and molecular map of Hippo signaling pathway in lower grade glioma: the perspective toward immune microenvironment and prognosis

Background

The activation of YAP/TAZ transcriptional co-activators, downstream effectors of the Hippo/YAP pathway, is commonly observed in human cancers, promoting tumor growth and invasion. The aim of this study was to use machine learning models and molecular map based on the Hippo/YAP pathway to explore the prognosis, immune microenvironment and therapeutic regimen of patients with lower grade glioma (LGG).

Methods

SW1783 and SW1088 cell lines were used as in vitro models for LGG, and the cell viability of the XMU-MP-1 (a small molecule inhibitor of the Hippo signaling pathway) treated group was evaluated using a Cell Counting Kit-8 (CCK-8). Univariate Cox analysis on 19 Hippo/YAP pathway related genes (HPRGs) was performed to identify 16 HPRGs that exhibited significant prognostic value in meta cohort. Consensus clustering algorithm was used to classify the meta cohort into three molecular subtypes associated with Hippo/YAP Pathway activation profiles. The Hippo/YAP pathway's potential for guiding therapeutic interventions was also investigated by evaluating the efficacy of small molecule inhibitors. Finally, a composite machine learning models was used to predict individual patients' survival risk profiles and the Hippo/YAP pathway status.

Results

The findings showed that XMU-MP-1 significantly enhanced the proliferation of LGG cells. Different Hippo/YAP Pathway activation profiles were associated with different prognostic and clinical features. The immune scores of subtype B were dominated by MDSC and Treg cells, which are known to have immunosuppressive effects. Gene Set Variation Analysis (GSVA) indicated that subtypes B with a poor prognosis exhibited decreased propanoate metabolic activity and suppressed Hippo pathway signaling. Subtype B had the lowest IC50 value, indicating sensitivity to drugs that target the Hippo/YAP pathway. Finally, the random forest tree model predicted the Hippo/YAP pathway status in patients with different survival risk profiles.

Conclusions

This study demonstrates the significance of the Hippo/YAP pathway in predicting the prognosis of patients with LGG. The different Hippo/YAP Pathway activation profiles associated with different prognostic and clinical features suggest the potential for personalized treatments.

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.

Long noncoding RNAs induced control of ferroptosis: Implications in cancer progression and treatment

A novel kind of nonapoptotic, iron-dependent cell death brought on by lipid peroxidation is known as ferroptosis. Numerous pathological processes, including neurotoxicity, neurological disorders, ischemia-reperfusion damage, and particularly cancer, have been demonstrated to be influenced by changes in the ferroptosis-regulating network. Recent studies have established the critical roles that ferroptosis can play in cancer development and the evolution of resistance to standard chemoradiotherapy, thus suggesting that ferroptosis may be a feasible therapeutic strategy for cancer management. Gene expression may be regulated at the transcriptional and posttranscriptional levels by long noncoding RNAs (lncRNAs). They have been implicated in tumorigenesis. Some lncRNAs participate in the biological process of ferroptosis, which represents an exciting alternative to regulate ferroptosis as a means of cancer therapy. Even though there is evidence that lncRNAs have a mechanistic role in the ferroptosis of cancer cells, research on the mechanism and potential treatments for these lncRNAs is still lacking. We elucidate the potential mechanisms by which lncRNAs modulate ferroptosis in cancer and examine the promise and challenges of employing lncRNAs as novel therapeutic targets in cancer.

TYROBP-positive endothelial cell-derived TWEAK as a promoter of osteosarcoma progression: insights from single-cell omics

Background

Endothelial cells (ECs) play a vital role in promoting the progression of malignant cells, and they exhibit heterogeneity in their phenotypic characteristics. We aimed to explore the initiating cells of ECs in osteosarcoma (OS) and investigate their potential interaction with malignant cells.

Method

We obtained scRNA-seq data from 6 OS patients, and datasets were batch-corrected to minimize variations among samples. Pseudotime analysis was performed to investigate the origin of differentiation of ECs. CellChat was employed to examine the potential communication between endothelial cells and malignant cells, and gene regulatory network analysis was performed to identify transcription factor activity changes during the conversion process. Importantly, we generated TYROBP-positive ECs in vitro and investigated its role in OS cell lines. Finally, we explored the prognosis of specific ECs cluster and their impact on the tumor microenvironment (TME) at the bulk transcriptome level.

Results

The results showed that TYROBP-positive ECs may play a crucial role in initiating the differentiation of ECs. TYROBOP-positive endothelial cells (ECs) exhibited the strongest crosstalk with malignant cells, likely mediated by TWEAK, a multifunctional cytokine. TYROBP-positive ECs exhibited significant expression of TME-related genes, unique metabolic and immunological profiles. Importantly, OS patients with low enrichment of TYROBP-positive ECs had better prognoses and a lower risk of metastasis. Finally, vitro assays confirmed that TWEAK was significantly increased in ECs-conditioned medium (ECs-CM) when TYROBP was over-expressed in EC cells, and could promote the proliferation and migration of OS cells.

Conclusion

We concluded that TYROBP-positive ECs may be the initiating cells and play a crucial role in the promotion of malignant cell progression. TYROBP-positive ECs have a unique metabolic and immunological profile and may interact with malignant cells through the secretion of TWEAK.

Machine learning-based neddylation landscape indicates different prognosis and immune microenvironment in endometrial cancer

Endometrial cancer (EC) is women's fourth most common malignant tumor. Neddylation plays a significant role in many diseases; however, the effect of neddylation and neddylation-related genes (NRGs) on EC is rarely reported. In this study, we first used MLN4924 to affect the activation of neddylation in different cell lines (Ishikawa and HEC-1-A) and determined the critical role of neddylation-related pathways for EC progression. Subsequently, we screened 17 prognostic NRGs based on expression files of the TCGA-UCEC cohort. Based on unsupervised consensus clustering analysis, patients with EC were classified into two neddylation patterns (C1 and C2). In terms of prognosis, substantial differences were observed between the two patterns. Compared with C2, C1 exhibited low levels of immune infiltration and promoted tumor progression. More importantly, based on the expression of 17 prognostic NRGs, we transformed nine machine-learning algorithms into 89 combinations. The random forest (RSF) was selected to construct the neddylation-related risk score according to the average C-index of different cohorts. Notably, our score had important clinical implications for EC. Patients with high scores have poor prognoses and a cold tumor state. In conclusion, neddylation-related patterns and scores can distinguish tumor microenvironment (TME) and prognosis and guide personalized treatment in patients with EC.

Deciphering the expression patterns of homologous recombination-related lncRNAs identifies new molecular subtypes and emerging therapeutic opportunities in epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is the leading killer among women with gynecologic malignancies. Homologous recombination deficiency (HRD) has attracted increasing attention due to its significant implication in the prediction of prognosis and response to treatments. In addition to the germline and somatic mutations of homologous recombination (HR) repair genes, to widely and deeply understand the molecular characteristics of HRD, we sought to screen the long non-coding RNAs (lncRNAs) with regard to HR repair genes and to establish a prognostic risk model for EOC. Herein, we retrieved the transcriptome data from the Genotype-Tissue Expression Project (GTEx) and The Cancer Genome Atlas (TCGA) databases. HR-related lncRNAs (HRRlncRNAs) associated with prognosis were identified by co-expression and univariate Cox regression analyses. The least absolute shrinkage and selection operator (LASSO) and multivariate stepwise Cox regression were performed to construct an HRRlncRNA risk model containing AC138904.1, AP001001.1, AL603832.1, AC138932.1, and AC040169.1. Next, Kaplan−Meier analysis, time-dependent receiver operating characteristics (ROC), nomogram, calibration, and DCA curves were made to verify and evaluate the model. Gene set enrichment analysis (GSEA), immune analysis, and prediction of the half-maximal inhibitory concentration (IC₅₀) in the risk groups were also analyzed. The calibration plots showed a good concordance with the prognosis prediction. ROCs of 1-, 3-, and 5-year survival confirmed the well-predictive efficacy of this model in EOC. The risk score was used to divide the patients into high-risk and low-risk subgroups. The low-risk group patients tended to exhibit a lower immune infiltration status and a higher HRD score. Furthermore, consensus clustering analysis was employed to divide patients with EOC into three clusters based on the expression of the five HRRlncRNAs, which exhibited a significant difference in checkpoints’ expression levels and the tumor microenvironment (TME) status. Taken together, the results of this project supported that the five HRRlncRNA models might function as a biomarker and prognostic indicator with respect to predicting the PARP inhibitor and immune treatment in EOC.

The role of hypoxia-related genes in TACE-refractory hepatocellular carcinoma: Exploration of prognosis, immunological characteristics and drug resistance based on onco-multi-OMICS approach

Transcatheter arterial chemoembolization (TACE) is an effective treatment for hepatocellular carcinoma (HCC). During TACE, chemotherapeutic agents are locally infused into the tumor and simultaneously cause hypoxia in tumor cells. Importantly, the poor effect of TACE in some HCC patients has been shown to be related to dysregulated expression of hypoxia-related genes (HRGs). Therefore, we identified 33 HRGs associated with TACE (HRGTs) by differential analysis and characterized the mutational landscape of HRGTs. Among 586 HCC patients, two molecular subtypes reflecting survival status were identified by consistent clustering analysis based on 24 prognosis-associated HRGs. Comparing the transcriptomic difference of the above molecular subtypes, three molecular subtypes that could reflect changes in the immune microenvironment were then identified. Ultimately, four HRGTs (CTSO, MMP1, SPP1, TPX2) were identified based on machine learning approachs. Importantly, risk assessment can be performed for each patient by these genes. Based on the parameters of the risk model, we determined that high-risk patients have a more active immune microenvironment, indicating “hot tumor” status. And the Tumor Immune Dysfunction and Exclusion (TIDE), the Cancer Immunome Atlas (TCIA), and Genome of Drug Sensitivity in Cancer (GDSC) databases further demonstrated that high-risk patients have a positive response to immunotherapy and have lower IC50 values for drugs targeting cell cycle, PI3K/mTOR, WNT, and RTK related signaling pathways. Finally, single-cell level analysis revealed significant overexpression of CTSO, MMP1, SPP1, and TPX2 in malignant cell after PD-L1/CTLA-4 treatment. In conclusion, Onco-Multi-OMICS analysis showed that HRGs are potential biomarkers for patients with refractory TACE, and it provides a novel immunological perspective for developing personalized therapies.

Integrated Machine Learning and Single-Sample Gene Set Enrichment Analysis Identifies a TGF-Beta Signaling Pathway Derived Score in Headneck Squamous Cell Carcinoma

Background

The TGF-β signaling pathway is clinically predictive of pan-cancer. Nevertheless, its clinical prognosis and regulation of immune microenvironment (TME) characteristics as well as the prediction of immunotherapy efficacy need to be further elucidated in head and neck squamous cell carcinoma.

Method

At first, we summarized TGF-β related genes from previous published articles, used ssGSEA to establish the TGF-β risk score. Considering the complexity of its clinical application, we improved it with the LASSO-COX algorithm to construct the model. In addition, we explored the predictive efficacy of TGF-β risk score in the observation of TME phenotype and immunotherapy effect. Finally, the potency of TGF-β risk score in adjusting precise treatment of HNSC was evaluated.

Results

We systematically established TGF-β risk score with multi-level predictive ability. TGF-β risk score was employed to predict the tumor microenvironment status, which was negatively associated with NK cells but positively related to macrophages and fibroblasts. It reveals that patients with high TGF-β risk score predict “cold” TME status. In addition, higher risk scores indicate higher sensitivity to immunotherapy.

Conclusion

We first construct and validate TGF-β characteristics that can predict immune microenvironment phenotypes and immunotherapeutic effect in multiple datasets. Noteworthy, TGF-β risk score is helpful for individualized precise treatment of patients with the head and neck squamous cell carcinoma.

A Prognostic Ferroptosis-Related lncRNA Model Associated With Immune Infiltration in Colon Cancer

Colon cancer (CC) is a common malignant tumor worldwide, and ferroptosis plays a vital role in the pathology and progression of CC. Effective prognostic tools are required to guide clinical decision-making in CC. In our study, gene expression and clinical data of CC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We identified the differentially expressed ferroptosis-related lncRNAs using the differential expression and gene co-expression analysis. Then, univariate and multivariate Cox regression analyses were used to identify the effective ferroptosis-related lncRNAs for constructing the prognostic model for CC. Gene set enrichment analysis (GSEA) was conducted to explore the functional enrichment analysis. CIBERSORT and single-sample GSEA were performed to investigate the association between our model and the immune microenvironment. Finally, three ferroptosis-related lncRNAs (XXbac-B476C20.9, TP73-AS1, and SNHG15) were identified to construct the prognostic model. The results of the validation showed that our model was effective in predicting the prognosis of CC patients, which also was an independent prognostic factor for CC. The GSEA analysis showed that several ferroptosis-related pathways were significantly enriched in the low-risk group. Immune infiltration analysis suggested that the level of immune cell infiltration was significantly higher in the high-risk group than that in the low-risk group. In summary, we established a prognostic model based on the ferroptosis-related lncRNAs, which could provide clinical guidance for future laboratory and clinical research on CC.

Long noncoding RNAs as therapeutic targets to overcome chemoresistance in ovarian cancer

Long noncoding RNAs (lncRNAs) have been characterized to play an essential role in ovarian tumorigenesis via controlling a variety of cellular processes, such as cell proliferation, invasion, apoptotic death, metastasis, cell cycle, migration, metabolism, immune evasion, and chemoresistance. The one obstacle for the therapeutic efficacy is due to the development of drug resistance in ovarian cancer patients. Therefore, in this review article, we describe the role of lncRNAs in chemoresistance in ovarian cancer. Moreover, we discuss the molecular mechanism of lncRNAs-involved drug resistance in ovarian cancer. We conclude that lncRNAs could be useful targets to overcome chemoresistance and improve therapeutic outcome in ovarian cancer patients.

Gastric Cancer Subtypes in Tumour and Nontumour Tissues by Immunologic and Hallmark Gene Sets

A previous research study on differentiating gastric cancer (GC) into distinct subtypes or prognostic models was mostly based on GC tissues, which neglected the role of nontumour tissues in GC subtypes. The purpose of the research was to identify GC subtypes on the basis of tumour and adjacent nontumour tissues to assess the prognosis of GC patients. We characterized three GC subtypes on the basis of the immunologic and hallmark gene sets in GC and adjacent nontumour tissues: among them, the GC patients with subtype I had the longest survival time compared to patients with other subtypes. The classification was closely associated with T stage and pathological stage of GC patients. A prognostic model containing two gene sets was constructed by LASSO analysis. Kaplan–Meier analysis showed that patients in the high-risk group survived longer than those in the low-risk group and the two prognostic genes sets in the model were strongly correlated with survival status. Then, GO and KEGG analyses and PPI network show that nontumour and tumour tissues are influencing the prognosis of GC patients in separate manners. In summary, we emphasized the prognostic value of nontumour tissue in GC patients and proposed a novel insight that both changes in tumour and nontumour tissues should be taken into account when selecting a treatment strategy for GC.

Current progress of ferroptosis study in ovarian cancer

Tumors are the leading cause of death all over the world, among which ovarian cancer ranks the third in gynecological malignancies. The current treatment for ovarian cancer is liable to develop chemotherapy resistance and high recurrence rate, in which a new strategy is demanded. Ferroptosis, a newly discovered manner of regulatory cell death, is shown to be induced by massive iron-dependent accumulation of lipid reactive oxygen species. With the in-depth study of ferroptosis, its associated mechanism with various tumors is gradually elucidated, including ovarian tumor, which probably promotes the application of ferroptosis in treating ovarian cancer. To this end, this review will focus on the history and current research progress of ferroptosis, especially its regulation mechanism, and its potential application as a novel treatment strategy for ovarian cancer.

A Novel Defined RAS-Related Gene Signature for Predicting the Prognosis and Characterization of Biological Function in Osteosarcoma

Background

Osteosarcoma (OS) is the most common primary bone malignancy in children and adolescents with a high incidence and poor prognosis. Activation of the RAS pathway promotes progression and metastasis of osteosarcoma. RAS has been studied in many different tumors; however, the prognostic value of RAS-associated genes in OS remains unclear. On this basis, we investigated the RAS-related gene signature and explored the intrinsic biological features of OS.

Methods

We obtained RNA transcriptome sequencing data and clinical information of osteosarcoma patients from the TARGET database. RAS pathway-related genes were obtained from the KEGG pathway database. Molecular subgroups and risk models were developed using consensus clustering and least absolute shrinkage and selection operator (LASSO) regression, respectively. ESTIMATE algorithm and ssGSEA analysis were used to assess the tumor microenvironment and immune penetrance between the two groups. A comprehensive review of gene ontology (GO) and KEGG analyses revealed inherent biological functional differences between the two groups.

Results

The consistent clustering showed stratification of osteosarcoma patients into two subtypes based on RAS-associated genes and provided a robust prediction of prognosis. A risk model further confirmed that RAS-related genes are the best prognostic indicators for OS patients. GO analysis showed that GDP/GTP binding, focal adhesion, cytoskeletal motor activity, and cell-matrix junctions were associated with the RAS-related model group. Furthermore, RAS signaling in osteosarcoma based on KEGG analysis was significantly associated with cancer progression, with immune function and tumor microenvironment particularly affected.

Conclusion

We constructed a prognostic model founded on RAS-related gene and demonstrated its predictive ability. Then, furtherly exploration of the molecular mechanisms and immune characteristics proved the role of RAS-related gene in the dysregulation in OS.

m6A-Related lncRNA Signature Is Involved in Immunosuppression and Predicts the Patient Prognosis of the Age-Associated Ovarian Cancer

Background

Epithelial ovarian cancers are age-associated diseases, usually diagnosed at an advanced stage. lncRNA has been discovered to interplay with N6-methyladenosine (m6A), working in tandem to promote cancer progression and worsening patient outcomes. This study is aimed at investigating the roles and mechanism of m6A-related lncRNA signature on ovarian cancers.

Methods

We retrieved TCGA and CGGA sequencing data to identify m6A-related lncRNA signature and constructed an m6A score (MS) using the LASSO algorithm. A clinical nomogram was then established to predict the overall survival of patients. Subsequently, GSEA analyses were conducted to obtain pathways involved. Expression of HLA genes, 28 tumor-infiltrating lymphocyte infiltration, and anticancer cycle were analyzed the immunological differences between high-MS and low-MS groups. Finally, immune checkpoint gene expressions and IC₅₀ of chemotherapeutic drugs were calculated, and CMap was run to identify the potential compounds and their corresponding mechanisms.

Results

We identified 16 m6A-related lncRNAs and constructed an MS model. The high-MS group showed a poor prognosis. A clinical nomogram consists of MS, and age was constructed and predicted the 1-, 3-, and 5-year survival with high accuracy. GSEA analyses presented downregulated antigen processing and presentation pathways. Immunocyte infiltrating analyses demonstrated that high-MS was associated with high infiltration of Treg cells, macrophages, and low Th1/Th2 rate. Also, high expression of immune checkpoint genes NRP1, TNFSF9, and VSIR was observed in the high-MS group. Finally, the high-MS group also predicted low IC₅₀ of vinorelbine and vorinostat.

Conclusion

This study constructed a robust prediction model for prognostic management and revealed the cross-talk between m6A and immunosuppression. Besides, the m6A lncRNA signature can predict the chemotherapeutic drug response. These will shed light on the development of novel therapeutic strategies and render survival benefits for ovarian patients.

Exploration of Potential Biomarkers and Immune Landscape for Hepatoblastoma: Evidence from Machine Learning Algorithm

This study aimed to investigate the immune landscape in hepatoblastoma (HB) based on deconvolution methods and identify a biomarkers panel for diagnosis based on a machine learning algorithm. Firstly, we identified 277 differentially expressed genes (DEGs) and differentiated and functionally identified the modules in DEGs. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and GO (gene ontology) were used to annotate these DEGs, and the results suggested that the occurrence of HB was related to DNA adducts, bile secretion, and metabolism of xenobiotics by cytochrome P450. We selected the top 10 genes for our final diagnostic panel based on the random forest tree method. Interestingly, TNFRSF19 and TOP2A were significantly down-regulated in normal samples, while other genes (TRIB1, MAT1A, SAA2-SAA4, NAT2, HABP2, CYP2CB, APOF, and CFHR3) were significantly down-regulated in HB samples. Finally, we constructed a neural network model based on the above hub genes for diagnosis. After cross-validation, the area under the ROC curve was close to 1 (AUC = 0.972), and the AUC of the validation set was 0.870. In addition, the results of single-sample gene-set enrichment analysis (ssGSEA) and deconvolution methods revealed a more active immune responses in the HB tissue. In conclusion, we have developed a robust biomarkers panel for HB patients.

Ferroptosis, necroptosis, and pyroptosis in the occurrence and development of ovarian cancer

Ovarian cancer (OC) is one of the most common malignancies that causes death in women and is a heterogeneous disease with complex molecular and genetic changes. Because of the relatively high recurrence rate of OC, it is crucial to understand the associated mechanisms of drug resistance and to discover potential target for rational targeted therapy. Cell death is a genetically determined process. Active and orderly cell death is prevalent during the development of living organisms and plays a critical role in regulating life homeostasis. Ferroptosis, a novel type of cell death discovered in recent years, is distinct from apoptosis and necrosis and is mainly caused by the imbalance between the production and degradation of intracellular lipid reactive oxygen species triggered by increased iron content. Necroptosis is a regulated non-cysteine protease–dependent programmed cell necrosis, morphologically exhibiting the same features as necrosis and occurring via a unique mechanism of programmed cell death different from the apoptotic signaling pathway. Pyroptosis is a form of programmed cell death that is characterized by the formation of membrane pores and subsequent cell lysis as well as release of pro-inflammatory cell contents mediated by the abscisin family. Studies have shown that ferroptosis, necroptosis, and pyroptosis are involved in the development and progression of a variety of diseases, including tumors. In this review, we summarized the recent advances in ferroptosis, necroptosis, and pyroptosis in the occurrence, development, and therapeutic potential of OC.

Integrative Analysis From Multicenter Studies Identifies a WGCNA-Derived Cancer-Associated Fibroblast Signature for Ovarian Cancer

Cancer-associated fibroblasts (CAFs) are a major contributor to tumor stromal crosstalk in the tumor microenvironment (TME) and boost tumor progression by promoting angiogenesis and lymphangiogenesis. This study aimed to identify prognostic genes associated with CAFs that lead to high morbidity and mortality in ovarian cancer (OC) patients. We performed bioinformatics analysis in 16 multicenter studies (2,742 patients) and identified CAF-associated hub genes using the weighted gene co-expression network analysis (WGCNA). A machine learning methodology was used to identify COL16A1, COL5A2, GREM1, LUM, SRPX, and TIMP3 and construct a prognostic signature. Subsequently, a series of bioinformatics algorithms indicated risk stratification based on the above signature, suggesting that high-risk patients have a worse prognosis, weaker immune response, and lower tumor mutational burden (TMB) status but may be more sensitive to routine chemotherapeutic agents. Finally, we characterized prognostic markers using cell lines, immunohistochemistry, and single-cell sequencing. In conclusion, these results suggest that the CAF-related signature may be a novel pretreatment guide for anti-CAFs, and prognostic markers in CAFs may be potential therapeutic targets to inhibit OC progression.

Comprehensive Analysis of LINC01615 in Head and Neck Squamous Cell Carcinoma: A Hub Biomarker Identified by Machine Learning and Experimental Validation

Background

Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers, but in clinical practice, the lack of precise biomarkers often results in an advanced diagnosis. Hence, it is crucial to explore novel biomarkers to improve the clinical outcome of HNSCC patients.

Methods

We downloaded RNA-seq data consisting of 502 HNSCC tissues and 44 normal tissues from the TCGA database, and lncRNA genomic sequence information was downloaded from the GENECODE database for annotating lncRNA expression profiles. We used Cox regression analysis to screen prognostic lncRNAs, the threshold as HR >1 and p value <0.05. Subsequently, three survival outcomes (overall survival, progress-free interval, and disease-specific survival)-related lncRNAs overlapped to get the common lncRNAs. The hub biomarker was identified using LASSO and random forest models. Subsequently, we used a variety of statistical methods to validate the prognostic ability of the hub marker. In addition, Spearman correlation analysis between the hub marker expression and genomic heterogeneity was conducted, such as instability (MSI), homologous recombination deficiency (HRD), and tumor mutational burden (TMB). Finally, we used enrichment analysis, ssGSEA, and ESTIMATE algorithms to explore the changes in the underlying immune-related pathway and function. Finally, the MTT assay and transwell assay were performed to determine the effect of LINC01615 silencing on tumor cell proliferation, invasion, and migration.

Results

Cox regression analysis revealed 133 lncRNAs with multiple prognostic significance. The machine learning algorithm screened out the hub lncRNA with the highest importance in the RF model: LINC01615. Clinical correlation analysis revealed that the LINC01615 increased with increasing the T stage, N stage, pathology grade, and clinical stage. LINC01615 could be used as a predictor of HNSCC prognosis validating by a variety of statistical methods. Subsequently, when clinical indicators were combined with the LINC01615 expression, the visualization model (nomogram) was more applicable to clinical practice. Finally, immune algorithms indicated that LINC01615 may be involved in the regulation of lymphocyte recruitment and immunological infiltration in HNSCC, and the LINC01615 expression represented genomic heterogeneity in pan-cancer. Functionally, silencing of LINC01615 suppresses cell proliferation, invasion, and migration in HEP-2 and TU212 cells.

Conclusion

LINC01615 may play an important role in the prostromal cell enrichment and immunosuppressive state and serve as a prognostic biomarker in HNSCC.

A Nomogram Based on SEER Database for Predicting Prognosis in Patients with Mucinous Ovarian Cancer: A Real-World Study

Purpose

Mucinous ovarian cancer (MOC) is a rare histological type of EOC. In order to guide the clinical diagnosis and management of MOC patients, we constructed and verified a nomogram for the estimation of overall survival in patients with MOC.

Patients and Methods

We collected 494 patients with MOC diagnosed from 2010 to 2015 in SEER database, and the following main inclusion criteria were used: (1) patients whose MOC was confirmed by pathology; (2) patients without a history of primary other cancer. Subsequently, we performed randomized grouping (6:4) and Cox hazard regression analysis in the training group. Subsequently, the nomogram was established. A variety of indicators were used to validate the prognosis value of nomogram, including the C-index, area under the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). Moreover, Kaplan–Meier analysis was used to compare the survival results among different risk subgroups.

Results

Cox hazard regression analysis revealed that age, grade, FIGO stage and log odds of positive lymph nodes stage were independent risk factors for patients with MOC. In the training group, the C-index of the nomogram was 0.827 (95% CI: 0.791–0.863) and the areas under the curve (AUC) predicting the 1-, 3- and 5-year survival rate were 0.853 (95% CI: 0.791–0.915), 0.886 (95% CI: 0.852–0.920) and 0.815 (95% CI: 0.766–0.864), respectively. The calibration curve revealed that the nomogram of the 1-, 3- and 5-year survival rate was consistent with the actual fact. Patients with high risk had a poorer prognosis than those with low risk (P < 0.001). DCA revealed that the nomogram had the best clinical value than other classical prognostic markers. Similarly, nomogram had excellent prognostic ability in the testing group.

Conclusion

The nomogram was constructed to predict overall survival in patients with MOC, which had the significance for clinical evaluation.

Ferroptosis-Related Long Noncoding RNAs as Prognostic Biomarkers for Ovarian Cancer

Ovarian cancer (OC) is a highly malignant gynecologic tumor with few treatments available and poor prognosis with the currently available diagnostic markers and interventions. More effective methods for diagnosis and treatment are urgently needed. Although the current evidence implicates ferroptosis in the development and therapeutic responses of various types of tumors, it is unclear to what extent ferroptosis affects OC. To explore the potential of ferroptosis-related genes as biomarkers and molecular targets for OC diagnosis and intervention, this study collected several datasets from The Cancer Genome Atlas-OC (TCGA-OC), analyzed and identified the coexpression profiles of 60 ferroptosis-related genes and two subtypes of OC with respect to ferroptosis and further examined and analyzed the differentially expressed genes between the two subtypes. The results indicated that the expression levels of ferroptosis genes were significantly correlated with prognosis in patients with OC. Single-factor Cox and LASSO analysis identified eight lncRNAs from the screened ferroptosis-related genes, including lncRNAs RP11-443B7.3, RP5-1028K7.2, TRAM2-AS1, AC073283.4, RP11-486G15.2, RP11-95H3.1, RP11-958F21.1, and AC006129.1. A risk scoring model was constructed from the ferroptosis-related lncRNAs and showed good performance in the evaluation of OC patient prognosis. The high- and low-risk groups based on tumor scores presented obvious differences in clinical characteristics, tumor mutation burden, and tumor immune cell infiltration, indicating that the risk score has a good ability to predict the benefit of immunotherapy and may provide data to support the implementation of precise immunotherapy for OC. Although in vivo tests and research are needed in the future, our bioinformatics analysis powerfully supported the effectiveness of the risk signature of ferroptosis-related lncRNAs for prognosis prediction in OC. The findings suggest that these eight identified lncRNAs have great potential for development as diagnostic markers and intervention targets for OC and that patients with high ferroptosis-related lncRNA expression will receive greater benefits from conventional chemotherapy or treatment with ferroptosis inducers.

Exploring the Potential of Pyroptosis-Related Genes in Predicting Prognosis and Immunological Characteristics of Pancreatic Cancer From the Perspective of Genome and Transcriptome

OBJECTIVE

To probe into the role of pyroptosis-related genes in pancreatic carcinoma.

METHODS

Herein, we conducted a comprehensive bioinformatics analysis to evaluate tumor-immune infiltration and tumor mutation burden, the correlations between PRGs, and microsatellite instability and found that 33 PRGS were up- or down-regulated in PC. Then we built the PPI network, which was downloaded from the STRING database. Using TCGA cohort median risk score, PC subjects from the Gene Expression Composite cohort (GEO) data resource were stratified into two risk categories, with the low-PC risk group harboring a higher overall survival (OS) (P = 0.011). We employed the ssGSEA approach to quantify immune cell abundance in separate risk groups separated by risk signature while assessing variations in immune cell invasion. Chemotherapeutic drugs were retrieved from the Genomics of Drug Sensitivity in Cancer (GDSC) data resource.

RESULTS

Eight prognostic PRG models (CASP4, GSDMC, IL-18, NLRP1, NLRP2, PLCG1, TIRAP, and TNF) were established via LASSO Cox regression to estimate the OS of PC subjects with medium-to-high accuracy.

CONCLUSION

Our study is the first to identify a pyroptotic-related prognostic gene feature for PC, providing more options for the prognostic prediction of PC.

Protective Prognostic Biomarkers Negatively Correlated with Macrophage M2 Infiltration in Low-Grade Glioma

Tumor-associated Macrophages (TAMs) play a vital role in the progression of glioma. Macrophage M2 has been confirmed to promote immunosuppression and proliferation of low-grade glioma (LGG). Here, we searched for genes negatively correlated with Macrophages M2 by bioinformatical methods and investigated their protective ability for prognosis. LGG and adjacent normal samples were screened out in TCGA and three GEO datasets. 326 overlapped differentially expressed genes were calculated, and their biological functions were investigated by Go and KEGG analyses. Macrophage M2 accounted for the highest proportion among all 22 immune cells by CIBERSORT deconvolution algorithm. The proportion of Macrophage M2 in LGG was also higher than that in normal tissue according to several deconvolution algorithms. 43 genes in the blue module negatively correlated with Macrophage M2 infiltration were identified by weighted gene coexpression network analysis (WGCNA). Through immune infiltration and correlation analysis, FGFBP3, VAX2, and SHD were selected and they were enriched in G protein-coupled receptors' signaling regulation and cytokine receptor interaction. They could prolong the overall and disease-free survival time. Univariate and multivariate Cox regression analyses were applied to evaluate prognosis prediction ability. Interestingly, FGFBP3 and AHD were independent prognostic predictors. A nomogram was drawn, and its 1-year, 3-year, and 5-year survival prognostic value was verified by ROC curves and calibration plots. In conclusion, FGFBP3, VAX2, and SHD were protective prognostic biomarkers against Macrophage M2 infiltration in low-grade glioma. The FGFBP3 and SHD were independent factors to effectively predict long-term survival probability.