Tumor Immune Microenvironment Related Gene-Based Model to Predict Prognosis and Response to Compounds in Ovarian Cancer

Identification of novel candidate biomarkers related to immune cell infiltration in peri-implantitis

OBJECTIVE

The present study was performed to identify key biomarkers associated with immune cell infiltration in peri-implantitis through bioinformatic analyses.

METHODS

Six peri-implantitis soft tissue samples and six healthy gingiva samples were obtained from GSE106090, and were used to identify immune-associated differentially expressed genes (DEGs) in peri-implantitis. The candidate biomarkers associated with immune cell infiltration were examined by immunohistochemical staining.

RESULTS

We identified 2089 upregulated and 2173 downregulated genes. Upregulated DEGs were significantly associated with immune response. Ten key candidate biomarkers were identified in the PPI network, including IL1B, TLR2, TLR4, CCL4, CXCL8, IL10, IL6, CD4, CCL3, and PTPRC. The expression level of the 10 genes increased in peri-implantitis soft tissue samples compared with healthy gingiva samples. The proportion of CD4+ T cells, iTreg, and Tfh in infiltration immune cells increased in peri-implantitis soft tissue samples and were positively correlated with the expression level of candidate biomarkers TLR4, CCL3, CXCL8, and IL1B. Immunohistochemistry showed that there were more lymphocytes in peri-implantitis soft tissue samples, with an increased expression level of TLR4, CCL3, CXCL8, and IL1B.

CONCLUSION

Identification of four novel diagnostic biomarkers was helpful for revealing the molecular mechanisms and could serve as a risk predictor for the immune microenvironment in peri-implantitis.

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.

The role of collagen triple helix repeat containing 1 (CTHRC1) in cancer development and progression

INTRODUCTION

Collagen triple helix repeat containing 1 (CTHRC1) is a protein that has been implicated in pro-migratory pathways, arterial tissue-repair processes, and inhibition of collagen deposition via the regulation of multiple signaling cascades. Studies have also demonstrated an upregulation of CTHRC1 in multiple cancers where it has been linked to enhanced proliferation, invasion, and metastasis. However, the understanding of the exact role and mechanisms of CTHRC1 in cancer is far from complete.

AREAS COVERED

This review focuses on analyzing the role of CTHRC1 in cancer as well as its associations with clinicopathologies and cancer-related processes and signaling. We have also summarized the available literature information regarding the role of CTHRC1 in tumor microenvironment and immune signaling. Finally, we have discussed the mechanisms associated with CTHRC1 regulations, and opportunities and challenges regarding the development of CTHRC1 as a potential target for cancer management.

EXPERT OPINION

CTHRC1 is a multifaceted protein with critical roles in cancer progression and other pathological conditions. Its association with lower overall survival in various cancers, and impact on the tumor immune microenvironment make it an intriguing target for further research and potential therapeutic interventions in cancer.

Genes associated with inflammation for prognosis prediction for clear cell renal cell carcinoma: a multi-database analysis

Background

Clear cell renal cell carcinoma (ccRCC) is the largest subtype of kidney tumour, with inflammatory responses characterising all stages of the tumour. Establishing the relationship between the genes related to inflammatory responses and ccRCC may help the diagnosis and treatment of patients with ccRCC.

Methods

First, we obtained the data for this study from a public database. After differential analysis and Cox regression analysis, we obtained the genes for the establishment of a prognostic model for ccRCC. As we used data from multiple databases, we standardized all the data using the surrogate variable analysis (SVA) package to make the data from different sources comparable. Next, we used a least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model of genes related to inflammation. The data used for modelling and internal validation came from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) series (GSE29609) databases. ccRCC data from the International Cancer Genome Consortium (ICGC) database were used for external validation. Tumour data from the E-MTAB-1980 cohort were used for external validation. The GSE40453 and GSE53757 datasets were used to verify the differential expression of inflammation-related gene model signatures (IRGMS). The immunohistochemistry of IRGMS was queried through the Human Protein Atlas (HPA) database. After the adequate validation of the IRGM, we further explored its application by constructing nomograms, pathway enrichment analysis, immunocorrelation analysis, drug susceptibility analysis, and subtype identification.

Results

The IRGM can robustly predict the prognosis of samples from patients with ccRCC from different databases. The verification results show that nomogram can accurately predict the survival rate of patients. Pathway enrichment analysis showed that patients in the high-risk (HR) group were associated with a variety of tumorigenesis biological processes. Immune-related analysis and drug susceptibility analysis suggested that patients with higher IRGM scores had more treatment options.

Conclusions

The IRGMS can effectively predict the prognosis of ccRCC. Patients with higher IRGM scores may be better candidates for treatment with immune checkpoint inhibitors and have more chemotherapy options.

Integrative evaluation and experimental validation of the immune-modulating potential of dysregulated extracellular matrix genes in high-grade serous ovarian cancer prognosis

BACKGROUND

High-grade serous ovarian cancer (HGSOC) is a challenging malignancy characterized by complex interactions between tumor cells and the surrounding microenvironment. Understanding the immune landscape of HGSOC, particularly the role of the extracellular matrix (ECM), is crucial for improving prognosis and guiding therapeutic interventions.

METHODS AND RESULTS

Using univariate Cox regression analysis, we identified 71 ECM genes associated with prognosis in seven HGSOC populations. The ECMscore signature, consisting of 14 genes, was validated using Cox proportional hazards regression with a lasso penalty. Cox regression analyses demonstrated that ECMscore is an excellent indicator for prognostic classification in prevalent malignancies, including HGSOC. Moreover, patients with higher ECMscores exhibited more active stromal and carcinogenic activation pathways, including apical surface signaling, Notch signaling, apical junctions, Wnt signaling, epithelial-mesenchymal transition, TGF-beta signaling, and angiogenesis. In contrast, patients with relatively low ECMscores showed more active immune-related pathways, such as interferon alpha response, interferon-gamma response, and inflammatory response. The relationship between the ECMscore and genomic anomalies was further examined. Additionally, the correlation between ECMscore and immune microenvironment components and signals in HGSOC was examined in greater detail. Moreover, the expression of MGP, COL8A2, and PAPPA and its correlation with FAP were validated using qRT-PCR on samples from HGSOC. The utility of ECMscore in predicting the prospective clinical success of immunotherapy and its potential in guiding the selection of chemotherapeutic agents were also explored. Similar results were obtained from pan-cancer research.

CONCLUSION

The comprehensive evaluation of the ECM may help identify immune activation and assist patients in HGSOC and even pan-cancer in receiving proper therapy.

Modulation of immunity by the secreted phospholipase A2 family

Among the phospholipase A2 (PLA2 ) superfamily, which typically catalyzes the sn-2 hydrolysis of phospholipids to yield fatty acids and lysophospholipids, the secreted PLA2 (sPLA2 ) family contains 11 isoforms in mammals. Individual sPLA2 s have unique enzymatic specificity toward fatty acids and polar heads of phospholipid substrates and display distinct tissue/cellular distributions, suggesting their distinct physiological functions. Recent studies using knockout and/or transgenic mice for a full set of sPLA2 s have revealed their roles in modulation of immunity and related disorders. Application of mass spectrometric lipidomics to these mice has enabled to identify target substrates and products of individual sPLA2 s in given tissue microenvironments. sPLA2 s hydrolyze not only phospholipids in the plasma membrane of activated, damaged or dying mammalian cells, but also extracellular phospholipids such as those in extracellular vesicles, microbe membranes, lipoproteins, surfactants, and dietary phospholipids, thereby exacerbating or ameliorating various diseases. The actions of sPLA2 s are dependent on, or independent of, the generation of fatty acid- or lysophospholipid-derived lipid mediators according to the pathophysiological contexts. In this review, we make an overview of our current understanding of the roles of individual sPLA2 s in various immune responses and associated diseases.

Worldwide productivity and research trend of publications concerning tumor immune microenvironment (TIME): a bibliometric study

BACKGROUND

As the complexity and diversity of the tumor immune microenvironment (TIME) are becoming better understood, burgeoning research has progressed in this field. However, there is a scarcity of literature specifically focused on the bibliometric analysis of this topic. This study sought to investigate the development pattern of TIME-related research from 2006 to September 14, 2022, from a bibliometric perspective.

METHODS

We acquired both articles and reviews related to TIME from the Web of Science Core Collection (WoSCC) (retrieved on September 14, 2022). R package "Bibliometrix" was used to calculate the basic bibliometric features, present the collaborative conditions of countries and authors, and generate a three-field plot to show the relationships among authors, affiliations, and keywords. VOSviewer was utilized for co-authorship analysis of country and institution and keyword co-occurrence analysis. CiteSpace was used for citation burst analysis of keywords and cited references. In addition, Microsoft Office Excel 2019 was used to develop an exponential model to fit the cumulative publication numbers.

RESULTS

A total of 2545 publications on TIME were included, and the annual publication trend exhibited a significant increase over time. China and Fudan University were the most productive country and institution, with the highest number of publications of 1495 and 396, respectively. Frontiers in Oncology held the highest number of publications. A number of authors were recognized as the main contributors in this field. The clustering analysis revealed six clusters of keywords that highlighted the research hot spots in the fields of basic medical research, immunotherapy, and various cancer types separately.

CONCLUSIONS

This research analyzed 16 years of TIME-related research and sketched out a basic knowledge framework that includes publications, countries, journals, authors, institutions, and keywords. The finding revealed that the current research hot spots of the TIME domain lie in "TIME and cancer prognosis", "cancer immunotherapy", and "immune checkpoint". Our researchers identified the following areas: "immune checkpoint-based immunotherapy", "precise immunotherapy" and "immunocyte pattern", which may emerge as frontiers and focal points in the upcoming years, offering valuable avenues for further exploration.

Development and validation of a tumor immune cell infiltration-related gene signature for recurrence prediction by weighted gene co-expression network analysis in prostate cancer

Introduction: Prostate cancer (PCa) is the second most common malignancy in men. Despite multidisciplinary treatments, patients with PCa continue to experience poor prognoses and high rates of tumor recurrence. Recent studies have shown that tumor-infiltrating immune cells (TIICs) are associated with PCa tumorigenesis.

Methods: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to derive multi-omics data for prostate adenocarcinoma (PRAD) samples. The CIBERSORT algorithm was used to calculate the landscape of TIICs. Weighted gene co-expression network analysis (WGCNA) was performed to determine the candidate module most significantly associated with TIICs. LASSO Cox regression was applied to screen a minimal set of genes and construct a TIIC-related prognostic gene signature for PCa. Then, 78 PCa samples with CIBERSORT output p-values of less than 0.05 were selected for analysis. WGCNA identified 13 modules, and the MEblue module with the most significant enrichment result was selected. A total of 1143 candidate genes were cross-examined between the MEblue module and active dendritic cell-related genes.

Results: According to LASSO Cox regression analysis, a risk model was constructed with six genes (STX4, UBE2S, EMC6, EMD, NUCB1 and GCAT), which exhibited strong correlations with clinicopathological variables, tumor microenvironment context, antitumor therapies, and tumor mutation burden (TMB) in TCGA-PRAD. Further validation showed that the UBE2S had the highest expression level among the six genes in five different PCa cell lines.

Discussion: In conclusion, our risk-score model contributes to better predicting PCa patient prognosis and understanding the underlying mechanisms of immune responses and antitumor therapies in PCa.

Identification of an Individualized Prognostic Biomarker for Serous Ovarian Cancer: A Qualitative Model

Serous ovarian cancer is the most common type of ovarian epithelial cancer and usually has a poor prognosis. The objective of this study was to construct an individualized prognostic model for predicting overall survival in serous ovarian cancer. Based on the relative expression orderings (Ea > Eb/Ea ≤ Eb) of gene pairs closely associated with serous ovarian prognosis, we tried constructing a potential individualized qualitative biomarker by the greedy algorithm and evaluated the performance in independent validation datasets. We constructed a prognostic biomarker consisting of 20 gene pairs (SOV-P20). The overall survival between high- and low-risk groups stratified by SOV-P20 was statistically significantly different in the training and independent validation datasets from other platforms (p < 0.05, Wilcoxon test). The average area under the curve (AUC) values of the training and three validation datasets were 0.756, 0.590, 0.630, and 0.680, respectively. The distribution of most immune cells between high- and low-risk groups was quite different (p < 0.001, Wilcoxon test). The low-risk patients tended to show significantly better tumor response to chemotherapy than the high-risk patients (p < 0.05, Fisher’s exact test). SOV-P20 achieved the highest mean index of concordance (C-index) (0.624) compared with the other seven existing prognostic signatures (ranging from 0.511 to 0.619). SOV-P20 is a promising prognostic biomarker for serous ovarian cancer, which will be applicable for clinical predictive risk assessment.

Preparation of Black pepper (Piper nigrum L.) essential oil nanoparticles and its antitumor activity on triple negative breast cancer in vitro

The active compounds isolated from Black pepper have anticancer effects, but the bioactivity of Black pepper essential oil (BP-EO) is rarely studied. BP-EO has poor stability and a suitable dose form should be prepared for in vivo delivery. Triple negative breast cancer (TNBC) has attracted more and more attention due to its high mitotic index, high metastasis rate and poor prognosis. In this study, the composition of BP-EO was analyzed by gas chromatography-mass spectrometry (GC-MS), and nanoparticles (NPs) loaded with BP-EO were prepared by nanoprecipitation method using Eudragit L100 as a carrier. We investigated the preparation, characterization, stability and in vitro release of nanoparticles. MTT assay, cell wound healing, Transwell invasion assay and Western blot were used to study the anti-tumor effect and mechanism of MDA-MB-231 cells. The GC-MS analysis identified a total of 33 compounds among which alkenes account for 63.55%. The prepared BP-EO NPs exhibited nanoscale morphology, good stability and pH-responsive and sustained release character which is suitable for in vivo delivery. BP-EO NPs significantly inhibited the proliferation, migration and invasion of MDA-MB-231 cells. Furthermore, BP-EO NPs significantly inhibited the expressions of Wnt and β-catenin and significantly activated the expression of GSK-3β in MDA-MB-231 cells. Therefore, BP-EO NPs prepared in this study provide a new effective strategy for the treatment of TNBC. PRACTICAL APPLICATIONS: Black pepper is rich in essential oil and has excellent antioxidant and antibacterial activities. However, the anti-tumor activity of BP-EO has not been studied. In this study, we found that BP-EO has excellent anticancer activity. To achieve effective encapsulation of black pepper essential oil and an excellent anti-triple negative breast cancer activity, nanoparticles loaded with BP-EO were prepared using Eudragit L100 as the carrier by the nanoprecipitation method. The in vitro study revealed that BP-EO NPs inhibited proliferation, migration and invasion of MDA-MB-231 cells via inhibiting the Wnt/β-Catenin signaling pathway. This study provides new ideas and innovations for the treatment of invasive triple negative breast cancer in the future. At the same time, we will further reveal the application potential, pharmacokinetic characteristics and precise mechanism of BP-EO NPs in vivo in subsequent studies.

Characteristic Genes and Immune Infiltration Analysis for Acute Rejection after Kidney Transplantation

Background

Renal transplantation can significantly improve the survival rate and quality of life of patients with end-stage renal disease, but the probability of acute rejection (AR) in adult renal transplant recipients is still approximately 12.2%. Machine learning (ML) is superior to traditional statistical methods in various clinical scenarios. However, the current AR model is constructed only through simple difference analysis or a single queue, which cannot guarantee the accuracy of prediction. Therefore, this study identified and validated new gene sets that contribute to the early prediction of AR and the prognosis prediction of patients after renal transplantation by constructing a more accurate AR gene signature through ML technology.

Methods

Based on the Gene Expression Omnibus (GEO) database and multiple bioinformatic analyses, we identified differentially expressed genes (DEGs) and built a gene signature via LASSO regression and SVM analysis. Immune cell infiltration and immunocyte association analyses were also conducted. Furthermore, we investigated the relationship between AR genes and graft survival status.

Results

Twenty-four DEGs were identified. A 5 gene signature (CPA6, EFNA1, HBM, THEM5, and ZNF683) were obtained by LASSO analysis and SVM analysis, which had a satisfied ability to differentiate AR and NAR in the training cohort, internal validation cohort and external validation cohort. Additionally, ZNF683 was associated with graft survival.

Conclusion

A 5 gene signature, particularly ZNF683, provided insight into a precise therapeutic schedule and clinical applications for AR patients.

Integrating cell cycle score for precise risk stratification in ovarian cancer

Background: Ovarian cancer (OC) is a highly heterogeneous disease, of which the mesenchymal subtype has the worst prognosis, is the most aggressive, and has the highest drug resistance. The cell cycle pathway plays a vital role in ovarian cancer development and progression. We aimed to screen the key cell cycle genes that regulated the mesenchymal subtype and construct a robust signature for ovarian cancer risk stratification.

Methods: Network inference was conducted by integrating the differentially expressed cell cycle signature genes and target genes between the mesenchymal and non-mesenchymal subtypes of ovarian cancer and identifying the dominant cell cycle signature genes.

Results: Network analysis revealed that two cell cycle signature genes (POLA2 and KIF20B) predominantly regulated the mesenchymal modalities of OC and used to construct a prognostic model, termed the Cell Cycle Prognostic Signature of Ovarian Cancer (CCPOC). The CCPOC-high patients showed an unfavorable prognosis in the GSE26712 cohort, consistent with the results in the seven public validation cohorts and one independent internal cohort (BL-OC cohort, qRT-PCR, n = 51). Functional analysis, drug-sensitive analysis, and survival analysis showed that CCPOC-low patients were related to strengthened tumor immunogenicity and sensitive to the anti-PD-1/PD-L1 response rate in pan-cancer (r = −0.47, OC excluded), which indicated that CCPOC-low patients may be more sensitive to anti-PD-1/PD-L1.

Conclusion: We constructed and validated a subtype-specific, cell cycle-based prognostic signature for ovarian cancer, which has great potential for predicting the response of anti-PD-1/PD-L1.

The pyroptosis-related gene signature predicts prognosis and reveals characterization of the tumor immune microenvironment in acute myeloid leukemia

Background: Pyroptosis is a novel inflammatory form of programmed cell death and a prospective target for cancer therapy. Nevertheless, little is known about the association between pyroptosis-related genes (PRGs) and acute myeloid leukemia (AML) prognosis. Herein, we systematically investigated the specific functions and clinical prognostic value of multiple PRGs in AML.

Methods: Univariate and LASSO Cox regression analyses based on TCGA and GTEx databases were used to generate the PRG signature, whose predictive efficacy of survival was evaluated using survival analysis, ROC, univariate and multivariate Cox analyses as well as subgroup analysis. The BeatAML cohort was used for data validation. The association between risk score and immune cell infiltration, HLA, immune checkpoints, cancer stem cell (CSC), tumor mutation burden (TMB), and therapeutic drug sensitivity were also analyzed.

Results: Six -PRG signatures, namely, CASP3, ELANE, GSDMA, NOD1, PYCARD, and VDR were generated. The high-risk score represented a poorer prognosis and the PRG risk score was also validated as an independent predictor of prognosis. A nomogram including the cytogenetic risk, age, and risk score was constructed for accurate prediction of 1-, 3-, and 5-year survival probabilities. Meanwhile, this risk score was significantly associated with the tumor immune microenvironment (TIME). A high-risk score is characterized by high immune cell infiltration, HLA, and immune checkpoints, as well as low CSC and TMB. In addition, patients with low-risk scores presented significantly lower IC50 values for ATRA, cytarabine, midostaurin, doxorubicin, and etoposide.

Conclusion: Our findings might contribute to further understanding of PRGs in the prognosis and development of AML and provide novel and reliable biomarkers for its precise prevention and treatment.

Cyclin-Dependent Kinase Inhibitors Function as Potential Immune Regulators via Inducing Pyroptosis in Triple Negative Breast Cancer

Background

Immunotherapy is the most promising treatment in triple-negative breast cancer (TNBC), and its efficiency is largely dependent on the intra-tumoral immune cells infiltrations. Thus, novel ways to assist immunotherapy by increasing immune cell infiltrations were highly desirable.

Methods

To find key immune-related genes and discover novel immune-evoking molecules, gene expression profiles of TNBC were downloaded from Gene Expression Omnibus (GEO). Single-sample gene set enrichment analysis (ssGSEA) and Weighted Gene Co-expression Network Analysis (WGCNA) were conducted to identified hub genes. The CMap database was used subsequently to predicate potential drugs that can modulate the overall hub gene expression network. In vitro experiments were conducted to assess the anti-tumor activity and the pyroptosis phenotypes induced by GW-8510.

Results

Gene expression profiles of 198 TNBC patients were downloaded from GEO dataset GSE76124, and ssGSEA was used to divide them into Immune Cell Proficiency (ICP) group and Immune Cell Deficiency (ICD) group. Hub differential expressed gene modules between two groups were identified by WGCNA and then annotated by Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. A cyclin-dependent kinase (CDK) 2 inhibitor, GW-8510 was then identified by the CMap database and further investigated. Treatment with GW-8510 resulted in potent inhibition of TNBC cell lines. More importantly, in vitro and in vivo studies confirmed that GW-8510 and other CDK inhibitors (Dinaciclib, and Palbociclib) can induce pyroptosis by activating caspase-3 and GSDME, which might be the mechanism for their immune regulation potentials.

Conclusion

GW-8510, as well as other CDK inhibitors, might serve as potential immune regulators and pyroptosis promotors in TNBC.

Necroptosis-Related LncRNAs Signature and Subtypes for Predicting Prognosis and Revealing the Immune Microenvironment in Breast Cancer

Purpose

Necroptosis is a mode of programmed cell death that overcomes apoptotic resistance. We aimed to construct a steady necroptosis-related signature and identify subtypes for prognostic and immunotherapy sensitivity prediction.

Methods

Necroptosis-related prognostic lncRNAs were selected by co-expression analysis, and were used to construct a linear stepwise regression model via univariate and multivariate Cox regression, along with least absolute shrinkage and selection operator (LASSO). Quantitative reverse transcription polymerase chain reaction (RT-PCR) was used to measure the gene expression levels of lncRNAs included in the model. Based on the riskScore calculated, we separated patients into high- and low-risk groups. Afterwards, we performed CIBERSORT and the single-sample gene set enrichment analysis (ssGSEA) method to explore immune infiltration status. Furthermore, we investigated the relationships between the signature and immune landscape, genomic integrity, clinical characteristics, drug sensitivity, and immunotherapy efficacy.

Results

We constructed a robust necroptosis-related 22-lncRNA model, serving as an independent prognostic factor for breast cancer (BRCA). The low-risk group seemed to be the immune-activated type. Meanwhile, it showed that the higher the tumor mutation burden (TMB), the higher the riskScore. PD-L1-CTLA4 combined immunotherapy seemed to be a promising treatment strategy. Lastly, patients were assigned to 4 clusters to better discern the heterogeneity among patients.

Conclusions

The necroptosis-related lncRNA signature and molecular clusters indicated superior predictive performance in prognosis and the immune microenvironment, which may also provide guidance to drug regimens for immunotherapy and provide novel insights into precision medicine.

Clinical Characteristics in the Prediction of Posttreatment Survival of Patients with Ovarian Cancer

Objective

To determine the efficacy of clinical characteristics in the prediction of prognosis in patients with ovarian cancer.

Methods

Clinical data were collected from 3 datasets from TCGA database, including 1680 cases of ovarian serous cystadenocarcinoma, and were analyzed. Patients with ovarian cancer admitted to our hospital in 2016 were retrieved and followed up for prognosis analysis.

Results

From the datasets, for patients > 75 years old at the time of diagnosis, histologic grade and mutation count were good predictors for disease-free survival, while for patients > 50 years old at the time of diagnosis, histologic grade, race, fraction genome altered, and mutation count were good predictors for overall survival. In the patients (n = 38) retrieved from our hospital, the longest dimension of lesion (cm) and body weight at admission were good predictors for overall survival.

Conclusions

Those clinical factors, together with the two predictive equations, could be used to comprehensively predict the long-term prognosis of patients with ovarian cancer.