Significance of tumor mutation burden combined with immune infiltrates in the progression and prognosis of ovarian cancer

Mutant TP53 promotes invasion of lung cancer cells by regulating desmoglein 3

PURPOSE

Targeted therapies have markedly improved the prognosis of lung cancer patients; nevertheless, challenges persist, including limited beneficiary populations and the emergence of drug resistance. This study investigates the molecular mechanisms of mutant TP53 in lung cancer, aiming to contribute to novel strategies for targeted therapy.

METHODS

The TCGA database was employed to delineate the mutational landscape of TP53 in lung cancer patients. Differential gene expression between TP53-mutant and wild-type patients was analyzed, followed by functional enrichment. DSG3 protein expression in lung cancer patients was assessed using IHC, and its impact on prognosis was analyzed in the TCGA database. The influence of TP53 on the downstream gene DSG3 was investigated using qPCR, ChIP-qPCR, and luciferase reporter gene assays. Protein enrichment in the DSG3 promoter region was examined through IP-MS, and the regulatory role of the HIF1-α/TP53 complex on DSG3 was explored using Co-IP, luciferase assays, and ChIP-qPCR. Molecular interactions between TP53 (R273H) and HIF1-α were detected through immunoprecipitation and molecular docking. The effects and mechanisms of DSG3 on lung cancer phenotypes were assessed through WB, transwell, and wound healing assays.

RESULTS

TP53 mutations were present in 47.44% of patients, predominantly as missense mutations. DSG3 exhibited high expression in TP53-mutant lung cancer patients, and this elevated expression correlated with a poorer prognosis. TP53 interference led to a reduction in DSG3 mRNA expression, with TP53 mutant P53 enriching at the P2 site of the DSG3 promoter region, a recruitment facilitated by HIF1-α. The DBD region of TP53 (R273H) demonstrated interaction with HIF1-α. DSG3, activated through Ezrin phosphorylation, played a role in promoting invasion and metastasis.

CONCLUSIONS

Mutant TP53 facilitates lung cancer cell invasion by modulating desmoglein 3.

The potent potential of MFAP2 in prognosis and immunotherapy of triple-negative breast cancer

BACKGROUNDS

Microfibril-associated protein 2 (MFAP2) is a protein presenting in the extracellular matrix that governs the activity of microfibrils through its interaction with fibrillin. While the involvement of MFAP2 in metabolic disorders has been documented, its expression and prognostic significance in triple-negative breast cancer (TNBC) remain unexplored.

METHODS

We acquired datasets pertaining to breast cancer (BC) from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Next, a Venn diagram was used to identify the differentially expressed genes (DEGs). The DEGs were used to perform Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI), immune and survival analysis. The expressions of MFAP2, PD-1 and PD-L1 were examined by immunohistochemistry and western blot and their relationship with clinical pathological parameters were analyzed by clinical specimen samples from patients with TNBC. Tumor Immune Estimation Resource (TIMER, https://cistrome.shinyapps.io/timer/ ) was adopted to calculate the immune infiltration level of TNBC. The link between gene expression and tumor mutational burden (TMB) was described using Spearman's correlation analysis.

RESULTS

We identified 66 differentially expressed genes (DEGs) that were up-regulated. Among these DEGs, MFAP2 was found to be overexpressed in TNBC and was associated with a lower probability of survival. This finding was confirmed through the use of immunohistochemistry and western blot techniques. Additionally, MFAP2 was found to be related to various pathological parameters in TNBC patients. Mechanistically, gene set enrichment analysis (GSEA) revealed that MFAP2 primarily influenced cellular biological behavior in terms of epithelial mesenchymal transition, glycolysis, and apical junction. Notably, MFAP2 expression was positively correlated with the abundance of macrophages, while a negative correlation was observed with the abundance of B cells, CD4 + T cells, CD8 + T cells, neutrophils and dendritic cells through immune analysis. Furthermore, it was observed that MFAP2 displayed a negative correlation not only with tumor mutational burden (TMB), a recognized biomarker for PD-1/PD-L1 immunotherapy, but also with PD-L1 in samples of TNBC.

CONCLUSION

MFAP2 may be an important prognostic biomarker for TNBC, as well as a viable target for immunotherapy in this disease.

Tumour mutation burden and infiltrating immune cell subtypes influenced the breast cancer prognosis

Background

Most of its issues are still undecided on the relationship between tumour mutation burden (TMB) and immune-related genes in the breast cancer. This study explores their relationship based on gene mutation and transcription data in The Cancer Genome Atlas (TCGA) database, and the effects of immune cells in TMB and tumour microenvironments on prognosis of breast cancer patients.

Methods

Cases were divided into low-TMB and high-TMB subgroups. Differentially expressed immune-related genes were identified in different TMB subgroups, and patient prognosis was predicted by gene function enrichment analysis, invasive immune cells and different clinical pathological features were compared among different TMB subgroups.

Results

A total of 986 mutation data from breast cancer patients were obtained. Compared with low-TMB group, the survival period of high-TMB group was relatively longer. A total of 337 differential expression genes were identified in this study. Of these genes, seven differentially expressed immune-related genes were associated with prognosis. In the high-TMB group, activated CD4+ memory T cells and other cells had high expression, the expression ratio of memory B cells and other cells in low-TMB group was high.

Conclusions

TMB-related immunological infiltration characteristics showed meaningful value for prognosis prediction for breast cancer patients. Differentially expressed immune-related genes in TMB subgroups provide important information on the survival prediction.

Comparison of target agent treatment strategies for platinum-resistant recurrent ovarian cancer: A Bayesian network meta-analysis

BACKGROUND

We aimed to compare 7 newer immunotherapies and targeted therapies for platinum-resistant relapsed ovarian cancer.

METHODS

We conducted a comprehensive search of PubMed, EMBASE, and Cochrane Library electronic databases for phase III trials involving platinum-resistant recurrent ovarian cancer (PRrOC) patients treated with immunotherapy or targeted therapy in combination with chemotherapy. The quality of the included trials was assessed using the GRADE method. The primary outcome of comparison was progression-free survival, and secondary outcomes included overall survival and safety.

RESULTS

This analysis included 7 randomized phase III controlled trials, encompassing 2485 PRrOC patients. Combining bevacizumab plus chemotherapy and lurbinectedin demonstrated statistically significant differences in progression-free survival compared to all other regimens of interest. However, no statistically significant differences were observed in the overall survival. Nivolumab and mirvetuximab exhibited fewer serious adverse events than the other regimens of interest.

CONCLUSIONS

Our findings indicate that bevacizumab combined with chemotherapy and lurbinectedin monotherapy has significant efficacy in patients with PRrOC. For patients with PRrOC who have exhausted treatment options, nivolumab and mirvetuximab may be considered as alternatives because of their better safety profiles.

Exploring the impact of insufficient thermal ablation on hepatocellular carcinoma: NDST2 overexpression mechanism and its role in facilitating growth and invasion of residual cancer cells

OBJECTIVE

Incomplete thermal ablation (ITA) fosters the malignancy of residual cells in Hepatocellular carcinoma (HCC) with unclear mechanisms now. This study aims to investigate the expression changes of NDST2 following ITA of HCC and its impact on residual cancer cells.

METHODS

An in vitro model of heat stress-induced liver cancer was constructed to measure the expression of NDST2 using Quantitative Real-Time PCR and Western blotting experiments. The sequencing data from nude mice were used for validation. The clinical significance of NDST2 in HCC was evaluated by integrating datasets. Gene ontology and pathway analysis were conducted to explore the potential signaling pathways regulated by NDST2. Additionally, NDST2 was knocked down in heat stress-induced HCC cells, and the effects of NDST2 on these cells were verified using Cell Counting Kit-8 assays, scratch assays, and Transwell assays.

RESULTS

NDST2 expression levels are elevated in HCC, leading to a decrease in overall survival rates of HCC patients. Upregulation of immune checkpoint levels in high NDST2-expressing HCC may contribute to immune evasion by liver cancer cells. Additionally, the low mutation rate of NDST2 in HCC suggests a relatively stable expression of NDST2 in this disease. Importantly, animal and cell models treated with ITA demonstrate upregulated expression of NDST2. Knockdown of NDST2 in heat stress-induced liver cancer cells results in growth inhibition associated with gene downregulation.

CONCLUSION

The upregulation of NDST2 can accelerate the progression of residual HCC after ITA, suggesting a potential role for NDST2 in the therapeutic efficacy and prognosis of residual HCC.

Endometriosis-Associated Ovarian Cancer: From Molecular Pathologies to Clinical Relevance

Endometriosis is a chronic condition affecting reproductive-aged women, characterized by the growth of ectopic endometrial tissue. Despite being benign, endometriosis is associated with an increased risk of certain cancers, including endometriosis-associated ovarian cancer (EAOC). Ovarian cancer is rare, but more common in women with endometriosis, particularly endometrioid and clear-cell carcinomas. Factors such as hormonal imbalance, reproductive history, environmental exposures, and genetic predisposition contribute to the malignant transformation of endometriosis. Thus, understanding potential risk factors causing malignancy is crucial. Over the past few decades, various genetic mutations, microRNAs, as well as tumor microenvironmental factors have been identified, impacting pathways like PI3K/AKT/mTOR, DNA repair mechanisms, oxidative stress, and inflammation. Thus, this review aims to summarize molecular studies involved in EAOC pathogenesis as potential therapeutic targets. However, further research is needed to better understand the molecular and environmental factors driving EAOC development, to target the susceptibility of endometriotic lesions to malignant progression, and to identify effective therapeutic strategies.

Prognostic and immunotherapeutic potential of regulatory T cell-associated signature in ovarian cancer

Tumour-induced immunosuppressive microenvironments facilitate oncogenesis, with regulatory T cells (Tregs) serving as a crucial component. The significance of Treg-associated genes within the context of ovarian cancer (OC) remains elucidated insufficiently. Utilizing single-cell RNA sequencing (scRNA-Seq) for the identification of Treg-specific biomarkers, this investigation employed single-sample gene set enrichment analysis (ssGSEA) for the derivation of a Treg signature score. Weighted gene co-expression network analysis (WGCNA) facilitated the identification of Treg-correlated genes. Machine learning algorithms were employed to determine an optimal prognostic model, subsequently exploring disparities across risk strata in terms of survival outcomes, immunological infiltration, pathway activation and responsiveness to immunotherapy. Through WGCNA, a cohort of 365 Treg-associated genes was discerned, with 70 implicated in the prognostication of OC. A Tregs-associated signature (TAS), synthesized from random survival forest (RSF) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, exhibited robust predictive validity across both internal and external cohorts. Low TAS OC patients demonstrated superior survival outcomes, augmented by increased immunological cell infiltration, upregulated immune checkpoint expression, distinct pathway enrichment and differential response to immunotherapeutic interventions. The devised TAS proficiently prognosticates patient outcomes and delineates the immunological milieu within OC, offering a strategic instrument for the clinical stratification and selection of patients.

Central neurocytoma exhibits radial glial cell signatures with FGFR3 hypomethylation and overexpression

We explored the genomic events underlying central neurocytoma (CN), a rare neoplasm of the central nervous system, via multiomics approaches, including whole-exome sequencing, bulk and single-nuclei RNA sequencing, and methylation sequencing. We identified FGFR3 hypomethylation leading to FGFR3 overexpression as a major event in the ontogeny of CN that affects crucial downstream events, such as aberrant PI3K-AKT activity and neuronal development pathways. Furthermore, we found similarities between CN and radial glial cells based on analyses of gene markers and CN tumor cells and postulate that CN tumorigenesis is due to dysregulation of radial glial cell differentiation into neurons. Our data demonstrate the potential role of FGFR3 as one of the leading drivers of tumorigenesis in CN.

Glycosylation-related genes mediated prognostic signature contribute to prognostic prediction and treatment options in ovarian cancer: based on bulk and single‑cell RNA sequencing data

BACKGROUND

Ovarian cancer (OC) is a complex disease with significant tumor heterogeneity with the worst prognosis and highest mortality among all gynecological cancers. Glycosylation is a specific post-translational modification that plays an important role in tumor progression, immune escape and metastatic spread. The aim of this work was to identify the major glycosylation-related genes (GRGs) in OC and construct an effective GRGs signature to predict prognosis and immunotherapy.

METHODS

AUCell algorithm was used to identify glycosylation-related genes (GRGs) based on the scRNA-seq and bulk RNA-seq data. An effective GRGs signature was conducted using COX and LASSO regression algorithm. The texting dataset and clinical sample data were used to assessed the accuracy of GRGs signature. We evaluated the differences in immune cell infiltration, enrichment of immune checkpoints, immunotherapy response, and gene mutation status among different risk groups. Finally, RT-qPCR, Wound-healing assay, Transwell assay were performed to verify the effect of the CYBRD1 on OC.

RESULTS

A total of 1187 GRGs were obtained and a GRGs signature including 16 genes was established. The OC patients were divided into high- and low- risk group based on the median riskscore and the patients in high-risk group have poor outcome. We also found that the patients in low-risk group have higher immune cell infiltration, enrichment of immune checkpoints and immunotherapy response. The results of laboratory test showed that CYBRD1 can promote the invasion, and migration of OC and is closely related to the poor prognosis of OC patients.

CONCLUSIONS

Our study established a GRGs signature consisting of 16 genes based on the scRNA-seq and bulk RNA-seq data, which provides a new perspective on the prognosis prediction and treatment strategy for OC.

A bioinformatic analysis found low expression and clinical significance of ATF4 in breast cancer

Background

Activating Transcription Factor 4 (ATF4) expression exhibits differential patterns across different types of tumors. Besides, the pathogenesis of breast cancer is complex, and the exact relationship between ATF4 and ATF4 remains uncertain.

Methods

The analysis of ATF4 expression was conducted by utilizing The Cancer Genome Atlas (TCGA) pan-cancer data, while the gene expression profile of breast cancer was checked by the comprehensive database-Gene Expression Omnibus database. In order to gain a more comprehensive understanding of the specific cell types that exhibit ATF4 expression within the microenvironment of breast cancer, we conducted a single-cell analysis of ATF4 using two distinct datasets of human breast cancer (GSE114717 and GSE11088, respectively). The spatial distribution of ATF4 within a tissue was demonstrated based on datasets obtained from the Human Protein Atlas (HPA) and SpatialDB. The clinical prognostic significance of ATF4 was assessed by analyzing clinical survival data obtained from TCGA, GSE4830, and GSE25055 datasets. We used the R package clusterProfiler to carry out an enrichment analysis of ATF4. We assessed how ATF4 impacts the growth and movement of breast cancer cell lines. We manipulated ATF4 levels using plasmid transfection techniques.

Results

The expression of ATF4 was found to be suboptimal and demonstrated a significant correlation with enhanced disease-specific survival (p = 0.012) and overall survival (p = 0.032) in breast cancer as well as other malignancies. We conducted an analysis to investigate the interaction between the infiltration level of immune cells and the expression of ATF4, using samples obtained from TCGA with known immune cell infiltration scores. Furthermore, a notable positive correlation exists between the elevated expression of ATF4 and immune-related genomes, specifically those associated with chemokine as well as immunity. Subsequent examination revealed a notable augmentation in the cytodifferentiation of T cells into regulatory T (Treg) cells within tissues exhibiting elevated levels of ATF4 expression. ATF4 exhibits notable upregulation in the MDA-MB-231 cell, thereby exerting a substantial impact on cell proliferation and migration upon its knockdown. Conversely, the overexpression of ATF4 in the MCF7 Luminal A breast cancer cell line can also modulate cellular function.

Conclusions

Our study suggests that ATF4 helps T cells differentiate into Treg cells in breast cancer. ATF4 can represent a clinically useful biomarker to predict the overall survival rate, especially in patients with different subtypes of breast cancer. Provide certain guidance value for the development of targeted drugs or inhibitors targeting ATF4.

Pharmacogenomic Analysis of Combined Therapies against Glioblastoma Based on Cell Markers from Single-Cell Sequencing

Glioblastoma is the most common and aggressive form of primary brain cancer and the lack of viable treatment options has created an urgency to develop novel treatments. Personalized or predictive medicine is still in its infancy stage at present. This research aimed to discover biomarkers to inform disease progression and to develop personalized prophylactic and therapeutic strategies by combining state-of-the-art technologies such as single-cell RNA sequencing, systems pharmacology, and a polypharmacological approach. As predicted in the pyroptosis-related gene (PRG) transcription factor (TF) microRNA (miRNA) regulatory network, TP53 was the hub gene in the pyroptosis process in glioblastoma (GBM). A LASSO Cox regression model of pyroptosis-related genes was built to accurately and conveniently predict the one-, two-, and three-year overall survival rates of GBM patients. The top-scoring five natural compounds were parthenolide, rutin, baeomycesic acid, luteolin, and kaempferol, which have NFKB inhibition, antioxidant, lipoxygenase inhibition, glucosidase inhibition, and estrogen receptor agonism properties, respectively. In contrast, the analysis of the cell-type-specific differential expression-related targets of natural compounds showed that the top five subtype cells targeted by natural compounds were endothelial cells, microglia/macrophages, oligodendrocytes, dendritic cells, and neutrophil cells. The current approach-using the pharmacogenomic analysis of combined therapies-serves as a model for novel personalized therapeutic strategies for GBM treatment.

Review of the Immune Checkpoint Inhibitors in the Context of Cancer Treatment

Checkpoint proteins are an integral part of the immune system and are used by the tumor cells to evade immune response, which helps them grow uncontrollably. By blocking these proteins, immune checkpoint inhibitors can restore the capability of the immune system to attack cancer cells and stop their growth. These findings are backed by adequate clinical trial data and presently, several FDA-approved immune checkpoint inhibitors exist in the market for treating various types of cancers, including melanoma, hepatocellular, endometrial, lung, kidney and others. Their mode of action is inhibition by targeting the checkpoint proteins CTLA-4, PD-1, PD-L1, etc. They can be used alone as well as in amalgamation with other cancer treatments, like surgery, radiation or chemotherapy. Since these drugs target only specific immune system proteins, their side effects are reduced in comparison with the traditional chemotherapy drugs, but may still cause a few affects like fatigue, skin rashes, and fever. In rare cases, these inhibitors are known to have caused more serious side effects, such as cardiotoxicity, and inflammation in the intestines or lungs. Herein, we provide an overview of these inhibitors and their role as biomarkers, immune-related adverse outcomes and clinical studies in the treatment of various cancers, as well as present some future perspectives.

Identification of Basement Membrane-Related Signatures in Gastric Cancer

BACKGROUND

The basement membrane (BM) serves as a major barrier to impede tumor cell invasion and extravasation during metastasis. However, the associations between BM-related genes and GC remain unclear.

METHODS

RNA expression data and corresponding clinical information of STAD samples were downloaded from the TCGA database. We identified BM-related subtypes and constructed a BM-related gene prognostic model using lasso-Cox regression analysis. We also investigated the single-cell properties of prognostic-related genes and the TME characteristic, TMB status, and chemotherapy response in high- and low-risk groups. Finally, we verified our results in the GEPIA database and human tissue specimens.

RESULTS

A six-gene lasso Cox regression model (APOD, CAPN6, GPC3, PDK4, SLC7A2, SVEP1) was developed. Activated CD4+ T cells and follicular T cells were shown to infiltrate more widely in the low-risk group. The low-risk group harbored significantly higher TMB and better prognosis, favoring immunotherapy.

CONCLUSIONS

We constructed a six-gene BM-related prognostic model for predicting GC prognosis, immune cell infiltration, TMB status, and chemotherapy response. This research provides new ideas for developing more effective individualized treatment of GC patients.

Molecular and phenotypic characteristics influencing the degree of cytoreduction in high-grade serous ovarian carcinomas

BACKGROUND

High-grade serous ovarian carcinoma (HGSOC) is the deadliest ovarian cancer subtype, and survival relates to initial cytoreductive surgical treatment. The existing tools for surgical outcome prediction remain inadequate for anticipating the outcomes of the complex relationship between tumour biology, clinical phenotypes, co-morbidity and surgical skills. In this genotype-phenotype association study, we combine phenotypic markers with targeted DNA sequencing to discover novel biomarkers to guide the surgical management of primary HGSOC.

METHODS

Primary tumour tissue samples (n = 97) and matched blood from a phenotypically well-characterised treatment-naïve HGSOC patient cohort were analysed by targeted massive parallel DNA sequencing (next generation sequencing [NGS]) of a panel of 360 cancer-related genes. Association analyses were performed on phenotypic traits related to complete cytoreductive surgery, while logistic regression analysis was applied for the predictive model.

RESULTS

The positive influence of complete cytoreductive surgery (R0) on overall survival was confirmed (p = 0.003). Before surgery, low volumes of ascitic fluid, lower CA125 levels, higher platelet counts and relatively lower clinical stage at diagnosis were all indicators, alone and combined, for complete cytoreduction (R0). Mutations in either the chromatin remodelling SWI_SNF (p = 0.036) pathway or the histone H3K4 methylation pathway (p = 0.034) correlated with R0. The R0 group also demonstrated higher tumour mutational burden levels (p = 0.028). A predictive model was developed by combining two phenotypes and the mutational status of five genes and one genetic pathway, enabling the prediction of surgical outcomes in 87.6% of the cases in this cohort.

CONCLUSION

Inclusion of molecular biomarkers adds value to the pre-operative stratification of HGSOC patients. A potential preoperative risk stratification model combining phenotypic traits and single-gene mutational status is suggested, but the set-up needs to be validated in larger cohorts.

Developing four cuproptosis-related lncRNAs signature to predict prognosis and immune activity in ovarian cancer

BACKGROUND

There has been a recent discovery of a new type of cell death produced by copper-iron ions, called Cuproptosis (copper death). The purpose of this study was to identify LncRNA signatures associated with Cuproptosis in ovarian cancer that could be used as prognostic indicators.

METHODS

RNA sequencing (RNA-seq) profiles with clinicopathological data from TCGA database were used to select prognostic CRLs and then constructed prognostic risk model using multivariate regression analysis and LASSO algorithms. An independent dataset from GEO database was used to validate the prognostic performance. Combined with clinical factors, we further constructed a prognostic nomogram. In addition, tumor immune microenvironment, somatic mutation and drug sensitivity were analyzed using ssGSEA, GSVA, ESTIMATE and CIBERSORT algorithms.

RESULT

A total of 129 CRLs were selected whose expression levels were significantly related to expression levels of 10 cuproptosis-related genes. The univariate Cox regression analysis showed that 12 CRLs were associated with overall survival (OS). Using LASSO algorithms and multivariate regression analysis, we constructed a four-CRLs prognostic signature in the training dataset. Patients in the training dataset could be classified into high- or low-risk subgroups with significantly different OS (log-rank p < 0.001). The prognostic performance was confirmed in TCGA-OC cohort (log-rank p < 0.001) and an independent GEO cohort (log-rank p = 0.023). Multivariate cox regression analysis proved the four-CRLs signature was an independent prognostic factor for OC. Additionally, different risk subtypes showed significantly different levels of immune cells, signal pathways, and drug response.

CONCLUSION

We established a prognostic signature based on cuproptosis-related lncRNAs for OC patients, which will be of great value in predicting the prognosis patients and may provide a new perspective for research and individualized treatment.

Comprehensive analysis of RNA-binding protein SRSF2-dependent alternative splicing signature in malignant proliferation of colorectal carcinoma

Aberrant expression of serine/arginine-rich splicing factor 2 (SRSF2) can lead to tumorigenesis, but its molecular mechanism in colorectal cancer is currently unknown. Herein, we found SRSF2 to be highly expressed in human colorectal cancer (CRC) samples compared with normal tissues. Both in vitro and in vivo, SRSF2 significantly accelerated the proliferation of colon cancer cells. Using RNA-seq, we screened and identified 33 alternative splicing events regulated by SRSF2. Knockdown of SLMAP-L or CETN3-S splice isoform could suppress the growth of colon cancer cells, predicting their role in malignant proliferation of colon cancer cells. Mechanistically, the in vivo crosslinking immunoprecipitation assay demonstrated the direct binding of the RNA recognition motif of SRSF2 protein to SLMAP and CETN3 pre-mRNAs. SRSF2 activated the inclusion of SLMAP alternative exon 24 by binding to constitutive exon 25, while SRSF2 facilitated the exclusion of CETN3 alternative exon 5 by binding to neighboring exon 6. Knockdown of SRSF2, its splicing targets SLMAP-L, or CETN3-S caused colon cancer cells to arrest in G1 phase of the cell cycle. Rescue of SLMAP-L or CETN3-S splice isoform in SRSF2 knockdown colon cancer cells could effectively reverse the inhibition of cell proliferation by SRSF2 knockdown through mediating cell cycle progression. Importantly, the percentage of SLMAP exon 24 inclusion increased and CETN3 exon 5 inclusion decreased in CRC samples compared to paired normal samples. Collectively, our findings identify that SRSF2 dysregulates colorectal carcinoma proliferation at the molecular level of splicing regulation and reveal potential splicing targets in CRC patients.

The integrative analysis based on super-enhancer related genes for predicting different subtypes and prognosis of patient with lower-grade glioma

Objective: Emerging evidence revealed that super-enhancer plays a crucial role in the transcriptional reprogramming for many cancers. The purpose aimed to explored how the super-enhancer related genes affects the prognosis and tumor immune microenvironment (TIME) of patients with low-grade glioma (LGG). Methods: In this study, the differentially expressed genes (DEGs) between LGG cohorts and normal brain tissue cohort were identified by the comprehensive analysis of the super-enhancer (SE) related genes. Then non-negative matrix factorization was performed to seek the optimal classification based on the DEGs, while investigating prognostic and clinical differences between different subtypes. Subsequently, a prognostic related signature (SERS) was constructed for the comprehensive evaluation in term of individualized prognosis, clinical characteristics, cancer markers, genomic alterations, and immune microenvironment of patients with LGG. Results: Based on the expression profiles of 170 DEGs, we identified three SE subtypes, and the three subtypes showed significant differences in prognostic, clinicopathological features. Then, nine optimal SE-related genes were selected to construct the SERS through the least absolute shrinkage and selection operator Cox regression analysis. Survival analysis showed that SERS had strong and stable predictive ability for the prognosis of LGG patients in the The Cancer Genome Atlas, China Glioma Genome Atlas, and Remdrandt cohorts, respectively. We also found that SERS was highly correlated with clinicopathological features, tumor immune microenvironment, cancer hallmarks, and genomic alterations in LGG patients. In addition, the predictive power of SERS for immune checkpoint inhibitor treatment is also superior. The qRT-PCR results and immunohistochemical results also confirmed the difference in the expression of four key genes in normal cells and tumors, as well as in normal tissues and tumor tissues. Conclusion: The SERS could be suitable to utilize individualized prognosis prediction and immunotherapy options for LGG patients in clinical application.

Implementation of Comprehensive Genomic Profiling in Ovarian Cancer Patients: A Retrospective Analysis

Comprehensive genomic profiling (CGP) allows for the detection of driver alterations at high resolution, but the limited number of approved targeted therapies and their high costs have contributed to its limited clinical utilization. We retrospectively compared data of 946 women with ovarian cancer (11.4% were referred to CGP, and 88.6% served as control) to examine whether CGP provides a prognosis benefit. Patient baseline parameters were similar between the groups. Cox regression analysis adjusted for age, disease stage at diagnosis, and recurrence status showed statistically significantly longer median overall survival (mOS) in the CGP group versus the control (73.4 versus 54.5 months, p < 0.001). Fifty-four patients (52.9%) had actionable mutations with potential treatments; twenty-six (48.2%) were treated with matched targeted therapy, showing a trend for longer mOS than the eighty-six women in the CGP group who were not given a suggested treatment (105.5 versus 63.6 months, p = 0.066). None of the genomic alterations predicted metastasis location. CCNE1 amplification and KRAS mutations were associated with shorter mOS. Patients with tumor mutation burden ≥4 mutations/megabase had longer mOS. High loss of heterozygosity was associated with longer mOS (99.0 versus 48.2 months, p = 0.004). CGP testing may provide both prognostic and predictive insights for treatment of patients with ovarian cancer. Prospective studies of larger cohorts are warranted.

Novel pyroptosis-associated genes signature for predicting the prognosis of sarcoma and validation

BACKGROUND

Sarcoma is a rare mesenchymal malignant tumor. Recently, pyroptosis has been reported to be a mode of programmed cell death. Nonetheless, levels of pyroptosis-associated genes in sarcoma and its relevance to prognostic outcomes are yet to be elucidated.

RESULTS

Sarcoma cases were classified into two subtypes with regards to differentially expressed genes. We established a profile composed of seven genes and classified the sarcoma patients into low- and high-risk groups through least absolute shrinkage and selection operator Cox regression. Survival rate of low-risk sarcoma patients was markedly higher, relative to high-risk group (P<0.001). In combination with clinical features, the risk score was established to be an independent predictive factor for OS of sarcoma patients. Chemotherapeutic drug sensitivity response analysis found 65 drugs with higher drug sensitivity in low-risk, than in high-risk group and 14 drugs with higher drug sensitivity in the high-risk patient group, compared with low-risk patient group. In addition, functional enrichment, pathway and gene mutation of the two modules were analyzed. Finally, we used qRT-PCR to detect the expression of seven pyroptosis-related genes in tumor cells, and human skeletal muscle cells, compared with human skeletal muscle cells, PODXL2, LRRC17, GABRA3, SCUBE3 and RFLNB genes show high expression levels in tumor cells, while IGHG2 and hepatic leukemia factor show low expression levels in tumor cells.

CONCLUSIONS

Our research suggest that pyroptosis is closely associated with sarcoma, and these findings confirm that pyroptosis-associated seven genes have a critical role in sarcoma and are potential prognostic factors for sarcoma.

Role of androgen receptor signaling pathway-related lncRNAs in the prognosis and immune infiltration of breast cancer

Androgen receptor (AR) is strong association with breast cancer (BRCA). We aimed to investigate the effect of the androgen receptor signaling pathway-related long non-coding RNAs (ARSP-related lncRNAs) on the process of subtype classification and the tumor microenvironment (TME) of breast cancer (BRCA). Our study screen ARSP-related lncRNAs for the construction of a risk model. The single-sample gene set enrichment analysis (ssGSEA) method was used to detect the differences between the immune responses generated by the patients belonging to the low- and high-risk groups. The relationship between the ARSP-related lncRNAs and TME was explored following the process of cluster analysis. The univariate Cox analysis and the Lasso regression analysis method was used to screen nine of these lncRNAs to develop a risk model. It was observed that risk score could function as an independent prognostic factor, affecting the prognoses of patients suffering from BRCA. The validity of the model was assessed by analyzing the generated calibration curves and a nomogram. Additionally, the effect of the risk score on the extent of immune cell infiltration realized in TME was explored. M2 macrophages correlated positively, whereas NK cells, CD4+ T cells, and naive B cells correlated negatively with the risk score. Results obtained using the cluster analysis indicated that immune scores correlated with clustered subtypes. Finally, the risk score and cluster subtypes were analyzed to study the sensitivity of the patients toward different drugs to identify the appropriate therapeutic agents. The prognoses of patients suffering from BRCA can be accurately predicted by ARSP-related lncRNAs.

Cuproptosis associated genes affect prognosis and tumor microenvironment infiltration characterization in lung adenocarcinoma

Cuproptosis, a newly discovered mechanism of programmed cell death, is important for detailing the metabolic aspects of cancer progression and thereby guiding cancer therapy. An exciting era of translational medicine has led to the rapid development of countless immunotherapeutic strategies. The existing successful cancer immunotherapies have sparked new hope for patients with solid and hematologic malignancies. Hence, it is important to characterize the link between the cuproptosis process and the immunity status in the tumor microenvironment (TME) in Lung Adenocarcinoma (LUAD), which may be able to predict patient's prognosis. In this study, we systematically assessed 10 cuproptosis-associated genes (CAGs) and comprehensively characterized the relationship between cuproptosis and the molecular characteristics and immune cell infiltration of tumor tissue, prognosis and clinical treatment of patients. Subsequently, the CAG_score for predicting overall survival (OS) was established and its reliable predictive ability in LUAD patients was confirmed. Next, we created a highly reliable nomogram to facilitate the clinical viability of the CAG_score. The low CAG_score group, with lower immune cell infiltration, and mutation burden, had a significantly superior OS, which was associated with a better response to immunotherapy. The present study revealed that cuproptosis play a significant role in TME regulation in LUAD. Collectively, we identified a prognostic CAGs-related signature for LUAD patients. This signature may contribute to clarifying the characteristics of TME and enable the exploration of more potent immunotherapy strategies.

Relationships of Ferroptosis and Pyroptosis-Related Genes with Clinical Prognosis and Tumor Immune Microenvironment in Head and Neck Squamous Cell Carcinoma

Ferroptosis and pyroptosis are two new programmed cell death (PCD) modes discovered in recent years. However, the potential value of ferroptosis and pyroptosis-related genes (FPRGs) in prognosis prediction and the tumor immune microenvironment of head and neck squamous cell carcinoma (HNSCC) is still unclear. We obtained 21 significant FPRGs based on the training dataset (TCGA- HNSC) using the univariate Cox and differential expression analysis. The TCGA- HNSC (n = 502) dataset was clustered into two group (clusters A and B) based on the 21 significant FPRGs. 1467 differentially expressed genes (DEGs) between cluster A and B were put into univariate Cox and Least absolute shrinkage and selection operator (LASSO) analysis to build a risk model. The predictive capability of the risk model was successfully confirmed by internal validation, external validation, and clinical sample validation. To improve the clinical applicability, a nomogram model combined risk score and clinical information were constructed. Moreover, the patients with lower risk score were characterized by increased immune response and tumor mutation burden (TMB), while the patients with higher risk score were characterized by increased TP53 mutation rate. In conclusion, our comprehensive analysis of the FPRGs revealed their significant role in prognosis prediction and the tumor immune microenvironment. The risk model containing 9 FPRGs could be a potential prognostic markers and effective immunotherapy targets for HNSCC.

Role of RBMS3 Novel Potential Regulator of the EMT Phenomenon in Physiological and Pathological Processes

RNA-binding protein 3 (RBMS3) plays a significant role in embryonic development and the pathogenesis of many diseases, especially cancer initiation and progression. The multiple roles of RBMS3 are conditioned by its numerous alternative expression products. It has been proven that the main form of RBMS3 influences the regulation of microRNA expression or stabilization. The absence of RBMS3 activates the Wnt/β-catenin pathway. The expression of c-Myc, another target of the Wnt/β-catenin pathway, is correlated with the RBMS3 expression. Numerous studies have focused solely on the interaction of RBMS3 with the epithelial-mesenchymal transition (EMT) protein machinery. EMT plays a vital role in cancer progression, in which RBMS3 is a new potential regulator. It is also significant that RBMS3 may act as a prognostic factor of overall survival (OS) in different types of cancer. This review presents the current state of knowledge about the role of RBMS3 in physiological and pathological processes, with particular emphasis on carcinogenesis. The molecular mechanisms underlying the role of RBMS3 are not fully understood; hence, a broader explanation and understanding is still needed.

Pyroptosis-related gene expression patterns and corresponding tumor microenvironment infiltration characterization in ovarian cancer

Pyroptosis, a form of inflammatory programmed cell death, is accompanied by inflammation and participate in the body's immune response. The expression of pyroptosis-related genes (PRGs) is associated with tumor prognosis in ovarian cancer (OC), but it is still unknown whether pyroptosis can affect tumor immune microenvironment (TME) of OC. Based on 30 PRGs, we comprehensively assessed the pyroptosis patterns by using PRGscore and correlated them with TME features in 474 OC patients. Finally, we identified three pyroptosis modification patterns and TME immune characteristics of these patterns were in response to three immune phenotypes (immune-desert, immune-inflamed, and immune-excluded phenotypes). PRGscore can predict patient survival, staging, grading, and immunotherapy efficacy. Low PRGscore was associated with better survival advantage and increased mutation burden. Low PRGscore patients showed significantly better therapeutic effects and clinical results in chemotherapy and immunotherapy. Besides, the capability of PRGscore in predicting prognosis and immunotherapy sensitivity was further verified in other three tumor cohorts. In conclusion, the comprehensive assessment of OC pyroptosis modifications can help enhancing our understanding of TME immune infiltration and provide better personalized treatment tactics for OC patients.

KLHL14: A Novel Prognostic Biomarker and Therapeutic Target for Ovarian Cancer

Ovarian cancer (OV) is a gynaecological malignancy that poses a serious risk to the health status of women. To date, effective molecular markers are unavailable for the diagnosis and management of ovarian malignancies. In this study, we aimed to investigate the molecular markers associated with the development of this cancer. We used bioinformatic analysis to determine the molecules and genes related to ovarian cancer using the gene expression profiling interactive analysis (GEPIA) and the cancer genome Atlas (TCGA) databases. In addition, we examined the genes and mechanisms underlying ovarian cancer. Our results showed that the KLHL14 gene is overexpressed in some cancers. An increase in the KLHL14 expression indicates a poor prognosis in patients with ovarian cancer. Results of immune cell infiltration analysis and half-maximal inhibitory concentration (IC50) analysis provide novel insights into the treatment of ovarian cancer. KLHL14 is anticipated to emerge as a novel molecular marker specifically for ovarian carcinoma.

Identification of necroptosis-related subtypes and prognosis model in triple negative breast cancer

Background

Necroptosis is considered to be a new form of programmed necrotic cell death, which is associated with metastasis, progression and prognosis of various types of tumors. However, the potential role of necroptosis-related genes (NRGs) in the triple negative breast cancer (TNBC) is unclear.

Methods

We extracted the gene expression and relevant clinicopathological data of TNBC from The Cancer Genome Atlas (TCGA) databases and the Gene Expression Omnibus (GEO) databases. We analyzed the expression, somatic mutation, and copy number variation (CNV) of 67 NRGs in TNBC, and then observed their interaction, biological functions, and prognosis value. By performing Lasso and COX regression analysis, a NRGs-related risk model for predicting overall survival (OS) was constructed and its predictive capabilities were verified. Finally, the relationship between risk_score and immune cell infiltration, tumor microenvironment (TME), immune checkpoint, and tumor mutation burden (TMB), cancer stem cell (CSC) index, and drug sensitivity were analyzed.

Results

A total 67 NRGs were identified in our analysis. A small number of genes (23.81%) detected somatic mutation, most genes appeared to have a high frequency of CNV, and there was a close interaction between them. These genes were remarkably enriched in immune-related process. A seven-gene risk_score was generated, containing TPSG1, KRT6A, GPR19, EIF4EBP1, TLE1, SLC4A7, ESPN. The low-risk group has a better OS, higher immune score, TMB and CSC index, and lower IC50 value of common therapeutic agents in TNBC. To improve clinical practicability, we added age, stage_T and stage_N to the risk_score and construct a more comprehensive nomogram for predicting OS. It was verified that nomogram had good predictive capability, the AUC values for 1-, 3-, and 5-year OS were 0.847, 0.908, and 0.942.

Conclusion

Our research identified the significant impact of NRGs on immunity and prognosis in TNBC. These findings were expected to provide a new strategy for personalize the treatment of TNBC and improve its clinical benefit.

CTNNB1 mutations, TERT polymorphism and CD8+ cell densities in resected hepatocellular carcinoma are associated with longer time to recurrence

Background

Hepatocellular carcinoma (HCC) is a fatal disease characterized by early genetic alterations in telomerase reverse transcriptase promoter (TERTp) and β-catenin (CTNNB1) genes and immune cell activation in the tumor microenvironment. As a novel approach, we wanted to assess patient survival influenced by combined presence of mutations and densities of CD8+ cytotoxic T cells.

Methods

Tissue samples were obtained from 67 HCC patients who had undergone resection. We analysed CD8+ T cells density, TERTp mutations, rs2853669 polymorphism, and CTNNB1 mutations. These variables were evaluated for time to recurrence (TTR) and disease free survival (DFS).

Results

TERTp mutations were found in 75.8% and CTNNB1 mutations in 35.6% of the patients. TERTp mutations were not associated with survival but polymorphism rs2853669 in TERTp was associated with improved TTR and DFS. CTNNB1 mutations were associated with improving TTR. High density of CD8+ T-lymphocytes in tumor center and invasive margin correlated with longer TTR and DFS. Combined genetic and immune factors further improved survival showing higher predictive values. E.g., combining CTNNB1 mutations and high density of CD8+ T-lymphocytes in tumor center yielded HRs of 0.12 (0.03–0.52), p = 0.005 for TTR and 0.25 (0.09–0.74), p = 0.01 for DFS.

Conclusion

The results outline a novel integrative approach for prognostication through combining independent predictive factors from genetic and immune cell profiles. However, larger studies are needed to explore multiple cell types in the tumor microenvironment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09989-0.

A novel tumor mutational burden-based risk model predicts prognosis and correlates with immune infiltration in ovarian cancer

Tumor mutational burden (TMB) has been reported to determine the response to immunotherapy, thus affecting the patient’s prognosis in many cancers. However, it is unclear whether TMB or TMB-related signature could be used as prognostic indicators for ovarian cancer (OC), as its potential association with immune infiltration remains poorly understood. Therefore, this study aimed to develop a novel TMB-related risk model (TMBrisk) to predict the prognosis of OC patients on the basis of exploring TMB-related genes, and to explore the potential association between TMB/TMBrisk and immune infiltration. The mutational landscape, TMB scores, and correlations between TMB and clinical characteristics and immune infiltration were investigated in The Cancer Genome Atlas (TCGA)-OV cohort. Differentially expressed gene (DEG) analyses and weighted gene co-expression network analysis (WGCNA) were performed to derive TMB-related genes. TMBrisk was constructed by Cox regression and further validated in Gene Expression Omnibus (GEO) datasets. The mRNA and protein expression levels and biological functions of TMBrisk hub genes were verified through Gene Expression Profiling Interactive Analysis (GEPIA), GSCA Lite, the Human Protein Atlas (HPA) database, and RT-qPCR. TMBrisk-related biological phenotypes were analyzed in function enrichment and tumor immune infiltration signature. Potential therapeutic regimens were inferred utilizing the Genomics of Drug Sensitivity in Cancer (GDSC) database and connectivity map (CMap). According to our results, higher TMB was associated with better survival and higher CD8+ T cell, regulatory T cell, and NK cell infiltration. TMBrisk was developed based on CBWD1, ST7L, RFX5-AS1, C3orf38, LRFN1, LEMD1, and HMGB1. High TMBrisk was identified as a poor factor for prognosis in TCGA and GEO datasets; the high-TMBrisk group comprised more higher-grade (G2 and G3) and advanced clinical stage (stage III/IV) tumors. Meanwhile, higher TMBrisk was associated with an immunosuppressive phenotype, with less infiltration of a majority of immunocytes and less expression of several genes of the human leukocyte antigen (HLA) family. Moreover, a nomogram containing TMBrisk showed a strong predictive ability demonstrated by time-dependent ROC analysis. Overall, this novel TMB-related risk model (TMBrisk) could predict prognosis, evaluate immune infiltration, and discover new therapeutic regimens in OC, which is very promising in clinical promotion.

Overexpression of LIMA1 Indicates Poor Prognosis and Promotes Epithelial-Mesenchymal Transition in Head and Neck Squamous Cell Carcinoma

Background:

LIMA1 encodes LIM domain and actin binding 1, a cytoskeleton-associated protein whose loss has been linked to migration and invasion behavior of cancer cells. However, the roles of LIMA1 underlying the malignant behavior of tumors in head and neck squamous cell carcinoma (HNSC) are not fully understood.

Methods:

We conducted a multi-omics study on the role of LIMA1 in HNSC based on The Cancer Genome Atlas data. Subsequent in vitro experiments were performed to validate the results of bioinformatic analysis. We first identified the correlation between LIMA1 and tumor cell functional states according to single-cell sequencing data in HNSC. The potential downstream effects of LIMA1 were explored for gene ontology and Kyoto Encyclopedia of Genes and Genomes pathways through functional enrichment analysis of the gene sets that correlated with LIMA1 in HNSC. The prognostic role of LIMA1 was assessed using the log rank test to compare difference in survival between LIMA1^High and LIMA1^Low patients. Univariate Cox regression and multivariate Cox regression were further carried out to identify the prognostic value of LIMA1 in HNSC.

Results:

LIMA1 was identified as a prognostic biomarker and is associated with epithelial-mesenchymal transition (EMT) progress in HNSC. In vitro silencing of LIMA1 suppressed EMT and related pathways in HNSC.

Conclusions:

LIMA1 promotes EMT and further leads to tumor invasion and metastasis. Increased expression of LIMA1 indicates poor survival, identifying it as a prognostic biomarker in HNSC.

Identification of pyroptosis related subtypes and tumor microenvironment infiltration characteristics in breast cancer

Understanding the association of pyroptosis with tumor progression, prognosis and effect on immunotherapeutic response in breast cancer (BC) is limited. This study analysed forty pyroptosis-related genes to construct the pyroptosis score. Association of the pyroptosis score with the overall survival, clinical features, tumor mutation load, immune cell infiltration, and treatment sensitivity of patients with BC was analysed. Out of 983 BC samples, 304 (30.93%) had genetic alterations with the highest TP53 frequency. We identified three separate subtypes associated with pyroptosis action. These subtypes correlate with the clinicopathological characteristics, TME immune cell infiltration, and disease prognosis. Based on the expression levels of the pyroptosis genes, we divided the pyroptosis score into a high group and a low group. The immune-activated pyroptosis subtype had a higher score with a better prognosis. We also observed that the pyroptosis score correlates with the tumor mutation burden. The pyroptosis score and disease prognosis were directly proportional. A higher pyroptosis score indicated a better prognosis. Results suggest that the pyroptosis-related gene prognosis model is closely related to the immune cell infiltration of BC. The three pyroptosis subtypes associated with BC assist in accurately identifying the tumor subtype, the prognosis of immunotherapy drugs and the patient’s therapeutic response.

Introduction of mutant TP53 related genes in metabolic pathways and evaluation their correlation with immune cells, drug resistance and sensitivity

BACKGROUND

Although the relationship between TP53 mutation, TP53 metabolism pathways, and tumorigenesis has been investigated, pan-cancer analysis of TP53 mutations and related metabolism pathways is not completely available in common types of human cancers. Thus, this study was going to represent TP53 mutant-related metabolism genes and pathways in a pan-cancer study and investigate the relationship between selected genes and drug resistance.

METHODS

The DNA-seq data, RNA-seq data, and clinical information of 12 types of cancer were downloaded from the cancer genome atlas (TCGA) database. GSE70479 data were obtained from GEO database for validation of our TCGA data. To evaluate the survival rate of patients, GEPIA2 was applied. The CCLE and GDSC database were used to investigate drug resistance and sensitivity.

RESULTS

Our findings indicated that TTN, MUC16, and TP53 were present in 12 types of cancer with high level of mutation frequency which abundance of TP53 mutations was higher. Mutant TP53-related (mTP53) pathways and genes including PKM, SLC16A3, HK2, PFKP, PHGDH, and CTSC were obtained from enrichment analysis and interestingly, top pathways were associated with metabolism including glycolysis and mTORC1 pathway. Our results showed the expression of some candidate genes correlated with immune markers, prognosis, and drug resistance.

CONCLUSIONS

Top mutant genes for 12 cancers were highlighted while TP53 was selected as top mutant gene, and metabolic genes associated with the TP53 mutation were identified that some of which are important in poor prognosis. In doing so, mutations in TP53 could run some metabolic pathways and drug resistance and sensitivity.

Construction of ovarian metastasis-related immune signature predicting prognosis of gastric cancer patients

BACKGROUND

Ovarian metastasis (OM) results in poor survival of gastric cancer (GC) patients. While immunotherapy has emerged as a promising approach for late-stage GC, validated immune-related prognostic signatures still remain in need. In this study, we constructed an ovarian metastasis- and immune-related prognostic signature (OMIRPS), characterized the molecular and immune features of OMIRPS-categorized subgroups and predicted their potential response to immunotherapy.

METHODS

Three individual cohorts were used to construct and evaluate OMIRPS: RNA-seq of matched primary GC and OM from Fudan University Shanghai Cancer Center (FUSCC) (discovery cohort, n = 4), The Cancer Genome Atlas (TCGA) (training cohort, n = 544) and GSE84437 (validation cohort, n = 433). Differentially expressed genes (DEGs) identified between primary GC and OM and immune-related genes (IRGs) from the ImmPort and InnateDB databases were used to identify immune-related prognostic hub genes, which were further used to construct OMIRPS by using LASSO regression analysis. Prognosis, molecular characteristics, immune features, and differential immunotherapy efficacy between different OMIRPS subgroups were analyzed.

RESULTS

Functional analyses of DEGs revealed the significance of immune-related signatures and pathways in the OM. Immune-related prognostic hub genes including TNFRSF18, CARD11, BCL11B, NRP1, BNIP3L, and ATF3 were utilized to construct OMIRPS, which was identified as an independent prognostic factor. Comprehensive analyses unveiled the distinctive molecular and immune characteristics of OMIRPS-high and -low subgroup in regard to enriched pathways, mutation rate, tumor mutation burden, microsatellite instability status, infiltrated immune cell, immune exclusion score, and the prediction of immunotherapy efficacy. Additionally, OMIRPS was associated with Immune Subtypes with borderline significance.

CONCLUSIONS

RNA-seq of paired primary and ovarian metastatic tumors unveiled the significance of immune-related pathways and tumor immune microenvironment in OM. OMIRPS served as a promising biomarker to predict the prognosis of GC patients and distinguish the molecular features, immune characteristics, and efficacy of immunotherapy between different subgroups.

A Risk Model of Eight Immune-Related Genes Predicting Prognostic Response to Immune Therapies for Gastric Cancer

Immune checkpoint inhibitor (ICI) treatment is considered as an innovative approach for cancers. Since not every patient responded well to ICI therapy, it is imperative to screen out novel signatures to predict prognosis. Based on 407 gastric cancer (GC) samples retrieved from The Cancer Genome Atlas (TCGA), 36 immune-related hub genes were identified by weighted gene co-expression network analysis (WGCNA), and eight of them (RNASE2, CGB5, INHBE, DUSP1, APOA1, CD36, PTGER3, CTLA4) were used to formulate the Cox regression model. The obtained risk score was proven to be significantly correlated with overall survival (OS), consistent with the consequence of the Gene Expression Omnibus (GEO) cohort (n = 433). Then, the relationship between the risk score and clinical, molecular and immune characteristics was further investigated. Results showed that the low-risk subgroup exhibited higher mutation rate, more M1 macrophages, CD8+ and CD4+ T cells infiltrating, more active MHC-I, and bias to “IFN-γ Dominant” immune type, which is consistent with our current understanding of tumor prognostic risk. Furthermore, it is suggested that our model can accurately predict 1-, 2-, and 3-year OS of GC patients, and that it was superior to other canonical models, such as TIDE and TIS. Thus, these eight genes are probably considered as potential signatures to predict prognosis and to distinguish patient benefit from ICI, serving as a guiding individualized immunotherapy.

Evaluation of the TCR Repertoire as a Predictive and Prognostic Biomarker in Cancer: Diversity or Clonality?

Simple Summary

The TCR is the T cell antigen receptor, and it is responsible of the T cell activation, through the HLA-antigen complex recognition. Studying the TCR repertoire in patients with cancer can help to better understand the anti-tumoural responses and it has been suggested to have predictive and or/prognostic values, both for the disease and in response to treatments. The aim of this review is to summarize TCR repertoire studies performed in patients with cancer found in the literature, thoroughly analyse the different factors that can be involved in shaping the TCR repertoire, and draw the current conclusions in this field, especially focusing on whether the TCR diversity—or its opposite, the clonality—can be used as predictors or prognostic biomarkers of the disease.

Abstract

T cells play a vital role in the anti-tumoural response, and the presence of tumour-infiltrating lymphocytes has shown to be directly correlated with a good prognosis in several cancer types. Nevertheless, some patients presenting tumour-infiltrating lymphocytes do not have favourable outcomes. The TCR determines the specificities of T cells, so the analysis of the TCR repertoire has been recently considered to be a potential biomarker for patients’ progression and response to therapies with immune checkpoint inhibitors. The TCR repertoire is one of the multiple elements comprising the immune system and is conditioned by several factors, including tissue type, tumour mutational burden, and patients’ immunogenetics. Its study is crucial to understanding the anti-tumoural response, how to beneficially modulate the immune response with current or new treatments, and how to better predict the prognosis. Here, we present a critical review including essential studies on TCR repertoire conducted in patients with cancer with the aim to draw the current conclusions and try to elucidate whether it is better to encounter higher clonality with few TCRs at higher frequencies, or higher diversity with many different TCRs at lower frequencies.

Next Generation Sequencing and Molecular Biomarkers in Ovarian Cancer—An Opportunity for Targeted Therapy

Ovarian cancer is the deadliest of all gynecologic malignancies claiming the lives of nearly 14,000 women in the United States annually. Despite therapeutic advances, the ovarian cancer mortality rate has remained stagnant since the 1980’s. The molecular heterogeneity of ovarian cancers suggest they may be more effectively treated via precision medicine. Current guidelines recommend germline and somatic testing for all new epithelial ovarian cancer diagnoses to assist providers in identifying candidates for targeted therapies. Next generation sequencing (NGS) identifies targetable, driver, and novel mutations used to guide treatment decisions. Performing NGS is standard of care in many other malignancies, but for ovarian cancer the use of NGS in daily practice is still emerging. This review discusses the targetable genetic mutations and role of NGS and molecular biomarker testing in the treatment of ovarian cancer.

Immune Subtypes Characterization Identifies Clinical Prognosis, Tumor Microenvironment Infiltration, and Immune Response in Ovarian Cancer

Objective: Because of the modest immunotherapeutic response among ovarian carcinoma (OC) patients, it is significant to evaluate antitumor immune response and develop more effective precision immunotherapeutic regimens. Here, this study aimed to determine diverse immune subtypes of OC.

Methods: This study curated the expression profiles of prognostic immunologically relevant genes and conducted consensus clustering analyses for determining immune subtypes among OC patients in TCGA cohort. With Boruta algorithm, characteristic genes were screened for conducting an immune scoring system through principal component analysis algorithm. The single-sample gene set enrichment analysis and ESTIAMTE methods were adopted for quantifying the immune infiltrates and responses to chemotherapeutic agents were estimated with pRRophetic algorithm. Two immunotherapeutic cohorts were used for investigating the efficacy of immune score in predicting therapeutic benefits.

Results: Two immune subtypes were conducted among 377 OC patients. Immune subtype 2 was characterized by worse clinical prognosis, more frequent genetic variations and mutations, enhanced immune infiltrates, and increased expression of MHC molecules and programmed cell death protein 1/programmed death ligand 1 (PD-1/PD-L1). In total, 30 prognosis-relevant characteristic immune subtype–derived genes were identified for constructing the immune score of OC patients. High immune score was linked with more dismal prognosis, decreased immune infiltrations, and expression of MHC molecules. High immune score presented favorable sensitivity to doxorubicin and vinorelbine and reduced sensitivity to cisplatin. In addition, immune score possessed the potential in predicting benefits from anti–PD-1/PD-L1 therapy.

Conclusion: Collectively, our findings propose two complex and diverse immune subtypes of OC. Quantitative assessment of immune subtypes in individual patients strengthens the understanding of tumor microenvironment features and promotes more effective immunotherapeutic regimens.

Significance of a PTEN Mutational Status-Associated Gene Signature in the Progression and Prognosis of Endometrial Carcinoma

Background

PTEN mutations have been reported to be involved in the development and prognosis of endometrial carcinoma (EC). However, a prognostic gene signature associated with PTEN mutational status has not yet been developed. In this study, we generated a PTEN mutation-associated prognostic gene signature for EC.

Methods

We obtained the single-nucleotide variation and transcriptomic profiling data from The Cancer Genome Atlas database as training data and implemented the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm to establish a PTEN mutation-associated prognostic gene signature. The overall survival rates of the high-risk and low-risk groups were determined with the Kaplan-Meier (K-M) method, and the accuracy of risk score prediction was tested by using the receiver operating characteristic (ROC) curve.

Results

The K-M curves revealed that the EC patients with PTEN mutations augured favorable survival outcomes. Differential expression analysis between the EC patients with PTEN mutation and wild-type PTEN identified 224 differentially expressed genes (DEGs). Eighty-four DEGs that manifested prognostic value were fitted into the LASSO-Cox analysis, and a PTEN gene signature with seven mutation-associated prognostic genes that showed robust prognostic ability was constructed; this signature was then successfully validated in the other two datasets from the cBioPortal database as well as with 60 clinical specimens. Furthermore, the PTEN mutation-associated prognostic gene signature proved to be an independent prognostic predictor of EC. Remarkably, the EC patients in the high-risk group were characterized by higher tumor stages and grades as well as lower tumor mutation burden with respect to EC, with a poor survival outcome. Collectively, the PTEN mutation-associated prognostic gene signature that we developed could now be used as a favorable prognostic biomarker for EC.

Conclusion

In summary, we developed and validated a prognostic predictor for EC associated with PTEN mutational status that may be used as a favorable prognostic biomarker and therapeutic target for EC.

Systematic evaluation of the prognostic and immunological role of PDLIM2 across 33 cancer types

The protein PDLIM2 regulates the stability of various transcription factors and is required for polarized cell migration. However, the clinical relevance and immune infiltration of PDLIM2 in cancer are not well-understood. We utilized The Cancer Genome Atlas and Genotype-Tissue Expression database to characterize alterations in PDLIM2 in pan-cancer. TIMER was used to explore PDLIM2 expression and immune infiltration levels. We assessed the correlation between PDLIM2 expression and immune-associated gene expression, immune score, tumor mutation burden, and DNA microsatellite instability. PDLIM2 significantly affected the prognosis of various cancers. Increased expression of PDLIM2 was significantly correlated with the tumor grade in seven types of tumors. The expression level of PDLIM2 was positively correlated with immune infiltrates, including B cells, CD8+ T cells, CD4+ T cells, neutrophils, macrophages, and dendritic cells in bladder urothelial, kidney renal papillary cell, and colon adenocarcinoma. High expression levels of PDLIM2 tended to be associated with higher immune and stromal scores. PDLIM2 expression was associated with the tumor mutation burden in 12 cancer types and microsatellite instability in 5 cancer types. PDLIM2 levels were strongly correlated with diverse immune-related genes. PDLIM2 can act as a prognostic-related therapeutic target and is correlated with immune infiltrates in pan-cancer.

Computational image features of immune architecture is associated with clinical benefit and survival in gynecological cancers across treatment modalities

BACKGROUND

We present a computational approach (ArcTIL) for quantitative characterization of the architecture of tumor-infiltrating lymphocytes (TILs) and their interplay with cancer cells from digitized H&E-stained histology whole slide images and evaluate its prognostic role in three different gynecological cancer (GC) types and across three different treatment types (platinum, radiation and immunotherapy).

METHODS

In this retrospective study, we included 926 patients with GC diagnosed with ovarian cancer (OC), cervical cancer, and endometrial cancer with available digitized diagnostic histology slides and survival outcome information. ArcTIL features quantifying architecture and spatial interplay between immune cells and the rest of nucleated cells (mostly comprised cancer cells) were extracted from the cell cluster graphs of nuclei within the tumor epithelial nests, surrounding stroma and invasive tumor front compartments on H&E-stained slides. A Cox proportional hazards model, incorporating ArcTIL features was fit on the OC training cohort (N=51), yielding an ArcTIL signature. A unique threshold learned from the training set stratified the patients into a low and high-risk group.

RESULTS

The seven feature ArcTIL classifier was found to significantly correlate with overall survival in chemotherapy and radiotherapy-treated validation cohorts and progression-free survival in an immunotherapy-treated validation cohort. ArcTIL features relating to increased density of TILs in the epithelium and invasive tumor front were found to be associated with better survival outcomes when compared with those patients with an increased TIL density in the stroma. A statistically significant association was found between the ArcTIL signature and signaling pathways for blood vessel morphogenesis, vasculature development, regulation of cell differentiation, cell-substrate adhesion, biological adhesion, regulation of vasculature development, and angiogenesis.

CONCLUSIONS

This study reveals that computationally-derived features from the spatial architecture of TILs and tumor cells are prognostic in GCs treated with chemotherapy, radiotherapy, and checkpoint blockade and are closely associated with central biological processes that impact tumor progression. These findings could aid in identifying therapy-refractory patients and further enable personalized treatment decision-making.

Development and Validation of a Tumor Mutation Burden-Related Immune Prognostic Signature for Ovarian Cancers

Ovarian cancer (OC), one of the most common malignancies of the female reproductive system, is characterized by high incidence and poor prognosis. Tumor mutation burden (TMB), as an important biomarker that can represent the degree of tumor mutation, is emerging as a key indicator for predicting the efficacy of tumor immunotherapy. In our study, the gene expression profiles of OC were downloaded from TCGA and GEO databases. Subsequently, we analyzed the prognostic value of TMB in OC and found that a higher TMB score was significantly associated with a better prognosis (p = 0.004). According to the median score of TMB, 9 key TMB related immune prognostic genes were selected by LASSO regression for constructing a TMB associated immune risk score (TMB-IRS) signature, which can effectively predict the prognosis of OC patients (HR = 2.32, 95% CI = 1.68–3.32; AUC = 0.754). Interestingly, TMB-IRS is also closely related to the level of immune cell infiltration and immune checkpoint molecules (PD1, PD-L1, CTLA4, PD-L2) in OC. Furthermore, the nomogram combined with TMB-IRS and a variety of clinicopathological features can more comprehensively evaluate the prognosis of patients. In conclusion, we explored the relationship between TMB and prognosis and validated the TMB-IRS signature based on TMB score in an independent database (HR = 1.60, 95% CI = 1.13–2.27; AUC = 0.639), which may serve as a novel biomarker for predicting OC prognosis as well as possible therapeutic targets.

The Role of the Extracellular Matrix and Tumor-Infiltrating Immune Cells in the Prognostication of High-Grade Serous Ovarian Cancer

Ovarian cancer is the eighth global leading cause of cancer-related death among women. The most common form is the high-grade serous ovarian carcinoma (HGSOC). No further improvements in the 5-year overall survival have been seen over the last 40 years since the adoption of platinum- and taxane-based chemotherapy. Hence, a better understanding of the mechanisms governing this aggressive phenotype would help identify better therapeutic strategies. Recent research linked onset, progression, and response to treatment with dysregulated components of the tumor microenvironment (TME) in many types of cancer. In this study, using bioinformatic approaches, we identified a 19-gene TME-related HGSOC prognostic genetic panel (19 prognostic genes (PLXNB2, HMCN2, NDNF, NTN1, TGFBI, CHAD, CLEC5A, PLXNA1, CST9, LOXL4, MMP17, PI3, PRSS1, SERPINA10, TLL1, CBLN2, IL26, NRG4, and WNT9A) by assessing the RNA sequencing data of 342 tumors available in the TCGA database. Using machine learning, we found that specific patterns of infiltrating immune cells characterized each risk group. Furthermore, we demonstrated the predictive potential of our risk score across different platforms and its improved prognostic performance compared with other gene panels.

Exploration of the Tumor Mutational Burden as a Prognostic Biomarker and Related Hub Gene Identification in Prostate Cancer

To explore the signature function of the tumor mutational burden (TMB) and potential biomarkers in prostate cancer (PCa), transcriptome profiles, somatic mutation data, and clinicopathologic feature information were downloaded from The Cancer Genome Atlas (TCGA) database. R software package was used to generate a waterfall plot to summarize the specific mutation information and calculate the TMB value of PCa. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to select the hub genes related to the TMB from the ImmPort network to build a risk score (RS) model to evaluate prognostic values and plot Kaplan–Meier (K-M) curves to predict PCa patients survival. The results showed that PCa patients with a high TMB exhibited higher infiltration of CD8+ T cells and CD4+ T cells and better overall survival (OS) than those with a low TMB. The anti-Mullerian hormone (AMH), baculoviral IAP repeat-containing 5 (BIRC5), and opoid receptor kappa 1 (OPRK1) genes were three hub genes and their copy number variation (CNV) was relatively likely to affect the infiltration of immune cells. Moreover, PCa patients with low AMH or BIRC5 expression had a longer survival time and lower cancer recurrence, while elevated AMH or BIRC5 expression favored PCa progression. In contrast, PCa patients with low OPRK1 expression had poorer OS in the early stage of PCa and a higher recurrent rate than those with high expression. Taken together, these results suggest that the TMB may be a promising prognostic biomarker for PCa and that AMH, OPRK1, and BIRC5 are hub genes affecting the TMB; AMH, OPRK1, and BIRC5 could serve as potential immunotherapeutic targets for PCa treatment.

Comprehensive of N1-Methyladenosine Modifications Patterns and Immunological Characteristics in Ovarian Cancer

Background

recently, many researches have concentrated on the relevance between N1-methyladenosine (m1A) methylation modifications and tumor progression and prognosis. However, it remains unknown whether m1A modification has an effect in the prognosis of ovarian cancer (OC) and its immune infiltration.

Methods

Based on 10 m1A modulators, we comprehensively assessed m1A modification patterns in 474 OC patients and linked them to TME immune infiltration characteristics. m1Ascore computed with principal component analysis algorithm was applied to quantify m1A modification pattern in OC patients. m1A regulators protein and mRNA expression were respectively obtained by HPA website and RT-PCR in clinical OC and normal samples.

Results

We finally identified three different m1A modification patterns. The immune infiltration features of these m1A modification patterns correspond to three tumor immune phenotypes, including immune-desert, immune-inflamed and immune-excluded phenotypes. The results demonstrate individual tumor m1A modification patterns can predict patient survival, stage and grade. The m1Ascore was calculated to quantify individual OC patient's m1A modification pattern. A high m1Ascore is usually accompanied by a better survival advantage and a lower mutational load. Research on m1Ascore in the treatment of OC patients showed that patients with high m1Ascore showed marked therapeutic benefits and clinical outcomes in terms of chemotherapy and immunotherapy. Lastly, we obtained four small molecule drugs that may potentially ameliorate prognosis.

Conclusion

This research demonstrates that m1A methylation modification makes an essential function in the prognosis of OC and in shaping the immune microenvironment. Comprehensive evaluation of m1A modifications improves our knowledge of immune infiltration profile and provides a more efficient individualized immunotherapy strategy for OC patients.

CSMD3 is Associated with Tumor Mutation Burden and Immune Infiltration in Ovarian Cancer Patients

Background

Globally, ovarian cancer (OC), the deadliest gynecologic malignancy, remains a major cause of mortality, with a rising number of cases in many low- and middle-income countries. Immunotherapy has been proven to be promising for OC. There is increasing awareness of the vital role that tumor mutation burden (TMB) plays in predicting the efficacy of immunotherapy. Women with a family history of OC are at higher risk of the disease due to gene mutations. However, whether these gene mutations are related to immune response and TMB remains to be explored.

Methods

Our present work analyzed genetic mutation data of OC patients obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts, and we identified 11 frequently mutated genes, namely, APOB, CSMD3, DST, FAT3, FLG, HMCN1, MUC16, RYR1, TP53, TTN, and USH2A, in accordance with the overlap of two databases.

Results

A statistically higher TMB was detected by whole-exome sequencing in patients with OC with CSMD3 mutation than in those with mutations in the other frequently mutated genes. Prognosis analysis performed with patients from the TCGA cohort revealed that those with CSMD3 mutation had an overall survival (OS) that was inferior to that of those with wild-type CSMD3. Gene set enrichment analysis (GSEA) and CIBERSORT analysis indicated that OC samples with CSMD3 mutations had significant involvement of pathways related to the immune response.

Conclusion

In summary, we found that CSMD3 mutation is highly correlated with increased TMB and poor clinical prognosis and that it might function as a biomarker for predicting prognosis and choosing an immunotherapy regimen.

Assessment of the immune landscapes of advanced ovarian cancer in an optimized in vivo model

BACKGROUND

Ovarian cancer (OC) is typically diagnosed late, associated with high rates of metastasis and the onset of ascites during late stage disease. Understanding the tumor microenvironment and how it impacts the efficacy of current treatments, including immunotherapies, needs effective in vivo models that are fully characterized. In particular, understanding the role of immune cells within the tumor and ascitic fluid could provide important insights into why OC fails to respond to immunotherapies. In this work, we comprehensively described the immune cell infiltrates in tumor nodules and the ascitic fluid within an optimized preclinical model of advanced ovarian cancer.

METHODS

Green Fluorescent Protein (GFP)-ID8 OC cells were injected intraperitoneally into C57BL/6 mice and the development of advanced stage OC monitored. Nine weeks after tumor injection, mice were sacrificed and tumor nodules analyzed to identify specific immune infiltrates by immunohistochemistry. Ascites, developed in tumor bearing mice over a 10-week period, was characterized by mass cytometry (CyTOF) to qualitatively and quantitatively assess the distribution of the immune cell subsets, and their relationship to ascites from ovarian cancer patients.

RESULTS

Tumor nodules in the peritoneal cavity proved to be enriched in T cells, antigen presenting cells and macrophages, demonstrating an active immune environment and cell-mediated immunity. Assessment of the immune landscape in the ascites showed the predominance of CD8+ , CD4+ , B- , and memory T cells, among others, and the coexistance of different immune cell types within the same tumor microenvironment.

CONCLUSIONS

We performed, for the first time, a multiparametric analysis of the ascitic fluid and specifically identify immune cell populations in the peritoneal cavity of mice with advanced OC. Data obtained highlights the impact of CytOF as a diagnostic tool for this malignancy, with the opportunity to concomitantly identify novel targets, and define personalized therapeutic options.

LPCAT1 overexpression promotes the progression of hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) remains one of the most common malignant neoplasms. Lysophosphatidylcholine acyltransferase 1 (LPCAT1) plays a key role in the lipid remodelling and is correlated with various neoplasms. Nonetheless, the biological functions and molecular mechanisms of LPCAT1 underlying HCC remain obscure.

METHODS

In the present study, we investigated the role of LPCAT1 in the progression of HCC. In-house RT-qPCR, tissue microarrays, and immunohistochemistry were performed to detect the expression levels and the clinical value of LPCAT1 in HCC. External datasets were downloaded to confirm the results. Proliferation, migration, invasiveness, cell cycle, and apoptosis assays were conducted to reveal the biological effects LPCAT1 has on SMMC-7721 and Huh7 cells. HCC differentially expressed genes and LPCAT1 co-expressed genes were identified to explore the molecular mechanisms underlying HCC progression.

RESULTS

LPCAT1 showed upregulated expression in 3715 HCC specimens as opposed to 3105 non-tumour specimens. Additionally, LPCAT1 might be an independent prognostic factor for HCC. LPCAT1-knockout hampered cellular proliferation, migration, and metastasis in SMMC-7721 and Huh7 cells. More importantly, the cell cycle and chemical carcinogenesis were the two most enriched signalling pathways.

CONCLUSIONS

The present study demonstrated that increased LPCAT1 correlated with poor prognosis in HCC patients and fuelled HCC progression by promoting cellular growth, migration, and metastasis.

Significance of Tumor Mutation Burden Combined With Immune Infiltrates in the Progression and Prognosis of Advanced Gastric Cancer

Gastric cancer (GC) is a serious malignant tumor with high mortality and poor prognosis. The prognosis and survival are much worse for advanced gastric cancer (AGC). Recently, immunotherapy has been widely promoted for AGC patients, and studies have shown that tumor mutation burden (TMB) is closely related to immunotherapy response. Here, RNA-seq data, matched clinical information, and MAF files were downloaded from the cancer genome atlas (TCGA)-STAD project in the TCGA database. The collation and visual analysis of mutation data were implemented by the “maftools” package in R. We calculated the TMB values for AGC patients and divided the patients into high- and low-TMB groups according to the median value of TMB. Then, the correlation between high or low TMB and clinicopathological parameters was calculated. Next, we examined the differences in gene expression patterns between the two groups by using the “limma” R package and identified the immune-related genes among the DEGs. Through univariate Cox regression analysis, 15 genes related to prognosis were obtained. Furthermore, the two hub genes (APOD and SLC22A17) were used to construct a risk model to evaluate the prognosis of AGC patients. ROC and survival curves and GEO data were used as a validation set to verify the reliability of this risk model. In addition, the correlation between TMB and tumor-infiltrating immune cells was examined. In conclusion, our results suggest that AGC patients with high TMB have a better prognosis. By testing the patient’s TMB, we could better guide immunotherapy and understand patient response to immunotherapy.

Significance of Tumor Mutation Burden Combined With Immune Infiltrates in the Progression and Prognosis of Advanced Gastric Cancer

Gastric cancer (GC) is a serious malignant tumor with high mortality and poor prognosis. The prognosis and survival are much worse for advanced gastric cancer (AGC). Recently, immunotherapy has been widely promoted for AGC patients, and studies have shown that tumor mutation burden (TMB) is closely related to immunotherapy response. Here, RNA-seq data, matched clinical information, and MAF files were downloaded from the cancer genome atlas (TCGA)-STAD project in the TCGA database. The collation and visual analysis of mutation data were implemented by the “maftools” package in R. We calculated the TMB values for AGC patients and divided the patients into high- and low-TMB groups according to the median value of TMB. Then, the correlation between high or low TMB and clinicopathological parameters was calculated. Next, we examined the differences in gene expression patterns between the two groups by using the “limma” R package and identified the immune-related genes among the DEGs. Through univariate Cox regression analysis, 15 genes related to prognosis were obtained. Furthermore, the two hub genes (APOD and SLC22A17) were used to construct a risk model to evaluate the prognosis of AGC patients. ROC and survival curves and GEO data were used as a validation set to verify the reliability of this risk model. In addition, the correlation between TMB and tumor-infiltrating immune cells was examined. In conclusion, our results suggest that AGC patients with high TMB have a better prognosis. By testing the patient’s TMB, we could better guide immunotherapy and understand patient response to immunotherapy.

Significance of N6-Methyladenosine RNA Methylation Regulators in Immune Infiltrates of Ovarian Cancer

N6-methyladenosine (m6A) RNA methylation regulators play an important role in the occurrence and development of tumors. Here, we aimed to identify the potential roles of m6A RNA methylation regulators in immune infiltrates of ovarian cancer. We obtained two distinct m6A patterns (m6Acluster.A and m6Acluster.B) based on the expression levels of all 21 m6A RNA methylation regulators from The Cancer Genome Atlas (TCGA) database using a consensus clustering algorithm. Differential analysis of m6Acluster.A and m6Acluster.B identified 196 m6A-related genes. We further validated the m6A regulation mechanism based on the 196 m6A-related genes using another consensus clustering algorithm. Considering individual differences, principal component analysis algorithms were used to calculate an m6A score for each sample in order to quantify the m6A patterns. A low m6A score was associated with immune activation and enhanced response to immune checkpoint inhibitors, whereas a high m6A score was associated with tumor progression. Finally, we successfully verified the correlation between m6A regulators and immune microenvironment in OC using our microarray analysis data. In summary, m6A regulators play non-negligible roles in immune infiltrates of ovarian cancer. Our investigation of m6A patterns may help to guide future immunotherapy strategies for advanced ovarian cancer.

Tumor Mutation Burden and Immune Invasion Characteristics in Triple Negative Breast Cancer: Genome High-Throughput Data Analysis

In recent years, the emergence of immunotherapy has provided a new perspective for the treatment and management of triple-negative breast cancer (TNBC). However, the relationship between tumor mutation burden (TMB) and immune infiltration and the prognosis of TNBC remains unclear. In this study, to explore the immunogenicity of TNBC, we divided patients with TNBC into high and low TMB groups based on the somatic mutation data of TNBC in The Cancer Genome Atlas (TCGA), and screened out genes with mutation rate ≥10. Then, Kaplan-Meier survival analysis revealed that the 5-year survival rate of the high TMB group was much higher than that of the low TMB group and the two groups also showed differences in immune cell infiltration. Further exploration found that the FAT3 gene, which displays significant difference and a higher mutation rate between the two groups, is not only significantly related to the prognosis of TNBC patients but also exhibits difference in immune cell infiltration between the wild group and the mutant group of the FAT3 gene. The results of gene set enrichment analysis and drug sensitivity analysis further support the importance of the FAT3 gene in TNBC. This study reveals the characteristics of TMB and immune cell infiltration in triple-negative breast cancer and their relationship with prognosis, to provide new biomarkers and potential treatment options for the future treatment of TNBC. The FAT3 gene, as a risk predictor gene of TNBC, is considered a potential biological target and may provide new insight for the treatment of TNBC.