TTN mutations predict a poor prognosis in patients with thyroid cancer

Perfluorooctanesulfonic acid (PFOS) induced cancer related DNA methylation alterations in human breast cells: A whole genome methylome study

DNA methylation plays a pivotal role in cancer. The ubiquitous contaminant perfluorooctanesulfonic acid (PFOS) has been epidemiologically associated with breast cancer, and can induce proliferation and malignant transformation of normal human breast epithelial cells (MCF-10A), but the information about its effect on DNA methylation is sparse. The aim of this study was to characterize the whole-genome methylome effects of PFOS in our breast cell model and compare the findings with previously demonstrated DNA methylation alterations in breast tumor tissues. The DNA methylation profile was assessed at single CpG resolution in MCF-10A cells treated with 1 μM PFOS for 72 h by using Enzymatic Methyl sequencing (EM-seq). We found 12,591 differentially methylated CpG-sites and 13,360 differentially methylated 100 bp tiles in the PFOS exposed breast cells. These differentially methylated regions (DMRs) overlapped with 2406 genes of which 494 were long non-coding RNA and 1841 protein coding genes. We identified 339 affected genes that have been shown to display altered DNA methylation in breast cancer tissue and several other genes related to cancer development. This includes hypermethylation of GACAT3, DELEC1, CASC2, LCIIAR, MUC16, SYNE1 and hypomethylation of TTN and KMT2C. DMRs were also found in estrogen receptor genes (ESR1, ESR2, ESRRG, ESRRB, GREB1) and estrogen responsive genes (GPER1, EEIG1, RERG). The gene ontology analysis revealed pathways related to cancer phenotypes such as cell adhesion and growth. These findings improve the understanding of PFOS's potential role in breast cancer and illustrate the value of whole-genome methylome analysis in uncovering mechanisms of chemical effects, identifying biomarker candidates, and strengthening epidemiological associations, potentially impacting risk assessment.

Construction and validation of a TTN mutation associated immune prognostic model for evaluating immune microenvironment and outcomes of gastric cancer: An observational study

Gastric cancer (GC) is a prevalent form of cancer worldwide, and TTN (titin) mutations are frequently observed in GC. However, the association between TTN mutations and immunotherapy for GC remains unclear, necessitating the development of novel prognostic models. The prognostic value and potential mechanisms of TTN in stomach adenocarcinoma were evaluated by TCGA (The Cancer Genome Atlas)-stomach adenocarcinoma cohort analysis, and an immune prognostic model was constructed based on TTN status. We validated it using the GSE84433 dataset. We performed Gene Set Enrichment Analysis and screened for differentially expressed genes, and used lasso (least absolute shrinkage and selection operator) regression analysis to screen for survival genes to construct a multifactorial survival model. In addition, we evaluated the relative proportions of 22 immune cells using the CIBERSORT algorithm for immunogenicity analysis. Finally, we constructed the nomogram integrating immune prognostic model and other clinical factors. GESA showed enrichment of immune-related phenotypes in patients with TTN mutations. We constructed an immune prognostic model based on 16 genes could identify gastric cancer patients with higher risk of poor prognosis. Immuno-microenvironmental analysis showed increased infiltration of naive B cells, plasma cells, and monocyte in high-risk patients. In addition, Nomo plots predicted the probability of 1-year, 3-year, and 5-year OS (overall survival) in GC patients, showing good predictive performance. In this study, we identified that TTN gene may be a potential clinical biomarker for GC and TTN mutations may be a predictor of immunotherapy in patients. We constructed and validated a new model for prognosis of GC patients based on immune characteristics associated with TTN mutations. This study may provide potential therapeutic strategies for gastric cancer.

Multi-omics data analysis reveals the complex roles of age in differentiated thyroid cancer

Aims

Age is a major risk factor for differentiated thyroid cancer (DTC); however, the mechanisms underlying aging-regulated progression of DTC remains unclear.

Methods

Based on multi-omics data (transcriptional files, somatic mutation files, methylation files) derived from the TCGA database, we comprehensively investigated the genomic and biological features associated with aging in patients with DTC.

Results

We confirmed that age was an independent risk factor for overall survival and progression-free survival of patients with DTC, and confirmed that 55 years of age (adopted in the 8th AJCC staging system) is an appropriate cutoff for patients with DTC rather than 45 years (adopted in the 7th AJCC staging system). Using 55 years as the cutoff, we demonstrated DNA methylation-driven transcriptional regulation during aging, and identified the landscape of somatic mutations in young and old patients with DTC along with two aging-related mutations: TTN and EIF1AX. Subsequently, we investigated the infiltration of immune cells in DTC, and found that old patients exhibited decreased CD8+ T cells infiltration with lower cytotoxicity. Finally, we constructed a prognosis prediction model based on three age-related genes (PTK2B, E2F1, and GHR) that showed satisfactory performance in predicting patients prognosis.

Conclusions

We comprehensively investigated the complex interplay between age and biological features of DTC, which may provide new insights into the role of aging in DTC.

The comprehensive roles of lncRNA FAM99A/FAM99B in hepatocellular carcinoma: Expressions, regulatory mechanisms and functional pathway analysis

AIMS

The incidence and mortality of liver hepatocellular carcinoma (LIHC) were increasing year by year. The aim of this study was to investigate the comprehensive roles of lncRNA FAM99A and FAM99B in LIHC.

MAIN METHODS

According to the data of TCGA and GTEx, the expression levels of FAM99A and FAM99B in LIHC were evaluated, and the overall survival (OS), disease-free survival (DFS), immune cell infiltration and tumor stage were analyzed. The subcellular localization of FAM99A and FAM99B in various cancer cell lines was predicted by lncATLAS database. In addition, we also used ENCORI, KEGG, LinkedOmics, Metascape and other databases. It was verified by in vivo and in vitro experiments.

KEY FINDINGS

Compared with adjacent normal tissues, FAM99A and FAM99B were down-regulated in LIHC tissues, and significantly correlated with immune cell infiltration. With the progression of tumor stage and grade, the expression of FAM99A and FAM99B showed a decreasing trend, and the prognosis of patients were also poor. In addition, the biological functions, signaling pathways and protein interactions of FAM99A and FAM99B in LIHC were enriched to study the potential molecular mechanisms. The overlapping RNA binding proteins (RBP) of FAM99A and FAM99B mainly included CSTF2T, BCCIP, RBFOX2 and SF3B4. Finally, experiments showed that overexpression of FAM99A attenuated the proliferation, invasion, colony formation and tumor growth of LIHC cells.

SIGNIFICANCE

Taken together, the above studies demonstrated that FAM99A and FAM99B had an inhibitory effect on the progression of LIHC, which might be promising diagnostic biomarkers and therapeutic targets for LIHC patients.

Analysis of the potential biological significance of glycosylation in triple-negative breast cancer on patient prognosis

BACKGROUND

Breast cancer is the most common malignancy in women, with its prognosis varying greatly according to its subtype. Triple-negative breast cancer (TNBC) has the worst prognosis among all subtypes. Glycosylation is a critical factor influencing the prognosis of patients with TNBC. Our aim is to develop a tumor prognosis model by analyzing genes related to glycosylation to predict patient outcomes.

METHODS

The dataset used in this study was downloaded from the Cancer Genome Atlas Program (TCGA) database, and predictive genes were identified through Cox one-way regression analysis. The model genes with the highest risk scores among the 18 samples were obtained by lasso regression analysis to establish the model. We analyzed the pathways affecting the progression of TNBC and discovered key genes for subsequent research.

RESULTS

Our model was constructed using data from TCGA database and validated through Kaplan-Meier curve analysis and Receiver Operating Characteristic (ROC) curve assessment. Our analysis revealed that a high expression of tumor-related chemokines in the high-risk group may be associated with poor tumor prognosis. Furthermore, we conducted a random survival forest analysis and identified two significant genes, namely DPM2 and PINK1, which have been selected for further investigation.

CONCLUSION

The prognostic analysis model, developed based on the glycosylation genes in TNBC, exhibits excellent validation efficacy. This model is valuable for the prognostic analysis of patients with TNBC.

Development of prognostic signatures and risk index related to lipid metabolism in ccRCC

Background

Clear cell renal cell carcinoma (ccRCC) is a metabolic disorder characterized by abnormal lipid accumulation in the cytoplasm. Lipid metabolism-related genes may have important clinical significance for prognosis prediction and individualized treatment.

Methods

We collected bulk and single-cell transcriptomic data of ccRCC and normal samples to identify key lipid metabolism-related prognostic signatures. qPCR was used to confirm the expression of signatures in cancer cell lines. Based on the identified signatures, we developed a lipid metabolism risk score (LMRS) as a risk index. We explored the potential application value of prognostic signatures and LMRS in precise treatment from multiple perspectives.

Results

Through comprehensive analysis, we identified five lipid metabolism-related prognostic signatures (ACADM, ACAT1, ECHS1, HPGD, DGKZ). We developed a risk index LMRS, which was significantly associated with poor prognosis in patients. There was a significant correlation between LMRS and the infiltration levels of multiple immune cells. Patients with high LMRS may be more likely to respond to immunotherapy. The different LMRS groups were suitable for different anticancer drug treatment regimens.

Conclusion

Prognostic signatures and LMRS we developed may be applied to the risk assessment of ccRCC patients, which may have potential guiding significance in the diagnosis and precise treatment of ccRCC patients.

Prognostic prediction of patients having classical papillary thyroid carcinoma with a 4 mRNA-based risk model

The dysregulation of protein-coding genes involved in various biological functions is closely associated with the progression of thyroid cancer. This study aimed to investigate the effects of dysregulated gene expressions on the prognosis of classical papillary thyroid carcinoma (cPTC). Using expression profiling datasets from the Cancer Genome Atlas (TCGA) database, we performed differential expression analysis to identify differentially expressed genes (DEGs). Cox regression and Kaplan-Meier analysis were used to identify DEGs, which were used to construct a risk model to predict the prognosis of cPTC patients. Functional enrichment analysis unveiled the potential significance of co-expressed protein-encoding genes in tumors. We identified 4 DEGs (SALL3, PPBP, MYH1, and SYNDIG1), which were used to construct a risk model to predict the prognosis of cPTC patients. These 4 genes were independent of clinical parameters and could be functional in cPTC carcinogenesis. Furthermore, PPBP exhibited a strong correlation with poorer overall survival (OS) in the advanced stage of the disease. This study suggests that the 4-gene signature could be an independent prognostic biomarker to improve prognosis prediction in cPTC patients older than 46.

Converting "cold" to "hot": epigenetics strategies to improve immune therapy effect by regulating tumor-associated immune suppressive cells

Significant developments in cancer treatment have been made since the advent of immune therapies. However, there are still some patients with malignant tumors who do not benefit from immunotherapy. Tumors without immunogenicity are called "cold" tumors which are unresponsive to immunotherapy, and the opposite are "hot" tumors. Immune suppressive cells (ISCs) refer to cells which can inhibit the immune response such as tumor-associated macrophages (TAMs), myeloid-derived suppressor cells (MDSCs), regulatory T (Treg) cells and so on. The more ISCs infiltrated, the weaker the immunogenicity of the tumor, showing the characteristics of "cold" tumor. The dysfunction of ISCs in the tumor microenvironment (TME) may play essential roles in insensitive therapeutic reaction. Previous studies have found that epigenetic mechanisms play an important role in the regulation of ISCs. Regulating ISCs may be a new approach to transforming "cold" tumors into "hot" tumors. Here, we focused on the function of ISCs in the TME and discussed how epigenetics is involved in regulating ISCs. In addition, we summarized the mechanisms by which the epigenetic drugs convert immunotherapy-insensitive tumors into immunotherapy-sensitive tumors which would be an innovative tendency for future immunotherapy in "cold" tumor.

Effect of radiotherapy on head and neck cancer tissues in patients receiving radiotherapy: a bioinformatics analysis-based study

Radiotherapy is pivotal in treating head and neck cancers including nasopharyngeal, tongue, hypopharyngeal, larynx, maxillary sinus, parotid gland, and oral cancers. It holds the potential for curative effects and finds application in conjunction with chemotherapy, either as a radical method to preserve organ function or as an adjuvant postoperative treatment. We used bioinformatics analysis to investigate the effects of radiotherapy on head and neck cancer tissues in patients who had received radiotherapy. In this study, the expression and mutation profiles of The Cancer Genome Atlas-Head-Neck Squamous Cell Carcinoma were downloaded from the UCSC-Xena database, categorizing patients into two groups-those receiving radiotherapy and those not receiving radiotherapy. Subsequently, differential expression analysis and gene set enrichment analysis (GSEA) were performed. Following this, single-sample GSEA (ssGSEA) scores related to glucose and lipid metabolism were compared between the two groups. Additionally, immune cell infiltration analysis and single-cell verification were performed. Finally, the mutation profiles of the two groups were compared. The analyses revealed that patients receiving radiotherapy exhibited prolonged survival, enhanced apoptosis in head and neck cancer tissue, and diminished keratinocyte proliferation and migration. A comparison of ssGSEA scores related to glucose and lipid metabolism between the two groups indicated a reduction in glycolysis, tricarboxylic acid cycle activity, and fat synthesis in tissues treated with radiotherapy, suggesting that radiotherapy can effectively inhibit tumour cell energy metabolism. Analyses of immune cell infiltration and single-cell verification suggested decreased infiltration of immune cells post-radiotherapy in head and neck cancer tissues. A comparison of mutation profiles revealed a higher frequency of TP53, TTN, and CDKN2A mutations in patients receiving radiotherapy for head and neck cancer. In conclusion, the bioinformatics analyses delved into the effect of radiotherapy on patients with head and neck carcinoma. This study provides a theoretical framework elucidating the molecular mechanisms underlying radiotherapy's efficacy in treating head and neck cancer and presents scientific recommendations for drug therapy following radiotherapy.

Impact of Nutritional Status of Patients with Head and Neck Squamous Cell Carcinoma on the Expression Profile of Ghrelin, Irisin, and Titin

The goal of this paper was the evaluation of the changes in the expression profile of irisin, ghrelin, and titin in the carcinoma tissue and in the blood of patients with head and neck squamous cell carcinoma (HNSCC), including determining the profile of their expression in relation to patient nutrition. The study included 56 patients with diagnosed squamous cell carcinoma of HNSCC in the T3 and T4 stages of the disease. Healthy control tissue specimens were collected from an area 10 mm outside the histologically negative margin. In turn, the blood and serum from the control group came from healthy volunteers treated for non-oncologic reasons (n = 70). The molecular analysis allowed us to determine the profile of irisin, ghrelin, and titin methylation, evaluate their expression on the level of mRNA (quantitative Reverse Transcription Polymerase Chain Reaction; qRT-PCR) and protein (Enzyme-Linked Immunosorbent Assay Reaction; ELISA) in the carcinoma tissue and the margin of healthy tissue, as well as in serum of patients in the study and control groups. At the start of our observations, a Body Mass Index (BMI) < 18.5 was noted in 42 of the patients, while six months after the treatment a BMI < 18.5 was noted in 29 patients. We also noted a decrease in the expression of irisin, ghrelin, and titin both on the level of mRNA and protein, as well as a potential regulation of their expression via DNA methylation. There is no convincing evidence that the proteins assayed in the present work are specific with regard to HNSSC.

Ovarian carcinoma immune-related microRNA affects the heterogeneity of the endocrine microenvironment and anti-tumor immune pattern

BACKGROUND

The eighth-leading cause of cancer-related mortality and the seventh-most prevalent malignancy in women globally is ovarian cancer (OV). However, 5-year survival expectancy after conventional treatment is not good. Therefore, there is an urgent need for novel signatures to guide the designation of therapeutic schemes for OV patients.

METHODS

We used univariate Cox analysis to screen hormone secretion regulation axis-related microRNAs (miRNAs), least absolute shrinkage and selection operator analysis to select candidate miRNAs and multivariate Cox analysis to build the risk model. To evaluate possible route and functional differences, enrichment analysis using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on the differentially expressed genes (DEGs) across various risk groups. We compared Tumor Immune Dysfunction and Exclusion (TIDE) scores across risk categories by analyzing immune cell infiltration, immune checkpoint gene expression, immunological function and TIDE scores. In the end, we determined the half maximal inhibitory concentration (IC50 ) of chemotherapy and targeted medicines for individual patients. Cell assays were determined to test the migration of the miRNA-target genes and western blotting was used to test the correlation of the miRNA-target genes and the pathways.

RESULTS

We finally identified hormone secretion regulation axis-related 13 microRNAs to build a risk model. The validation of observed and anticipated values revealed a fair level of agreement. To evaluate the molecular pathways between various groups in accordance with the GO and KEGG analyses, we then discovered 173 DEGs between distinct risk groups. The risk score was shown to be inversely related to the number of immune cells, including myeloid dendritic, granulocytes, M1 and M2 macrophages, B cells, t-lymphocytes, and CD4+ and CD8+ cells, suggesting that immune cells are more frequent in the low-risk group. Immune cell infiltration investigation yielded these results. Finally, we recognized 11 chemotherapeutic drugs and 30 novels targeted drugs on the basis of IC50 between the different risk groups. GJB5 was determined to be the mir-219 target gene and was identified as promoting the cell cycle process. In addition, hormone secretion regulation axis related miRNAs were reported to affects the heterogeneity of endocrine microenvironment and anti-tumor immune pattern.

CONCLUSIONS

In conclusion, a 13-miRNA prognostic model was constructed to know the immune status, prognosis, immunotherapeutic response and anti-tumor drug sensitivity for OV, which provides theoretical guidance for the effective and individualized treatment of OV patients.

A comprehensive prognostic score for head and neck squamous cancer driver genes and phenotype traits

BACKGROUND

Head and neck squamous cancer (HNSCC) presents variable phenotype and progression features. Clinically applicable, high-accuracy multifactorial prognostic models for HNSCC survival outcomes are warranted and an active area of research. This study aimed to construct a comprehensive prognostic tool for HNSCC overall survival by integrating cancer driver genes with tumor clinical and phenotype information.

METHODS

Key overall survival-related cancer driver genes were screened from among main effector and reciprocal gene pairs using TCGA data using univariate Cox proportional hazard regression analysis. Independent validation was performed using the GSE41613 dataset. The main effector genes among these were selected using LASSO regression and transcriptome score modeling was performed using multivariate Cox regression followed by validation analysis of the prognostic score. Next, multivariate Cox regression analysis was performed using the transcriptome score combined with age, grade, gender, and stage. An 'Accurate Prediction Model of HNSCC Overall Survival Score' (APMHO) was computed and validated. Enriched functional pathways, gene mutational landscape, immune cell infiltration, and immunotherapy sensitivity markers associated with high and low APMHO scores were analyzed.

RESULTS

Screening 107 overall survival-related cancer genes and 402 interacting gene pairs, 6 genes: CRLF2, HSP90AA1, MAP2K1, PAFAH1B2, MYCL and SET genes, were identified and a transcriptional score was obtained. Age, stage and transcriptional score were found to be significant predictors in Cox regression analysis and used to construct a final APMHO model showing an AUC > 0.65 and validated. Transcriptional score, age, pathologic_N, pathologic_T, stage, and TCGA_subtype were significantly different in distribution between high and low APMHO groups. High APMHO samples showed significantly higher mutation rate, enriched tumor-related pathways including Hypoxia, unfold_protein_response, Glycolysis, and mTORC1 signaling, along with differences in immune cell infiltration and immune checkpoint, interferon-γ pathway and m6A regulator expression patterns.

CONCLUSION

The APMHO score combining transcriptional and clinical variables showed good prognostic ability for HNSCC overall survival outcomes and was associated with different patterns of phenotypical features, immune and mutational landscape, and immunotherapy sensitivity marker expression. Future studies should validate this score in independent clinical cohorts.

Integrating single-cell and bulk RNA sequencing to predict prognosis and immunotherapy response in prostate cancer

Prostate cancer (PCa) stands as a prominent contributor to morbidity and mortality among males on a global scale. Cancer-associated fibroblasts (CAFs) are considered to be closely connected to tumour growth, invasion, and metastasis. We explored the role and characteristics of CAFs in PCa through bioinformatics analysis and built a CAFs-based risk model to predict prognostic treatment and treatment response in PCa patients. First, we downloaded the scRNA-seq data for PCa from the GEO. We extracted bulk RNA-seq data for PCa from the TCGA and GEO and adopted "ComBat" to remove batch effects. Then, we created a Seurat object for the scRNA-seq data using the package "Seurat" in R and identified CAF clusters based on the CAF-related genes (CAFRGs). Based on CAFRGs, a prognostic model was constructed by univariate Cox, LASSO, and multivariate Cox analyses. And the model was validated internally and externally by Kaplan-Meier analysis, respectively. We further performed GO and KEGG analyses of DEGs between risk groups. Besides, we investigated differences in somatic mutations between different risk groups. We explored differences in the immune microenvironment landscape and ICG expression levels in the different groups. Finally, we predicted the response to immunotherapy and the sensitivity of antitumour drugs between the different groups. We screened 4 CAF clusters and identified 463 CAFRGs in PCa scRNA-seq. We constructed a model containing 10 prognostic CAFRGs by univariate Cox, LASSO, and multivariate Cox analysis. Somatic mutation analysis revealed that TTN and TP53 were significantly more mutated in the high-risk group. Finally, we screened 31 chemotherapeutic drugs and targeted therapeutic drugs for PCa. In conclusion, we identified four clusters based on CAFs and constructed a new CAFs-based prognostic signature that could predict PCa patient prognosis and response to immunotherapy and might suggest meaningful clinical options for the treatment of PCa.

Disulfidptosis-associated LncRNAs index predicts prognosis and chemotherapy drugs sensitivity in cervical cancer

Disulfidptosis is a newly discovered form of cell death. Not yet clearly classified as programmed cell death or accidental cell death. This study aimed to create a novel disulfidptosis-related lncRNA index (DLI) that can be used to predict survival and chemotherapy drugs sensitivity in patients with cervical cancer. First of all, we found lncRNAs associated with disulfidptosis between cervical cancer tissues and normal tissues. By LASSO-Cox analysis, overlapping lncRNAs were then used to construct lncRNA index associated with disulfidptosis, which can be served to predict the prognosis of patients with CC, especially the chemotherapy drugs sensitivity. ROC curves and PCA based on DLI and clinical signatures were developed and demonstrated to have good predictive potential. In addition, differences in immune cell subset infiltration and differences in immune checkpoint expression between high-DLI and low-DLI groups were analyzed, and we investigated the relationship between the DLI and tumor mutation burden (TMB). In summary, we constructed a lncRNA prediction index associated with disulfidptosis. This has important clinical implications, including improving the predictive value of cervical cancer patients and providing a biomarker for cervical cancer guiding individualized treatment.

Identification of Somatic Mutations in Plasma Cell-Free DNA from Patients with Metastatic Oral Squamous Cell Carcinoma

The accurate diagnosis and treatment of oral squamous cell carcinoma (OSCC) requires an understanding of its genomic alterations. Liquid biopsies, especially cell-free DNA (cfDNA) analysis, are a minimally invasive technique used for genomic profiling. We conducted comprehensive whole-exome sequencing (WES) of 50 paired OSCC cell-free plasma with whole blood samples using multiple mutation calling pipelines and filtering criteria. Integrative Genomics Viewer (IGV) was used to validate somatic mutations. Mutation burden and mutant genes were correlated to clinico-pathological parameters. The plasma mutation burden of cfDNA was significantly associated with clinical staging and distant metastasis status. The genes TTN, PLEC, SYNE1, and USH2A were most frequently mutated in OSCC, and known driver genes, including KMT2D, LRP1B, TRRAP, and FLNA, were also significantly and frequently mutated. Additionally, the novel mutated genes CCDC168, HMCN2, STARD9, and CRAMP1 were significantly and frequently present in patients with OSCC. The mutated genes most frequently found in patients with metastatic OSCC were RORC, SLC49A3, and NUMBL. Further analysis revealed that branched-chain amino acid (BCAA) catabolism, extracellular matrix-receptor interaction, and the hypoxia-related pathway were associated with OSCC prognosis. Choline metabolism in cancer, O-glycan biosynthesis, and protein processing in the endoplasmic reticulum pathway were associated with distant metastatic status. About 20% of tumors carried at least one aberrant event in BCAA catabolism signaling that could possibly be targeted by an approved therapeutic agent. We identified molecular-level OSCC that were correlated with etiology and prognosis while defining the landscape of major altered events of the OSCC plasma genome. These findings will be useful in the design of clinical trials for targeted therapies and the stratification of patients with OSCC according to therapeutic efficacy.

Comprehensive Analysis of Purine-Metabolism-Related Gene Signature for Predicting Ovarian Cancer Prognosis, Immune Landscape, and Potential Treatment Options

Purine metabolism is an important branch of metabolic reprogramming and has received increasing attention in cancer research. Ovarian cancer is an extremely dangerous gynecologic malignancy for which there are no adequate tools to predict prognostic risk. Here, we identified a prognostic signature consisting of nine genes related to purine metabolism, including ACSM1, CACNA1C, EPHA4, TPM3, PDIA4, JUNB, EXOSC4, TRPM2, and CXCL9. The risk groups defined by the signature are able to distinguish the prognostic risk and the immune landscape of patients. In particular, the risk scores offer promising personalized drug options. By combining risk scores with clinical characteristics, we have created a more detailed composite nomogram that allows for a more complete and individualized prediction of prognosis. In addition, we demonstrated metabolic differences between platinum-resistant and platinum-sensitive ovarian cancer cells. In summary, we have performed the first comprehensive analysis of genes related to purine metabolism in ovarian cancer patients and created a feasible prognostic signature that will aid in risk prediction and support personalized medicine.

In silico Screening and Validation of Achyranthes aspera as a Potential Inhibitor of BRAF and NRAS in Controlling Thyroid Cancer

BACKGROUND

Thyroid carcinoma (THCA) is one of the most prevalent endocrine tumors, accounting for 3.4% of all cancers diagnosed annually. Single Nucleotide Polymorphisms (SNPs) are the most prevalent genetic variation associated with thyroid cancer. Understanding thyroid cancer genetics will enhance diagnosis, prognosis, and treatment.

METHODS

This TCGA-based study analyzes thyroid cancer-associated highly mutated genes through highly robust in silico techniques. Pathway, gene expression, and survival studies were performed on the top 10 highly mutated genes (BRAF, NRAS, TG, TTN, HRAS, MUC16, ZFHX3, CSMD2, EIFIAX, SPTA1). Novel natural compounds from Achyranthes aspera Linn were discovered to target two highly mutated genes. The natural compounds and synthetic drugs used to treat thyroid cancer were subjected to comparative molecular docking against BRAF and NRAS targets. The ADME characteristics of Achyranthes aspera Linn compounds were also investigated.

RESULTS

The gene expression analysis revealed that the expression of ZFHX3, MCU16, EIF1AX, HRAS, and NRAS was up-regulated in tumor cells while BRAF, TTN, TG, CSMD2, and SPTA1 were down-regulated in tumor cells. In addition, the protein-protein interaction network demonstrated that HRAS, BRAF, NRAS, SPTA1, and TG proteins have strong interactions with each other as compared to other genes. The ADMET analysis shows that seven compounds have druglike properties. These compounds were further studied for molecular docking studies. The compounds MPHY012847, IMPHY005295, and IMPHY000939 show higher binding affinity with BRAF than pimasertib. In addition, IMPHY000939, IMPHY000303, IMPHY012847, and IMPHY005295 showed a better binding affinity with NRAS than Guanosine Triphosphate.

CONCLUSION

The outcomes of docking experiments conducted on BRAF and NRAS provide insight into natural compounds with pharmacological characteristics. These findings indicate that natural compounds derived from plants as a more promising cancer treatment option. Thus, the results of docking investigations conducted on BRAF and NRAS substantiate the conclusions that the molecule possesses the most suited drug-like qualities. Compared to other compounds, natural compounds are superior, and they are also druggable. This demonstrates that natural plant compounds can be an excellent source of potential anti-cancer agents. The preclinical research will pave the road for a possible anti-cancer agent.

The gene regulatory molecule GLIS3 in gastric cancer as a prognostic marker and be involved in the immune infiltration mechanism

Background

Gastric cancer is the most prevalent solid tumor form. Even after standard treatment, recurrence and malignant progression are nearly unavoidable in some cases of stomach cancer. GLIS Family Zinc Finger 3 (GLIS3) has received scant attention in gastric cancer research. Therefore, we sought to examine the prognostic significance of GLIS3 and its association with immune infiltration in gastric cancer.

Method

Using public data from The Cancer Genome Atlas (TCGA), we investigated whether GLIS3 gene expression was linked with prognosis in patients with stomach cancer (STAD). The following analyses were performed: functional enrichment analysis (GSEA), quantitative real-time PCR, immune infiltration analysis, immunological checkpoint analysis, and clinicopathological analysis. We performed functional validation of GLIS3 in vitro by plate cloning and CCK8 assay. Using univariate and multivariate Cox regression analyses, independent prognostic variables were identified. Additionally, a nomogram model was built. The link between OS and subgroup with GLIS3 expression was estimated using Kaplan-Meier survival analysis. Gene set enrichment analysis utilized the TCGA dataset.

Result

GLIS3 was significantly upregulated in STAD. An examination of functional enrichment revealed that GLIS3 is related to immunological responses. The majority of immune cells and immunological checkpoints had a positive correlation with GLIS3 expression. According to a Kaplan-Meier analysis, greater GLIS3 expression was related to adverse outcomes in STAD. GLIS3 was an independent predictive factor in STAD patients, as determined by Cox regression (HR = 1.478, 95%CI = 1.478 (1.062-2.055), P=0.02).

Conclusion

GLIS3 is considered a novel STAD patient predictive biomarker. In addition, our research identifies possible genetic regulatory loci in the therapy of STAD.