Multi-staged gene expression profiling reveals potential genes and the critical pathways in kidney cancer

The Role of HSP90 and TRAP1 Targets on Treatment in Hepatocellular Carcinoma

Hepatocellular Carcinoma (HCC) is the predominant form of liver cancer and arises due to dysregulation of the cell cycle control machinery. Heat Shock Protein 90 (HSP90) and mitochondrial HSP90, also referred to as TRAP1 are important critical chaperone target receptors for early diagnosis and targeting HCC. Both HSP90 and TRAP1 expression was found to be higher in HCC patients. Hence, the importance of HSP90 and TRAP1 inhibitors mechanism and mitochondrial targeted delivery of those inhibitors function is widely studied. This review also focuses on importance of protein-protein interactions of HSP90 and TRAP1 targets and association of its interacting proteins in various pathways of HCC. To further elucidate the mechanism, systems biology approaches and computational biology approach studies are well explored in the association of inhibition of herbal plant molecules with HSP90 and its mitochondrial type in HCC.

Comprehensive Molecular and Genomic Analysis of NCI-MATCH Subprotocol Y: Capivasertib in Patients With an AKT1 E17K-Mutated Tumor

PURPOSE

NCI-MATCH (EAY131) is a precision medicine trial using genomic testing to allocate patients with advanced malignancies to targeted treatments. Arm Y evaluated capivasertib, a pan AKT inhibitor, in patients with an AKT1 E17K-mutated tumor. Here, we report on the translational objectives of the study, a molecular and genomic analysis of specimens to identify potential biomarkers of response or resistance to capivasertib.

METHODS

Eligible patients had AKT1 E17K-mutated metastatic tumors that progressed with standard treatment and received capivasertib 480 mg orally twice daily for 4 days on and 3 days off weekly in 28-day cycles. The primary end point was objective response rate (ORR). We performed whole-exome sequencing, RNA sequencing, and gene set and pathway enrichment analysis on 25 pretreatment tissue samples and evaluated findings in responders (complete response [CR], n = 0, and partial response, n = 9) and nonresponders (stable disease, n = 13, and progressive disease, n = 3).

RESULTS

The ORR was 28.6% (10 of 35) in the reported primary trial and 36% (9 of 25) in this translational cohort. Mutations in the TP53 gene were more frequent in responders, whereas the PI3K/AKT/mTOR pathway genes TYRO3, SYNJ1, and CDIPT were significantly altered in nonresponders. DNA repair, p53, E2F, and Wnt-beta catenin pathways were enriched in the responder group. Unsupervised clustering of gene expression identified five genes, ANKRD30A, SUSD4, TTC6, POTEJ, and POTEI, that were significantly higher in responders and lower in nonresponders. In addition, EGFR expression was significantly increased in nonresponders.

CONCLUSION

In patients with AKT1 E17K-mutated tumors, capivasertib achieved a clinically significant ORR. TP53 mutations appear to be associated with response, whereas certain additional PI3K/AKT/mTOR pathway mutations and EGFR overexpression appear to be associated with nonresponse to capivasertib. Further investigation of predictive biomarkers is warranted.

The Role of Zuo Jin Wan in Modulating the Tumor Microenvironment of Colorectal Cancer

INTRODUCTION

Traditional Chinese medicine (TCM) can modulate the immune function of tumor patients in various ways. Zuojin Wan (ZJW, a 6:1 ratio of Huang Lian and Wu Zhu Yu) can modulate the microenvironment of ulcerative colitis, but its role in regulating the colorectal cancer (CRC) microenvironment remains unclear. Exploring the role of ZJW in CRC immunomodulation may improve the antitumor effect of existing immunotherapeutic strategies.

MATERIAL AND METHODS

The active compounds of each herb in ZJW were obtained from the HIT2.0 database with literature evidence. Single-cell RNA sequencing data of CRC were obtained from published studies (PMID: 32451460, 32103181, and 32561858). Pathway enrichment was analyzed using the reactome database, and intergenic correlation analysis was performed using the corrplot R software package. ZJW-regulated gene expression was verified by RT-qPCR.

RESULTS

Huang Lian and Wu Zhu Yu contain 19 and 4 compounds, respectively. Huang Lian targets 146 proteins, and Wu Zhu Yu targets 28 proteins based on evidence from the literature. ZJW regulates a range of biological processes associated with immune function, including cytokine signaling and Toll-Like Receptor 4 (TLR4) cascade. ZJW regulates malignant CRC cells, immune cells (including T-cells, B-cells, mast cells, NK/NKT cells, and myeloid cells), and other nonimmune cells (including endothelial cells, enteric glial cells, and pericytes). We confirmed that ZJW significantly downregulated the expression of TIMP1 and MTDHin CRC cell lines.

CONCLUSIONS

ZJW regulates a range of cells in the CRC microenvironment, including malignant CRC, immune cells, and stromal cells. In CRC cell lines, downregulation of TIMP1 and MTDH by ZJW may play an important role in the immunomodulation in CRC.

Deciphering molecular landscape of breast cancer progression and insights from functional genomics and therapeutic explorations followed by in vitro validation

Breast cancer is caused by aberrant breast cells that proliferate and develop into tumors. Tumors have the potential to spread throughout the body and become lethal if ignored. Metastasis is the process by which invasive tumors move to neighboring lymph nodes or other organs. Metastasis can be lethal and perhaps fatal. The objective of our study was to elucidate the molecular mechanisms underlying the transition of Ductal Carcinoma In Situ (DCIS) to Invasive Ductal Carcinoma (IDC), with a particular focus on hub genes and potential therapeutic agents. Using Weighted Gene Co-expression Network Analysis (WGCNA), we built a comprehensive network combining clinical and phenotypic data from both DCIS and IDC. Modules within this network, correlated with specific phenotypic traits, were identified, and hub genes were identified as critical markers. Receiver Operating Characteristic (ROC) analysis assessed their potential as biomarkers, while survival curve analysis gauged their prognostic value. Furthermore, molecular docking predicted interactions with potential therapeutic agents. Ten hub genes-CDK1, KIF11, NUF2, ASPM, CDCA8, CENPF, DTL, EXO1, KIF2C, and ZWINT-emerged as pivotal fibroblast-specific genes potentially involved in the DCIS to IDC transition. These genes exhibited pronounced positive correlations with key pathways like the cell cycle and DNA repair, Molecular docking revealed Fisetin, an anti-inflammatory compound, effectively binding to both CDK1 and DTL underscoring their role in orchestrating cellular transformation. CDK1 and DTL were selected for molecular docking with CDK1 inhibitors, revealing effective binding of Fisetin, an anti-inflammatory compound, to both. Of the identified hub genes, DTL-an E3 ubiquitin ligase linked to the CRL4 complex-plays a central role in cancer progression, impacting tumor growth, invasion, and metastasis, as well as cell cycle regulation and epithelial-mesenchymal transition (EMT). CDK1, another hub gene, is pivotal in cell cycle progression and associated with various biological processes. In conclusion, our study offers insights into the complex mechanisms driving the transition from DCIS to IDC. It underscores the importance of hub genes and their potential interactions with therapeutic agents, particularly Fisetin. By shedding light on the interplay between CDK1 and DTL expression, our findings contribute to understanding the regulatory landscape of invasive ductal carcinoma and pave the way for future investigations and novel therapeutic avenues.

Prognostic value of FDX1, the cuprotosis key gene, and its prediction models across imaging modalities and histology

BACKGROUND

Cuprotosis has been identified as a novel way of cell death. The key regulator ferredoxin 1 (FDX1) was explored via pan-cancer analysis, and its prediction models were proposed across seven malignancies and two imaging modalities.

METHODS

The prognostic value of FDX1 was explored via 1654 cases of 33 types of cancer in the Cancer Genome Atlas database. The MRI cohort of hepatocellular carcinoma in the First Affiliated Hospital of Fujian Medical University, and CT and MRI images from the Cancer Imaging Archive, REMBRANDT and Duke databases were exploited to formulate radiomic models to predict FDX1 expression. After segmentation of volumes of interest and feature extraction, the recursive feature elimination algorithm was used to screen features, logistic regression was used to model features, immunohistochemistry staining with FDX1 antibody was performed to test the radiomic model.

RESULTS

FDX1 was found to be prognostic in various types of cancer. The area under the receiver operating characteristic curve of radiomic models to predict FDX1 expression reached 0.825 (95% CI = 0.739-0.911). Cross-tissue compatibility was confirmed in pan-cancer validation and test cohorts. Mechanistically, the radiomic score was significantly correlated with various immunosuppressive genes and gene mutations. The radiomic score was also found to be an independent prognostic factor, making it a potentially actionable biomarker in the clinical setting.

CONCLUSIONS

The expression of FDX1 could be non-invasively predicted via radiomics. The radiomic patterns with biological and clinical relevance across histology and modalities could have a broad impact on a larger population of patients.

The role of cuproptosis-related genes in pan-cancer and the development of cuproptosis-related risk model in colon adenocarcinoma

Cancer is widely regarded as a leading cause of death in humans, with colon adenocarcinoma (COAD) ranking among the most prevalent types. Cuproptosis is a novel form of cell death mediated by protein lipoylation. Cuproptosis-related genes (CRGs) participate in tumourigenesis and development. Their role in pan-cancer and COAD require further investigation. This study comprehensively evaluated the relationship among CRGs, pan-cancer, and COAD. Our research revealed the differential expression of CRGs and the cuproptosis potential index (CPI) between normal and tumour tissues, and further explored the correlation of CRGs or CPI with prognosis, immune infiltration, tumor mutant burden(TMB), microsatellite instability (MSI), and drug sensitivity in pan-cancer. Gene set enrichment analysis (GSEA) revealed that oxidative phosphorylation and fatty acid metabolism pathways were significantly enriched in the high CPI group of most tumours. FDX1 and CDKN2A were chosen for further exploration, and we found an independent association between FDX1 and CDKN2A and prognosis, immune infiltration, TMB, and MSI in pan-cancer. Furthermore, a prognostic risk model based on the association between CRGs and COAD was built, and the correlations between the risk score and prognosis, immune-related characteristics, and drug sensitivity were analysed. COAD was then divided into three subtypes using cluster analysis, and the differences among the subtypes in prognosis, CPI, immune-related characteristics, and drug sensitivity were determined. Due to the level of LIPT1 was notably positive related with the risk score, the cytological identification was carried out to identify the association of LIPT1 with proliferation and migration of colon cancer cells. In summary, CRGs can be used as potential prognostic biomarkers to predict immune infiltration levels in patients with pan-cancer. In addition, the risk model could more accurately predict the prognosis and immune infiltration levels of COAD and better guide the direction of clinical medication. Thus, FDX1, CDKN2A, and LIPT1 may serve as prospective new targets for cancer therapy.

Pharmacogenomics: A Genetic Approach to Drug Development and Therapy

The majority of the well-known pharmacogenomics research used in the medical sciences contributes to our understanding of medication interactions. It has a significant impact on treatment and drug development. The broad use of pharmacogenomics is required for the progress of therapy. The main focus is on how genes and an intricate gene system affect the body's reaction to medications. Novel biomarkers that help identify a patient group that is more or less likely to respond to a certain medication have been discovered as a result of recent developments in the field of clinical therapeutics. It aims to improve customized therapy by giving the appropriate drug at the right dose at the right time and making sure that the right prescriptions are issued. A combination of genetic, environmental, and patient variables that impact the pharmacokinetics and/or pharmacodynamics of medications results in interindividual variance in drug response. Drug development, illness susceptibility, and treatment efficacy are all impacted by pharmacogenomics. The purpose of this work is to give a review that might serve as a foundation for the creation of new pharmacogenomics applications, techniques, or strategies.

The prognostic biomarker TPGS2 is correlated with immune infiltrates in pan-cancer: a bioinformatics analysis

Background

Tubulin polyglutamylase complex subunit 2 (TPGS2) is an element of the neuronal polyglutamylase complex that plays a role in the post-translational addition of glutamate residues to C-terminal tubulin tails. Recent research has shown that TPGS2 is associated with some tumors, but the roles of TPGS2 in tumor immunity remain unclear.

Methods

The research data were mainly sourced from The Cancer Genome Atlas. The data were analyzed to identify potential correlations between TPGS2 expression and survival, gene alterations, the tumor mutational burden (TMB), microsatellite instability (MSI), immune infiltration, and various immune-related genes across various cancers. The Wilcoxon rank-sum test was used to identify the significance. A log-rank test and univariate Cox regression analysis were performed to assess the survival state of the patients. Spearman's correlation coefficients were used to show the correlations.

Results

TPGS2 exhibited abnormal expression patterns in most types of cancers, and has promising prognostic potential in adrenocortical carcinoma and liver hepatocellular carcinoma. Further, TPGS2 expression was significantly correlated with molecular and immune subtypes. Moreover, the single-cell analyses showed that the expression of TPGS2 was associated with the cell cycle, metastasis, invasion, inflammation, and DNA damage. In addition, the immune cell infiltration analysis and gene-set enrichment analysis demonstrated that a variety of immune cells and immune processes were associated with TPGS2 expression in various cancers. Further, immune regulators, including immunoinhibitors, immunostimulators, the major histocompatibility complex, chemokines, and chemokine receptors, were correlated with TPGS2 expression in different cancer types. Finally, the TMB and MSI, which have been identified as powerful predictors of immunotherapy, were shown to be correlated with the expression of TPGS2 across human cancers.

Conclusions

TPGS2 is aberrantly expressed in most cancer tissues and might be associated with immune cell infiltration in the tumor microenvironment. TPGS2 could serve not only as a biomarker for predicting clinical outcomes, but also as a promising biomarker for evaluating and developing new approaches to immunotherapy in many types of cancers, especially colon adenocarcinoma and stomach adenocarcinoma.

Impact of curcumin on ferroptosis-related genes in colorectal cancer: Insights from in-silico and in-vitro studies

Colorectal cancer (CRC) is responsible for a significant number of cancer-related fatalities worldwide. Researchers are investigating the therapeutic potential of ferroptosis, a type of iron-dependent controlled cell death, in the context of CRC. Curcumin, a natural compound found in turmeric, exhibits anticancer properties. This study explores the effects of curcumin on genes related to ferroptosis (FRGs) in CRC. To gather CRC data, we used the Gene Expression Profiling Interactive Analysis (GEPIA) and Gene Expression Omnibus (GEO) databases, while FRGs were obtained from the FerrDb database and PubMed. We identified 739 CRC differentially expressed genes (DEGs) in CRC and discovered 39 genes that were common genes between FRGs and CRC DEGs. The DEGs related to ferroptosis were enriched with various biological processes and molecular functions, including the regulation of signal transduction and glucose metabolism. Using the Drug Gene Interaction Database (DGIdb), we predicted drugs targeting CRC-DEGs and identified 17 potential drug targets. Additionally, we identified eight essential proteins related to ferroptosis in CRC, including MYC, IL1B, and SLC1A5. Survival analysis revealed that alterations in gene expression of CDC25A, DDR2, FABP4, IL1B, SNCA, and TFAM were associated with prognosis in CRC patients. In SW480 human CRC cells, treatment with curcumin decreased the expression of MYC, IL1B, and EZH2 mRNA, while simultaneously increasing the expression of SLCA5 and CAV1. The findings of this study suggest that curcumin could regulate FRGs in CRC and have the potential to be utilized as a therapeutic agent for treating CRC.

Predicting congenital renal tract malformation genes using machine learning

Congenital renal tract malformations (RTMs) are the major cause of severe kidney failure in children. Studies to date have identified defined genetic causes for only a minority of human RTMs. While some RTMs may be caused by poorly defined environmental perturbations affecting organogenesis, it is likely that numerous causative genetic variants have yet to be identified. Unfortunately, the speed of discovering further genetic causes for RTMs is limited by challenges in prioritising candidate genes harbouring sequence variants. Here, we exploited the computer-based artificial intelligence methodology of supervised machine learning to identify genes with a high probability of being involved in renal development. These genes, when mutated, are promising candidates for causing RTMs. With this methodology, the machine learning classifier determines which attributes are common to renal development genes and identifies genes possessing these attributes. Here we report the validation of an RTM gene classifier and provide predictions of the RTM association status for all protein-coding genes in the mouse genome. Overall, our predictions, whilst not definitive, can inform the prioritisation of genes when evaluating patient sequence data for genetic diagnosis. This knowledge of renal developmental genes will accelerate the processes of reaching a genetic diagnosis for patients born with RTMs.

Multi-omics analysis uncovers clinical, immunological, and pharmacogenomic implications of cuproptosis in clear cell renal cell carcinoma

OBJECTIVE

The latest research proposed a novel copper-dependent programmed cell death named cuproptosis. We aimed to elucidate the influence of cuproptosis in clear cell renal cell carcinoma (ccRCC) from a multi-omic perspective.

METHODS

This study systematically assessed mRNA expression, methylation, and genetic alterations of cuproptosis genes in TCGA ccRCC samples. Through unsupervised clustering analysis, the samples were classified as different cuproptosis subtypes, which were verified through NTP method in the E-MTAB-1980 dataset. Next, the cuproptosis score (Cuscore) was computed based on cuproptosis-related genes via PCA. We also evaluated clinical and immunogenomic features, drug sensitivity, immunotherapeutic response, and post-transcriptional regulation.

RESULTS

Cuproptosis genes presented multi-layer alterations in ccRCC, and were linked with patients' survival and immune microenvironment. We defined three cuproptosis subtypes [C1 (moderate cuproptosis), C2 (low cuproptosis), and C3 (high cuproptosis)], and the robustness and reproducibility of this classification was further proven. Overall survival was best in C3, moderate in C1, and worst in C2. C1 had the highest sensitivity to pazopanib, and sorafenib, while C2 was most sensitive to sunitinib. Furthermore, C1 patients benefited more from anti-PD-1 immunotherapy. Patients with high Cuscore presented the notable survival advantage. Cuscore was highly linked with immunogenomic features, and post-transcriptional events that contributed to ccRCC development. Finally, several potential compounds and druggable targets (NMU, RARRES1) were selected for low Cuscore group.

CONCLUSION

Overall, our study revealed the non-negligible role of cuproptosis in ccRCC development. Evaluation of the cuproptosis subtypes improves our cognition of immunogenomic features and better guides personalized prognostication and precision therapy.

Construction and validation of a novel cuproptosis-mitochondrion prognostic model related with tumor immunity in osteosarcoma

BACKGROUND

The purpose of this study was to develop a new prognostic model for osteosarcoma based on cuproptosis-mitochondrion genes.

MATERIALS AND METHODS

The data of osteosarcoma were obtained from TARGET database. By using Cox regression and LASSO regression analysis, a novel risk score was constructed based on cuproptosis-mitochondrion genes. Kaplan-Meier, ROC curve and independent prognostic analyses were performed to validate the risk score in GSE21257 dataset. Then, a predictive nomogram was constructed and further validated by calibration plot, C-index and ROC curve. Based on the risk score, all patients were divided into high-risk and low-risk group. GO and KEGG enrichment, immune correlation and drug sensitivity analyses were performed between groups. Real-time quantitative PCR verified the expression of cuproptosis-mitochondrion prognostic model genes in osteosarcoma. And we explored the function of FDX1 in osteosarcoma by western blotting, CCK8, colony formation assay, wound healing assay and transwell assays.

RESULTS

A total of six cuproptosis-mitochondrion genes (FDX1, COX11, MFN2, TOMM20, NDUFB9 and ATP6V1E1) were identified. A novel risk score and associated prognostic nomogram were constructed with high clinical application value. Strong differences in function enrichment and tumor immune microenvironment were shown between groups. Besides, the correlation of cuproptosis-mitochondrion genes and drug sensitivity were revealed to search for potential therapeutic target. The expression of FDX1, COX11, MFN2, TOMM20 and NDUFB9 at mRNA level was elevated in osteosarcoma cells compared with normal osteoblast hFOB1.19. The mRNA expression level of ATP6V1E1 was decreased in osteosarcoma. Compared with hFOB1.19, western blotting revealed that the expression of FDX1 was significantly elevated in osteosarcoma cells. Functional experiments indicated that FDX1 mainly promoted the migration of osteosarcoma rather than proliferation.

CONCLUSIONS

We developed a novel prognostic model of osteosarcoma based on cuproptosis-mitochondrion genes, which provided great guidance in survival prediction and individualized treatment decision making for patients with osteosarcoma.

Role of ZNF143 and Its Association with Gene Expression Patterns, Noncoding Mutations, and the Immune System in Human Breast Cancer

The function of noncoding sequence variations at ZNF143 binding sites in breast cancer cells is currently not well understood. Distal elements and promoters, also known as cis-regulatory elements, control the expression of genes. They may be identified by functional genomic techniques and sequence conservation, and they frequently show cell- and tissue-type specificity. The creation, destruction, or modulation of TF binding and function may be influenced by genetic modifications at TF binding sites that affect the binding affinity. Therefore, noncoding mutations that affect the ZNF143 binding site may be able to alter the expression of some genes in breast cancer. In order to understand the relationship among ZNF143, gene expression patterns, and noncoding mutations, we adopted an integrative strategy in this study and paid close attention to putative immunological signaling pathways. The immune system-related pathways ErbB, HIF1a, NF-kB, FoxO, JAK-STAT, Wnt, Notch, cell cycle, PI3K-AKT, RAP1, calcium signaling, cell junctions and adhesion, actin cytoskeleton regulation, and cancer pathways are among those that may be significant, according to the overall analysis.

An Approach for Systems-Level Understanding of Prostate Cancer from High-Throughput Data Integration to Pathway Modeling and Simulation

To understand complex diseases, high-throughput data are generated at large and multiple levels. However, extracting meaningful information from large datasets for comprehensive understanding of cell phenotypes and disease pathophysiology remains a major challenge. Despite tremendous advances in understanding molecular mechanisms of cancer and its progression, current knowledge appears discrete and fragmented. In order to render this wealth of data more integrated and thus informative, we have developed a GECIP toolbox to investigate the crosstalk and the responsible genes'/proteins' connectivity of enriched pathways from gene expression data. To implement this toolbox, we used mainly gene expression datasets of prostate cancer, and the three datasets were GSE17951, GSE8218, and GSE1431. The raw samples were processed for normalization, prediction of differentially expressed genes, and the prediction of enriched pathways for the differentially expressed genes. The enriched pathways have been processed for crosstalk degree calculations for which number connections per gene, the frequency of genes in the pathways, sharing frequency, and the connectivity have been used. For network prediction, protein-protein interaction network database FunCoup2.0 was used, and cytoscape software was used for the network visualization. In our results, we found that there were enriched pathways 27, 45, and 22 for GSE17951, GSE8218, and GSE1431, respectively, and 11 pathways in common between all of them. From the crosstalk results, we observe that focal adhesion and PI3K pathways, both experimentally proven central for cellular output upon perturbation of numerous individual/distinct signaling pathways, displayed highest crosstalk degree. Moreover, we also observe that there were more critical pathways which appear to be highly significant, and these pathways are HIF1a, hippo, AMPK, and Ras. In terms of the pathways' components, GSK3B, YWHAE, HIF1A, ATP1A3, and PRKCA are shared between the aforementioned pathways and have higher connectivity with the pathways and the other pathway components. Finally, we conclude that the focal adhesion and PI3K pathways are the most critical pathways, and since for many other pathways, high-rank enrichment did not translate to high crosstalk degree, the global impact of one pathway on others appears distinct from enrichment.

The cuproptosis related genes signature predicts the prognosis and correlates with the immune status of clear cell renal cell carcinoma

Background: Clear cell renal cell carcinoma (CCRCC) has a high incidence and poor prognosis. Cuproptosis, an independent pattern of cell death associated with copper, plays an important role in cancer proliferation and metastasis. The role of cuproptosis-related genes (CRGs) in CCRCC is unclear.

Methods: Transcriptome and clinical information for CCRCC were downloaded from The Cancer Genome Atlas (TCGA) database. After dividing the training and testing cohort, a 4-CRGs risk signature (FDX1, DLD, DLAT, CDKN2A) was identified in the training cohort using Least absolute shrinkage and selection operator (LASSO) and Cox regression analysis. The effect of the 4-CRGs risk signature on prognosis was assessed using Kaplan-Meier (KM) curves and time-dependent receiver operating characteristic (ROC) curves and verified using the testing cohort. For different risk groups, the immune statue was assessed using the CIBERSORT algorithm, the ssGSEA method and immune checkpoint expression data. Finally, a competitive endogenous RNA (ceRNA) network was constructed using miRTarbase and starBase databases to identify molecules that may have a regulatory relationship with CRCCC.

Results: There were significant changes in the overall survival (OS), immune microenvironment, immune function, and checkpoint gene expression among the different risk groups. A ceRNA network consisting of one mRNA, two miRNAs, and 12 lncRNAs was constructed.

Conclusion: The 4-CRGs risk signature provides a new method to predict the prognosis of patients with CCRCC and the effect of immunotherapy. We propose a new cuproptosis-associated ceRNA network that can help to further explore the molecular mechanisms of CCRCC.

Construction of cuproptosis-related gene signature to predict the prognosis and immunotherapy efficacy of patients with bladder cancer through bioinformatics analysis and experimental validation

Background: A new form of cell death, copper-dependent cell death (termed cuproptosis), was illustrated in a recent scientific study. However, the biological function or prognostic value of cuproptosis regulators in bladder cancer (BLCA) remains unknown. Materials and Methods: Sequencing data obtained from BLCA samples in TCGA and GEO databases were preprocessed for analysis. Biological function and immune cell infiltration levels evaluated by gene set variation analysis (GSVA) were employed to calculate enrichment scores. Iteration least absolute shrinkage and selection operator (LASSO) and COX regression model were employed to select feature genes and construct a novel cuproptosis-related (CR) score signature. The genomics of drug sensitivity in cancer (GDSC) and tumor immune dysfunction and exclusion (TIDE) analysis were used to predict the chemotherapy and immunotherapy efficacy for BLCA patients. The relative expression of the genes involved in the signature was also verified by real-time quantitative PCR (qRT-PCR) in cell lines and tissues. Results: Expression abundance and the prognostic value of cuproptosis regulators proved that cuproptosis might play a vital part in the carcinogenesis of BLCA. GSVA revealed that cuproptosis regulators might be associated with metabolism and metastasis-related pathways such as TGF-β, protein secretion, oxidative Phosphorylation, MYC targets, MTORC1, and adipogenesis pathways. CR scores could predict the prognosis and evaluate the chemotherapy and immunotherapy efficacies of BLCA. CR scores were positively correlated with EMT, MYC, MTORC1, HEDGEHOG, and E2F signaling pathways; meanwhile, they were negatively correlated with several immune cell infiltration levels such as CD8+ T cells, γδT cells, and activated dendritic cells. Several GEO datasets were used to validate the power of prognostic prediction, and a nomogram was also established for clinical use. The expressions of DDX10, RBM34, and RPL17 were significantly higher in BLCA cell lines and tissues in comparison with those in the corresponding normal controls. Conclusion: Cuproptosis might play an essential role in the progression of BLCA. CR scores could be helpful in the investigation of prognostic prediction and therapeutic efficacy and could make contributions to further studies in BLCA.

Genomic profiling and network-level understanding uncover the potential genes and the pathways in hepatocellular carcinoma

Data integration with phenotypes such as gene expression, pathways or function, and protein-protein interactions data has proven to be a highly promising technique for improving human complex diseases, particularly cancer patient outcome prediction. Hepatocellular carcinoma is one of the most prevalent cancers, and the most common cause is chronic HBV and HCV infection, which is linked to the majority of cases, and HBV and HCV play a role in multistep carcinogenesis progression. We examined the list of known hepatocellular carcinoma biomarkers with the publicly available expression profile dataset of hepatocellular carcinoma infected with HCV from day 1 to day 10 in this study. The study covers an overexpression pattern for the selected biomarkers in clinical hepatocellular carcinoma patients, a combined investigation of these biomarkers with the gathered temporal dataset, temporal expression profiling changes, and temporal pathway enrichment following HCV infection. Following a temporal analysis, it was discovered that the early stages of HCV infection tend to be more harmful in terms of expression shifting patterns, and that there is no significant change after that, followed by a set of genes that are consistently altered. PI3K, cAMP, TGF, TNF, Rap1, NF-kB, Apoptosis, Longevity regulating pathway, signaling pathways regulating pluripotency of stem cells, Cytokine-cytokine receptor interaction, p53 signaling, Wnt signaling, Toll-like receptor signaling, and Hippo signaling pathways are just a few of the most commonly enriched pathways. The majority of these pathways are well-known for their roles in the immune system, infection and inflammation, and human illnesses like cancer. We also find that ADCY8, MYC, PTK2, CTNNB1, TP53, RB1, PRKCA, TCF7L2, PAK1, ITPR2, CYP3A4, UGT1A6, GCK, and FGFR2/3 appear to be among the prominent genes based on the networks of genes and pathways based on the copy number alterations, mutations, and structural variants study.