Prognostic Gene Research Articles

A four-gene model for prognostic prediction in bladder urothelial carcinoma

BackgroundBladder urothelial carcinoma (BLCA) is one of the most common malignant tumors in urinary system worldwide. High possibility of recurrence and progression leads to poor prognosis, revealing the significant role of long-term postoperative monitoring to the patients. However, effective noninvasive diagnosis is currently limited. Materials and methodsDifferentially expressed genes (DEGs) were analyzed using R-packages. Functional enrichment analyses were performed on Metascape. The prognostic model was established by multi-step cox regression and evaluated by survival plots and receiver operating characteristic (ROC) curves. The nomogram was then constructed based on three identified prognostic factors. C-index and calibration curves were calculated to testify the capacity for predicting survival possibility of BLCA patients. The transcription levels of model genes were further verified in a gemcitabine-resistant bladder cancer cell line T24 (TGR) by quantitative real-time PCR (qRT-PCR). Results360 genes were differentially expressed between BLCA and normal bladder mucosae simultaneously in three GEO datasets, of which 59 were up-regulated and 301 were down-regulated. 159 prognostic genes were obtained from DEGs. Lasso and multivariate cox regression were conducted in sequence and the prognostic model was eventually optimized to four genes (EHBP1, RHOJ, FASN, STXBP6). Survival analyses demonstrated that the overall survival (OS) of patients in high-risk group was significantly shorter than that in low-risk group. The area under the curve (AUC) values of 3–5 years survival were basically above 0.7. Moreover, cox regression analyses showed that age, T stage and risk score were independent indicators for BLCA prognosis. For further clinical application, a nomogram was then constructed by integrating these factors. The C-index (0.72, CI 95 %, 0.669–0.775) and calibration curves demonstrated the good performance of nomogram. Importantly, the mRNA level of model genes was significantly up-regulated in TGR compared to T24, indicating a better prediction for chemotherapy-resistant BLCA patients. ConclusionCollectively, our findings suggest a novel four-gene predictive model for BLCA prognosis. It is expected to provide a valuable reference for prognostic evaluation and treatment in BLCA patients.

APOA1 mRNA and serum APOA1 protein as diagnostic and prognostic biomarkers in gastric cancer.

Gastric cancer (GC) remains a formidable challenge in oncology, ranking as a leading cause of cancer mortality globally. This underscores an urgent need for innovative prognostic markers that can revolutionize patient management and outcomes. Recent insights into cancer biology have spotlighted the profound influence of lipid metabolism alterations on tumorigenesis, tumor progression, and the tumor microenvironment. These alterations not only fuel cancer cell growth and proliferation but also play a strategic role in evading immune surveillance and promoting metastasis. The intricate web of lipid metabolism in cancer cells, characterized by deregulated uptake, synthesis, and oxidation of fatty acids (FAs), opens new avenues for targeted therapeutic interventions and prognostic evaluations. Specifically, this study zeroes in on apolipoprotein A-I (APOA1), a key player in lipid metabolism, to unearth its prognostic value in GC. By delving into the role of lipid metabolism-related genes, particularly APOA1, we aim to unveil their potential as groundbreaking biomarkers for GC prognosis. This endeavor not only aims to enhance our understanding of the molecular underpinnings of GC but also to spearhead the development of lipid metabolism-based strategies for improved diagnostic, prognostic, and therapeutic outcomes. Transcriptomic and clinical data from GC patients and healthy individuals were sourced from The Cancer Genome Atlas (TCGA) database, a comprehensive project that molecularly characterizes over 20,000 primary cancer and matched normal samples across 33 cancer types. Significantly differentially expressed lipid metabolism-related genes were identified using the "limma" package in R. Prognostic genes were selected via univariate Cox regression analysis. Differential gene enrichment analysis was performed using Metascape (http://www.metascape.org). The Human Protein Atlas (HPA, https://www.proteinatlas.org) provided information on APOA1 protein expression in GC and healthy tissues. Immune cell infiltration was analyzed using the CIBERSORT algorithm (http://cibersort.stanford.edu). Significant differences in lipid metabolism-related gene expression were observed between GC and normal tissues, closely linked to FA metabolism, oxidoreductase activity, and sphingolipid metabolism. APOA1 emerged as a potential prognostic biomarker by intersecting prognostic and differentially expressed lipid metabolism genes. Immunohistochemical analysis confirmed APOA1 downregulation in GC. The receiver operating characteristic (ROC) analysis demonstrated its predictive value, with the area under the curve (AUC) being 0.64 [95% confidence interval (CI): 0.52-0.76]. APOA1 expression correlated with immune cell infiltrations. Clinical serum APOA1 results revealed lower levels in GC patients (1.38 vs. 1.26; P<0.05), associated with poor prognosis (hazard ratio =1.50; P<0.001) and clinical characteristics. ROC analysis of serum APOA1 demonstrated good diagnostic ability (AUC: 0.63, 95% CI: 0.61-0.65). Serum APOA1 levels significantly increased after treatment. This study highlights lipid metabolism reprogramming in GC and identifies APOA1 as a potential diagnostic and prognostic biomarker, suggesting its clinical utility in managing GC.

Pomiferin targeting SLC9A9 based on histone acetylation modification pattern is a potential therapeutical option for gastric cancer with high malignancy

Changes in histone acetylation status are associated with gastric cancer (GC) progression. Pomiferin is a natural flavonoid, however, the specific role of pomiferin in the treatment of GC is still unclear, and its targets are not well clarified. In this work, the prognostic genes related with histone acetylation in GC were screened by univariate Cox analysis. Next, a risk model of was constructed using least absolute shrinkage and selection operator-Cox regression analyses, and multivariate Cox analysis was used for identifying the independent risk factor. Molecular docking was performed using AutoDock Vina to validate the interaction between solute carrier family 9 member A9 (SLC9A9) and pomiferin. In vitro and in vivo models were applied to investigate the tumor-suppressive role of pomiferin against GC. The inhibitory effects of pomiferin on EGFR/PI3K/AKT signaling were valdiated by Western blotting, immunofluorescence staining and qPCR. Here, a prognostic risk model based on histone acetylation regulators was established, and SLC9A9 was identified as a risk factor associated with histone acetylation status in GC. SLC9A9 expression was associated with abnormal immune microenvironment of tumor. Pomiferin had a high binding affinity with SLC9A9, and both pomiferin treatment and depletion of SLC9A9 repressed the malignant phenotypes of GC cells. Mechanistically, pomiferin inactivates EGFR/PI3K/AKT signaling in GC cells. In summary, SLC9A9, as a indicator of abnormal histone acetylation status of GC, functions as an oncogenic factor. Pomiferin binds with SLC9A9 to inactivate EGFR/PI3K/AKT pathway, to block GC progression, suggesting it is a promising drug for the patients with highly malignant GC.

A novel lactylation-related gene signature for effectively distinguishing and predicting the prognosis of ovarian cancer.

Lactylation has been found to regulate several types of biological processes in cancer. However, there is limited research on lactylation-related genes in predicting the prognosis of ovarian cancer (OC). This study aimed to explore the functional roles of lactylation-related genes in OC. Based on TCGA database, we obtained RNA sequencing data and clinical characteristics of patients with OC. Fourteen lactylation-related genes were screened for bioinformatic analysis in OC. Tumor classification of OC was constructed via a consistency cluster analysis. We examined the prognosis, immune-cell infiltration, and immunotherapy in relation to a lactylation-related model for OC. A total of 707 prognostic genes and 14 key lactylation-related genes (SNRPA1, MPHOSPH6, POLDIP3, RB1, AHNAK, MAGOHB, CALM1, EP300, HDAC1, HDAC2, HDAC3, SIRT1, SIRT2, and SIRT3) were identified in TCGA-OC patients. Based on 14 genes involved in lactylation, TCGA-OC patients were split into low-risk (G1) and high-risk (G2) groups. Downregulated differentially expressed genes (DEGs) in the low-risk G1 group were associated with thermogenesis, oxidative phosphorylation, neutrophil extracellular trap formation, and interleukin 17 (IL-17) signaling pathway, whereas upregulated DEGs were associated with proteoglycans in cancer, focal adhesion, Wnt signaling pathway, extracellular matrix (ECM)-receptor interaction, and the adherens junction. The immune activity of the low-risk G1 group was lower than that of the high-risk G2 group. Gemcitabine, bleomycin, and doxorubicin had lower half-maximal inhibitory concentration (IC50) values in the high-risk G2 patients with OC, while cisplatin and paclitaxel had higher IC50 values compared to the low-risk G1 patients. The prognosis of patients with OC was also predicted with the help of an eight-lactylation-related gene prognostic model, comprising SNRPA1, MPHOSPH6, POLDIP3, RB1, HDAC1, CALM1, HDAC2, and SIRT2. The lactylation-related genes are closely related to tumor classification and immunity in patients with OC. There was good prognostic predictive performance for OC based on a lactylation-related signature. Our findings may offer new insights into the diagnosis and treatment of OC.

Machine learning-derived immunosenescence index for predicting outcome and drug sensitivity in patients with skin cutaneous melanoma.

The functions of immunosenescence are closely related to skin cutaneous melanoma (SKCM). The aim of this study is to uncover the characteristics of immunosenescence index (ISI) to identify novel biomarkers and potential targets for treatment. Firstly, integrated bioinformatics analysis was carried out to identify risk prognostic genes, and their expression and prognostic value were evaluated. Then, we used the computational algorithm to estimate ISI. Finally, the distribution characteristics and clinical significance of ISI in SKCM by using multi-omics analysis. Patients with a lower ISI had a favorable survival rate, lower chromosomal instability, lower somatic copy-number alterations, lower somatic mutations, higher immune infiltration, and sensitive to immunotherapy. The ISI exhibited robust, which was validated in multiple datasets. Besides, the ISI is more effective than other published signatures in predicting survival outcomes for patients with SKCM. Single-cell analysis revealed higher ISI was specifically expressed in monocytes, and correlates with the differentiation fate of monocytes in SKCM. Besides, individuals exhibiting elevated ISI levels could potentially receive advantages from chemotherapy, and promising compounds with the potential to target high ISI were recognized. The ISI model is a valuable tool in categorizing SKCM patients based on their prognosis, gene mutation signatures, and response to immunotherapy.

Targeting TUBB3 Suppresses Anoikis Resistance and Bone Metastasis in Prostate Cancer.

Bone metastases occur in more than 70% of advanced prostate cancer (PCa) patients, leading to a poor prognosis. Resistance to detachment-induced apoptosis, also known as anoikis, plays a crucial role in the onset of tumor metastasis. Targeting anoikis resistance is of immense therapeutic significance in repression of metastatic spread. In this study, based on an anoikis-related prognostic risk model of PCa, we identify TUBB3 as a key anoikis-related prognostic gene that is highly expressed in bone metastatic PCa. TUBB3 expression is increased in anoikis-resistant PCa cells, and TUBB3 depletion significantly reverses anoikis resistance during extracellular matrix (ECM) detachment and inhibits anoikis-resistance-induced PCa cell invasion and migration as well as epithelial-mesenchymal transition (EMT) process. TUBB3 knockdown significantly reduces αvβ3/FAK/Src axis activation, blocking its downstream oncogenic signaling. In addition, we develop bone-targeting lipid nanoparticles (BT-LNP) based on bisphosphonate-modified ionizable lipid for systemic delivery of siRNA targeting TUBB3 (siTUBB3). BT-LNP-delivered siTUBB3 therapy with localization in the bone microenvironment significantly attenuate PCa bone metastasis progression in vivo upon intravenous administration. These findings pinpoint that TUBB3, as a key regulator of anoikis resistance, is an effective therapeutic target in bone metastatic PCa and that BT-LNP-mediated systemic delivery of siTUBB3 can be developed as a novel therapeutic strategy for this disease. This article is protected by copyright. All rights reserved.

Tumorigenic role of Pak4 in ovarian cancer and its correlation with immune infiltration

BackgroundOvarian cancer is the most common cause of gynecological cancer death. Pak4 has been proved to be tumorigenic in many types of cancers, but its role in ovarian cancer is still not clarified.MethodsIn this study, we used immunohistochemistry to investigate into Pak4 expression in different histological types of ovarian cancer. TIMER, TISCH2, GEPIA, ualcan, KM plotter, GSCA and GeneMANIA were used to identify the prognostic roles and gene regulation networks of Pak4 in ovarian cancer. Immune infiltration levels were investigated using TIMER database.ResultsPak4 was highly expressed in ovarian cancers, regardless of different FIGO stages and histological grades. Single cell sequencing database proved that Pak4 was highly expressed in malignant ovarian cancer cells. Pak4 level was significantly correlated with different histological types of ovarian cancer. Pak4 expression was negatively connected with OS and PFS of ovarian cancer patients. Functions of Pak4 and its interacted genes were mainly involved in protein serine/threonine kinase activity, regulation of actin filament-based process and regulation of cytoskeleton organization. Pak4 level was negatively correlated with immune biomarkers of B cell infiltration (p = 2.39e-05), CD8 + T cell infiltration (p = 1.51e-04), neutrophil (p = 1.74e-06) and dendritic cell (p = 4.41e-08). Close correlation was found between Pak4 expression and T cell exhaustion (p < 0.05).ConclusionsOur results demonstrated the expression level, gene interaction networks and immune infiltration levels of Pak4 in ovarian cancer. And the results revealed role of Pak4 in tumorigenesis and the possibility to be a potential immunotherapeutic target.

Integrated analysis of tumor-associated macrophages and M2 macrophages in CRC: unraveling molecular heterogeneity and developing a novel risk signature.

Emerging investigations have increasingly highlighted the critical role of tumor-associated macrophages (TAMs) and M2 macrophages in cancer development, progression, and metastasis, marking them as potential targets in various cancer types. The main objective of this research is to discover new biomarkers associated with TAM-M2 macrophages in colorectal cancer (CRC) and to dissect the molecular heterogeneity of CRC by combining single-cell RNA sequencing and bulk RNA-seq data. By utilizing weighted gene co-expression network analysis (WGCNA), we acquired TAM-M2-associated genes by intersecting TAM marker genes obtained from scRNA-seq data with module genes of M2 macrophages derived from bulk RNA-seq data. We employed least absolute shrinkage and selection operator (LASSO) Cox analysis to select predictive biomarkers from these TAM-M2-related genes. Quantitative polymerase chain reaction (qPCR) was employed to validate the mRNA expression levels of the genes identified in the screening. This led to the development of the TAM-M2-related signature (TAMM2RS). We also conducted functional and immune landscape analyses of different risk groups. The combination of scRNA-seq and bulk RNA-seq analyses yielded 377 TAM-M2-related genes. DAPK1, NAGK, and TRAF1 emerged as key prognostic genes in CRC, which were identified through LASSO Cox analysis. Utilizing these genes, we constructed and validated the TAMM2RS, demonstrating its effectiveness in predicting survival in CRC patients. Our research offers a thorough investigation into the molecular mechanisms associated with TAM-M2 macrophages in CRC and unveils potential therapeutic targets, offering new insights for treatment strategies in colorectal cancer.

The prognostic genes model of breast cancer drug resistance based on single-cell sequencing analysis and transcriptome analysis

Breast cancer (BC) represents a multifaceted malignancy, with escalating incidence and mortality rates annually. Chemotherapy stands as an indispensable approach for treating breast cancer, yet drug resistance poses a formidable challenge. Through transcriptome data analysis, we have identified two sets of genes exhibiting differential expression in this context. Furthermore, we have confirmed the overlap between these genes and those associated with exosomes, which were subsequently validated in cell lines. The investigation screened the identified genes to determine prognostic markers for BC and utilized them to formulate a prognostic model. The disparities in prognosis and immunity between the high- and low-risk groups were validated using the test dataset. We have discerned different BC subtypes based on the expression levels of prognostic genes in BC samples. Variations in prognosis, immunity, and drug sensitivity among distinct subtypes were examined. Leveraging data from single-cell sequencing and prognostic gene expression, the AUCell algorithm was employed to score individual cell clusters and analyze the pathways implicated in high-scoring groups. Prognostic genes (CCT4, CXCL13, MTDH, PSMD2, and RAB27A) were subsewoquently validated using RT-qPCR. Consequently, we have established a model for predicting prognosis in breast cancer that hinges on drug resistance and ERGs. Furthermore, we have evaluated the prognostic value of this model. The genes identified as prognostic markers can now serve as a reference for precise treatment of this condition.

Impact of RANGAP1 SUMOylation on Smad4 nuclear export by bioinformatic analysis and cell assays.

Small Ubiquitin-like Modifier (SUMOylation) regulates a variety of cellular activities, and its dysregulation has been associated with glioma etiology. The aim of this research was to clarify the function of SUMOylation-related genes in glioma and determine relevant prognostic markers. The Cancer Genome Atlas (TCGA) Glioma and GSE16011 datasets were analyzed through bioinformatics to identify Ran GTPase activating protein 1 (RANGAP1) as the hub gene for further study. Experimental validation consisted of quantitative real-time polymerase chain reaction (qRT-PCR), western blotting (WB), and immunoprecipitation (IP) to evaluate RANGAP1 expression, function, and interaction with SUMO1. To assess the role of RANGAP1 knockdown and SUMOylation in glioma cells, various assays were conducted, including cell proliferation, migration, invasion, and apoptosis. In addition, cell cycle analysis and immunofluorescence were performed. Through bioinformatics, RANGAP1 was identified as a crucial prognostic gene for glioma. Experimental studies confirmed the downregulation of RANGAP1 in glioma cells and verified that RANGAP1 repair impedes tumor growth. When it comes to RANGAP1 silencing, it enhanced cell proliferation, invasion and migration. Additionally, SUMO1 was identified as a specific SUMO molecule coupled to RANGAP1, affecting the location of Sma and Mad related protein 4 (Smad4) in the nucleocytoplasm and the transforming growth factor (TGF)-β/Smad signaling pathway. The functional impact of RANGAP1 SUMOylation on cell proliferation and migration was further confirmed through experiments using a SUMOylation-impairing mutation (K524R). Our findings suggest that RANGAP1 may be a potential prognostic marker in gliomas and could play a role in regulating cell proliferation, migration, and invasion. SUMOylation of RANGAP1 is responsible for regulating the TGF-β/Smad signaling pathway, which is crucial for the progression of tumors. Further investigations and experiments are necessary to confirm these results.

Bioinformatics screening of prognostic immune-related genes in renal clear cell carcinoma.

This study aims to harness bioinformatics to identify prognostic immune-related genes in clear cell renal cell carcinoma (ccRCC), focusing particularly on LILRB3. It evaluates LILRB3's expression in ccRCC, its association with patient prognosis, and its potential as a biomarker for predicting survival, thereby providing a preliminary basis for the diagnosis of ccRCC. Utilizing The Cancer Genome Atlas (TCGA) datasets and an immune gene set, we sought immune-related genes with elevated expression in ccRCC. Seventy-two normal tissue samples and 531 ccRCC samples were analyzed, and differential genes were identified with a screening criterion of fold change (FC) > 2 and P value < 0.01. Survival analysis and receiver operating characteristic (ROC) curve analysis were employed to discover genes of prognostic and diagnostic relevance to ccRCC. Pearson correlation analysis with a cutoff of |r|≥ 0.5, facilitated by cBioPortal, assessed genes co-expressed with LILRB3. The DAVID online tool conducted functional and pathway enrichment analyses for LILRB3-coexpressed genes. The TIMER and TCIA databases were utilized to explore LILRB3's influence on immune infiltration in the tumor microenvironment and its relation to key immunological checkpoints. Screening the TCGA database revealed 3719 up-regulated differential genes in ccRCC, with 355 overlapping immune-related genes. Survival analysis of these 355 genes revealed 100 with significant survival impact. ROC curve analysis pinpointed the top 10 genes, including LILRB3, with the highest diagnostic efficiency. LILRB3 emerged as an independent risk factor from the Cox risk regression model. GO and KEGG analyses linked LILRB3 to various biological processes, including chemokine signaling pathways, immunological response, antigen processing and presentation, inflammatory response, T cell co-stimulation, and signal transduction. LILRB3 significantly affected ccRCC immune infiltration and correlated positively with several immunological checkpoints, such as PD-1, LAG3, IDO1, PD-L1, CTLA4, TIM3, TIGIT, and VISTA. LILRB3 shows higher expression levels in ccRCC than in normal tissues and correlates with poor patient prognosis. Its impactful role in the immune infiltration of the RCC microenvironment suggests that LILRB3 could serve as a novel target for ccRCC treatment and prognosis, underlining its diagnostic and prognostic significance.

Identification of microtubule-associated biomarker using machine learning methods in osteonecrosis of the femoral head and osteosarcoma

BackgroundThis study aims to explore the microtubule-associated gene signatures and molecular processes shared by osteonecrosis of the femoral head (ONFH) and osteosarcoma (OS). MethodsDatasets from the TARGET and GEO databases were subjected to bioinformatics analysis, including the functional enrichment analysis of genes shared by ONFH and OS. Prognostic genes were identified using univariate and multivariate Cox regression analyses to develop a risk score model for predicting overall survival and immune characteristics. Furthermore, LASSO and SVM-RFE algorithms identified biomarkers for ONFH, which were validated in OS. Function prediction, ceRNA network analysis, and gene–drug interaction network construction were subsequently conducted. Biomarker expression was then validated on clinical samples by using qPCR. ResultsA total of 14 microtubule-associated disease genes were detected in ONFH and OS. Subsequently, risk score model based on four genes was then created, revealing that patients with low-risk exhibited superior survival outcomes compared with those with high-risk. Notably, ONFH with low-risk profiles may manifest an antitumor immune microenvironment. Moreover, by utilizing LASSO and SVM-RFE algorithms, four diagnostic biomarkers were pinpointed, enabling effective discrimination between patients with ONFH and healthy individuals as well as between OS and normal tissues. Additionally, 21 drugs targeting these biomarkers were predicted, and a comprehensive ceRNA network comprising four mRNAs, 71 miRNAs, and 98 lncRNAs was established. The validation of biomarker expression in clinical samples through qPCR affirmed consistency with the results of bioinformatics analysis. ConclusionMicrotubule-associated genes may play pivotal roles in OS and ONFH. Additionally, a prognostic model was constructed, and four genes were identified as potential biomarkers and therapeutic targets for both diseases.

Analysis of prognostic value of lactate metabolism-related genes in ovarian cancer based on bioinformatics

BackgroundRecent studies have provided evidence supporting the functional role and mechanism of lactate in suppressing anticancer immunity. However, there is no systematic analysis of lactate metabolism-related genes (LMRGs) and ovarian cancer (OV) prognosis.ResultsSix genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) were selected as prognostic genes and a prognostic model was utilized. Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) analyses were further performed and indicated that the prognostic model was effective. Subsequently, the neoplasm_cancer_status and RiskScore were determined as independent prognostic factors, and a nomogram was established with relatively accurate forecasting ability. Additionally, 2 types of immune cells (Central memory CD8 T cell and Immature B cell), 4 types of immune functions (APC co inhibition, DCs, Tfh and Th1 cells), 9 immune checkpoints (BTLA, CTLA4, IDO1, LAG3, VTCN1, CXCL10, CXCL9, IFNG, CD27) and tumor immune dysfunction and exclusion (TIDE) scores were significantly different between risk groups. The expression of 6 genes were verified by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) and the expression of 6 genes were higher in the high-grade serous carcinoma (HGSC) samples.ConclusionA prognostic model related to lactate metabolism was established for OV based on six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) that could provide new insights into therapy.

Comprehensive analysis of KLF family reveals KLF6 as a promising prognostic and immune biomarker in pancreatic ductal adenocarcinoma

BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the deadliest tumors worldwide, with extremely aggressive and complicated biology. Krüppel-like factors (KLFs) encode a series of transcriptional regulatory proteins and play crucial roles in a variety of processes, including tumor cell differentiation and proliferation. However, the potential biological functions and possible pathways of KLFs in the progression of PDAC remain elusive.MethodsWe systematically evaluated the transcriptional variations and expression patterns of KLFs in pancreatic cancer from the UCSC Xena. Based on difference analysis, the non-negative matrix factorization (NMF) algorithm was utilized to identify the immune characteristics and clinical significance of two different subtypes. The multivariate Cox regression was used to construct the risk model and then explore the differences in tumor immune microenvironment (TIME) and drug sensitivity between high and low groups. Through single-cell RNA sequencing (scRNA-seq) analysis, we screened KLF6 and further investigated its biological functions in pancreatic cancer and pan-cancer.ResultsThe KLFs exhibited differential expression and mutations in the transcriptomic profile of PDAC. According to the expression of KLFs, patients were classified into two distinct subtypes, each exhibiting significant differences in prognosis and TIME. Moreover, the KLF signature was developed using univariate Cox and Lasso regression, which proved to be a reliable and effective prognostic model. Furthermore, the KLF_Score was closely associated with immune infiltration, response to immunotherapy, and drug sensitivity and we screened small molecule compounds targeting prognostic genes separately. Through scRNA-seq analysis, KLF6 was selected to further demonstrate its role in the malignance of PC in vitro. Finally, pan-cancer analysis emphasized the biological significance of KLF6 in multiple types of tumors and its clinical utility in assessing cancer prognosis.ConclusionThis study elucidated the pivotal role of KLF family genes in the malignant development of PC through comprehensive analysis and revealed that KLF6 would be a novel diagnostic biomolecule marker and potential therapeutic target for PDAC.

Programmed Cell Death-Related Gene Signature Associated with Prognosis and Immune Infiltration and the Roles of HMOX1 in the Proliferation and Apoptosis were Investigated in Uveal Melanoma

BackgroundUveal melanoma (UVM) is the most common primary ocular malignancy, with a wide range of symptoms and outcomes. The programmed cell death (PCD) plays an important role in tumor development, diagnosis, and prognosis. There is still no research on the relationship between PCD-related genes and UVM. A novel PCD-associated prognostic model is urgently needed to improve treatment strategies.ObjectiveWe aim to screen PCD-related prognostic signature and investigate its proliferation ability and apoptosis in UVM cells.MethodsThe clinical information and RNA-seq data of the UVM patients were collected from the TCGA cohort. All the patients were classified using consensus clustering by the selected PCD-related genes. After univariate Cox regression and PPI network analysis, the prognostic PCD-related genes were then submitted to the LASSO regression analysis to build a prognostic model. The level of immune infiltration of 8-PCD signature in high- and low-risk patients was analyzed using xCell. The prediction on chemotherapy and immunotherapy response in UVM patients was assessed by GDSC and TIDE algorithm. CCK-8, western blot and Annexin V-FITC/PI staining were used to explore the roles of HMOX1 in UVM cells.ResultsA total of 8-PCD signature was constructed and the risk score of the PCD signature was negatively correlated with the overall survival, indicating strong predictive ability and independent prognostic value. The risk score was positively correlated with CD8 Tcm, CD8 Tem and Th2 cells. Immune cells in high-risk group had poorer overall survival. The drug sensitivity demonstrated that cisplatin might impact the progression of UVM and better immunotherapy responsiveness in the high-risk group. Finally, Overespression HMOX1 (OE-HMOX1) decreased the cell viability and induced apoptosis in UVM cells. Recuse experiment results showed that ferrostatin-1 (fer-1) protected MP65 cells from apoptosis and necrosis caused by OE-HMOX1.ConclusionThe PCD signature may have a significant role in the tumor microenvironment, clinicopathological characteristics, prognosis and drug sensitivity. More importantly, HMOX1 depletion greatly induced tumor cell growth and inhibited cell apoptosis and fer-1 protected UVM cells from apoptosis and necrosis induced by OE-HMOX1. This work provides a foundation for effective therapeutic strategy in tumour treatment.

Identification of a methyltransferase-related long noncoding RNA signature as a novel prognosis biomarker for lung adenocarcinoma.

Lung adenocarcinoma (LUAD) accounts for a high proportion of tumor deaths globally, while methyltransferase-related lncRNAs in LUAD were poorly studied. In our study, we focused on two distinct cohorts, TCGA-LUAD and GSE3021, to establish a signature of methyltransferase-related long non-coding RNAs (MeRlncRNAs) in LUAD. We employed univariate Cox and LASSO regression analyses as our main analytical tools. The GSE30219 cohort served as the validation cohort for our findings. Furthermore, to explore the differential pathway enrichments between groups stratified by risk, we utilized Gene Set Enrichment Analysis (GSEA). Additionally, single-sample GSEA (ssGSEA) was conducted to assess the immune infiltration landscape within each sample. Reverse transcription quantitative PCR (RT-qPCR) was also performed to verify the expression of prognostic lncRNAs in both clinically normal and LUAD samples. In LUAD, we identified a set of 32 MeRlncRNAs. We further narrowed our focus to six prognostic lncRNAs to develop gene signatures. The TCGA-LUAD cohort and GSE30219 were utilized to validate the risk score model derived from these signatures. Our analysis showed that the risk score served as an independent prognostic factor, linked to immune-related pathways. Additionally, the analysis of immune infiltration revealed that the immune landscape in high-risk groups was suppressed, which could contribute to poorer prognoses. We also constructed a regulatory network comprising 6 prognostic lncRNAs, 19 miRNAs, and 21 mRNAs. Confirmatory RT-qPCR results aligned with public database findings, verifying the expression of these prognostic lncRNAs in the samples. The prognostic gene signature of LUAD associated with MeRlncRNAs that we provided, may offer us a comprehensive picture of the prognosis prediction for LUAD patients.

The mitochondria-related gene risk mode revealed p66Shc as a prognostic mitochondria-related gene of glioblastoma.

Numerous studies have highlighted the pivotal role of mitochondria-related genes (MRGs) in the initiation and progression of glioblastoma (GBM). However, the specific contributions of MRGs coding proteins to GBM pathology remain incompletely elucidated. The identification of prognostic MRGs in GBM holds promise for the development of personalized targeted therapies and the enhancement of patient prognosis. We combined differential expression with univariate Cox regression analysis to screen prognosis-associated MRGs in GBM. Based on the nine MRGs, the hazard ratio model was conducted using a multivariate Cox regression algorithm. SHC-related survival, pathway, and immune analyses in GBM cohorts were obtained from the Biomarker Exploration of the Solid Tumor database. The proliferation and migration of U87 cells were measured by CCK-8 and transwell assay. Apoptosis in U87 cells was evaluated using flow cytometry. Confocal microscopy was employed to measure mitochondrial reactive oxygen species (ROS) levels and morphology. The expression levels of SHC1 and other relevant proteins were examined via western blotting. We screened 15 prognosis-associated MRGs and constructed a 9 MRGs-based model. Validation of the model's risk score confirmed its efficacy in predicting the prognosis of patients with GBM. Furthermore, analysis revealed that SHC1, a constituent MRG of the prognostic model, was upregulated and implicated in the progression, migration, and immune infiltration of GBM. In vitro experiments elucidated that p66Shc, the longest isoform of SHC1, modulates mitochondrial ROS production and morphology, consequently promoting the proliferation and migration of U87 cells. The 9 MRGs-based prognostic model could predict the prognosis of GBM. SHC1 was upregulated and correlated with the prognosis of patients by involvement in immune infiltration. Furthermore, in vitro experiments demonstrated that p66Shc promotes U87 cell proliferation and migration by mediating mitochondrial ROS production. Thus, p66Shc may serve as a promising biomarker and therapeutic target for GBM.

Integrated analysis reveals critical cisplatin-resistance regulators E2F7 contributed to tumor progression and metastasis in lung adenocarcinoma

BackgroundDrug resistance poses a significant challenge in cancer treatment, particularly as a leading cause of therapy failure. Cisplatin, the primary drug for lung adenocarcinoma (LUAD) chemotherapy, shows effective treatment outcomes. However, the development of resistance against cisplatin is a major obstacle. Therefore, identifying genes resistant to cisplatin and adopting personalized treatment could significantly improve patient outcomes.MethodsBy examining transcriptome data of cisplatin-resistant LUAD cells from the GEO database, 181 genes associated with cisplatin resistance were identified. Using univariate regression analysis, random forest and multivariate regression analyses, two prognostic genes, E2F7 and FAM83A, were identified. This study developed a prognostic model utilizing E2F7 and FAM83A as key indicators. The Cell Counting Kit 8 assay, Transwell assay, and flow cytometry were used to detect the effects of E2F7 on the proliferation, migration, invasiveness and apoptosis of A549/PC9 cells. Western blotting was used to determine the effect of E2F7 on AKT/mTOR signaling pathway.ResultsThis study has pinpointed two crucial genes associated with cisplatin resistance, E2F7 and FAM83A, and developed a comprehensive model to assist in the diagnosis, prognosis, and evaluation of relapse risk in LUAD. Analysis revealed that patients at higher risk, according to these genetic markers, had elevated levels of immune checkpoints (PD-L1 and PD-L2). The prognostic and diagnosis values of E2F7 and FAM83A were further confirmed in clinical data. Furthermore, inhibiting E2F7 in lung cancer cells markedly reduced their proliferation, migration, invasion, and increased apoptosis. In vivo experiments corroborated these findings, showing reduced tumor growth and lung metastasis upon E2F7 suppression in lung cancer models.ConclusionOur study affirms the prognostic value of a model based on two DEGs, offering a reliable method for predicting the success of tumor immunotherapy in patients with LUAD. The diagnostic and predictive model based on these genes demonstrates excellent performance. In vitro, reducing E2F7 levels shows antitumor effects by blocking LUAD growth and progression. Further investigation into the molecular mechanisms has highlighted E2F7’s effect on the AKT/mTOR signaling pathway, underscoring its therapeutic potential. In the era of personalized medicine, this DEG-based model promises to guide clinical practice.

Uncovering miRNA-mRNA Regulatory Networks Related to Olaparib Resistance and Resensitization of BRCA2MUT Ovarian Cancer PEO1-OR Cells with the ATR/CHK1 Pathway Inhibitors.

Resistance to olaparib is the major obstacle in targeted therapy for ovarian cancer (OC) with poly(ADP-ribose) polymerase inhibitors (PARPis), prompting studies on novel combination therapies to enhance olaparib efficacy. Despite identifying various mechanisms, understanding how OC cells acquire PARPi resistance remains incomplete. This study investigated microRNA (miRNA) expression in olaparib-sensitive (PEO1, PEO4) and previously established olaparib-resistant OC cell lines (PEO1-OR) using high-throughput RT-qPCR and bioinformatic analyses. The role of miRNAs was explored regarding acquired resistance and resensitization with the ATR/CHK1 pathway inhibitors. Differentially expressed miRNAs were used to construct miRNA-mRNA regulatory networks and perform functional enrichment analyses for target genes with miRNet 2.0. TCGA-OV dataset was analyzed to explore the prognostic value of selected miRNAs and target genes in clinical samples. We identified potential processes associated with olaparib resistance, including cell proliferation, migration, cell cycle, and growth factor signaling. Resensitized PEO1-OR cells were enriched in growth factor signaling via PDGF, EGFR, FGFR1, VEGFR2, and TGFβR, regulation of the cell cycle via the G2/M checkpoint, and caspase-mediated apoptosis. Antibody microarray analysis confirmed dysregulated growth factor expression. The addition of the ATR/CHK1 pathway inhibitors to olaparib downregulated FGF4, FGF6, NT-4, PLGF, and TGFβ1 exclusively in PEO1-OR cells. Survival and differential expression analyses for serous OC patients revealed prognostic miRNAs likely associated with olaparib resistance (miR-99b-5p, miR-424-3p, and miR-505-5p) and resensitization to olaparib (miR-324-5p and miR-424-3p). Essential miRNA-mRNA interactions were reconstructed based on prognostic miRNAs and target genes. In conclusion, our data highlight distinct miRNA profiles in olaparib-sensitive and olaparib-resistant cells, offering molecular insights into overcoming resistance with the ATR/CHK1 inhibitors in OC. Moreover, some miRNAs might serve as potential predictive signature molecules of resistance and therapeutic response.

Evaluating the predictive value of angiogenesis-related genes for prognosis and immunotherapy response in prostate adenocarcinoma using machine learning and experimental approaches.

Angiogenesis, the process of forming new blood vessels from pre-existing ones, plays a crucial role in the development and advancement of cancer. Although blocking angiogenesis has shown success in treating different types of solid tumors, its relevance in prostate adenocarcinoma (PRAD) has not been thoroughly investigated. This study utilized the WGCNA method to identify angiogenesis-related genes and assessed their diagnostic and prognostic value in patients with PRAD through cluster analysis. A diagnostic model was constructed using multiple machine learning techniques, while a prognostic model was developed employing the LASSO algorithm, underscoring the relevance of angiogenesis-related genes in PRAD. Further analysis identified MAP7D3 as the most significant prognostic gene among angiogenesis-related genes using multivariate Cox regression analysis and various machine learning algorithms. The study also investigated the correlation between MAP7D3 and immune infiltration as well as drug sensitivity in PRAD. Molecular docking analysis was conducted to assess the binding affinity of MAP7D3 to angiogenic drugs. Immunohistochemistry analysis of 60 PRAD tissue samples confirmed the expression and prognostic value of MAP7D3. Overall, the study identified 10 key angiogenesis-related genes through WGCNA and demonstrated their potential prognostic and immune-related implications in PRAD patients. MAP7D3 is found to be closely associated with the prognosis of PRAD and its response to immunotherapy. Through molecular docking studies, it was revealed that MAP7D3 exhibits a high binding affinity to angiogenic drugs. Furthermore, experimental data confirmed the upregulation of MAP7D3 in PRAD, correlating with a poorer prognosis. Our study confirmed the important role of angiogenesis-related genes in PRAD and identified a new angiogenesis-related target MAP7D3.