4-Hydroxynonenal Promotes Colorectal Cancer Progression through Regulating Cancer Stem Cell Fate.

AIMS: Tumor microenvironment (TME) plays a crucial role in sustaining cancer stem cells (CSCs). 4-hydroxynonenal (4-HNE) is abundantly present in the TME of colorectal cancer (CRC). However, the contribution of 4-HNE to CSCs and cancer progression remains unclear. This study aimed to investigate the impact of 4-HNE on the regulation of CSC fate and tumor progression. METHODS: Human CRC cells were exposed to 4-HNE, and CSC signaling was analyzed using quantitative real-time PCR, immunofluorescent staining, fluorescence-activated cell sorting, and bioinformatic analysis. Tumor-promoting role of 4-HNE was confirmed using a xenograft model. RESULTS: Exposure of CRC cells to 4-HNE activated non-canonical Hedgehog (HH) signaling and homologous recombination repair (HRR) pathways in LGR5+ CSCs. Furthermore, blocking HH signaling led to a significant increase in the expression of γH2AX, indicating that 4-HNE induces double-stranded DNA breaks (DSBs) and simultaneously activates HH signaling to protect CSCs from 4-HNE-induced damage via the HRR pathway. Additionally, 4-HNE treatment increased the population of LGR5+ CSCs and promoted asymmetric division in these cells, leading to enhanced self-renewal and differentiation. Notably, 4-HNE also promoted xenograft tumor growth and activated CSC signaling in vivo. INNOVATION AND CONCLUSION: These findings demonstrate that 4-HNE, as a signaling inducer in the TME, activates the non-canonical HH pathway to shield CSCs from oxidative damage, enhances the proliferation and asymmetric division of LGR5+ CSCs, and thereby facilitates tumor growth. These novel insights shed light on the regulation of CSC fate within the oxidative TME, offering potential implications for understanding and targeting CSCs for CRC therapy.

Anoikis-related lncRNA signature predicts prognosis and is associated with immune infiltration in hepatocellular carcinoma.

Anoikis is a programmed cell death process triggered when cells are dislodged from the extracellular matrix. Numerous long noncoding RNAs (lncRNAs) have been identified as significant factors associated with anoikis resistance in various tumor types, including glioma, breast cancer, and bladder cancer. However, the relationship between lncRNAs and the prognosis of hepatocellular carcinoma (HCC) has received limited research attention. Further research is needed to investigate this potential link and understand the role of lncRNAs in the progression of HCC. We developed a prognostic signature based on the differential expression of lncRNAs implicated in anoikis in HCC. A co-expression network of anoikis-related mRNAs and lncRNAs was established using data obtained from The Cancer Genome Atlas (TCGA) for HCC. Cox regression analyses were conducted to formulate an anoikis-related lncRNA signature (ARlncSig) in a training cohort, which was subsequently validated in both a testing cohort and a combined dataset comprising the two cohorts. Receiver operating characteristic curves, nomograms, and decision curve analyses based on the ARlncSig score and clinical characteristics demonstrated robust predictive ability. Moreover, gene set enrichment analysis revealed significant enrichment of several immune processes in the high-risk group compared to the low-risk group. Furthermore, significant differences were observed in immune cell subpopulations, expression of immune checkpoint genes, and response to chemotherapy and immunotherapy between the high- and low-risk groups. Lastly, we validated the expression levels of the five lncRNAs included in the signature using quantitative real-time PCR. In conclusion, our ARlncSig model holds substantial predictive value regarding the prognosis of HCC patients and has the potential to provide clinical guidance for individualized immunotherapy. In this study, we obtained 36 genes associated with anoikis from the Gene Ontology and Gene Set Enrichment Analysis databases. We also identified 22 differentially expressed lncRNAs that were correlated with these genes using data from TCGA. Using Cox regression analyses, we developed an ARlncSig in a training cohort, which was then validated in both a testing cohort and a combined cohort comprising data from both cohorts. Additionally, we collected eight pairs of liver cancer tissues and adjacent tissues from the Affiliated Tumor Hospital of Nantong University for further analysis. The aim of this study was to investigate the potential of ARlncSig as a biomarker for liver cancer prognosis. The study developed a risk stratification system called ARlncSig, which uses five lncRNAs to categorize liver cancer patients into low- and high-risk groups. Patients in the high-risk group exhibited significantly lower overall survival rates compared to those in the low-risk group. The model's predictive performance was supported by various analyses including the receiver operating characteristic curve, nomogram calibration, clinical correlation analysis, and clinical decision curve. Additionally, differential analysis of immune function, immune checkpoint, response to chemotherapy, and immune cell subpopulations revealed significant differences between the high- and low-risk groups. Finally, quantitative real-time PCR validated the expression levels of the five lncRNAs. In conclusion, the ARlncSig model demonstrates critical predictive value in the prognosis of HCC patients and may provide clinical guidance for personalized immunotherapy.

Upregulation of TTK expression is associated with poor prognosis and immune infiltration in endometrial cancer patients.

BACKGROUND: Threonine and tyrosine kinase (TTK) is associated with invasion and metastasis in various tumors. However, the prognostic importance of TTK and its correlation with immune infiltration in endometrial cancer (EC) remain unclear. METHODS: The expression profile of TTK was analyzed using data from The Cancer Genome Atlas (TCGA) and the Clinical Proteome Cancer Analysis Consortium (CPTAC). TTK protein and mRNA levels were verified in EC cell lines. Receiver operating characteristic (ROC) curve analysis was used to evaluate the ability of TTK to distinguish between normal and EC tissues. K-M survival analysis was also conducted to evaluate the impact of TTK on survival outcomes. Protein‒protein interaction (PPI) networks associated with TTK were explored using the STRING database. Functional enrichment analysis was performed to elucidate the biological functions of TTK. TTK mRNA expression and immune infiltration correlations were examined using the Tumor Immune Estimation Resource (TIMER) and the Tumor-Immune System Interaction Database (TISIDB). RESULTS: TTK expression was significantly greater in EC tissues than in adjacent normal tissues. Higher TTK mRNA expression was associated with tumor metastasis and advanced TNM stage. The protein and mRNA expression of TTK was significantly greater in tumor cell lines than in normal endometrial cell lines. ROC curve analysis revealed high accuracy (94.862%), sensitivity (95.652%), and specificity (94.894%) of TTK in differentiating EC from normal tissues. K-M survival analysis demonstrated that patients with high TTK expression had worse overall survival (OS) and disease-free survival (DFS) rates. Correlation analysis revealed that TTK mRNA expression was correlated with B cells and neutrophils. CONCLUSION: TTK upregulation is significantly associated with poor survival outcomes and immune infiltration in patients with EC. TTK can serve as a potential biomarker for poor prognosis and a promising immunotherapy target in EC. Further investigation of the role of TTK in EC may provide valuable insights for therapeutic interventions and personalized treatment strategies.

Minichromosome maintenance protein family member 6 mediates hepatocellular carcinoma progression by recruiting UBE3A to induce P53 ubiquitination.

With limited therapeutic options for hepatocellular carcinoma (HCC), it is of great significance to investigate the underlying mechanisms and identifying tumor drivers. MCM6, a member of minichromosome maintenance proteins (MCMs), was significantly elevated in HCC progression and associated with poor prognosis. Knockdown of MCM6 significantly inhibited the proliferation and migration of HCC cells with the increased apoptosis ratio and cell cycle arrest, whereas overexpression of MCM6 induced adverse effects. Mechanistically, MCM6 could decrease the P53 activity by inducing the degradation of P53 protein. In addition, MCM6 enhanced the ubiquitination of P53 by recruiting UBE3A to form a triple complex. Furthermore, overexpression of UBE3A significantly rescued the P53 activation and suppression of malignant behaviors mediated by MCM6 inhibition. In conclusion, MCM6 facilitated aggressive phenotypes of HCC cells by UBE3A/P53 signaling, providing potential biomarkers and targets for HCC.

CRTC2 promotes paclitaxel resistance by inducing autophagy in ovarian cancer in part via the PI3K-AKT signaling axis.

Background: Ovarian cancer is the most malignant gynecological disease, which seriously threatens female physical and mental health. Paclitaxel is a first-line chemotherapy drug in the clinical treatment of ovarian cancer, but drug resistance has become an important factor affecting the survival of ovarian cancer patients. However, the main mechanism of chemotherapy resistance in ovarian cancer remains unclear. In this study, we analyzed the Integrated Gene Expression Database (GEO) dataset using comprehensive bioinformatics tools to provide new therapeutic strategies and search for prognostic targets for ovarian cancer. Methods: Ovarian cancer related genes were extracted from GSE18520 by bioinformatics method. Differentially expressed genes (DEGs) were obtained by differential analysis, and related genes and functions were elucidated. The key gene CRTC2 was identified by prognostic analysis. Immunohistochemistry was used to detect the expression of CRTC2 in chemotherapy-resistant and chemotherapy-sensitive ovarian cancer tissues. Functional analysis (cell assay) confirmed the role of CRTC2 in paclitaxel resistance. Autophagy related proteins were detected by Western blot. Autophagy flux analysis was performed using the GFP/RFP-LC3 adenovirus reporter. Results: A total of 3,852 DEGs were identified in the GEO microarray dataset. Key genes were screened by prognostic analysis. We found that CRTC2 was highly expressed in chemoresistant tissues of ovarian cancer. In 110 patients with ovarian cancer, high expression of CRTC2 was associated with poorer prognostic factors and shorter survival. At the same time, we found that CRTC2 can promote the proliferation and invasion ability of ovarian cancer cells. In addition, CRTC2 can affect the expression of PI3K, AKT, autophagic flux and sensitivity to paclitaxel chemotherapy in ovarian cancer. Conclusion: CRTC2 can affect autophagy partially through PI3K-AKT signaling pathway, and then affect the sensitivity of ovarian cancer to paclitaxel chemotherapy. CRTC2 may be a potential predictor or target for ovarian cancer therapy.

An immune-related signature for optimizing prognosis prediction and treatment decision of hepatocellular carcinoma.

BACKGROUND: An immune-related gene signature (IGS) was established for discriminating prognosis, predicting benefit of immunotherapy, and exploring therapeutic options in hepatocellular carcinoma (HCC). METHODS: Based on Immune-related hub genes and The Cancer Genome Atlas (TCGA) LIHC dataset (n = 363), an immune-related gene signature (IGS) was established by least absolute shrinkage and selection operator (LASSO) analysis. The prognostic significance and clinical implications of IGS were verified in International Cancer Genome Consortium (ICGC) and Chinese HCC (CHCC) cohorts. The molecular and immune characteristics and the benefit of immune checkpoint inhibitor (ICI) therapy in IGS-defined subgroups were analyzed. In addition, by leveraging the Cancer Therapeutics Response Portal (CTRP) and PRISM Repurposing datasets, we determined the potential therapeutic agents for high IGS-risk patients. RESULTS: The IGS was constructed based on 8 immune-related hub genes with individual coefficients. The IGS risk model could robustly predict the survival of HCC patients in TCGA, ICGC, and CHCC cohorts. Compared with 4 previous established immune genes-based signatures, IGS exhibited superior performance in survival prediction. Additionally, for immunological characteristics and enriched pathways, a low-IGS score was correlated with IL-6/JAK/STAT3 signaling, inflammatory response and interferon α/γ response pathways, low TP53 mutation rate, high infiltration level, and more benefit from ICI therapy. In contrast, high IGS score manifested an immunosuppressive microenvironment and activated aggressive pathways. Finally, by in silico screening potential compounds, vindesine, ispinesib and dasatinib were identified as potential therapeutic agents for high-IGS risk patients. CONCLUSIONS: This study developed a robust IGS model for survival prediction of HCC patients, providing new insights into integrating tailored risk stratification with precise immunotherapy and screening potentially targeted agents.

Significance of Spliceosome-Related Genes in the Prediction of Prognosis and Treatment Strategies for Lung Adenocarcinoma.

Background: The leading cause of cancer-related fatalities globally is lung cancer; lung adenocarcinoma (LUAD) is the most common histological type in it. The spliceosome plays an important role in a majority of malignancies. However, it is yet unclear how spliceosome-related genes affect patients with LUAD in terms of treatment course and prognosis. Methods: Spliceosome-related genes were assessed from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database to obtain clinical information and gene expression in patients with LUAD. A spliceosome-related gene signature and prognostic model were constructed by using the least absolute shrinkage and selection operator (LASSO), time-dependent receiver operating characteristic (ROC), and nomogram. Immune infiltrate levels, mutation analysis, and pathway enrichment were predicted potential mechanisms of the signature by using single-sample gene set enrichment analysis (ssGSEA), Gene Set Cancer Analysis (GSCA) database, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) database. Then, a protein-protein interaction (PPI) network and transcription factor- (TF-) hub gene and drug mining network were also established by Cytoscape software. Results: Firstly, we constructed a prognostic model for 11 spliceosome signature genes. Based on the prognostic risk score, we stratified patients with LUAD into high- and low-risk groups. The high- and low-risk groups were closely related to the OS, tumor immune infiltration level, immune checkpoint molecules, and tumor mutation burden (TMB) of LUAD patients. Based on PPI networks, we also predict relevant TF genes that may regulate signature prognostic genes. Finally, drugs including oxaliplatin, arsenic trioxide, cisplatin, and sunitinib were excavated for the treatment of the 11 spliceosome signature genes in LUAD patients. Conclusion: In conclusion, this study is the first to explore the importance of spliceosome-related genes in the prognosis and treatment of LUAD. Through our study, we have innovatively provided potential prognosis genes and new therapeutic drug targets for the treatment of LUAD patients.

Clinical Significance of Serum Biomarkers in Stage IV Non-Small-Cell Lung Cancer Treated with PD-1 Inhibitors: LIPI Score, NLR, dNLR, LMR, and PAB.

Background: To assess the prognostic value of pretreatment serum biomarkers in stage IV non-small-cell lung cancer (NSCLC) patients treated with PD-1 (programmed cell death protein 1) inhibitors and their value as a predictor of benefit. Methods: We performed a retrospective study including patients with stage IV NSCLC who were treated with anti-PD-1 drugs in first or advanced lines of therapy in the Affiliated Tumor Hospital of Nantong University. Serum biomarkers such as NLR, dNLR, LMR, PAB, ALB, and LIPI scores were calculated and analyzed in detail. Results: A total of 85 patients with stage IV NSCLC treated with PD-1 inhibitors in the first or advanced lines of therapy were included in this subject. According to the tumor response of PD-1-based treatment, ORR was 42.4% (36/85) and DCR was 68.2% (58/85). The median OS and PFS were 20.0 months and 7.0 months, respectively. The ROC curves showed that the serum biomarkers of NLR, dNLR, LDH, LMR, PAB, and ALB were significantly associated with overall survival and helped to determine the cut-off value. The multivariate Cox proportional hazard analyses for stage IV NSCLC patients treated with PD-1 inhibitors indicated that dNLR (P < 0.001) and ALB (P = 0.033) were independent prognostic indicators of PFS, while liver metastasis (P = 0.01), NLR (P = 0.01), dNLR (P = 0.001), and LMR (P = 0.006) were independent prognostic indicators of OS. Moreover, patients of the good LIPI group showed prolonged PFS and OS than those with intermediate/poor LIPI score (P < 0.001 and P = 0.006, respectively). Conclusions: Pretreatment dNLR is an independent prognostic indicator of both PFS and OS in stage IV NSCLC patients treated with PD-1 inhibitors. Pretreatment LIPI, combining dNLR > 3 and LDH>ULN, was correlated with worse outcome for stage IV NSCLC patients treated with ICI. High NLR, high dNLR, low LMR, and low ALB at baseline might be useful as an early predictive biomarker of benefit.

Based on the Development and Verification of a Risk Stratification Nomogram: Predicting the Risk of Lung Cancer-Specific Mortality in Stage IIIA-N2 Unresectable Large Cell Lung Neuroendocrine Cancer Compared With Lung Squamous Cell Cancer and Lung Adenocarcinoma.

Background: The purpose of this study is to predict overall survival (OS) and lung cancer-specific survival (LCSS) in patients with stage IIIA-N2 unresectable lung squamous cell cancer (LUSC), lung adenocarcinoma (LUAD), and large cell neuroendocrine cancer (LCNEC) by constructing nomograms and to compare risk and prognostic factors affecting survival outcomes in different histological subtypes. Methods: We included 11,505 unresectable NSCLC patients at stage IIIA-N2 between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Moreover, competition models and nomograms were developed to predict prognostic factors for OS and LCSS. Results: Analysis of the SEER database identified 11,505 NSCLC patients, of whom 5,559 (48.3%) have LUAD, 5,842 (50.8%) have LUSC, and 104 (0.9%) have LCNEC. Overall, both OS and LCSS were significantly better in stage IIIA-N2 unresectable LUAD than in LCNEC, while there was no statistically significant difference between LUSC and LCNEC. Age, gender, T stage, chemotherapy, and radiotherapy were significantly associated with OS rates in LUAD and LUSC. However, chemotherapy was the only independent factor for LCNEC (p < 0.01).From competitive risk models, we found that older age, larger tumors, non-chemotherapy and non-radiotherapy were associated with a increased risk of death from LUAD and LUSC. Unlike prognostic factors for OS, our study showed that both chemotherapy and radiotherapy were all LCNEC-specific survival factors for both LCSS and non-LCSS LCNEC. Conclusion: Our study reports that unresectable patients with stage IIIA-N2 LCNEC and LUSC have worse LCSS than LUAD. The study's first prognostic nomogram constructed for patients with unresectable stage IIIA-N2 NSCLC can accurately predict the survival of different histological types, which may provide a practical tool to help clinicians assess prognosis and stratify these prognostic risks to determine which patients should be given an optimized individual treatment strategy based on histology.

Joint Progressive and Coarse-to-Fine Registration of Brain MRI via Deformation Field Integration and Non-Rigid Feature Fusion.

Registration of brain MRI images requires to solve a deformation field, which is extremely difficult in aligning intricate brain tissues, e.g., subcortical nuclei, etc. Existing efforts resort to decomposing the target deformation field into intermediate sub-fields with either tiny motions, i.e., progressive registration stage by stage, or lower resolutions, i.e., coarse-to-fine estimation of the full-size deformation field. In this paper, we argue that those efforts are not mutually exclusive, and propose a unified framework for robust brain MRI registration in both progressive and coarse-to-fine manners simultaneously. Specifically, building on a dual-encoder U-Net, the fixed-moving MRI pair is encoded and decoded into multi-scale sub-fields from coarse to fine. Each decoding block contains two proposed novel modules: i) in Deformation Field Integration (DFI), a single integrated deformation sub-field is calculated, warping by which is equivalent to warping progressively by sub-fields from all previous decoding blocks, and ii) in Non-rigid Feature Fusion (NFF), features of the fixed-moving pair are aligned by DFI-integrated deformation field, and then fused to predict a finer sub-field. Leveraging both DFI and NFF, the target deformation field is factorized into multi-scale sub-fields, where the coarser fields alleviate the estimate of a finer one and the finer field learns to make up those misalignments insolvable by previous coarser ones. The extensive and comprehensive experimental results on both private and two public datasets demonstrate a superior registration performance of brain MRI images over progressive registration only and coarse-to-fine estimation only, with an increase by at most 8% in the average Dice.