Identification and Validation of an Invasion-Related Disease-Free Survival Prognostic Model for Tongue Squamous Cell Carcinoma.

INTRODUCTION: Tongue squamous cell carcinoma (TSCC) is a common malignant tumour type with aggressive invasion and a poor prognosis. To date, invasion-related gene expression signatures for the prognostic stratification of TSCC patients are unavailable in clinical practice. This study aimed to assess the impact of invasion-related genes on the prognosis of TSCC patients. METHODS: We obtained mRNA profiles and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (TCGA-TSCC and GSE41116, respectively). The TSCC samples from the TCGA-TSCC cohort were randomly divided into TCGA training and TCGA test datasets at a 7:3 ratio. Next, a disease-free survival (DFS) prognostic risk model was established on the basis of univariate and stepwise multivariate Cox regression analyses of the TCGA training cohort. Moreover, prognostic genes were screened. The model was subsequently evaluated and validated using the TCGA test and GSE41116 datasets. In addition, the prognostic genes were validated in the human TSCC cell line UM1 and the human oral keratinocyte (HOK) cell line using quantitative real-time polymerase chain reaction (qRT-PCR) analysis. RESULTS: A total of 70 candidate genes related to invasion were identified in the TCGA-TSCC cohort. DFS data were subsequently constructed, and 6 prognostic genes, HMGN2, MYL12B, ACTB, PPP1CA, PSMB9, and IFITM3, were identified. The TSCC samples were divided into high- and low-risk groups in the TCGA training, TCGA test, and GSE41116 cohorts, respectively. In particular, patients with TSCC in the low-risk group had longer DFS than those in the high-risk group. Furthermore, qRT-PCR analysis confirmed that the expression levels of the 6 prognostic genes were significantly greater in the TSCC cell line UM1 than in the HOK cell line. CONCLUSION: This study identified new invasion-related target genes related to poor prognosis in TSCC patients, providing new insights into the underlying mechanisms of TSCC invasion.

Construction of a 12-Gene Prognostic Risk Model and Tumor Immune Microenvironment Analysis Based on the Clear Cell Renal Cell Carcinoma Model.

OBJECTIVES: Accurate survival predictions and early interventional therapy are crucial for people with clear cell renal cell carcinoma (ccRCC). METHODS: In this retrospective study, we identified differentially expressed immune-related (DE-IRGs) and oncogenic (DE-OGs) genes from The Cancer Genome Atlas (TCGA) dataset to construct a prognostic risk model using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. We compared the immunogenomic characterization between the high- and low-risk patients in the TCGA and the PUCH cohort, including the immune cell infiltration level, immune score, immune checkpoint, and T-effector cell- and interferon (IFN)-γ-related gene expression. RESULTS: A prognostic risk model was constructed based on 9 DE-IRGs and 3 DE-OGs and validated in the training and testing TCGA datasets. The high-risk group exhibited significantly poor overall survival compared with the low-risk group in the training (P < 0.0001), testing (P = 0.016), and total (P < 0.0001) datasets. The prognostic risk model provided accurate predictive value for ccRCC prognosis in all datasets. Decision curve analysis revealed that the nomogram showed the best net benefit for the 1-, 3-, and 5-year risk predictions. Immunogenomic analyses of the TCGA and PUCH cohorts showed higher immune cell infiltration levels, immune scores, immune checkpoint, and T-effector cell- and IFN-γ-related cytotoxic gene expression in the high-risk group than in the low-risk group. CONCLUSION: The 12-gene prognostic risk model can reliably predict overall survival outcomes and is strongly associated with the tumor immune microenvironment of ccRCC.

Construction of oxidative phosphorylation-related prognostic risk score model in uveal melanoma.

BACKGROUND: Uveal melanoma (UVM) is a malignant intraocular tumor in adults. Targeting genes related to oxidative phosphorylation (OXPHOS) may play a role in anti-tumor therapy. However, the clinical significance of oxidative phosphorylation in UVM is unclear. METHOD: The 134 OXPHOS-related genes were obtained from the KEGG pathway, the TCGA UVM dataset contained 80 samples, served as the training set, while GSE22138 and GSE39717 was used as the validation set. LASSO regression was carried out to identify OXPHOS-related prognostic genes. The coefficients obtained from Cox multivariate regression analysis were used to calculate a risk score, which facilitated the construction of a prognostic model. Kaplan-Meier survival analysis, logrank test and ROC curve using the time "timeROC" package were conducted. The immune cell frequency in low- and high-risk group was analyzed through Cibersort tool. The specific genomic alterations were analyzed by "maftools" R package. The differential expressed genes between low- or high-risk group were analyzed and performed Gene Ontology (GO) and GSEA. Finally, we verified the function of CYC1 in UVM by gene silencing in vitro. RESULTS: A total of 9 OXPHOS-related prognostic genes were identified, including NDUFB1, NDUFB8, ATP12A, NDUFA3, CYC1, COX6B1, ATP6V1G2, ATP4B and NDUFB4. The UVM prognostic risk model was constructed based on the 9 OXPHOS-related prognostic genes. The prognosis of patients in the high-risk group was poorer than low-risk group. Besides, the ROC curve demonstrated that the area under the curve of the model for predicting the 1 to 5-year survival rate of UVM patients were all more than 0.88. External validation in GSE22138 and GSE39717 dataset revealed that these 9 genes could also be utilized to evaluate and predict the overall survival of patients with UVM. The risk score levels related to immune cell frequency and specific genomic alterations. The DEGs between the low- and high- risk group were enriched in tumor OXPHOS and immune related pathway. In vitro experiments, CYC1 silencing significantly inhibited UVM cell proliferation and invasion, induced cell apoptosis. CONCLUSION: In sum, a prognostic risk score model based on oxidative phosphorylation-related genes in UVM was developed to enhance understanding of the disease. This prognostic risk score model may help to find potential therapeutic targets for UVM patients. CYC1 acts as an oncogene role in UVM.

Development of a prognostic risk model of uveal melanoma based on N7-methylguanosine-related regulators.

BACKGROUND: Uveal melanoma (UVM) stands as the predominant type of primary intraocular malignancy among adults. The clinical significance of N7-methylguanosine (m7G), a prevalent RNA modifications, in UVM remains unclear. METHODS: Primary information from 80 UVM patients were analyzed as the training set, incorporating clinical information, mutation annotations and mRNA expression obtained from The Cancer Genome Atlas (TCGA) website. The validation set was carried out using Gene Expression Omnibus (GEO) database GSE22138 and GSE84976. Kaplan-Meier and Cox regression of univariate analyses were subjected to identify m7G-related regulators as prognostic genes. RESULT: A prognostic risk model comprising EIF4E2, NUDT16, SNUPN and WDR4 was established through Cox regression of LASSO. Evaluation of the model's predictability for UVM patients' prognosis by Receiver Operating Characteristic (ROC) curves in the training set, demonstrated excellent performance Area Under the Curve (AUC) > 0.75. The high-risk prognosis within the TCGA cohort exhibit a notable worse outcome. Additionally, an independent correlation between the risk score and overall survival (OS) among UVM patients were identified. External validation of this model was carried out using the validation sets (GSE22138 and GSE84976). Immune-related analysis revealed that patients with high score of m7G-related risk model exhibited elevated level of immune infiltration and immune checkpoint gene expression. CONCLUSION: We have developed a risk prediction model based on four m7G-related regulators, facilitating effective estimate UVM patients' survival by clinicians. Our findings shed novel light on essential role of m7G-related regulators in UVM and suggest potential novel targets for the diagnosis, prognosis and therapy of UVM.

An innovative predictive model for cervical cancer constructed around a gene profile associated with cholesterol metabolism.

Cholesterol metabolism is crucial for cell survival and cancer progression. The prognostic patterns of genes linked to cholesterol metabolism (CMAGs) in CESC, however, have received very little attention in research. From public databases, TCGA-CESC cohorts with mRNA expression patterns and the accompanying clinical information of patients were gathered. Consensus clustering was used to find the molecular subtype connected to cholesterol metabolism. In the TCGA-CESC cohort, a predictive risk model with 28 CMAGs was created using Lasso-Cox regression. The function enrichment analysis between groups with high-and low-risk were investigated by employing GO, KEGG, and GSVA software. The immune cell infiltration was analyzed using ESTIMATE, CIBERSORT, and MCPCOUNTER methods. Finally, we select 7 genes in risk model for further multivariate Cox analysis, and ultimately a hub gene, CHIT1, was identified. Meanwhile, the function of CHIT1 was preliminarily verified in cell and mice tumor model. In conclusion, the abundance of the CHIT1 gene might be beneficial for forecasting the prognosis of CESC, demonstrating that cholesterol metabolism could be a promising treatment target for CESC.

Oncological outcomes of minimally invasive surgery in non-endometrioid endometrial Cancer patients with varying prognostic risks: a retrospective cohort study based on the ESGO/ESTRO/ESP 2020 guidelines.

BACKGROUND: Non-endometrioid endometrial carcinomas (NEEC) are characterized by their rarity and adverse prognoses. This study evaluates the outcomes of open versus minimally invasive surgery (MIS) in NEEC patients stratified by prognostic risks according to the 2020 ESGO-ESTRO-ESP risk classification guidelines. METHODS: A retrospective analysis was performed on 99 NEEC patients who underwent initial surgery at Fujian University Cancer Hospital. Patients were categorized into two groups: those undergoing MIS and those undergoing open surgery. We compared disease-free survival (DFS) and overall survival (OS) between these groups. Cox regression analysis was employed to identify risk factors for DFS, which were further validated via bootstrap statistical methods. RESULTS: The study included 31 patients in the MIS group and 68 in the open surgery group. The demographics and clinical characteristics such as age, body mass index, comorbidities, histological subtypes, and FIGO stage were similar between groups (P > 0.05). The MIS group experienced ten recurrences (1 vaginal, 2 lymph nodes, 7 distant metastases), whereas the open surgery group had seven recurrences (1 vaginal, 3 lymph nodes, 1 pelvis, 2 distant metastases), yielding recurrence rates of 10.3% versus 25.6% (P = 0.007). Besides lymphovascular space invasion (LVSI), surgical approach was also identified as an independent prognostic factor for DFS in high-risk patients (P = 0.037, 95% CI: 1.062-7.409). The constructed nomogram demonstrated a robust predictive capability with an area under the curve (AUC) of 0.767. Survival analysis for high- and intermediate-risk patients showed no significant differences in OS between the two groups (Phigh risk = 0.275; Pintermediate-risk = 0.201). However, high-risk patients in the MIS group exhibited significantly worse DFS (P = 0.001). CONCLUSION: This investigation is the inaugural study to assess the impact of surgical approaches on NEEC patients within the framework of the latest ESGO-ESTRO-ESP risk classifications. Although MIS may offer clinical advantages, it should be approached with caution in high-risk NEEC patients due to associated poorer DFS outcomes.

Analyzing the Functional Roles and Immunological Features of Chemokines in COAD.

Chemokines are key proteins that regulate cell migration and immune responses and are essential for modulating the tumor microenvironment. Despite their close association with colon cancer, the expression patterns, prognosis, immunity, and specific roles of chemokines in colon cancer are still not fully understood. In this study, we investigated the mutational features, differential expression, and immunological characteristics of chemokines in colon cancer (COAD) by analyzing the Tumor Genome Atlas (TCGA) database. We clarified the biological functions of these chemokines using Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. By univariate and multivariate COX regression analyses, we developed chemokine-based prognostic risk models. In addition, using Gene Set Enrichment Analysis (GSEA) and Gene Set Variant Analysis (GSVA), we analyzed the differences in immune responses and signaling pathways among different risk groups. The results showed that the mutation rate of chemokines was low in COAD, but 25 chemokines were significantly differentially expressed. These chemokines function in several immune-related biological processes and play key roles in signaling pathways including cytokine-cytokine receptor interactions, NF-kappa B, and IL-17. Prognostic risk models based on CCL22, CXCL1, CXCL8, CXCL9, and CXCL11 performed well. GSEA and GSVA analyses showed significant differences in immune responses and signaling pathways across risk groups. In conclusion, this study reveals the potential molecular mechanisms of chemokines in COAD and proposes a new prognostic risk model based on these insights.

Mismatch Repair Deficiency as a Predictive and Prognostic Biomarker in Endometrial Cancer: A Review on Immunohistochemistry Staining Patterns and Clinical Implications.

Among the four endometrial cancer (EC) TCGA molecular groups, the MSI/hypermutated group represents an important percentage of tumors (30%), including different histotypes, and generally confers an intermediate prognosis for affected women, also providing new immunotherapeutic strategies. Immunohistochemistry for MMR proteins (MLH1, MSH2, MSH6 and PMS2) has become the optimal diagnostic MSI surrogate worldwide. This review aims to provide state-of-the-art knowledge on MMR deficiency/MSI in EC and to clarify the pathological assessment, interpretation pitfalls and reporting of MMR status.

Poor pretransplantation minimal residual disease clearance as an independent prognostic risk factor for survival in myelodysplastic syndrome with excess blasts: A multicenter, retrospective cohort study.

BACKGROUND: Minimal residual disease (MRD) is an important prognostic factor for survival in adults with acute leukemia. The role of pretransplantation MRD status in myelodysplastic syndrome with excess blasts (MDS-EB) is unknown. This study retrospectively analyzed the relationship between pretransplantation MRD status and long-term survival. MATERIALS AND METHODS: Patients with MDS-EB who underwent allogeneic hematopoietic stem cell transplantation (allo-HSCT) from March 5, 2005, to November 8, 2020, were included. The relationship between pretransplantation MRD status and long-term survival was analyzed using univariate and multivariate logistic regression models. RESULTS: Of 220 patients with MDS-EB who underwent allo-HSCT, 198 were eligible for inclusion in this multicenter, retrospective cohort study. Complete remission was attained in 121 (61.1%) patients, and 103 patients underwent detection of MRD pretransplantation, with 67 patients being MRD-positive and 36 patients being MRD-negative. The median follow-up time was 16 months, the median age was 41 years (6-65 years), and 58% of the patients were men. The 3-year disease-free survival (DFS) and overall survival (OS) probabilities for all patients were 70.1% and 72.9%, respectively. For patients in complete remission, the 3-year DFS and OS probabilities were 72.2% and 74.8%, respectively. Further analysis found that the 3-year DFS rates of MRD-negative and MRD-positive patients were 85.6% and 66.5% (p = .045), respectively, whereas the 3-year OS rates were 91.3% and 66.4% (p = .035), respectively. Univariate and multivariate analyses showed that poor pretransplantation MRD clearance was an independent prognostic risk factor for DFS and OS. CONCLUSION: Poor pretransplantation MRD clearance is an independent prognostic risk factor for long-term survival after allo-HSCT for patients with MDS-EB. PLAIN LANGUAGE SUMMARY: Poor minimal residual disease clearance pretransplanation is an independent prognostic risk factor for long-term survival after allogeneic hematopoietic stem cell transplantation for patients with myelodysplastic syndrome with excess blasts.

Elucidating the role of T-cell exhaustion-related genes in colorectal cancer: a single-cell bioinformatics perspective.

Colorectal cancer (CRC) remains a significant global health issue. In this study, the role of T-cell exhaustion-related genes (TEXs) in CRC was investigated using single-cell and bulk RNA-seq analysis. This research involved extensive data analysis using multiple databases, including the TCGA-COAD cohort, GSE14333, and GSE39582. Through single-cell analysis, distinct cell populations within CRC samples were identified and classified T-cells into four subgroups: regulatory T-cells (Tregs), conventional CD4+ T-cells (CD4+ T conv), CD8+ T, and CD8+ T exhausted cells. Intercellular communication networks and signaling pathways associated with TEXs using computational tools such as CellChat and PROGENy. Additionally, TEX-related alterations in tumor gene pathways were analyzed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Prognostic models were developed, and their correlation with immune infiltration was assessed. The study revealed the presence of distinct cell populations within CRC, with TEXs playing a crucial role in the tumor microenvironment. CD8+ T exhausted cells exhibited expression of specific markers, indicating their involvement in tumor immune evasion. CellChat and PROGENy analyses revealed intricate communication networks and signaling pathways associated with TEXs, including RNA splicing and viral carcinogenesis. Furthermore, the prognostic risk model developed on the basis of TEXs demonstrated its efficacy in stratifying CRC patients. This risk model exhibited strong correlations with immune infiltration by various effector immune cells, highlighting the influence of TEXs on the tumor immune response. The complex interactions and signaling pathways underlying TEX-associated immune dysregulation in CRC were revealed by employing advanced analytical approaches. The development of a prognostic risk model based on TEXs offers a promising tool for prognostic stratification in patients with CRC. Furthermore, the correlations observed between TEXs and immune infiltration provide valuable insights into the potential of TEXs as therapeutic targets and highlight the need for further investigation into TEX-mediated immune evasion mechanisms. This study thus provides valuable insights into the role of TEXs in CRC.

Prognostic analysis and validation of lncRNAs in bladder cancer on the basis of neutrophil extracellular traps.

BACKGROUND: Complex interactions in the tumor microenvironment (TME) between bladder cancer (BLCA) and immune cells are critical for cancer progression. However, studies of neutrophil extracellular trap-associated long non-coding RNAs (NET-lncRNAs) in the TME of BLCA have not been reported. This study aims to screen for NET-lncRNAs in BLCA and to preliminarily explore their effects on BLCA development. METHODS: The correlation of NET-related gene sets, which were identified from the cancer genome atlas (TCGA) BLCA datasets, with lncRNAs was analyzed and the prognosis-related genes were identified through random forest analysis. The least absolute shrinkage and selection operator (LASSO) model was utilized to obtain prognostic risk scores for NET-lncRNAs (NET-Score). We collected clinical BLCA samples, as well as SV-HUC-1 and BLCA cells, to validate the expression of NET-lncRNAs. Survival and independent prognostic analysis were performed. In J82 and UM-UC-3 cells, after NKILA expression was inhibited, cell proliferation and apoptosis levels were detected. RESULTS: NET-related gene sets mainly included CREB5, MMP9, PADI4, CRISPLD2, CD93, DYSF, MAPK3, TECPR2, MAPK1 and PIK3CA. Then, four NET-lncRNAs, MAP 3 K4-AS1, MIR100HG, NKILA and THY1-AS1, were identified. NET-Score had the highest hazard ratio for BLCA. An elevated NET-Score was linked to a significant increase in immune cell infiltration and copy number variation, as well as a notable decrease in survival rate and drug sensitivity. NET-lncRNA-related genes were mainly enriched in the pathways of angiogenesis, immune response, cell cycle and T cell activation. MAP 3 K4-AS1, MIR100HG, NKILA and THY1-AS1 expressions were significantly increased in BLCA tissues. Compared with SV-HUC-1 cells, NKILA expression was elevated in J82 and UM-UC-3 cells. Inhibition of NKILA expression inhibited the proliferation and promoted apoptosis of J82 and UM-UC-3 cells. CONCLUSIONS: Several NET-lncRNAs, including MAP 3 K4-AS1, MIR100HG, NKILA and THY1-AS1, were successfully screened in the BLCA. The NET-Score was an independent prognostic factor for BLCA. In addition, inhibition of NKILA expression suppressed BLCA cell development. The above NET-lncRNAs could serve as potential prognostic markers and targets in BLCA.

Immune-Related Genes' Prognostic, Therapeutic and Diagnostic Value in Ovarian Cancer Immune-Related Gene Biomarker in Ovarian Cancer.

OBJECTIVES: The abnormal expression of immune-related genes (IRGs) plays an important role in the occurrence and progression of ovarian cancer (OC), which is the main cause of mortality among gynecological cancer patients. This study aims to establish a prognostic risk model and comprehensively analyze the relationship between OC risk score and prognosis, immune cell infiltration (ICI) and therapeutic sensitivity in OC. METHODS: We retrospectively evaluated the clinicopathological characteristics of consecutive OC patients in the Cancer Genome Atlas (TCGA) database. First, the prognostic risk model was constructed by bioinformatics methods. And then, we systematically assessed model robustness, and correlations between risk score and prognosis, and immune cell infiltration. The ICGC cohort was used to verify the prognostic risk model. Finally, we evaluated their value in the treatment of OC immunotherapy and chemotherapy. RESULTS: A total of 10 IRGs were identified to construct the prognostic risk model. Survival analysis revealed that patients in the low-risk group had a better prognosis (P < .01), and the risk score might be considered an independent predictor for predicting the prognosis. In addition, risk scores and patient clinical information were used to construct clinical nomograms, improving the prediction's precision. We also explored the relationship between the risk score and ICI, immunotherapy and drug sensitivity. CONCLUSIONS: Collectively, we identified a novel ten IRGs signature that may be applied as a prognostic predictor of OC, thereby benefiting clinical decision-making and personalized treatment of patients.

Construction of a novel signature based on immune-related lncRNA to identify high and low risk pancreatic adenocarcinoma patients.

BACKGROUND: Pancreatic adenocarcinoma is one of the most lethal tumors in the world with a poor prognosis. Thus, an accurate prediction model, which identify patients within high risk of pancreatic adenocarcinoma is needed to adjust the treatment and elevate the prognosis of these patients. METHODS: We obtained RNAseq data of The Cancer Genome Atlas (TCGA) pancreatic adenocarcinoma (PAAD) from UCSC Xena database, identified immune-related lncRNAs (irlncRNAs) by correlation analysis, and identified differential expressed irlncRNAs (DEirlncRNAs) between pancreatic adenocarcinoma tissues from TCGA and normal pancreatic tissues from TCGA and Genotype-Tissue Expression (GTEx). Further univariate and lasso regression analysis were performed to construct prognostic signature model. Then, we calculated the areas under curve and identified the best cut-off value to identify high- and low-risk patients with pancreatic adenocarcinoma. The clinical characteristics, immune cell infiltration, immunosuppressive microenvironment, and chemoresistance were compared between high- and low-risk patients with pancreatic adenocarcinoma. RESULTS: We identified 20 DEirlncRNA pairs and grouped the patients by the best cut-off value. We proved that our prognostic signature model possesses a remarkable efficiency to predict prognosis of PAAD patients. The AUC for ROC curve was 0.905 for 1-year prediction, 0.942 for 2-year prediction, and 0.966 for 3-year prediction. Patients in high-risk group have poor survival rate and worse clinical characteristics. We also proved that patients in high-risk groups were in immunosuppressive status and may be resistant to immunotherapy. Anti-cancer drug evaluation was performed based on in-silico predated tool, such as paclitaxel, sorafenib, and erlotinib, may be suitable for PAAD patients in high-risk group. CONCLUSIONS: Overall, our study constructed a novel prognostic risk model based on pairing irlncRNAs, exhibited a promising prediction value in patients with pancreatic adenocarcinoma. Our prognostic risk model may help distinguish PAAD patients suitable for medical treatments.

Pan-sarcoma characterization of lncRNAs in the crosstalk of EMT and tumour immunity identifies distinct clinical outcomes and potential implications for immunotherapy.

The epithelial-to-mesenchymal transition (EMT) is a reversible process that may interact with tumour immunity through multiple approaches. There is increasing evidence demonstrating the interconnections among EMT-related processes, the tumour microenvironment, and immune activity, as well as its potential influence on the immunotherapy response. Long non-coding RNAs (lncRNAs) are emerging as critical modulators of gene expression. They play fundamental roles in tumour immunity and act as promising biomarkers of immunotherapy response. However, the potential roles of lncRNA in the crosstalk of EMT and tumour immunity are still unclear in sarcoma. We obtained multi-omics profiling of 1440 pan-sarcoma patients from 19 datasets. Through an unsupervised consensus clustering approach, we categorised EMT molecular subtypes. We subsequently identified 26 EMT molecular subtype and tumour immune-related lncRNAs (EILncRNA) across pan-sarcoma types and developed an EILncRNA signature-based weighted scoring model (EILncSig). The EILncSig exhibited favourable performance in predicting the prognosis of sarcoma, and a high-EILncSig was associated with exclusive tumour microenvironment (TME) characteristics with desert-like infiltration of immune cells. Multiple altered pathways, somatically-mutated genes and recurrent CNV regions associated with EILncSig were identified. Notably, the EILncSig was associated with the efficacy of immune checkpoint inhibition (ICI) therapy. Using a computational drug-genomic approach, we identified compounds, such as Irinotecan that may have the potential to convert the EILncSig phenotype. By integrative analysis on multi-omics profiling, our findings provide a comprehensive resource for understanding the functional role of lncRNA-mediated immune regulation in sarcomas, which may advance the understanding of tumour immune response and the development of lncRNA-based immunotherapeutic strategies for sarcoma.

A lipid metabolism-based prognostic risk model for HBV-related hepatocellular carcinoma.

BACKGROUND: Up to 85% of hepatocellular carcinoma (HCC) cases in China can be attributed to infection of hepatitis B virus (HBV). Lipid metabolism performs important function in hepatocarcinogenesis of HBV-related liver carcinoma. However, limited studies have explored the prognostic role of lipid metabolism in HBV-related HCC. This study established a prognostic model to stratify HBV-related HCC based on lipid metabolisms. METHODS: Based on The Cancer Genome Atlas HBV-related HCC samples, this study selected prognosis-related lipid metabolism genes and established a prognosis risk model by performing uni- and multi-variate Cox regression methods. The final markers used to establish the model were selected through the least absolute shrinkage and selection operator method. Analysis of functional enrichment, immune landscape, and genomic alteration was utilized to investigate the inner molecular mechanism involved in prognosis. RESULTS: The risk model independently stratified HBV-infected patients with liver cancer into two risk groups. The low-risk groups harbored longer survival times (with P < 0.05, log-rank test). TP53, LRP1B, TTN, and DNAH8 mutations and high genomic instability occurred in high-risk groups. Low-risk groups harbored higher CD8 T cell infiltration and BTLA expression. Lipid-metabolism (including "Fatty acid metabolism") and immune pathways were significantly enriched (P < 0.05) in the low-risk groups. CONCLUSIONS: This study established a robust model to stratify HBV-related HCC effectively. Analysis results decode in part the heterogeneity of HBV-related liver cancer and highlight perturbation of lipid metabolism in HBV-related HCC. This study's findings could facilitate patients' clinical classification and give hints for treatment selection.

Long-term analysis of prognostic risk factors impacting surgical outcomes in patients with external auditory canal carcinoma.

BACKGROUND: Malignant neoplasms of the external auditory canal (EAC) are rare. No consensus on management has emerged. OBJECTIVE: To determine possible risk factors influencing tumorgenesis and prognosis of EAC carcinoma. MATERIALS AND METHODS: 108 patients (87 men/21 women) with an average age of 74 ± 13.8 years were recruited from 2005 to 2019 at Department of Otorhinolaryngology, Head and Neck Surgery Heidelberg. The follow-up interval was 43.62 ± 55.39 months. Partial and (sub)total ablative otis, supplementary surgery (petrosectomy, parotidectomy, neck dissection, mastoidectomy) and adjuvant radio(chemo)therapy belonged to treatment options. TNM status was determined at time of diagnosis using the AJCC staging system. RESULTS: 63.9% of patients underwent a total ablative otis. Tumor recurrence was seen in 24.1%. The 1-year survival rate was 87%, the 5-year survival rate was 52%, the mean overall survival (OS) was 3.82 ± 4.6 years. Male EAC carcinoma patients had a better OS (p < 0.001), PFS (p < 0.001) and DSS (p = 0.02) than females. T1 patients had a better OS (p = 0.01), PFS (p = 0.01) and DSS (p < 0.001) than T4 patients. Lymph node but not distant metastasis, tumor grading, perineural, venous and lymphatic invasion, histology, age and tumor localization influenced the OS in EAC carcinoma patients (p = 0.04). The more radical the ablative otis, the worse the OS (p = 0.002), PFS (p = 0.02) and DSS (p < 0.001). Radio(chemo)therapy did not improve the OS. CONCLUSIONS: EAC carcinoma are difficult to treat and benefit from early diagnosis so that a radical combined treatment approach does not need to be used.

A multitask deep learning radiomics model for predicting the macrotrabecular-massive subtype and prognosis of hepatocellular carcinoma after hepatic arterial infusion chemotherapy.

BACKGROUND: The macrotrabecular-massive (MTM) is a special subtype of hepatocellular carcinoma (HCC), which has commonly a dismal prognosis. This study aimed to develop a multitask deep learning radiomics (MDLR) model for predicting MTM and HCC patients' prognosis after hepatic arterial infusion chemotherapy (HAIC). METHODS: From June 2018 to March 2020, 158 eligible patients with HCC who underwent surgery were retrospectively enrolled in MTM related cohorts, and 752 HCC patients who underwent HAIC were included in HAIC related cohorts during the same period. DLR features were extracted from dual-phase (arterial phase and venous phase) contrast-enhanced computed tomography (CECT) of the entire liver region. Then, an MDLR model was used for the simultaneous prediction of the MTM subtype and patient prognosis after HAIC. The MDLR model for prognostic risk stratification incorporated DLR signatures, clinical variables and MTM subtype. FINDINGS: The predictive performance of the DLR model for the MTM subtype was 0.968 in the training cohort [TC], 0.912 in the internal test cohort [ITC] and 0.773 in the external test cohort [ETC], respectively. Multivariable analysis identified portal vein tumor thrombus (PVTT) (p = 0.012), HAIC response (p < 0.001), HAIC sessions (p < 0.001) and MTM subtype (p < 0.001) as indicators of poor prognosis. After incorporating DLR signatures, the MDLR model yielded the best performance among all models (AUC, 0.855 in the TC, 0.805 in the ITC and 0.792 in the ETC). With these variables, the MDLR model provided two risk strata for overall survival (OS) in the TC: low risk (5-year OS, 44.9%) and high risk (5-year OS, 4.9%). INTERPRETATION: A tool based on MDLR was developed to consider that the MTM is an important prognosis factor for HCC patients. MDLR showed outstanding performance for the prognostic risk stratification of HCC patients who underwent HAIC and may help physicians with therapeutic decision making and surveillance strategy selection in clinical practice.

Prognostic risk assessment model and drug sensitivity analysis of colon adenocarcinoma (COAD) based on immune-related lncRNA pairs.

PURPOSE: The aim of this study was to identify and screen long non-coding RNA (lncRNA) associated with immune genes in colon cancer, construct immune-related lncRNA pairs, establish a prognostic risk assessment model for colon adenocarcinoma (COAD), and explore prognostic factors and drug sensitivity. METHOD: Our method was based on data from The Cancer Genome Atlas (TCGA). To begin, we obtained all pertinent demographic and clinical information on 385 patients with COAD. All lncRNAs significantly related to immune genes and with differential expression were identified to construct immune lncRNA pairs. Subsequently, least absolute shrinkage and selection operator and Cox models were used to screen out prognostic-related immune lncRNAs for the establishment of a prognostic risk scoring formula. Finally, We analysed the functional differences between subgroups and screened the drugs, and establish an individual prediction nomogram model. RESULTS: Our final analysis confirmed eight lncRNA pairs to construct prognostic risk assessment model. Results showed that the high-risk and low-risk groups had significant differences (training (n = 249): p < 0.001, validation (n = 114): p = 0.022). The prognostic model was certified as an independent prognosis model. Compared with the common clinicopathological indicators, the prognostic model had better predictive efficiency (area under the curve (AUC) = 0.805). Finally, We have analysed highly differentiated cellular pathways such as mucosal immune response, identified 9 differential immune cells, 10 sensitive drugs, and establish an individual prediction nomogram model (C-index = 0.820). CONCLUSION: Our study verified that the eight lncRNA pairs mentioned can be used as biomarkers to predict the prognosis of COAD patients. Identified cells, drugs may have an positive effect on colon cancer prognosis.

Novel six-gene prognostic signature based on colon adenocarcinoma immune-related genes.

BACKGROUND: Colon adenocarcinoma (COAD) is one of the most common gastrointestinal tumors worldwide, and immunotherapy is one of the most promising treatments for it. Identifying immune genes involved in the development and maintenance of cancer is key to the use of tumor immunotherapy. This study aimed to determine the prognostic value of immune genes in patients with COAD and to establish an immune-related gene signature. Differentially expressed genes, immune-related genes (DEIGs), and transcription factors (DETFs) were screened using the following databases: Cistrome, The Cancer Genome Atlas (TCGA), the Immunology Database and Analysis Portal, and InnateDB. We constructed a network showing the regulation of DEIGs by DETFs. Using weighted gene co-expression network analysis, we prepared 5 co-expressed gene modules; 6 hub genes (CD1A, CD1B, FGF9, GRP, SERPINE1, and F2RL2) obtained using univariate and multivariate regression analysis were used to construct a risk model. Patients from TCGA database were divided into high- and low-risk groups based on whether their risk score was greater or less than the mean; the public dataset GSE40967, which contains gene expression profiles of 566 colon cancer patients, was used for validation. RESULTS: Survival analysis, somatic gene mutations, and tumor-infiltrating immune cells differed significantly between the high- and low-risk groups. CONCLUSIONS: This immune-related gene signature could play an important role in guiding treatment, making prognoses, and potentially developing future clinical applications.

Molecular mechanism by which CDCP1 promotes proneural-mesenchymal transformation in primary glioblastoma.

BACKGROUND: Compared with the proneural (PN) subtype of glioblastoma (GBM), the mesenchymal (MES) subtype is more invasive and immune evasive and is closely related to poor prognosis. Here, we used transcriptome data and experimental evidence to indicate that CUB domain-containing protein 1 (CDCP1) is a novel regulator that facilitates the transformation of PN-GBM to MES-GBM. METHODS: The mRNA expression data of CDCP1 in glioma were collected from the TCGA, CGGA and GEO databases, and in vitro experiments verified CDCP1 expression in glioma tissue samples. Independent prognostic analysis revealed the correlation of the CDCP1 expression level and patient survival. Bioinformatics analysis and experiments verified the biological function of CDCP1. Multivariate proportional hazards models and a PPI network were used to select key genes. A prognostic risk model for predicting the survival of glioma patients was constructed based on the selected genes. RESULTS: The results showed that the expression of CDCP1 increased with increasing tumor grade and that the overexpression of CDCP1 correlated with a poor prognosis. CDCP1 was highly expressed in MES-GBM but weakly expressed in PN-GBM. The risk model (considering CDCP1 combined with CD44 and ITGAM expression) could represent a tool for predicting survival and prognosis in glioma patients. CONCLUSIONS: Our study indicates that CDCP1 plays an important role in facilitating the transformation of PN-GBM to MES-GBM.