Patients In Different Risk Groups Research Articles

The potential of cancer stem cells for personalized risk assessment and therapeutic intervention in individuals with intrahepatic cholangiocarcinoma

BackgroundAccumulating evidence suggests that intrahepatic cholangiocarcinoma (ICC) is a stem cell-based disease, but information on the biology of cancer stem cells (CSC) in ICC is very limited.MethodsICC RNA-seq cohorts from three different public databases were integrated and the protein-coding genes were divided into different modules using "WGCNA" to screen the most relevant modules with CSC scores. Least Absolute Shrinkage and Selection Operator (LASSO) regression were introduced to construct prognostic classification models. In addition, the extent of immune cell infiltration in patients in different risk groups was assessed based on the ESTIMATE, CIBERSORT, MCP-Counter, and single sample gene set enrichment analysis (ssGSEA) algorithms. Finally, the correlation between different risk scores and common drugs was analyzed by pRRophetic package and Spearman method.ResultsIn the present study, we found that a high CSC score was associated with a poorer prognosis in patients with ICC. The yellow module obtained by WGCNA was significantly positively correlated with the CSCs score, in which 8 genes were served to build a prognostic classification model, and the obtained risk score was negatively correlated with CSCs score and prognosis. The low-risk score was more suitable for immunotherapy, and the high-risk score was more suitable for treatment with 11 antitumor drugs.ConclusionThis study revealed the regulatory role of CSC-mediated EMT, angiogenesis, and immunomodulatory biological processes in ICC, and applied a prognostic classification model to highlight the great potential of CSC for personalized risk assessment, chemotherapy, and immunotherapy intervention in ICC individuals.

Application of 21-gene recurrence risk score in patients with breast cancer

Objective: To investigate the relationship between 21-gene recurrence risk score (21-Gene RS) and the prognosis and clinicopathological features of hormone receptor (HR) positive, HER2-negative early breast cancer patients who did not receive neoadjuvant therapy. Methods: A total of 469 patients with HR positive and HER2-negative early breast cancer who received surgical treatment in the First Affiliated Hospital, Zhejiang University School of Medicine from January 2014 to October 2017 were selected. Their clinicopathological data were retrospectively analyzed. Tumor tissue samples were collected from patients, and the expression of 21-gene was detected by reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR). The 21-Gene RS was calculated according to the Trial Assigning Individualized Options for Treatment (TAILORx) RS grouping and National Surgical Adjuvant Breast and Bowel Project B-20 (NSABP B-20) RS grouping principles. Patients were divided into low (21-Gene RS<11 or 21-Gene RS<18), intermediate (11≤21-Gene RS<26 or 18≤21-Gene RS<31) and high (21-Gene RS≥26 or 21-Gene RS≥31) risk groups, and the clinicopathological features and prognostic differences of patients in different risk groups were compared. Statistical data were compared by chi-square test. Survival analysis was performed using Kaplan-Meier curve analysis and the differences between groups were compared using Log-rank test. Multivariate analysis was conducted by COX regression analysis. Results: Based on TAILORx RS grouping, the proportions of low-risk, intermediate-risk and high-risk groups among the 469 patients were 18.8% (88/469), 48.2% (226/469) and 33.0% (155/469), respectively. Based on NSABP B-20 RS grouping, the proportion of low-risk, intermediate-risk and high-risk groups were 43.1% (202/469), 37.5% (176/469) and 19.4% (91/469), respectively. The association of 21-Gene RS with histological grading, luminal typing, Ki-67 expression, and chemotherapy and treatment modalities were statistically significant (P<0.05) regardless of TAILORx RS grouping or NSABP B-20 RS grouping. Kaplan-Meier survival curve suggested poor prognosis in high-risk group (P<0.05, Log-rank test). Multivariate COX regression analysis showed that surgical method and 21-Gene RS were risk factors affecting the prognosis of patients. Conclusions: 21-Gene RS is significantly associated with the prognosis of patients with HR-positive, HER2-negative, early-stage breast cancer not receiving neoadjuvant therapy, as well as with their clinicopathological characteristics such as patients' histologic grade, luminal typing, Ki-67 expression, and whether or not they are treated with chemotherapy or other treatment modalities.The 21-Gene RS threshold of 11 and 26 or 18 and 31 can be used to grade the prognosis in Chinese patients with early-stage breast cancer. More researches are needed to guide the selection of postoperative adjuvant therapy for patients with HR-positive and HER2-negative early-stage breast cancer.

The Construction of a Nomogram Using the Pan-Immune-Inflammation Value Combined with a PILE Score for Immunotherapy Prediction Prognosis in Advanced NSCLC.

The purpose of this study was to investigate the predictive value of Pan-Immune-Inflammation Value (PIV) combined with the PILE score for immunotherapy in patients with advanced non-small cell lung cancer (NSCLC) and to construct a nomogram prediction model to provide reference for clinical work. Patients with advanced NSCLC who received ICIs treatment in Qingdao Municipal Hospital from January 2019 to December 2021 were selected as the study subjects. The chi-square test, Kaplan-Meier survival analysis, and Cox proportional risk regression analysis were used to evaluate the prognosis. The results were visualized by a nomogram, and the performance of the model was judged by indicators such as the area under the subject operating characteristic curve (AUC) and C-index. The patients were divided into high- and low-risk groups by PILE score, and the prognosis of patients in different risk groups was evaluated. Multivariate Cox regression analysis showed that immune-related adverse events (irAEs) were prognostic factors for overall survival (OS) improvement, and ECOG PS score ≥2, bone metastases before treatment, and high PIV expression were independent risk factors for OS. The C index of OS predicted by the nomogram model is 0.750 (95% CI: 0.677-0.823), and the Calibration and ROC curves show that the model has good prediction performance. Compared with the low-risk group, patients in the high-risk group of PILE were associated with a higher inflammatory state and poorer physical condition, which often resulted in a poorer prognosis. PIV can be used as a prognostic indicator for patients with advanced NSCLC treated with ICIs, and a nomogram prediction model can be constructed to evaluate the survival prediction of patients, thus contributing to better clinical decision-making and prognosis assessment.

A prognostic binary classifier comprised of five critical mRNAs stratified pancreatic cancer patients following resection

BackgroundPancreatic cancer is characterized by an extremely poor prognosis, even following potentially curative resection. Classical prognostic markers such as histopathological or clinical parameters have limited predictive power. The present study aimed to establish a prognostic model combining mRNA expression data with histopathological and clinical data to better predict survival and stratify pancreatic cancer patients following resection. We pioneered three models in one study and systematically evaluated the clinical benefits of all three models. MethodsTo identify differentially expressed genes in pancreatic cancer, mRNA data from normal (GTEx database) and pancreatic cancer (TCGA database) tissues were used. Survival analysis was carried out to identify prognosis-relevant genes from the identified differentially expressed genes and LASSO regression was used to filter out hub genes. The risk score of several hub genes was calculated according to gene expression and coefficients. Validation was carried out using an independent set of GEO microarray data. Multivariate COX regression was used for identifying independent clinical and pathological risk factors related to patient's survival in the TCGA database and a prognostic model combining mRNA expression data with histopathological and clinical data was established. Another prognostic model using clinicopathological factors from the SEER database was conceived based on multivariate COX regression. NRI (net reclassification improvement) and IDI (integrated discrimination index) were used to compare the predictive capabilities of the different models. ResultsWe identified 1589 differentially expressed genes (DEGs) through the comparison of normal and pancreatic cancer tissues, of whom 317 were associated with prognosis(p < 0.05). LASSO regression identified five hub genes, MYEOV, ANXA2P2, MET, CEP55, and KRT7, that were used for the five-mRNA-classifier prognostic model. The classifier could stratify patients into a short and long survival group: 5-year overall survival in the training set (TCGA, 6 % vs 52 %, p < 0.001), test set (TCGA, 18 % vs 55 %,p < 0.01) and external validation set (GEO, 0 % vs 25 %, p < 0.05). Sensitivity analysis showed that the mRNA model (model 1) was better than the clinicopathological no-mRNA model (model 2) in predicting 5-year survival in the TCGA database (AUC: 0.877 vs 0.718, z = 3.165, p < 0.01) and better than the multi-factor prognostic model (model 3) from the SEER database (AUC: 0.754, z = 2.637, p < 0.01). On predictive performance, model 1 improved model 2 (NRI = 0.084, z = 1.288, p = 0.198; IDI = 0.055, z = 1.041,p = 0.298) and model 3 (NRI = 0.167,z = 1.961,p = 0.05; IDI = 0.086, z = 1.427, p = 0.154). ConclusionThe five-mRNA-classifier is a reliable and feasible instrument to predict the prognosis of pancreatic cancer patients following resection. It might help in patiens counseling and assist clinicians in providing individualized treatment for patients in different risk groups.

Machine Learning-Based Prediction of Pathological Responses and Prognosis After Neoadjuvant Chemotherapy for Non–Small-Cell Lung Cancer: A Retrospective Study

BackgroundNeoadjuvant chemotherapy has variable efficacy in patients with non–small-cell lung cancer (NSCLC), yet reliable noninvasive predictive markers are lacking. This study aimed to develop a radiomics model predicting pathological complete response and postneoadjuvant chemotherapy survival in NSCLC. Materials and MethodsRetrospective data collection involved 130 patients with NSCLC who underwent neoadjuvant chemotherapy and surgery. Patients were randomly divided into training and independent testing sets. Nine radiomics features from prechemotherapy computed tomography (CT) images were extracted from intratumoral and peritumoral regions. An auto-encoder model was constructed, and its performance was evaluated. X-tile software classified patients into high and low-risk groups based on their predicted probabilities. survival of patients in different risk groups and the role of postoperative adjuvant chemotherapy were examined. ResultsThe model demonstrated area under the receiver operating characteristic (ROC) curve of 0.874 (training set) and 0.876 (testing set). The larger the area under curve (AUC), the better the model performance. Calibration curve and decision curve analysis indicated excellent model calibration (Hosmer–Lemeshow test, P = .763, the higher the P-value, the better the model fit) and potential clinical applicability. Survival analysis revealed significant differences in overall survival (P = .011) and disease-free survival (P = .017) between different risk groups. Adjuvant chemotherapy significantly improved survival in the low-risk group (P = .041) but not high-risk group (P = 0.56). ConclusionThis study represents the first successful prediction of pathological complete response achievement after neoadjuvant chemotherapy for NSCLC, as well as the patients’ survival, utilizing intratumoral and peritumoral radiomics features.

A Nomogram Model Based on the Inflammation-Immunity-Nutrition Score (IINS) and Classic Clinical Indicators for Predicting Prognosis in Extranodal Natural Killer/T-Cell Lymphoma.

Systemic inflammation, immunity, and nutritional status are closely related to patients' outcomes in several kinds of cancers. This study aimed to establish a new nomogram based on inflammation-immunity-nutrition score (IINS) to predict the prognosis of extranodal natural killer/T-cell lymphoma (ENKTL) patients. The clinical data of 435 patients with ENTKL were retrospectively reviewed and randomly assigned to training cohort (n=305) and validation cohort (n=131) at a ratio of 7:3. Cox regression analysis was employed to identify independent prognostic factors and develop a nomogram in the training cohort. Harrell's concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) curve were employed to assess the performance of the nomogram and compare it with traditional prognostic systems (PINK, IPI, KPI). Internal validation was performed using 1000 bootstrap resamples in the validation cohort. Kaplan-Meier survival analyses were conducted to compare the overall survival (OS) of patients in different risk groups. In the training cohort, in addition to several classic parameters, IINS was identified as an independent prognostic factor significantly associated with the OS of patients. The nomogram established based on the independent prognostic indicators showed superior survival prediction efficacy, with C-index of 0.733 in the training cohort and 0.759 in the validation cohort compared to the PINK (0.636 and 0.737), IPI (0.81 and 0.707), and KPI (0.693 and 0.639) systems. Furthermore, compared with PINK, IPI, and IPI systems, the nomogram showed relatively superior calibration curves and more powerful prognostic discrimination ability in predicting the OS of patients. DCA curves revealed some advantages in terms of clinical applicability of the nomogram compared to the PINK, IPI, and IPI systems. Compared with traditional prognostic systems, the nomogram showed promising prospects for risk stratification in ENKTL patient prognosis, providing new insights into the personalized treatment.

Significance of Aneuploidy in Predicting Prognosis and Treatment Response of Uveal Melanoma.

This study aimed to improve personalized treatment strategies and predict survival outcomes for patients with uveal melanoma (UM). Copy number aberrations (CNAs) have been considered as a main feature of metastatic UM. This study was designed to explore the feasibility of using copy number variation (CNV) in UM classification, prognosis stratification and treatment response. The CNV data in the TCGA-UVM cohort were used to classify the samples. The differentially expressed genes (DEGs) between subtypes were screened by the "Limma" package. The module and hub genes related to aneuploidy score were identified by performing weighted gene co-expression network analysis (WGCNA) on the DEGs. Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to train the hub genes for developing a prognosis model for UM. Finally, the expression levels of the screened prognostic key genes were verified in UM cells, and the cell migration and invasion abilities were detected using real-time quantitative PCR (qRT-PCR) and transwell assay. The UM samples were divided into 3 CNV subtypes, which differed significantly in overall survival (OS) and disease-specific survival (DSS). C1 had the shortest OS and DSS and the highest level of immune infiltration. A total of 2036 DEGs were obtained from the three subtypes. Eighty hub genes with the closest correlation with aneuploidy scores were selected by WGCNA. Univariate Cox and LASSO regression-based analyses finally determined eight genes as the key prognostic genes, including HES6, RNASEH2C, NQO1, NUDT14, TTYH3, GJC1, FKBP10, and MRPL24. A prognostic model was developed using the eight genes, demonstrating a strong prediction power. Differences in the response to immunotherapy among patients in different risk groups were significant. We found that high-risk patients were more sensitive to two drugs (Palbociclib_ 1054 and Ribociclib_1632), while low-risk patients were more sensitive to AZD1208_1449, ERK_2440_1713, Mirin_1048, and Selumetinib_1736. UM in this study was divided into three CNV subtypes, and a model based on eight aneuploidy score-related genes was established to evaluate the prognosis and drug treatment efficacy of UM patients. The current results may have the potential to help the clinical decision-making process for UM management.

A Treg-related riskscore model may improve the prognosis evaluation of colorectal cancer.

Colorectal cancer (CRC) poses a significant health challenge. This study aims to investigate the prognostic value of a regulatory T cell (Treg)-related gene signature in CRC. We extracted the gene expression and clinical data on CRC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The gene module related to Treg was identified by weighted gene co-expression network analysis (WGCNA). The genes in the significant module were filtered by univariate Cox, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. A riskscore model was established in terms of the key Treg-related genes. The reliability of this riskscore model was validated using the external GEO dataset. The association of riskscore with clinical features, mutation patterns and signaling pathways was explored. Genes in the blue module showed the strongest association with Tregs. After a series of filtering cycles, seven Treg-related key genes, GDE1, GSR, HSPB1, AOC2, TBX19, TAMM41 and TIGD6, were selected to construct a riskscore model. This model performed well in evaluating the patients' survival in TCGA cohort, and was further affirmed by the GSE17536 validation cohort. For precise evaluation of the patients' survival, we established a nomogram in light of riskscore and clinical factors. Patients in different risk groups had distinct clinical features, mutation patterns and signaling pathway activities. The expression of five key genes was significantly associated with Treg infiltration in the CRC samples. We established a useful riskscore model in light of seven Treg-related genes. This model may contribute to the prognosis evaluation, direct tailored treatment, and hopefully improve clinical outcomes of the CRC patients.

Development of a Novel CD8+ T Cell-Associated Signature for Prognostic Assessment in Hepatocellular Carcinoma.

The aim of this study was to analyze the clinical significance and prognostic value of CD8+ T cell-related regulatory genes in hepatocellular carcinoma (HCC). This was a retrospective study. We combined TCGA-LIHC and single-cell RNA sequencing data for Lasso-Cox regression analysis to screen for CD8+ T cell-associated genes to construct a novel signature. The expression of the signature genes was detected at cellular and tissue levels using qRT-PCR, immunohistochemistry, and tissue microarrays. The CIBERSORT algorithm was then used to assess the immune microenvironmental differences between the different risk groups and a drug sensitivity analysis was performed to screen for potential HCC therapeutic agents. An 8-gene CD8 + T cell-associated signature (FABP5, GZMH, ANXA2, KLRB1, CD7, IL7R, BATF, and RGS2) was constructed. Survival analysis showed that high-risk patients had a poorer prognosis in all cohorts. Tumor immune microenvironment analysis revealed 22 immune cell types that differed significantly between patients in different risk groups, with patients in the low-risk group having an immune system that was more active in terms of immune function. Patients in the high-risk group were more prone to immune escape and had a poorer response to immunotherapy, and AZD7762 was screened as the most sensitive drug in the high-risk group. Finally, preliminary experiments have shown that BATF has a promoting effect on the proliferation, migration and invasion of HuH-7 cells. The CD8+ T-cell-associated signature is expected to be a tool for optimizing individual patient decision-making and monitoring protocols, and to provide new ideas for treatment and prognostic assessment of HCC.

Development of an m6A-Related lncRNAs Signature Predicts Tumor Stemness and Prognosis for Low-Grade Glioma Patients.

Growing evidence has revealed that m6A modification of long noncoding RNAs (lncRNAs) dynamically controls tumor stemness and tumorigenesis-related processes. However, the prognostic significance of m6A-related lncRNAs and their associations with stemness in low-grade glioma (LGG) remain to be clarified. A multicenter transcriptome analysis of lncRNA expression in 1,247 LGG samples was performed in this study. The stemness landscape of LGG tumors was presented and associations with clinical features were revealed. The m6A-related lncRNAs were identified between stemness groups and were further prioritized via least absolute shrinkage and selection operator Cox regression analysis. A risk score model based on m6A-related lncRNAs was constructed and validated in external LGG datasets. Based on the expression of LINC02984, PFKP-DT, and CRNDE, a risk model and nomogram were constructed; they successfully predicted the survival of patients and were extended to external datasets. Significant correlations were observed between the risk score and tumor stemness. Moreover, patients in different risk groups exhibited distinct tumor immune microenvironments and immune signatures. We finally provided several potential compounds suitable for specific risk groups, which may aid in LGG treatment. This novel signature presents noteworthy value in the prediction of prognosis and stemness status for LGG patients and will foster future research on the development of clinical regimens.

Involvement of ICAM5 in Carcinostasis Effects on LUAD Based on the ROS1-Related Prognostic Model.

Lung cancer is the most common type of cancer in the world. In lung adenocarcinoma (LUAD), studies on receptor tyrosine kinase ROS proto-oncogene 1 (ROS1) have mainly focused on the oncogenic effects of its fusion mutations, whereas ROS1 has been reported to be aberrantly expressed in a variety of cancers and can extensively regulate the growth, survival, and proliferation of tumor cells through multiple signaling pathways. The comprehensive analysis of ROS1 expression has not been fully investigated regarding its predictive value for LUAD patients. Gene expression profiles collected from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were used to build and validate prognostic risk models. The association of ROS1 with overall survival and the immune landscape was obtained from the Tumor Immune Estimation Resource (TIMER) database. The following analyses were performed using the R package to determine the model's validity: pathway dysregulation analysis, gene set enrichment analysis, Gene Oncology analysis, immune invasion analysis, chemotherapy, radiotherapy, and immunotherapy sensitivity analysis. Finally, we conducted a pan-cancer analysis and performed in vitro experiments to explore the regulatory role of intercellular adhesion molecule 5 (ICAM5) in the progression of LUAD. We constructed a 17-gene model that categorized patients into two risk groups. The model had predictive accuracy for tumor prognosis and was specific for patients with high ROS1 expression. Comprehensive analysis showed that patients in the high-risk group were characterized by marked dysregulation of multiple pathways (eg, unfolded protein response), immune suppression of the tumor microenvironment, and poor benefit from immunotherapy and radiotherapy compared with patients in the low-risk group. PLX4720 may be a suitable treatment for the high-risk patient population. The ICAM5 gene has been demonstrated to inhibit the proliferation, cell cycle, invasion, and migration of LUAD cells. We constructed a 17-gene prognostic risk model and found differences in immune-related cells, biological processes, and prognosis among patients in different risk groups based on the correlation between ROS1 and immunity. Personalized therapy may play an essential role in treatment. We further investigated the role of ICAM5 in inhibiting the malignant bioactivity of LUAD cells.

Prognostic and immunotherapeutic significances of M2 macrophage-related genes signature in lung cancer.

Objective: We aimed to investigate the immunological significance of M2 macrophage-related genes in lung cancer (LC) patients, specifically focusing on constructing a risk score to predict patient prognosis and response to immunotherapy. Methods: We developed a novel risk score by identifying and incorporating 12 M2 macrophage-related genes. The risk score was calculated by multiplying the expression levels of risk genes by their respective coefficients. Through comprehensive enrichment analysis, we explored the potential functions distinguishing high- and low-risk groups. Moreover, we examined the relationship between patients in different risk groups and immune infiltration as well as their response to immunotherapy. The single-cell RNA sequencing data were acquired to ascertain the spatial pattern of RNF130 expression. The expression of RNF130 was examined using TCGA datasets and verified by HPA. The qRT-PCR was employed to examine RNF130 expression in LC cells. Finally, in vitro experiments were carried out to validate the expression and function of RNF130. Results: Our results indicated that the risk score constructed from 12 M2 macrophage-related genes was an independent prognostic factor. Patients in the high-risk group had a significantly worse prognosis compared to those in the low-risk group. Functional enrichment analysis showed a significant relationship between the risk score and immunity. Furthermore, we explored immune infiltration in different risk groups using seven immune algorithms. The results demonstrated a negative correlation between high-risk group patients and immune infiltration of B cells, CD4+ cells, and CD8+ cells. We further validated these findings using an immunotherapy response database, which revealed that high-risk patients were more likely to exhibit immune evasion and might have poorer immunotherapy outcomes. Additionally, drug sensitivity analysis indicated that patients in the high-risk group were more sensitive to certain chemotherapeutic and targeted drugs than those in the low-risk group. Single-cell analysis indicated that macrophages were the primary site of RNF130 distribution. The results from the TCGA and HPA database demonstrated a trend toward a low expression of RNF130 in LC. Finally, in vitro experiments further validated the expression and function of RNF130 in LC cells. Conclusions: The high-risk group constructed with M2 macrophage-related genes in LC was closely associated with poor prognosis, low immune cell infiltration, and poorer response to immunotherapy. This risk score can help differentiate and predict the prognosis and immune status of LC patients, thereby aiding in the development of precise and personalized immunotherapy strategies.

Validation and Comparison of Prognostic Models in Renal Carcinoma in a Tertiary Hospital.

Renal cell carcinoma (RCC) is the third most frequent urological neoplasia. Proper risk stratification is essential for adequate management. Various calculators are available. This project aims to evaluate the accuracy of the calculators applied to our patients. We performed a retrospective study of the nephrectomies due to RCC performed from January 2008 to December 2013. We applied the most widely used predictive models (University of California, Los Angeles Integrated Staging System (UISS), Stage, Size, Grade and Necrosis (SSIGN), Memorial Sloan Kettering Cancer Center (MSKCC) and International Metastatic RCC Database Consortium (IMDC)) to stratify patients in different risk groups. We evaluated progression-free survival (PFS) or death caused by RCC (cancer-specific survival (CSS)) or other causes (overall survival (OS)). We analysed 238 patients. The 5-year OS, CSS and PFS were 76%, 85% and 83%, whereas the 10-year OS, CSS and PFS were 47%, 75% and 77%, respectively. The 5-year survival analysis by risk groups according to the prognostic models showed that the PFS was 0% and 20.4% in high- and intermediate-risk metastatic RCC (mRCC). Moreover, the PFS was 90%, 95.2% and 98.9% in localised high-, intermediate- and low-risk RCC according to the UISS (area under the receiver operating characteristics curve (AUC): 0.93). The SSIGN model showed a CSS of 99% for the group with the lowest score and 5.3% for the group with the worst prognosis (AUC: 0.91). The OS of mRCC showed medians of 13.25 and 87 months according to MSKCC (AUC: 0.75) and 16, 23 and 85 months according to IMDC (AUC: 0.71) (high risk, intermediate and low). The validation of the predictive models carried out with our patients showed consistency in many of the results. Risk stratification should be implemented.

Integrated analysis of necroptosis related gene signature to predict clinical outcomes, immune status and drug sensitivity in lower grade Glioma

BackgroundThe treatment of lower grade gliomas (LGG) is currently the most challenging dilemma in the management of intracranial tumors. Necroptosis is a type of programmed cell death that is closely associated with tumor progression, However, the role of necroptosis related genes in LGG is not yet well elucidated. MethodsOnline databases were used to obtain gene expression and clinical information. After gene differential expression analysis, a risk score model based on prognostic differentially expressed necroptosis-related genes (DENGs) were constructed to predict prognosis for LGG patients. The validity of the risk score model was then assessed with Kaplan-Meier survival curve. The prognostic DENGs included in the risk score model were then subjected to gene expression analysis, functional enrichment analysis, consensus clustering analysis, and single cell sequencing analysis. Finally, we investigated the correlation of the risk score and immune infiltration in LGG tumor microenvironment and drug sensitivity for LGG patients in different risk groups. ResultsA survival risk score model was constructed based on seven prognostic DENGs, which demonstrated satisfactory performance in predicting the prognosis of LGG patients. According to functional enrichment analyses, these seven DENGs may play a regulatory role in LGG tumorigenesis through several immune and metabolic pathways. LGG patients could be categorized into two clusters with distinct prognosis and clinicopathologic characteristics based on the expression of seven DENGs. Single-cell sequencing analysis demonstrated that the DENG signature was differentially expressed in various types of cells in LGG and may play a vital role in oncogenesis. Additionally, drug sensitivity analysis suggested that the seven-gene signature could guide clinical medication for LGG patients. ConclusionOur study developed a reliable necroptosis-related signature to predict the prognosis of LGG patients. This gene signature may also help estimate immune status and anti-cancer drug sensitivity in LGG patients. Our findings may pave the way to enhance our understanding of necroptosis in LGG.

Metabolomic profile of acute myeloid leukaemia parallels of prognosis and response to therapy

The heterogeneity of acute myeloid leukemia (AML), a complex hematological malignancy, is caused by mutations in myeloid cells affecting their differentiation and proliferation. Thus, various cytogenetic alterations in AML cells may be characterized by a unique metabolome and require different treatment approaches. In this study, we performed untargeted metabolomics to assess metabolomics differences between AML patients and healthy controls, AML patients with different treatment outcomes, AML patients in different risk groups based on the 2017 European LeukemiaNet (ELN) recommendations for the diagnosis and management of AML, AML patients with and without FLT3-ITD mutation, and a comparison between patients with FLT3-ITD, CBF-AML (Core binding factor acute myelogenous leukemia), and MLL AML (mixed-lineage leukemia gene) in comparison to control subjects. Analyses were performed in serum samples using liquid chromatography coupled with mass spectrometry (LC–MS). The obtained metabolomics profiles exhibited many alterations in glycerophospholipid and sphingolipid metabolism and allowed us to propose biomarkers based on each of the above assessments as an aid for diagnosis and eventual classification, allowing physicians to choose the best-suited treatment approach. These results highlight the application of LC–MS-based metabolomics of serum samples as an aid in diagnostics and a potential minimally invasive prognostic tool for identifying various cytogenetic and treatment outcomes of AML.

Haematological and Clinical Characterization of Chronic Myeloid Leukemia

A myeloproliferative disorder known as chronic myeloid leukemia (CML) is caused by the clonal proliferation of haematopoietic progenitor stem cell leading to a marked raise in the granulocyte series of cells in the peripheral blood and bone marrow. The present study focused on evaluating clinical and haematological parameters. It is an observational study conducted over a period of 3 years which constitutes 50 diagnosed CML patients. Clinical and haematological details were collected. Prognostic Sokal and European treatment and Outcomes Study (EUTOS) scoring were applied to stratify different risk groups of patients. Study population showed male preponderance. The most common presenting symptom was splenomegaly (98%) followed by anaemia (94%). The patients were divided into Chronic Phase (CP), Accelerated Phase (AP) and Blast phase (BP) in males and females. In males, there was a significant reduction in haemoglobin levels as the disease progressed whereas in females, there was a reduction in haemoglobin levels. Haemoglobin count reflected the anaemic picture and was lowest in the blast phase. The blood cell indices and differential counts were within their normal range in both male and female patients. There was variation in the levels of lymphocytes among males and females but lower than the normal range. Basophil levels were significant between the different phases in females whereas platelet count was significant in males. The current study expected that there would be significant variations between male and female CML patients in a number of haematological measures which can be used as a good prognostic indicator.

System analysis based on the pyroptosis-related genes identifes GSDMD as a novel therapy target for skin cutaneous melanoma

BackgroundSkin cutaneous melanoma (SKCM) is the most aggressive skin cancer, accounting for more than 75% mortality rate of skin-related cancers. As a newly identified programmed cell death, pyroptosis has been found to be closely associated with tumor progression. Nevertheless, the prognostic significance of pyroptosis in SKCM remains elusive.MethodsA total of 469 SKCM samples and 812 normal samples were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Firstly, differentially expressed pyroptosis-related genes (PRGs) between normal samples and SKCM samples were identified. Secondly, we established a prognostic model based on univariate Cox and LASSO Cox regression analyses, which was validated in the test cohort from GSE65904. Thirdly, a nomogram was used to predict the survival probability of SKCM patients. The R package “pRRophetic” was utilized to identify the drug sensitivity between the low- and high-risk groups. Tumor immune infiltration was evaluated using “immuneeconv” R package. Finally, the function of GSDMD and SB525334 was explored in A375 and A2058 cells.ResultsBased on univariate Cox and LASSO regression analyses, we established a prognostic model with identified eight PRGs (AIM2, CASP3, GSDMA, GSDMC, GSDMD, IL18, NLRP3, and NOD2), which was validated in the test cohort. SKCM patients were divided into low- and high-risk groups based on the median of risk score. Kaplan–Meier survival analysis showed that high-risk patients had shorter overall survival than low-risk patients. Additionally, time-dependent ROC curves validated the accuracy of the risk model in predicting the prognosis of SKCM. More importantly, 4 small molecular compounds (SB525334, SR8278, Gemcitabine, AT13387) were identified, which might be potential drugs for patients in different risk groups. Finally, overexpression of GSDMD and SB525334 treatment inhibit the proliferation, migration, and invasion of SKCM cells.ConclusionIn this study, we constructed a prognostic model based on PRGs and identified GSDMD as a potential therapeutic target, which provide new insights into SKCM treatment.

A prognostic and immunotherapy effectiveness model for pancreatic adenocarcinoma based on cuproptosis-related lncRNAs signature.

Pancreatic adenocarcinoma (PAAD) results in one of the deadliest solid tumors with discouraging clinical outcomes. Growing evidence suggests that long non-coding RNAs (lncRNAs) play a crucial role in altering the growth, prognosis, migration, and invasion of pancreatic cancer cells. Cuproptosis is a novel type of cell death induced by copper (Cu) and is associated with mitochondrial respiration during the tricarboxylic acid cycle. However, the relationship between lncRNAs related to cuproptosis and PAAD is poorly studied. In this study, we investigated the association between a signature of cuproptosis-related lncRNAs and the diagnosis of PAAD. Genomic data and clinical information were obtained using the TCGA dataset, while cuproptosis-related genes (CRGs) from previous studies. Co-expression analysis was utilized to identify lncRNAs associated with cuproptosis. We developed and verified a prognostic risk model following a classification of patients into high- and low-risk categories. The prediction capacity of the risk model was assessed using a number of methods including Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, nomograms, and principal component analysis (PCA). Furthermore, differentially expressed genes (DEGs) were used to perform functional enrichment analyses, and to examine the behaviors of various risk groups in terms of immune-related activities and medication sensitivity. We identified 7 cuproptosis-related lncRNA signatures, including CASC19, FAM83A-AS1, AC074099.1, AC007292.2, AC026462.3, AL358944.1, and AC009019.1, as overall survival (OS) predictors. OS and progression-free survival (PFS) showed significant differences among patients in different risk groups. Independent prognostic analysis revealed that the cuproptosis-related lncRNA signatures can independently achieve patient prognosis. The risk model demonstrated strong predictive ability for patient outcomes, as evidenced by ROC curves, nomograms, and PCA. Higher tumor mutation burden (TMB) and lower tumor immune dysfunction and exclusion (TIDE) scores were observed in the high-risk group. Additionally, the low-risk group was hypersensitive to 3 anti-cancer medications, whereas the high-risk group was hypersensitive to one. A prognostic risk model with a good predictive ability based on cuproptosis-related lncRNAs was developed, providing a theoretical basis for personalized treatment and immunotherapeutic responses in pancreatic cancer.

Construction of an anoikis‒related prognostic signature to predict immunotherapeutic response and prognosis in hepatocellular carcinoma.

Anoikis resistance is an important inducer of tumor metastasis. The role of anoikis-related genes (ARGs) in hepatocellular carcinoma (HCC) remains unclear. A list of ARGs was obtained and regression analysis was employed to assemble an anoikis-related prognostic signature and risk score formula from mRNA data and clinical prognostic data downloaded from The Cancer Genome Atlas database. Quantitative real-time PCR (qRT-PCR) was performed on clinical samples to validate the selected ARGs expressions. Kaplan‒Meier curves, ROC curves, and Cox regression analyses were used to demonstrated the prognostic value of this signature. Biological functional enrichment analysis and immune infiltration analysis were utilized to analyze the differences in potential biological functions, immune cell infiltration, immune functions, and immunotherapeutic responses. A prognostic signature based on 6 ARGs and corresponding prognostic nomogram were established. Our qRT-PCR results showed a higher expression of 6 ARGs in HCC tissues (p value < 0.05). Kaplan‒Meier curves, ROC curves, and Cox regression analyses demonstrated good prognostic value of the signature in HCC (p value < 0.05). Significant differences between the enriched biological functions and immune landscapes of patients in different risk groups were discovered (p value < 0.05). In addition, patients with higher risk scores possibly had poor therapeutic response to transhepatic arterial chemotherapy and embolization or sorafenib, but their responses to immunotherapy might be more effective. A successful anoikis-related prognostic signature and corresponding clinical nomogram were established, which might facilitate better predictions of prognosis and therapeutic responses for HCC patients.

Target Genes of c-MYC and MYCN with Prognostic Power in Neuroblastoma Exhibit Different Expressions during Sympathoadrenal Development.

Deregulation of the MYC family of transcription factors c-MYC (encoded by MYC), MYCN, and MYCL is prevalent in most human cancers, with an impact on tumor initiation and progression, as well as response to therapy. In neuroblastoma (NB), amplification of the MYCN oncogene and over-expression of MYC characterize approximately 40% and 10% of all high-risk NB cases, respectively. However, the mechanism and stage of neural crest development in which MYCN and c-MYC contribute to the onset and/or progression of NB are not yet fully understood. Here, we hypothesized that subtle differences in the expression of MYCN and/or c-MYC targets could more accurately stratify NB patients in different risk groups rather than using the expression of either MYC gene alone. We employed an integrative approach using the transcriptome of 498 NB patients from the SEQC cohort and previously defined c-MYC and MYCN target genes to model a multigene transcriptional risk score. Our findings demonstrate that defined sets of c-MYC and MYCN targets with significant prognostic value, effectively stratify NB patients into different groups with varying overall survival probabilities. In particular, patients exhibiting a high-risk signature score present unfavorable clinical parameters, including increased clinical risk, higher INSS stage, MYCN amplification, and disease progression. Notably, target genes with prognostic value differ between c-MYC and MYCN, exhibiting distinct expression patterns in the developing sympathoadrenal system. Genes associated with poor outcomes are mainly found in sympathoblasts rather than in chromaffin cells during the sympathoadrenal development.