4-gene Signature Research Articles

A deep neural network predictor to predict the sensitivity of neoadjuvant chemoradiotherapy in locally advanced rectal cancer

Neoadjuvant chemoradiotherapy (NCRT) is widely used for locally advanced rectal cancer (LARC). This study aimed to conduct an effective model to predict NCRT sensitivity and provide guidance for clinical treatment. Biomarkers for NCRT sensitivity were identified by applying transcriptome profiles using logistic regression and subsequently screened out by Spearman correlation analysis and four machine learning algorithms. A deep neural network (DNN) predictor was constructed by using in-house dataset and validated in two independent datasets. Additionally, a web-based program was developed. Wnt/β-catenin signaling and linoleic acid metabolism (LA) pathways were associated with NCRT sensitivity and prognosis in LARC, antagonistically. A DNN predictor with an 18-gene signature was conducted within in-house datasets. In two validation cohorts, area under ROC curve (AUC) achieved 0.706 and 0.897. The DNN subtypes were significantly associated with NCRT sensitivity, survival status et al. Moreover, NK and cytotoxic T cells were observed contribution to NCRT sensitivity while regulatory T, myeloid-derived suppressor cells and dysfunction of CD4 T effector memory cells could impede NCRT response. A DNN predictor could predict NCRT sensitivity in LARC and stratify LARC patients with different clinical and immunity characteristic.

Development and validation of epigenetic modification-related signals for the diagnosis and prognosis of colorectal cancer

BackgroundColorectal cancer (CRC) is one of the world's most common malignancies. Epigenetics is the study of heritable changes in characteristics beyond the DNA sequence. Epigenetic information is essential for maintaining specific expression patterns of genes and the normal development of individuals, and disorders of epigenetic modifications may alter the expression of oncogenes and tumor suppressor genes and affect the development of cancer. This study elucidates the relationship between epigenetics and the prognosis of CRC patients by developing a predictive model to explore the potential value of epigenetics in the treatment of CRC.MethodsGene expression data of CRC patients’ tumor tissue and controls were downloaded from GEO database. Combined with the 720 epigenetic-related genes (ERGs) downloaded from EpiFactors database, prognosis-related epigenetic genes were selected by univariate cox and LASSO analyses. The Kaplan–Meier and ROC curve were used to analyze the accuracy of the model. Data of 238 CRC samples with survival data downloaded from the GSE17538 were used for validation. Finally, the risk model is combined with the clinical characteristics of CRC patients to perform univariate and multivariate cox regression analysis to obtain independent risk factors and draw nomogram. Then we evaluated the accuracy of its prediction by calibration curves.ResultsA total of 2906 differentially expressed genes (DEGs) were identified between CRC and control samples. After overlapping DEGs with 720 ERGs, 56 epigenetic-related DEGs (DEERGs) were identified. Combining univariate and LASSO regression analysis, the 8 epigenetic-related genes-based risk score model of CRC was established. The ROC curves and survival difference of high and low risk groups revealed the good performance of the risk score model based on prognostic biomarkers in both training and validation sets. A nomogram with good performance to predict the survival of CRC patients were established based on age, NM stage and risk score. The calibration curves showed that the prognostic model had good predictive performance.ConclusionIn this study, an epigenetically relevant 8-gene signature was constructed that can effectively predict the prognosis of CRC patients and provide potential directions for targeted therapies for CRC.

A risk model based on 10 ferroptosis regulators and markers established by LASSO-regularized linear Cox regression has a good prognostic value for ovarian cancer patients

Ovarian cancer is the deadliest gynecologic cancer due to its high rate of recurrence and limited early diagnosis. For certain patients, particularly those with recurring disorders, standard treatment alone is insufficient in the majority of cases. Ferroptosis, an iron- and ROS (reactive oxygen species)-reliant cell death, plays a vital role in the occurrence of ovarian cancer. Herein, subjects from TCGA-OV were calculated for immune scores using the ESTIMATE algorithm and assigned into high- (N = 185) or low-immune (N = 193) score groups; 259 ferroptosis regulators and markers were analyzed for expression, and 64 were significantly differentially expressed between two groups. These 64 differentially expressed genes were applied for LASSO-regularized linear Cox regression for establishing ferroptosis regulators and a markers-based risk model, and a 10-gene signature was established. The ROC curve indicated that the risk score-based curve showed satisfactory predictive efficiency. Univariate and multivariate Cox risk regression analyses showed that age and risk score were risk factors for ovarian cancer patients’ overall survival; patients in the high-risk score group obtained lower immune scores. The Nomogram analysis indicated that the model has a good prognostic performance. GO functional enrichment annotation confirmed again the involvement of these 10 genes in ferroptosis and immune activities. TIMER online analysis showed that risk factors and immune cells were significantly correlated. In conclusion, the risk model based on 10 ferroptosis regulators and markers has a good prognostic value for ovarian cancer patients.

Identification of a Notch transcriptomic signature for breast cancer

BackgroundDysregulated Notch signalling contributes to breast cancer development and progression, but validated tools to measure the level of Notch signalling in breast cancer subtypes and in response to systemic therapy are largely lacking. A transcriptomic signature of Notch signalling would be warranted, for example to monitor the effects of future Notch-targeting therapies and to learn whether altered Notch signalling is an off-target effect of current breast cancer therapies. In this report, we have established such a classifier.MethodsTo generate the signature, we first identified Notch-regulated genes from six basal-like breast cancer cell lines subjected to elevated or reduced Notch signalling by culturing on immobilized Notch ligand Jagged1 or blockade of Notch by γ-secretase inhibitors, respectively. From this cadre of Notch-regulated genes, we developed candidate transcriptomic signatures that were trained on a breast cancer patient dataset (the TCGA-BRCA cohort) and a broader breast cancer cell line cohort and sought to validate in independent datasets.ResultsAn optimal 20-gene transcriptomic signature was selected. We validated the signature on two independent patient datasets (METABRIC and Oslo2), and it showed an improved coherence score and tumour specificity compared with previously published signatures. Furthermore, the signature score was particularly high for basal-like breast cancer, indicating an enhanced level of Notch signalling in this subtype. The signature score was increased after neoadjuvant treatment in the PROMIX and BEAUTY patient cohorts, and a lower signature score generally correlated with better clinical outcome.ConclusionsThe 20-gene transcriptional signature will be a valuable tool to evaluate the response of future Notch-targeting therapies for breast cancer, to learn about potential effects on Notch signalling from conventional breast cancer therapies and to better stratify patients for therapy considerations.

The Ability of a 3-Gene Host Signature in Blood to Distinguish Tuberculous Meningitis From Other Brain Infections.

Tuberculous meningitis (TBM) is difficult to diagnose. We investigated whether a 3-gene host response signature in blood can distinguish TBM from other brain infections. The expression of 3 genes (dual specificity phosphatase 3 [DUSP3], guanylate-binding protein [GBP5], krupple-like factor 2 [KLF2]) was analyzed by RNA sequencing of archived whole blood from 4 cohorts of Vietnamese adults: 281 with TBM, 279 with pulmonary tuberculosis, 50 with other brain infections, and 30 healthy controls. Tuberculosis scores (combined 3-gene expression) were calculated following published methodology and discriminatory performance compared using area under a receiver operator characteristic curve (AUC). GBP5 was upregulated in TBM compared to other brain infections (P < .001), with no difference in DUSP3 and KLF2 expression. The diagnostic performance of GBP5 alone (AUC, 0.74; 95% confidence interval [CI], .67-.81) was slightly better than the 3-gene tuberculosis score (AUC, 0.66; 95% CI, .58-.73) in TBM. Both GBP5 expression and tuberculosis score were higher in participants with human immunodeficiency virus (HIV; P < .001), with good diagnostic performance of GBP5 alone (AUC, 0.86; 95% CI, .80-.93). The 3-gene host signature in whole blood has the ability to discriminate TBM from other brain infections, including in individuals with HIV. Validation in large prospective diagnostic study is now required.

Identification and validation of novel prognostic fatty acid metabolic gene signatures in colon adenocarcinoma through systematic approaches.

Colorectal cancer (CRC) belongs to the class of significantly malignant tumors found in humans. Recently, dysregulated fatty acid metabolism (FAM) has been a topic of attention due to its modulation in cancer, specifically CRC. However, the regulatory FAM pathways in CRC require comprehensive elucidation. The clinical and gene expression data of 175 fatty acid metabolic genes (FAMGs) linked with colon adenocarcinoma (COAD) and normal cornerstone genes were gathered through The Cancer Genome Atlas (TCGA)-COAD corroborating with the Molecular Signature Database v7.2 (MSigDB). Initially, crucial prognostic genes were selected by uni- and multi-variate Cox proportional regression analyses; then, depending upon these identified signature genes and clinical variables, a nomogram was generated. Lastly, to assess tumor immune characteristics, concomitant evaluation of tumor immune evasion/risk scoring were elucidated. A 8-gene signature, including ACBD4, ACOX1, CD36, CPT2, ELOVL3, ELOVL6, ENO3, and SUCLG2, was generated, and depending upon this, CRC patients were categorized within high-risk (H-R) and low-risk (L-R) cohorts. Furthermore, risk and age-based nomograms indicated moderate discrimination and good calibration. The data confirmed that the 8-gene model efficiently predicted CRC patients' prognosis. Moreover, according to the conjoint analysis of tumor immune evasion and the risk scorings, the H-R cohort had an immunosuppressive tumor microenvironment, which caused a substandard prognosis. This investigation established a FAMGs-based prognostic model with substantially high predictive value, providing the possibility for improved individualized treatment for CRC individuals.

Differentiation of ovarian serous carcinoma from ovarian clear cell carcinoma using a 10-gene signature selected by comprehensive gene expression analysis.

Ovarian serous carcinoma (OSC) and ovarian clear cell carcinoma (OCCC) are two major histological types of epithelial ovarian carcinoma (EOC), each with different biological features and clinical behaviors. Although immunostaining is commonly used for differential diagnosis between OSC and OCCC, correct identification of EOC with mixed-type histology is sometimes a diagnostic challenge. The aim of the present study was to explore candidate genes as potential diagnostic biomarkers that distinguish OSC from OCCC. A total of 57 surgical specimens were obtained from EOC patients who had previously undergone primary debulking surgery. Total RNAs were extracted from fresh-frozen tissues of EOC patients, and were used for comprehensive gene expression analysis using DNA microarray technology. Ten candidate genes, FXYD2, TMEM101, GABARAPL1, ARG2, GLRX, RBPMS, GDF15, PPP1R3B, TOB1, and GSTM3 were up-regulated in OCCC compared to OSC. All EOC patients were divided into two groups according to hierarchical clustering using a 10-gene signature. Our data suggest that the 10 candidate genes would be an excellent marker for distinguishing OSC from OCCC. Furthermore, the molecular signatures of the 10 genes may enlighten us on the differences in carcinogenesis, and provide a theoretical basis for OCCC's resistance to chemotherapy in the future.

Identification and validation of a novel apoptosis-related prognostic risk score model for lung adenocarcinoma.

The prognostic roles of apoptosis-related genes (ARGs) in lung adenocarcinoma (LUAD) have not been fully elucidated. In this study, differentially expressed genes (DEGs) associated with apoptosis and the hub genes were further identified. The prognostic values of the ARGs were evaluated using the LASSO Cox regression method. Prognostic values were determined using Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves in the TCGA and GEO datasets. The correlations, mutation data, and protein expression of the 10 ARGs predictive models were also analyzed. We identified 130 differentially expressed ARGs. DEGs were used to split LUAD cases into two subtypes whose overall survival (OS) were significantly different (P = 0.025). We developed a novel 10-gene signature using LASSO Cox regression. In both TCGA and GEO datasets, the results of the K-M curve and log-rank test showed significant difference in the survival rate of patients in the high-risk group and low-risk group (P < 0.0001). According to the GO and KEGG analyses, ARGs were enriched in cancer-related terms. In both cohorts, the immune status of the high-risk group was significantly lower than that of the low-risk group. Based on the differential expression of the ARGs, we established a new risk model to predict the prognosis of patients with LUAD.

TARGET: A phase I/II open-label multicenter study to assess safety and efficacy of fexagratinib in patients with relapsed/refractory FGFR fusion-positive glioma

Abstract Background Oncogenic FGFR–TACC fusions are present in 3–5% of high-grade gliomas (HGGs). Fexagratinib (AZD4547) is an oral FGFR1–3 inhibitor with preclinical activity in FGFR–TACC+ gliomas. We tested its safety and efficacy in patients with recurrent FGFR–TACC + HGGs. Patients and Methods TARGET (NCT02824133) is a phase I/II open-label multicenter study that included adult patients with FGFR–TACC + HGGs relapsing after ≥1 line of standard chemoradiation. Patients received fexagratinib 80 mg bd on a continuous schedule until disease progression or unacceptable toxicity. The primary endpoint was the 6-month progression-free survival rate (PFS6). Results Twelve patients with recurrent IDH wildtype FGFR–TACC + HGGs (all FGFR3–TACC3+) were included in the efficacy cohort (male/female ratio = 1.4, median age = 61.5 years). Most patients (67%) were included at the first relapse. The PFS6 was 25% (95% confidence interval 5–57%), with a median PFS of 1.4 months. All patients without progression at 6 months (n = 3) were treated at first recurrence (versus 56% of those in progression) and remained progression-free for 14–23 months. The best response was RANO partial response in 1 patient (8%), stable disease in 5 (42%), and progressive disease in 6 (50%). Median survival was 17.5 months from inclusion. Grade 3 toxicities included lymphopenia, hyperglycaemia, stomatitis, nail changes, and alanine aminotransferase increase (n = 1 each). No grade 4–5 toxicities were seen. A 32-gene signature was associated with the benefit of FGFR inhibition in FGFR3–TACC3 + HGGs. Conclusions Fexagratinib exhibited acceptable toxicity but limited efficacy in recurrent FGFR3–TACC3 + HGGs. Patients treated at first recurrence appeared more likely to benefit, yet additional evidence is required.

NAD metabolism-related genes provide prognostic value and potential therapeutic insights for acute myeloid leukemia.

Acute myeloid leukemia (AML) is an aggressive blood cancer with high heterogeneity and poor prognosis. Although the metabolic reprogramming of nicotinamide adenine dinucleotide (NAD) has been reported to play a pivotal role in the pathogenesis of acute myeloid leukemia (AML), the prognostic value of NAD metabolism and its correlation with the immune microenvironment in AML remains unclear. We utilized our large-scale RNA-seq data on 655 patients with AML and the NAD metabolism-related genes to establish a prognostic NAD metabolism score based on the sparse regression analysis. The signature was validated across three independent datasets including a total of 1,215 AML patients. ssGSEA and ESTIMATE algorithms were employed to dissect the tumor immune microenvironment. Ex vivo drug screening and in vitro experimental validation were performed to identify potential therapeutic approaches for the high-risk patients. In vitro knockdown and functional experiments were employed to investigate the role of SLC25A51, a mitochondrial NAD+ transporter gene implicated in the signature. An 8-gene NAD metabolism signature (NADM8) was generated and demonstrated a robust prognostic value in more than 1,800 patients with AML. High NADM8 score could efficiently discriminate AML patients with adverse clinical characteristics and genetic lesions and serve as an independent factor predicting a poor prognosis. Immune microenvironment analysis revealed significant enrichment of distinct tumor-infiltrating immune cells and activation of immune checkpoints in patients with high NADM8 scores, acting as a potential biomarker for immune response evaluation in AML. Furthermore, ex vivo drug screening and in vitro experimental validation in a panel of 9 AML cell lines demonstrated that the patients with high NADM8 scores were more sensitive to the PI3K inhibitor, GDC-0914. Finally, functional experiments also substantiated the critical pathogenic role of the SLC25A51 in AML, which could be a promising therapeutic target. Our study demonstrated that NAD metabolism-related signature can facilitate risk stratification and prognosis prediction in AML and guide therapeutic decisions including both immunotherapy and targeted therapies.

A CD8+ T cell related immune score predicts survival and refines the risk assessment in acute myeloid leukemia.

Although advancements in genomic and epigenetic research have deepened our understanding of acute myeloid leukemia (AML), only one-third of patients can achieve durable remission. Growing evidence suggests that the immune microenvironment in bone marrow influences prognosis and survival in AML. There is a specific association between CD8+ T cells and the prognosis of AML patients. To develop a CD8+ T cell-related immune risk score for AML, we first evaluated the accuracy of CIBERSORTx in predicting the abundance of CD8+ T cells in bulk RNA-seq and found it significantly correlated with observed single-cell RNA sequencing data and the proportions of CD8+ T cells derived from flow cytometry. Next, we constructed the CTCG15, a 15-gene prognostic signature, using univariate and LASSO regression on the differentially expressed genes between CD8+ THigh and CD8+ TLow groups. The CTCG15 was further validated across six datasets in different platforms. The CTCG15 has been shown to be independent of established prognostic markers, and can distill transcriptomic consequences of several genetic abnormalities closely related to prognosis in AML patients. Finally, integrating this model into the 2022 European LeukemiaNet contributed to a higher predictive power for prognosis prediction. Collectively, our study demonstrates that CD8+ T cell-related signature could improve the comprehensive risk stratification and prognosis prediction in AML.

Constructing a Prognostic Model of Uterine Corpus Endometrial Carcinoma and Predicting Drug-Sensitivity Responses Using Programmed Cell Death-Related Pathways.

Background: Uterine Corpus Endometrial Carcinoma (UCEC) is the most common type of cancer that develops in the uterus, specifically originating from the endometrium, the inner lining of the uterus. Programmed cell death (PCD) is a highly regulated process that eliminates damaged, aged, or unwanted cells in the body. Dysregulation of PCD pathways can contribute to the formation and progression of various cancers, including UCEC. Methods: Fourteen PCD pathways (autophagy-dependent cell death, alkaliptosis, apoptosis, cuproptosis, entotic cell death, ferroptosis, immunogenic cell death, lysosome-dependent cell death, MPT-driven necrosis, necroptosis, netotic cell death, oxeiptosis, parthanatos, and pyroptosis) were involved in building a prognostic signature. The model was trained and tested using data from the TCGA-UCEC and validated with the GSE119041 dataset. Results: A 12-gene PCD signature (DRAM1, ELAPOR1, MAPT, TRIM58, UCHL1, CDKN2A, CYFIP2, AKT2, LINC00618, TTPA, TRIM46, and NOS2) was established and validated in an independent dataset. UCEC patients with a high PCD score (PCDS) exhibited worse prognosis. Furthermore, PCDS was found to be associated with immune related cells and key tumor microenvironment components through multiple methods. It was observed that UCEC patients with a high PCD score may not benefit from immunotherapy, but some chemo drugs like Bortezomib may be useful. Conclusion: In conclusion, a novel PCD model was established by comprehensively analyzing diverse cell death patterns. This model accurately predicts the clinical prognosis and drug sensitivity of UCEC. The findings suggest that the PCD signature can serve as a valuable tool in assessing prognosis and guiding treatment decisions for UCEC patients.

Identification of a 5-Gene Cuproptosis Signature Predicting the Prognosis for Colon Adenocarcinoma Based on WGCNA.

Background: Colorectal cancer is a highly aggressive malignant tumor that primarily affects the digestive system. It is frequently diagnosed at an advanced stage. Cuproptosis is a copper-dependent form cell death mechanism, distinct from all other known pathways underlying cell death, tumor progression, prognosis, and immune response. Although the role of cuproptosis in colorectal cancer has been investigated over time, there is still an urgent need to explore new methods and insights to understand its potential function. Methods: The Gene Expression Omnibus and The Cancer Genome Atlas gene expression data were systematically explored to investigate the role of cuproptosis in colon adenocarcinoma. The weighted gene coexpression network analysis was used to construct a gene coexpression network and identify the critical module and cuproptosis-related genes correlated with colon adenocarcinoma prognosis. A cuproptosis-related genes prognostic signature for colon adenocarcinoma was identified and validated. To validate the identified gene signature, quantitative reverse transcription-polymerase chain reaction was performed. Cell proliferation assays were analyzed by CCK8 and cell cycle detection. In addition, reactive oxygen species assay was also analyzed. Results: Five hub cuproptosis-related genes (Dihydrolipoamide S-acetyltransferase, Cyclin-dependent kinase inhibitor 2A, ATOX1, VEGFA, and ULK1) were screened and a prognostic risk model for predicting overall survival was established based on these genes. The model was successfully tested in the validation cohort and the GEPIA database. Colon adenocarcinoma patients were categorized into high-risk and low-risk groups based on risk scores. The study revealed that patients with higher risk scores were more likely to have a poor prognosis. Moreover, Dihydrolipoamide S-acetyltransferase was a tumor suppressor gene that can induce cell death and affected the redox reactions in the colon cancer cell line. Conclusions: These findings suggest that the newly identified 5-gene signature may serve as a more reliable prognostic factor than clinical factors such as age and stage of disease. These findings offer a theoretical foundation for further investigation into potential cuproptosis-related biomarkers for predicting colon adenocarcinoma prognosis in the future.

The Role of GNMT and MMP12 Expression in Determining TACE Efficacy: Validation at Transcription and Protein Levels.

Transarterial chemoembolization (TACE) represents a significant therapeutic modality for hepatocellular carcinoma (HCC). We aimed to develop a gene signature to accurately predict patient TACE response and explore the underlying mechanisms. Three independent datasets were utilized, including GSE104580, GSE14520 and external validation from the Cancer Hospital Chinese Academy of Medical Sciences. GSE104580 was randomly partitioned into a training set and a validation set, whereas GSE14520 was categorized into a resection group and a TACE group. Logistic regression was used to develop a TACE effectiveness model. Immunohistochemistry is utilized to confirm the protein expression trends of the signature genes. Immune infiltration and functional enrichment analyses were conducted to investigate the potential underlying mechanisms. A 2-gene signature consisting of glycine N-methyltransferase (GNMT) and matrix metalloproteinase-12 (MMP12) was constructed, and based on this, all the patients were assigned TACE effectiveness scores and categorized into high effectiveness (HE) and low effectiveness (LE) groups. The HE group exhibited a better prognosis than the LE group in the various cohorts (p < 0.05). In the external validation set, immunohistochemistry confirmed the expression of the signature genes exhibiting an upregulated trend of GNMT in the HE group and MMP12 in the LE group, the LE group also exhibited a poorer prognosis [for overall survival (OS), HE group: 881 days vs LE group: 273 days (p < 0.05), and for progression-free survival (PFS), HE group: 458 days vs LE group: 136 days (p < 0.05)]. Multivariate analysis in all the datasets identified LE status as an independent risk factor for OS, disease-free survival (DFS) and PFS. The infiltration level of M0 macrophages and activated mast cells in the LE group was significantly higher than in the HE group. The hypoxia signaling pathway and glycolysis pathway were significantly enriched in the LE group. The loss of GNMT and the overexpression of MMP12 may be critical factors influencing TACE efficacy.

Retracted: Identifying a 6-Gene Prognostic Signature for Lung Adenocarcinoma Based on Copy Number Variation and Gene Expression Data.

[This retracts the article DOI: 10.1155/2022/6962163.].

The value of subcutaneous xenografts for individualised radiotherapy in HNSCC: Robust gene signature correlates with radiotherapy outcome in patients and xenografts

PurposeTo assess the robustness of prognostic biomarkers and molecular tumour subtypes developed for patients with head and neck squamous cell carcinoma (HNSCC) on cell-line derived HNSCC xenograft models, and to develop a novel biomarker signature by combining xenograft and patient datasets. Materials and methodsMice bearing xenografts (n = 59) of ten HNSCC cell lines and a retrospective, multicentre patient cohort (n = 242) of the German Cancer Consortium-Radiation Oncology Group (DKTK-ROG) were included. All patients received postoperative radiochemotherapy (PORT-C). Gene expression analysis was conducted using GeneChip Human Transcriptome Arrays. Xenografts were stratified based on their molecular subtypes and previously established gene classifiers. The dose to control 50 % of tumours (TCD50) was compared between these groups. Using differential gene expression analyses combining xenograft and patient data, a gene signature was developed to define risk groups for the primary endpoint loco-regional control (LRC). ResultsTumours of mesenchymal subtype were characterized by a higher TCD50 (xenografts, p < 0.001) and lower LRC (patients, p < 0.001) compared to the other subtypes. Similar to previously published patient data, hypoxia- and radioresistance-related gene signatures were associated with high TCD50 values. A 2-gene signature (FN1, SERPINE1) was developed that was prognostic for TCD50 (xenografts, p < 0.001) and for patient outcome in independent validation (LRC: p = 0.007). ConclusionGenetic prognosticators of outcome for patients after PORT-C and subcutaneous xenografts after primary clinically relevant irradiation show similarity. The identified robust 2-gene signature may help to guide patient stratification, after prospective validation. Thus, xenografts remain a valuable resource for translational research towards the development of individualized radiotherapy.

Construction of novel 7 integrin-related gene signatures in thyroid cancer construction of model based on integrin genes.

Advanced and metastatic THCA patients usually have a poor prognosis. Thus, this study aimed to establish a risk model to discriminate the high risk population. The expression and clinical data were obtained from TCGA database. The cluster analysis, lasso, univariate and multivariate cox analyses were used to construct risk model. K-M, ROC and DCA were applied to validate the efficiency and stability of the model. GO, KEGG, and ssGSEA analysis were performed to identify the potential mechanism of signatures. The 7-gene prognosis model was constructed, including FAM27E3, FIGN, GSTM4, BEX5, RBPMS2, PHF13, and DCSTAMP. ROC and DCA results showed our model had a better prognosis prediction performance than other risk models. The high risk score was associated with the poor prognosis of THCA patients with different clinical characteristics. The risk score was closely related to cell cycle. Further, we found that the expressions of signatures were significantly dysregulated in THCA and associated with prognosis. These gene expressions were affected by some clinical characteristics, methylation and CNV. Some signatures played a role in drug sensitivity and pathway activation. We constructed a 7-gene signature model based on the integrin-related genes, which showed a great prognostic value in THCA.

Colorectal cancer subtyping and prognostic model construction based on interleukin-related genes.

Members of the interleukin (IL) family are closely linked to cancer development and progression. However, research on the prognosis of colorectal cancer (CRC) related to IL is still lacking. This study investigated new CRC prognostic markers and offered new insights for CRC prognosis and treatment. CRC-related data and IL gene data were collected from public databases. Sample clustering was done with the NMF package to divide samples into different subtypes. Differential, enrichment, survival, and immune analyses were conducted on subtypes. A prognostic model was constructed using regression analysis. Drug sensitivity analysis was performed using GDSC database. Western blot analysis was performed to assess the effect of IL-7 on the JAK/STAT signaling pathway. Flow cytometry was used to examine the impact of IL-7 on CD8+ T cell apoptosis. Two CRC subtypes based on IL-associated genes were obtained. Cluster 1 had a higher survival rate than cluster 2, and they showed differences in some immune levels. The two clusters were mainly enriched in the JAK-STAT signaling pathway, T helper 17 cell differentiation, and the IL-17 signaling pathway. An 11-gene signature was built, and risk score was an independent prognosticator for CRC. The low-risk group showed a higher sensitivity to nine common targeted anticancer drugs. Western blot and flow cytometry results demonstrated that IL-7 could phosphorylate STAT5 and promote survival of CD8+ T cells. In conclusion, this study divided CRC samples into two IL-associated subtypes and obtained an 11-gene signature. In addition, targeted drugs that may improve the prognosis of patients with CRC were identified. These findings are of paramount importance for patient prognosis and CRC treatment.NEW & NOTEWORTHY We identified two clusters with significant survival differences in colorectal cancer (CRC) based on interleukin-related genes, constructed an 11-gene risk score model that can independently predict the prognosis of CRC, and explored some targeted drugs that may improve the prognosis of patients with CRC. The results of this study have important implications for the prognosis and treatment of CRC.

Crosstalk of cuproptosis-related prognostic signature and competing endogenous RNAs regulation in hepatocellular carcinoma.

Cuproptosis is a new type of programmed cell death involved in the regulation of neuroendocrine tumors, immune microenvironment, and substance metabolism. However, the role of cuproptosis-related genes (CRGs) in Hepatocellular carcinoma (HCC) remains unclear. Through multiple bioinformatics analysis, we constructed a prognostic gene model and competing endogenous RNA (ceRNA) network. The correlation between CRGs and prognosis, immune infiltration, immune checkpoints, microsatellite instability (MSI) and tumor mutational burden (TMB) was analyzed by Kaplan-Meier curve, univariate Cox, multivariate regression, and Spearman's analysis in HCC patients. Besides, the qRT-PCR and immunohistochemistry assays were used to determine prognostic CRGs mRNA and protein expression in HCC. We established a novel 3-gene signature related to CRGs for evaluating the prognosis of HCC patients. HCC patients with high risk scores had a poor prognosis with an area under the curve of 0.737, 0.646, and 0.634 on 1-year, 3-year, and 5-year receiver operating characteristic curves. Significant correlation was observed between prognostic CRGs and immune infiltration, immune checkpoints, MSI and TMB. We also developed five ceRNA networks to regulate the occurrence and progression of HCC. CDKN2A, DLAT, and PDHA1 protein expression was up-regulated in HCC versus normal tissues. Besides, the mRNA expression levels of CDKN2A, DLAT, GLS, and PDHA1 were elevated in the HCC cell lines compared to the normal liver cell lines. This novel prognostic CRGs signature could be accurately predict the prognosis of patients with HCC. The ceRNA regulatory network might be potential prognostic biomarkers and therapeutic targets for HCC patients.

Randomized Phase 3 Trial of the Hypoxia Modifier Nimorazole Added to Radiation Therapy With Benefit Assessed in Hypoxic Head and Neck Cancers Determined Using a Gene Signature (NIMRAD)

BackgroundTumour hypoxia is an adverse prognostic factor in head and neck squamous cell carcinoma (HNSCC). We assessed whether patients with hypoxic HNSCC benefited from the addition of nimorazole to definitive intensity-modulated radiotherapy (IMRT). MethodsNIMRAD was a phase III, multi-centre, placebo controlled, double-anonymized trial in patients with HNSCC unsuitable for concurrent platinum chemotherapy or cetuximab with definitive IMRT (NCT01950689). Patients were randomized 1:1 to receive IMRT (65 Gy in 30 fractions over 6 weeks) plus nimorazole (1.2 g/m2 daily, prior to IMRT) or placebo. The primary endpoint was freedom from loco-regional progression (FFLRP) in patients with hypoxic tumours, defined as greater than or equal to the median tumour hypoxia score of the first 50 patients analysed (≥0.079), using a validated 26-gene signature. The planned sample size was 340 patients allowing for signature generation in 85%, assumed HR 0.50 for nimorazole effectiveness in the hypoxic group, and requiring 66 loco-regional failures to have 80% power in a two-tail log-rank test at the 5% significance level. Results338 patients were randomised by 19 UK centres from May 2014 to May 2019, with a median follow-up of 3.1 years (95%CI 2.9-3.4). Hypoxia scores were available for 286 (85%). The median patient age was 73 years (range: 44-88, IQR: 70-76). There were 36 (25.9%) loco-regional failures in the hypoxic group, where nimorazole + IMRT did not improve FFLRP (adjusted HR 0.72; 95% CI 0.36-1.44; p=0.35), or overall survival (adjusted HR 0.96; 0.53-1.72; p=0.88) compared with placebo + IMRT. Similarly, nimorazole + IMRT did not improve FFLRP or OS in the whole population. In total (n=338), 73% of patients allocated nimorazole adhered to the drug for ≥50% of IMRT fractions. Nimorazole + IMRT caused more acute nausea compared with placebo + IMRT (CTCAE v4.0 G1+2: 56.6% vs 42.4%, G3: 10.1% vs 5.3%, respectively; p<0.05). ConclusionsAddition of the hypoxia modifier nimorazole to IMRT for locally advanced HNSCC in older and less fit patients did not improve loco-regional control or survival.