LASSO Cox Regression Model Research Articles

Integrative analysis indicates the prognostic value of circadian rhythm disruption in liver cancer: Potential for therapeutic targeting.

Circadian rhythms regulate various biological processes, such as cell division and metabolism. Circadian rhythm disruption (CRD) is often associated with malignant tumor progression and poor prognosis. However, the effect of CRD on liver cancer prognosis has not been systematically analyzed or fully elucidated. Here, we developed a method to quantify and assess intratumoral CRD in a single-cell transcriptomic analysis of liver cancer and systematically analyzed the role of CRD in tumor progression and prognosis. Furthermore, a LASSO-Cox regression model based on 14 CRD genes was used to predict overall patient survival across multiple datasets. We found that malignant cells with high CRD scores were enriched in specific metabolic pathways, such as fatty acid metabolism and the trichloroacetic acid cycle. Intercellular communication analysis suggested that CRD regulates chemokine-mediated interactions. With the bulk transcriptomic datasets, we determined that LiverCRD scores were significantly correlated with macrophage infiltration levels and could guide targeted immunotherapy and chemotherapy strategies. In addition, LiverCRD is also associated with the mutational landscape-for example, TP53 mutation frequency was higher in high-CRD samples. Finally, the 14-gene-based LASSO-Cox regression model could accurately predict overall patient survival across datasets. In conclusion, Our proposed analysis reflects the relationship between CRD and the immune environment in liver cancer, suggesting that CRD may serve as a potential prognostic indicator. Our results may help guide targeted anti-tumor strategies.

Development and validation of a nomogram to predict liver metastasis for pancreatic ductal adenocarcinoma after radical resection.

This study aimed to develop and externally validate a nomogram for predicting liver metastasis after radical resection in patients with pancreatic ductal adenocarcinoma (PDAC). A total of 247 PDAC patients who underwent radical resection were retrospectively reviewed from January 2015 to March 2022 at Ningbo Medical Centre Lihuili Hospital Eastern Section, and used as a training cohort to develop the nomogram. 83 PDAC patients from the Ningbo Medical Centre Lihuili Hospital Xingning Section were enrolled as the validation cohort. The postoperative liver metastasis was recorded during the follow-up, and the liver metastasis-free survival was defined as the time from operation to the date of liver metastasis diagnosis or death. The nomogram was established based on independent prognostic factors selected by LASSO and multivariate Cox regression model. The performance was assessed using the concordance index (C-index) and calibration curves. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to determine the clinical utility of the nomogram model. From the training cohort of 247 patients, a total of 132 patients developed liver metastasis during the follow-up, the 1-, 2- and 3- year liver metastasis-free survival were 52.4%, 43.5% and 40% respectively. The LASSO and multivariate Cox regression analysis indicated that postoperative CA125 (hazard ratio [HR] = 1.007, p <0.001), tumor differentiation (HR = 1.640, p = 0.010), tumor size (HR = 1.520, p = 0.029), lymph node ratio (HR = 1.897, p = 0.002) and portal/superior mesenteric/splenic vein invasion degree (PV/SMV/SV) (HR = 2.829, p <0.001) were the independent factors of liver metastasis. A nomogram with independent factors was developed and the C-index was 0.760 (95% confidence interval [CI], 0.720-0.799) and 0.739 (95% CI, 0.669-0.810) in the training and validation cohorts, respectively. The areas under curve (AUC) of the nomogram at 1-, 2- and 3-year were 0.815, 0.803 and 0.773 in the training cohort, and 0.765, 0.879 and 0.908 in the validation cohort, respectively, higher than those in TNM stage. Decision curve analysis (DCA) analysis revealed that the nomogram model provided superior net benefit in clinical utility. Liver metastasis-free survival curves showed a significant discriminatory ability for liver metastasis risk based on the nomogram (p <0.001). The nomogram showed high accuracy in predicting liver metastasis for PDAC after radical resection, and may serve as a clinical support tool to guide personalized and prescient intervention.

Whole-genome sequencing of extrachromosomal circular DNA of cerebrospinal fluid of medulloblastoma.

Medulloblastoma (MB) is a malignant tumor associated with a poor prognosis in part due to a lack of effective detection methods. Extrachromosomal circular DNA (eccDNA) has been associated with multiple tumors. Nonetheless, little is currently known on eccDNA in MB. Genomic features of eccDNAs were identified in MB tissues and matched cerebrospinal fluid (CSF) and compared with corresponding normal samples using Circle map. The nucleotides on both sides of the eccDNAs' breakpoint were analyzed to understand the mechanisms of eccDNA formation. Bioinformatics analysis combined with the Gene Expression Omnibus (GEO) database identified features of eccDNA-related genes in MB. Lasso Cox regression model, univariate and multivariate Cox regression analysis, time-dependent ROC, and Kaplan-Meier curve were used to assess the potential diagnostic and prognostic value of the hub genes. EccDNA was profiled in matched tumor and CSF samples from MB patients, and control, eccDNA-related genes enriched in MB were identified. The distribution of eccDNAs in the genome was closely related to gene density and the mechanism of eccDNA formation was evaluated. EccDNAs in CSF exhibited similar distribution with matched MB tissues but were differentially expressed between tumor and normal. Ten hub genes prominent in both the eccDNA dataset and the GEO database were selected to classify MB patients to either high- or low-risk groups, and a prognostic nomogram was thus established. This study provides preliminary evidence of the characteristics and formation mechanism of eccDNAs in MB and CSF. Importantly, eccDNA-associated hub genes in CSF could be used as diagnostic and prognostic biomarkers for MB.

Construction of a B cell-related gene pairs signature for predicting prognosis and immunotherapeutic response in non-small cell lung cancer.

Accumulating evidence indicates that the B cells play important roles in anti-tumor immunity and shaping tumor development. This study aimed to explore the expression profiles of B cell marker genes and construct a B cell-related gene pairs (BRGPs) signature associated with the prognosis and immunotherapeutic efficiency in non-small cell lung cancer (NSCLC) patients. B cell-related marker genes in NSCLC were identified using single-cell RNA sequencing data. TCGA and GEO datasets were utilized to identify the prognostic BRGPs based on a novel algorithm of cyclically single pairing along with a 0-or-1 matrix. BRGPs signature was then constructed using Lasso-Cox regression model. Its prognostic value, associated immunogenomic features, putative molecular mechanism and predictive ability to immunotherapy were investigated in NSCLC patients. The BRGPs signature was composed of 23 BRGPs including 28 distinct B cell-related genes. This predictive signature demonstrated remarkable power in distinguishing good or poor prognosis and can serve as an independent prognostic factor for NSCLC patients in both training and validation cohorts. Furthermore, BRGPs signature was significantly associated with immune scores, tumor purity, clinicopathological characteristics and various tumor-infiltrating immune cells. Besides, we demonstrated that the tumor mutational burden scores and TIDE scores were positively correlated with the risk score of the model implying immune checkpoint blockade therapy may be more effective in NSCLC patients with high-risk scores. This novel BRGPs signature can be used to assess the prognosis of NSCLC patients and may be useful in guiding immune checkpoint inhibitor treatment in our clinical practice.

Development of a 4-miRNA prognostic signature for endometrial cancer.

To develop an effective uterine corpus endometrial carcinoma (UCEC) risk assessment tool to monitor treatment outcomes. Limma package was used to analyze differentially expressed microRNAs (miRNAs) between UCEC tissues and normal tissues in the TCGA database. According to univariate Cox risk regression, least absolute shrinkage, and selection operator (LASSO) Cox analysis were performed to screen prognostic miRNAs and construct a risk scoring model. The prognostic performance of signature was evaluated by Kaplan–Meier and receiver operating characteristic. Multivariate Cox regression analysis was used to determine the independent prognostic factors of UCEC. Nomogram was constructed according to age, clinical stage, and risk score. A 4-miRNA signature based on miR-31-5p, miR-34a-5p, miR-26a-1-3p and miR-4772-3p was established. Risk scores of each patient were calculated by the 4-miRNA signature. After z-score, the patients were divided into high- and low-risk groups. The overall survival of high-risk patients was significantly shorter than that of low-risk patients, pointing to the high performance and independence of the 4-miRNA signature in predicting UCEC prognosis. The nomogram showed a high accuracy in predicting overall survival of UCEC patients. We developed a 4-miRNA signature that could effectively predict the prognosis of UCEC.

NXPH4 Used as a New Prognostic and Immunotherapeutic Marker for Muscle-Invasive Bladder Cancer.

Background One of the most common malignant tumors of the urinary system is muscle-invasive bladder cancer (MIBC). With the increased use of immunotherapy, its importance in the field of cancer is becoming abundantly evident. This study classifies MIBC according to GSVA score from the perspective of the GSEA immune gene set. Methods This study integrated the sequencing and clinical data of MIBC patients in TCGA and GEO databases, then scored the data using the GSVA algorithm, the CNMF algorithm was implemented to divide the subtypes of GEO and TCGA datasets, respectively, and finally screened and determined the key pathways in combination with clinical data. Simultaneously, LASSO Cox regression model was constructed based on key pathway genes to assess the model's predictive ability (ROC) and describe the immune landscape differences between high- and low-risk groups; key genes were further analyzed and verified in patient tissues. Results 404 TCGA and 297 GEO datasets were divided into C1-3 groups (TCGA-C1:120/C2:152/C3:132; GEO- C1:112/C2:101/C3:84), of which TCGA-C2 (n = 152) subtype and GEO-C1 (n = 112) subtype had the worst prognosis. LASSO Cox regression model with ROC (train set = 0.718, test set = 0.667) could be constructed. When combined with the Cancer Immunome Atlas database, it was found that patients with high-risk scores were more sensitive to PD-1 inhibitor and PD-1 inhibitor combined with CTLA-4. NXPH4, as a key gene, plays a role in MIBC with tissue validation results show that nxph4 is highly expressed in tumor. Conclusion The immune gene score of MIBC data in TCGA and GEO databases was successfully evaluated using GSVA in this research. The lasso Cox expression model was successfully constructed by screening immune genes, the high-risk group had a worse prognosis and higher sensitivity to immunotherapy, PD-1 inhibitors or PD-1 combined with CTLA-4 inhibitors can be preferentially used in high-risk patients who are sensitive to immunotherapy, and NXPH4 may be a molecular target to adjust the effect of immunotherapy.

Microglia-Based Gene Expression Signature Highly Associated with Prognosis in Low-Grade Glioma

Simple SummaryGliomas make up ~80% of malignant brain tumors in adults and are responsible for the majority of deaths from primary brain tumors. Consequently, a better understanding of the malignant features of the TME in glioma is pertinent. The aim of our study was to evaluate the expression of immune-related genes (IRGs) in glioma and their association with patient prognosis. We utilized several approaches to interrogate the glioma immune microenvironment. We found that immune genes are generally negatively associated with survival and that overall survival was significantly lower in those with a high level of microglia infiltration. The microglia abundance was significantly associated with common genomic aberrations. Lastly, we generated a 23-gene expression signature that is highly associated with patient prognosis, independent of clinical variables. These findings are relevant to investigators in the glioma field, those working in biomarker development, but also to individuals working on glioma therapeutics.Gliomas make up ~80% of malignant brain tumors in adults and are responsible for the majority of deaths from primary brain tumors. The glioma tumor microenvironment (TME) is a dynamic, heterogeneous mixture of extracellular matrix and malignant and non-malignant cells. Several ongoing clinical trials are evaluating the efficacy of therapies that target non-malignant cells, particularly immune cells. Consequently, a better understanding of the TME in glioma is pertinent. We utilized several gene expression datasets to evaluate the relationship between immune-related genes (IRGs) and patient prognosis. We generated microglia signatures using single-cell RNAseq data from human and mouse glioma cells to infer microglia abundance. Lastly, we built a LASSO Cox regression model that predicts patient survival. We found that 428 IRGs were negatively associated with survival in glioma patients. Overall survival was significantly lower in those with a high level of microglia infiltration. In addition, we also found that microglia abundance was significantly associated with several common genomic aberrations, including IDH2 and TP53 mutations. Furthermore, we found that patients with high risk scores had significantly worse overall survival than those with low risk scores in several independent datasets. Altogether, we characterized immune features predictive of overall survival in glioma and found that microglia abundance is negatively associated with survival. We developed a 23-gene risk score that can significantly stratify patients into low- and high-risk categories.

A novel T-cell proliferation-associated regulator signature pre-operatively predicted the prognostic of bladder cancer.

BackgroundBladder cancer (BCa) is a remarkably malignant and heterogeneous neoplastic disease, and its prognosis prediction is still challenging. Even with the mounting researches on the mechanisms of tumor immunotherapy, the prognostic value of T-cell proliferation regulators in bladder cancer remains elusive.MethodsHerein, we collected mRNA expression profiles and relevant clinical information of bladder cancer sufferers from a publicly available data base. Then, the LASSO Cox regression model was utilized to establish a multi-gene signature for the TCGA cohort to predict the prognosis and staging of bladder cancer. Eventually, the predictive power of the model was validated by randomized grouping.ResultsThe outcomes revealed that most genes related to T-cell proliferation in the TCGA cohort exhibited different expressions between BCa cells and neighboring healthy tissues. Univariable Cox regressive analyses showed that four DEGs were related to OS in bladder cancer patients (p<0.05). We constructed a histogram containing four clinical characteristics and separated sufferers into high- and low-risk groups. High-risk sufferers had remarkably lower OS compared with low-risk sufferers (P<0.001). Eventually, the predictive power of the signature was verified by ROC curve analyses, and similar results were obtained in the validation cohort. Functional analyses were also completed, which showed the enrichment of immune-related pathways and different immune status in the two groups. Moreover, by single-cell sequencing, our team verified that CXCL12, a T-lymphocyte proliferation regulator, influenced bladder oncogenesis and progression by depleting T-lymphocyte proliferation in the tumor microenvironment, thus promoting tumor immune evasion.ConclusionThis study establishes a novel T cell proliferation-associated regulator signature which can be used for the prognostic prediction of bladder cancer. The outcomes herein facilitate the studies on T-cell proliferation and its immune micro-environment to ameliorate prognoses and immunotherapeutic responses.

Osteosarcoma subtypes based on platelet-related genes and tumor microenvironment characteristics.

BackgroundOsteosarcoma is a common metastatic tumor in children and adolescents. Because of its easy metastasis, patients often show a poor prognosis. Recently, researchers have found that platelets are closely related to metastasis of a variety of malignant tumors, but the role of platelets related characteristics in osteosarcoma is still unknown. The purpose of this study is to explore the characteristics of platelet-related subtypes and cell infiltration in tumor microenvironment.MethodsWe collected osteosarcoma cohorts from TCGA and GEO databases, and explored the molecular subtypes mediated by platelet-related genes and the related TME cell infiltration according to the expression of platelet-related genes in osteosarcoma. In addition, we also explored the differentially expressed genes (DEGs) among different molecular subtypes and established a protein-protein interaction network (PPI). Then we constructed a platelet scoring model by Univariate cox regression and least absolute shrinkage and selection operator (Lasso) cox regression model to quantify the characteristics of platelet in a single tumor. RT-PCR was used to investigate the expression of six candidate genes in osteosarcoma cell lines and normal osteoblast lines. Finally, we also predicted potential drugs with therapeutic effects on platelet-related subtypes.ResultsWe found that platelet-related genes (PRGs) can distinguish osteosarcoma into two different platelet-related subtypes, C1 and C2. And the prognosis of the C2 subtype was significantly worse than that of C1 subtype. The results of ESTIMATE analysis and GO/KEGG enrichment showed that the differences between different subtypes were mainly concentrated in immune response pathways, and the immune response of C2 was inhibited relative to C1. We further studied the relationship between platelet-related subtypes and immune cell infiltration. We found that the distribution of most immune cells in C1 subtype was higher than that in C2 subtype, and there was a correlation between C1 subtype and more immune cells. Finally, we screened the PRGs related to the prognosis of osteosarcoma through Univariate Cox regression, established independent prognostic platelet characteristics consisting of six genes to predict the prognosis of patients with OS, and predicted the drugs that may be used in the treatment of osteosarcoma. RT-PCR was used to verify the expression of candidate genes in osteosarcoma cells.ConclusionPlatelet scoring model is a significant biomarker, which is of great significance to determine the prognosis, molecular subtypes, characteristics of TME cell infiltration and therapy in patients with OS.

Identification of the Mechanism of Matrine Combined with Glycyrrhizin for Hepatocellular Carcinoma Treatment through Network Pharmacology and Bioinformatics Analysis.

Matrine and glycyrrhizin are representative active ingredients of traditional Chinese medicine (TCM) used in clinical practice. Studies have demonstrated that matrine has antitumor pharmacological effects and that glycyrrhizin protects liver function. However, the potential bioactive compounds and mechanisms remain unknown, as well as whether they have synergistic effects in killing cancer cells and protecting liver cells. To investigate the synergistic effects and mechanism of matrine combined with glycyrrhizin in hepatocellular carcinoma (HCC) treatment, we used both network pharmacology and bioinformatics analyses. First, the chemical gene interaction information of matrine and glycyrrhizin was obtained from the PubChem database. The pathogenic genes of HCC were accessed from five public databases. The RNA sequencing data and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA). Next, the overlapping genes among the potential targets of matrine and glycyrrhizin and HCC-related targets were determined using bioinformatics analysis. We constructed the drug-target interaction network. Prognosis-associated genes were acquired through the univariate Cox regression model and Lasso-Cox regression model. The results were verified by the International Cancer Genome Consortium (ICGC) database. Finally, we predicted the immune function of the samples. The drug-target interaction network consisted of 10 matrine and glycyrrhizin targets. We selected a Lasso-Cox regression model consisting of 3 differentially expressed genes (DEGs) to predict the efficacy of the combination in HCC. Subsequently, we successfully predicted the overall survival of HCC patients using the constructed prognostic model and investigated the correlation of the immune response. Matrine and glycyrrhizin have synergistic effects on HCC. The model we obtained consisted of three drug-target genes by Lasso-Cox regression analysis. The model independently predicted the combined effect of matrine and glycyrrhizin in HCC treatment and OS, which will be helpful for guiding clinical treatment. The prognostic model was correlated with the immune cells and immune checkpoints of patients, which had an adjuvant effect on HCC immunotherapy. Matrine and glycyrrhizin can have therapeutic effects on HCC by promoting the production or enhancing the core gene activity in the drug network and improving the immune system function of patients.

The benefit of radiotherapy in distant metastatic small-cell lung cancer: A retrospective study based on propensity score matching (PSM).

Due to extensive tumor spread, systemic chemotherapy is the main treatment for distant metastatic small-cell lung cancer (DM-SCLC). It is still unclear whether adding local radiotherapy (RT) on the basis of chemotherapy can improve the long-term survival of patients with DM-SCLC. This study aims to explore the population with DM-SCLC who can benefit from RT. Patients with metastatic SCLC with complete data were collected from the Surveillance, Epidemiology, and End Results database and divided into 2 groups according to whether RT was given or not. The propensity score matching method was used to balance the covariate differences between the RT group and the non-RT group. Lasso Cox regression model and Cox proportional hazards regression analyses were used to identifying independent risk factors affecting survival. Kaplan-Meier method was used to calculate the survival rate. P < .05 was considered statistically significant. After matching, there were 3150 patients in both groups. Sex, tumor size, N stage, RT, chemotherapy, brain metastasis, liver metastasis, age, and site metastasis were independent factors of survival in DM-SCLC. The 1- and 2-year survival rates were 24.5% and 5.8% in the RT group and 14.8% and 2.3% in the non-RT group (P < .001). The median survival time of the RT group was 9 months, and that of the non-RT group was 7 months, and the difference was statistically significant (P < .001). RT improved survival in all sex subgroups, any N stage subgroup, any tumor size subgroup, no brain metastases subgroup, no liver metastases subgroup, any age subgroup, and 1-2 organ metastases subgroup. RT improves 1- and 2-year survival in DM-SCLC.

Identification of the collagen family as prognostic biomarkers in papillary thyroid carcinoma

The aim of this study was to construct a collagen-related prognostic model for thyroid cancer and to investigate prognostic value of collagen family genes for thyroid cancer. A LASSO Cox regression model for thyroid cancer was developed based on the expression profiles of collagen-related genes. Kaplan-Meier survival analysis was performed for high and low risk groups. The ROC method was used to assess its predictive performance. Predictive independence was verified by multivariate Cox regression analysis. The relationship between this feature and immune cell infiltration was analyzed by tumor microenvironment. COL18A1 was validated by immunohistochemistry and RT-PCR in thyroid cancer tissues. The effect of COL18A1 on cell proliferation, migration and invasion ability of tumor cells were further valuated by CCK-8 assay and transwell assay. The effect of COL18A1 on the immune escape ability of tumor cells was further valuated by cytotoxicity assays. A model including 4 collagen family genes was developed to predict thyroid cancer prognosis. Patients with high-risk score had a poorer prognosis than those with low-risk scores for 1-, 2-, 3-, and 5- year survival. The model independently predicted prognosis after adjusting for other prognostic factors. A nomogram combining risk score and age was constructed with high sensitivity and specificity. This feature was significantly associated with immune cell infiltration. COL18A1 was aberrantly over-expressed in thyroid cancer compared with control tissues and significantly increased proliferative capacity, migration capacity, invasion capacity, and immune escape ability of tumor cells. Our findings establish a signature associated with collagen family genes that can be a promising tool to predict the prognosis of thyroid cancer. High COL18A1 expression significantly correlates with the poor prognosis of patients and enhances the immune escape ability of tumor cells.

Prediction of recurrence-free survival and adjuvant therapy benefit in patients with gastrointestinal stromal tumors based on radiomics features.

Development and validation of a radiomics nomogram for predicting recurrence and adjuvant therapy benefit populations in high/intermediate-risk gastrointestinal stromal tumors (GISTs) based on computed tomography (CT) radiomic features. Retrospectively collected from 2009.07 to 2015.09, 220 patients with pathological diagnosis of intermediate- and high-risk stratified gastrointestinal stromal tumors and received imatinib treatment were randomly divided into (6:4) training cohort and validation cohort. The 2D-tumor region of interest (ROI) was delineated from the portal-phase images on contrast-enhanced (CE) CT, and radiological features were extracted. The most valuable radiological features were obtained using a Lasso-Cox regression model. Integrated construction was conducted of nomograms of radiomics characteristics to predict recurrence-free survival (RFS) in patients receiving adjuvant therapy. Eight radiomic signatures were finally selected. The area under the curve (AUC) of the radiomics signature model for predicting 3-, 5-, and 7-year RFS in the training and validation cohorts (training cohort AUC = 0.80, 0.84, 0.76; validation cohort AUC = 0.78, 0.80, 0.76). The constructed radiomics nomogram was more accurate than the clinicopathological nomogram for predicting RFS in GIST (C-index: 0.864 95%CI, 0.817-0.911 vs. 0.733 95%CI, 0.675-0.791). Kaplan-Meier survival curve analysis showed a greater benefit from adjuvant therapy in patients with high radiomics scores (training cohort: p < 0.0001; validation cohort: p = 0.017), while there was no significant difference in the low-score group (p > 0.05). In this study, a nomogram constructed based on preoperative CT radiomics features could be used for RFS prediction in high/intermediate-risk GISTs and assist the clinical decision-making for GIST patients.

Identification of ferroptosis-related genes for the prediction of prognosis and chemotherapy benefit of gastric cancer.

As a newly reported programmed cell death, ferroptosis plays a crucial role in tumor progression. We aimed to comprehensively analyze the prognostic value of ferroptosis-related genes and identify the genes for the prediction of prognosis and chemotherapy benefit of gastric cancer (GC) patients. Public microarray data and corresponding clinical information of GC were downloaded from GEO databases. Patients in GSE66229 were randomly separated into discovery and internal validation at a ratio of 2:1. GSE15459 was set as the external validation set. LASSO Cox regression model was performed to identify the most significant prognostic ferroptosis-related genes. Twenty ferroptosis-related genes were finally identified to establish the predictive signature. In the discovery data set, the signature could divide patients into low- and high-risk groups with significantly different overall survival (HR: 2.11, 95% CI: 1.40-3.17, p<0.001). These results were successfully validated in the internal validation cohort (HR 2.04, 95% CI 1.18-3.52; p=0.008) and external validation cohort (HR 4.87, 95% CI 2.99-7.93; p=0.008). Survival ROC at 5-year revealed a remarkably higher predictive ability of the ferroptosis classifier (AUC=0.835) compared with other clinical features in predicting prognosis. Further GSEA analysis showed that samples of high risk were related to several established tumor invasion and metastatic signaling pathways. Further experiments revealed that VLDLR and GCH1 were two newly identified genes associated with chemotherapy sensitivity in GC. The developed ferroptosis gene-set based prognostic signature indicated superior prognostic and predictive value, suggesting new possibilities for individualized treatment of GC patients.

Costimulatory molecule expression profile as a biomarker to predict prognosis and chemotherapy response for patients with small cell lung cancer

Owing to the paucity of specimens, progress in identifying prognostic and therapeutic biomarkers for small cell lung cancer (SCLC) has been stagnant for decades. Considering that the costimulatory molecules are essential elements in modulating immune responses and determining therapeutic response, we systematically revealed the expression landscape and identified a costimulatory molecule-based signature (CMS) to predict prognosis and chemotherapy response for SCLCs for the first time. We found T cell activation was restrained in SCLCs, and costimulatory molecules exhibited widespread abnormal genetic alterations and expression. Using a LASSO Cox regression model, the CMS was built with a training cohort of 77 cases, which successfully divided patients into high- or low-risk groups with significantly different prognosis and chemotherapy benefit (both P < 0.001). The CMS was well validated in an independent cohort containing 131 samples with qPCR data. ROC and C-index analysis confirmed the superior predictive performance of the CMS in comparison with other clinicopathological parameters from different cohorts. Importantly, the CMS was confirmed as a significantly independent prognosticator for clinical outcomes and chemotherapy response in SCLCs through multivariate Cox analysis. Further analysis revealed that low-risk patients were characteristic by an activated immune phenotype with distinct expression of immune checkpoints. In summary, we firstly uncovered the expression heterogeneity of costimulatory molecules in SCLC and successfully constructed a novel predictive CMS. The identified signature contributed to more accurate patient stratification and provided robust prognostic value in estimating survival and the clinical response to chemotherapy, allowing optimization of treatment and prognosis management for patients with SCLC.

Identification of a Novel Cuproptosis-Related Gene Signature for Prognostic Implication in Head and Neck Squamous Carcinomas

Simple SummaryHead and neck squamous carcinoma (HNSC) is a common malignancy that requires novel therapeutic targets. Cuproptosis is an emerging research hotspot. The purpose of this study is to mine the cuproptosis-related genes to find prognosis-related genes. We successfully identified a 24-gene signature for predicting overall survival (OS) in HNSC patients and may expand the range of potential targets for treating HNSC.Head and neck squamous carcinoma (HNSC) is a frequent and deadly malignancy that is challenging to manage. The existing treatment options have considerable efficacy limitations. Hence, the identification of new therapeutic targets and the development of efficacious treatments are urgent needs. Cuproptosis, a non-apoptotic programmed cell death caused by excess copper, has only very recently been discovered. The present study investigated the prognostic importance of genes involved in cuproptosis through the mRNA expression data and related clinical information of HNSC patients downloaded from public databases. Our results revealed that many cuproptosis-related genes were differentially expressed between normal and HNSC tissues in the TCGA cohort. Moreover, 39 differentially expressed genes were associated with the prognosis of HNSC patients. Then, a 24-gene signature was identified in the TCGA cohort utilizing the LASSO Cox regression model. HNSC expression data used for validation were obtained from the GEO database. Consequently, we divided patients into high- and low-risk groups based on the 24-gene signature. Furthermore, we demonstrated that the high-risk group had a worse prognosis when compared to the low-risk group. Additionally, significant differences were found between the two groups in metabolic pathways, immune microenvironment, etc. In conclusion, we found a cuproptosis-related gene signature that can be used effectively to predict OS in HNSC patients. Thus, targeting cuproptosis might be an alternative and promising strategy for HNSC patients.

Prognostic Signature, Immune Features, and Therapeutic Responses of a Novel Ubiquitination-Related Gene Signature in Lung Adenocarcinoma.

Growing studies have implicated the association of ubiquitination-related genes (UbRGs) with the cancer progression and the long-term survival of patients. However, the prognostic values of UbRGs in lung adenocarcinoma (LUAD) have not been investigated. Our study aimed to establish a ubiquitination-related model for prognosis prediction and internal mechanism investigation. The transcriptome expression profiles and corresponding clinical information of LUAD were obtained from TCGA and GEO datasets. Differentially expressed genes (DEGs) were screened between LUAD specimens and nontumor specimens. Kaplan–Meier analysis and univariate assays were carried out on DEGs to preliminarily screen survival-related UbRGs. Then, the LASSO Cox regression model was applied to develop a multigene signature, which was then demonstrated in two GEO datasets by the use of Kaplan-Meier, ROC, and Cox analyses. We estimated the immune cell infiltration in tumor microenvironment via CIBERSORT and immunotherapy response through the TIDE algorithm. In this study, a total of 71 ubiquitination-related DEGs were identified. Nine UbRGs, including TUBA4A, TRIM2, PLK1, ARRB1, TRIM58, PLK1, ARRB1, CCNB1, TRIM6, PTTG1, and CCT2, were included to establish a risk model, which was validated in TCGA and GEO datasets. The multivariate assays demonstrated that the 9-UbRGs signature was a robust independent prognostic factor in the overall survival of LUAD patients. The abundance of CD8 T cells, activated CD4 T memory cells, resting NK cells and macrophages was higher in the high-risk group, and the TMB of high-risk group was statistically higher than the low-risk group. Multiple drugs approved by FAD, targeting UbRGs, were available for the treatment of LUAD. Overall, we identified a nine ubiquitination-related gene signature, and the signature may be applied to be a potential biomarker for CD8 T cells response and clinical responses to immune checkpoint inhibitors for LUAD.

Construction of a solid Cox model for AML patients based on multiomics bioinformatic analysis.

Acute myeloid leukemia (AML) is a highly heterogeneous hematological malignancy. The bone marrow (BM) microenvironment in AML plays an important role in leukemogenesis, drug resistance and leukemia relapse. In this study, we aimed to identify reliable immune-related biomarkers for AML prognosis by multiomics analysis. We obtained expression profiles from The Cancer Genome Atlas (TCGA) database and constructed a LASSO-Cox regression model to predict the prognosis of AML using multiomics bioinformatic analysis data. This was followed by independent validation of the model in the GSE106291 (n=251) data set and mutated genes in clinical samples for predicting overall survival (OS). Molecular docking was performed to predict the most optimal ligands to the genes in prognostic model. The single-cell RNA sequence dataset GSE116256 was used to clarify the expression of the hub genes in different immune cell types. According to their significant differences in immune gene signatures and survival trends, we concluded that the immune infiltration-lacking subtype (IL type) is associated with better prognosis than the immune infiltration-rich subtype (IR type). Using the LASSO model, we built a classifier based on 5 hub genes to predict the prognosis of AML (risk score = -0.086×ADAMTS3 + 0.180×CD52 + 0.472×CLCN5 - 0.356×HAL + 0.368×ICAM3). In summary, we constructed a prognostic model of AML using integrated multiomics bioinformatic analysis that could serve as a therapeutic classifier.

Identification and Validation of an Immune Evasion Molecular Subgroup of Patients With Colon Cancer for Implications of Immunotherapy.

Immune evasion (IEV) plays a critical role in the development and progression of colon cancer. However, studies to predict the prognosis of colon cancer via IEV-related genes are limited. Therefore, based on the 182 IEV-related genes, we used the univariate and Lasso Cox regression model to construct the IEV-related genes signature (IEVSig) of 16 prognostic IEV-related genes using the Gene Expression Omnibus and The Cancer Genome Atlas online databases. We found that IEVSig was an independent prognostic factor, and patients with high IEVSig had higher TNM stage and shorter recurrence-free survival than their counterparts. Kyoto Encyclopedia of Genes and Genomes and gene set enrichment analyses revealed that patients with high and low IEVSig had significantly different enrichment pathways. Immune cell infiltration analysis showed that nine immune cells obviously increased in the high-IEVSig group, whereas five immune cells increased in the low-IEVSig group. Immunotherapy cohort analysis revealed that patients with high IEVSig had a higher proportion of progressive disease or stable disease after receiving immunotherapy than patients with low IEVSig. Furthermore, patients with low IEVSig had higher tumor mutation load and neoantigen burden, which indicated an improved response to immunotherapy, than patients with high IEVSig. Thus, an IEV-related prognostic signature was established to predict the prognosis of patients with colon cancer and derive a prediction marker to offer insights into therapeutic strategies.

A New Prognostic Indicator of Immune Microenvironment and Therapeutic Response in Lung Adenocarcinoma Based on Peroxisome-Related Genes.

Lung adenocarcinoma (LUAD) has been the major cause of tumor-associated mortality in recent years and exhibits a poor outcome. New data revealed that peroxisomes have a function in the regulation of the development and progression of several tumors. However, the prognostic values of peroxisome-related genes (PRGs) were rarely reported. Genomic sequence, mutation, and clinical data of 535 LUAD tissues were obtained from TCGA data sets. Within the TCGA cohort, a multigene signature was constructed with the assistance of the LASSO Cox regression model. Three GEO data sets, including GSE3141, GSE31210, and GSE72094, were obtained as validation cohorts. ROC assays, Kaplan-Meier methods, and multivariate assays were applied to examine the prognostic capacities of the novel signature. Gene Set Enrichment Analysis (GSEA) was performed to further understand the underlying molecular mechanisms. In this study, we identified 47 differentially expressed peroxisome-related genes (PRGs), including 25 increased and 22 decreased PRGs. A prognostic model of six PRGs was established. The univariate and multivariate Cox analyses both showed that the p value of risk score was less than 0.05. In LUAD patients, the strong connection between the risk score and overall survival was further verified in three other GEO data sets. TMB and cancer stem cell infiltration were shown to be significantly higher in the high-risk group in comparison to the low-risk group. The TIDE score of the group with the low risk was considerably greater than that of the group with the high risk. Several drugs, targeting PRG-related genes, were available for the treatments of LUAD. Overall, we developed a novel peroxisome-related prognostic signature for LUAD patients. This signature could successfully indicate LUAD patients' chances of survival as well as their immune system's responsiveness to treatments. In addition, it has the potential to serve as immunotherapeutic targets for LUAD patients.