Independent Prognostic Signature Research Articles

Identification and validation of ferroptosis-related prognostic gene signature in patients with cervical cancer.

Ferroptosis is an iron-dependent cell death, which is distinct from the other types of regulated cell death. Considerable studies have demonstrated that ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in cervical cancer (CC) remains unclear. This study aims to explore the ferroptosis-related prognostic genes (FRPGs) expression profiles and their prognostic values in CC. The ferroptosis-related genes (FRGs) were obtained from The Cancer Genome Atlas (TCGA) and FerrDb databases. Core FRGs were determined by the Search Tool for the Retrieval of Interacting Genes (STRING) website. FRPGs were identified using univariate and multivariate Cox regressions, and the ferroptosis-related prognostic model was constructed. FRPGs were verified in clinical specimens. The relationship between FRPGs and tumor infiltrating immune cells were assessed through the CIBERSORT algorithm and the LM22 signature matrix. Bioinformatics functions of FRPGs were explored with the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Thirty-three significantly up-regulated and 28 down-regulated FRGs were screened from databases [P<0.05; false discovery rate (FDR) <0.05; and |log2 fold change (FC)| ≥2]. Twenty-four genes were found closely interacting with each other and regarded as hub genes (degree ≥3). Solute carrier family 2 member 1 (SLC2A1), carbonic anhydrases IX (CA9), and dual oxidase 1 (DUOX1) were identified as independent prognostic signatures for overall survival (OS) in a Cox regression. Time-dependent receiver operating characteristic (ROC) curves showed the predictive ability of the ferroptosis-related prognostic model, especially for 1-year OS [area under the curve (AUC) =0.76]. Consistent with the public data, our experiments demonstrated that the mRNA levels of SLC2A1 and DUOX1, and the protein levels of SLC2A1, DUOX1, and CA9 were significantly higher in the tumor tissues. Further analysis showed that there was a significant difference in the proportion of tumor infiltrating immune cells between the low- and high-risk group based on our prognostic model. The function enrichment of FRPGs was explored by applying Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. In this study, the features of FRPGs in CC were pictured. The results implicated that targeting ferroptosis may be a new reliable biomarker and an alternative therapy for CC.

Construction of a potential long noncoding RNA prognostic model involved competitive endogenous RNA for patients with gastric cancer.

Increasing evidence has underscored the role of long noncoding RNAs (lncRNAs) make up the major proportion of the competing endogenous RNAs (ceRNAs) network and can regulate gene expression by competitively binding to miRNAs in the development and progression of tumors. Nevertheless, the role of lncRNA-mediated ceRNAs in gastric cancer (GC) and their regulatory mechanisms have not been elucidated to some extent. This study is aimed at constructing a prognostic risk model for GC based on lncRNAs. A TCGA (The Cancer Genome Atlas) dataset was analyzed using edgeR to identify differentially expressed lncRNAs (DElncRNAs) in GC tissues vs normal tissues. Subsequently, DElncRNAs that could predict GC prognosis were determined using a training set. A prognostic risk model based on the DElncRNAs was then constructed. The performance of the model was tested using a test set. The functions of these lncRNAs in GC were investigated using a lncRNA-miRNA-mRNA network. Analysis of lncRNA expression in 407 TCGA GC cases identified 3 lncRNAs that significantly correlated with prognosis. GC cases with high-risk scores showed markedly poor prognosis relative to those with low-risk scores in both the training and test sets. Univariate and multivariate Cox regression analysis of the relationship between various clinical features and prognosis found that these lncRNAs and stage significantly correlated with GC prognosis. A lncRNA-miRNA-mRNA network based on 3 lncRNAs and functional enrichment analysis of interacting mRNA indicated that these genes are enriched in various intracellular receptor signaling pathways, including regulation of muscle system process, and protein deubiquitylation. The current study provides novel insights into the lncRNA-related ceRNA network in GC and sheds lights on underlying 3 lncRNA biomarkers may be independent prognostic signatures in predicting the survival of GC patients.

Identification of necroptosis-related gene signatures for predicting the prognosis of ovarian cancer

Ovarian cancer (OC) is one of the most prevalent and fatal malignant tumors of the female reproductive system. Our research aimed to develop a prognostic model to assist inclinical treatment decision-making.Utilizing data from The Cancer Genome Atlas (TCGA) and copy number variation (CNV) data from the University of California Santa Cruz (UCSC) database, we conducted analyses of differentially expressed genes (DEGs), gene function, and tumor microenvironment (TME) scores in various clusters of OC samples.Next, we classified participants into low-risk and high-risk groups based on the median risk score, thereby dividing both the training group and the entire group accordingly. Overall survival (OS) was significantly reduced in the high-risk group, and two independent prognostic factors were identified: age and risk score. Additionally, three genes—C-X-C Motif Chemokine Ligand 10 (CXCL10), RELB, and Caspase-3 (CASP3)—emerged as potential candidates for an independent prognostic signature with acceptable prognostic value. In Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, pathways related to immune responses and inflammatory cell chemotaxis were identified. Cellular experiments further validated the reliability and precision of our findings. In conclusion, necroptosis-related genes play critical roles in tumor immunity, and our model introduces a novel strategy for predicting the prognosis of OC patients.

Identification of cuproptosis-related long non-coding RNA and construction of a novel prognostic signature for bladder cancer: An observational study.

Bladder Urothelial Carcinoma (BLCA), a prevalent and lethal cancer, lacks understanding regarding the roles and prognostic value of cuproptosis-related lncRNAs (CRLs), a novel form of cell death induced by copper. We collected RNA-seq data, clinical information, and prognostic data for 414 BLCA samples and 19 matched controls from The Cancer Genome Atlas. Using multivariate and univariate Cox regression analyses, we identified CRLs to create a prognostic signature. Patients were then divided into low- and high-risk groups based on their risk scores. We analyzed overall survival using the Kaplan-Meier method, evaluated stromal and immune scores, and explored functional differences between these risk groups with gene set enrichment analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were also conducted to understand the links between CRLs and BLCA development. We developed a prognostic signature using 4 independent CRLs: RC3H1-IT1, SPAG5-AS1, FAM13A-AS1, and GNG12-AS1. This signature independently predicted the prognosis of BLCA patients. High-risk patients had worse outcomes, with gene set enrichment analysis revealing enrichment in tumor- and immune-related pathways in the high-risk group. Notably, high-risk patients exhibited enhanced responses to immunotherapy and conventional chemotherapy drugs like sunitinib, paclitaxel, and gemcitabine. The independent prognostic signature variables RC3H1-IT1, SPAG5-AS1, FAM13A-AS1, and GNG12-AS1 predicted the prognoses of BLCA patients and provided a basis for the study of the mechanism of CRLs in BLCA development and progression, and the guidance of clinical treatments for patients with BLCA.

Construction and analysis of a reliable five-gene prognostic signature for colon adenocarcinoma associated with the wild-type allelic state of the COL6A6 gene.

Tumors emerge by acquiring a number of mutations over time. The first mutation provides a selective growth advantage compared to adjacent epithelial cells, allowing the cell to create a clone that can outgrow the cells that surround it. Subsequent mutations determine the risk of the tumor progressing to metastatic cancer. Some secondary mutations may inhibit the aggressiveness of the tumor while still increasing the survival of the clone. Meaningful mutations in genes may provide a strong molecular foundation for developing novel therapeutic strategies for cancer. The somatic mutation and prognosis in colon adenocarcinoma (COAD) were analyzed. The copy number variation (CNV) and differentially expressed genes (DEGs) between the collagen type VI alpha 6 chain (COL6A6) mutation (COL6A6-MUT) and the COL6A6 wild-type (COL6A6-WT) subgroups were evaluated. The independent prognostic signatures based on COL6A6-allelic state were determined to construct a Cox model. The biological characteristics and the immune microenvironment between the two risk groups were compared. COL6A6 was found to be highly mutated in COAD at a frequency of 9%. Patients with COL6A6-MUT had a good overall survival (OS) compared to those with COL6A6-WT, who had a different CNV pattern. Significant differences in gene expression were established for 593 genes between the COL6A6-MUT and COL6A6-WT samples. Among them, MUC16, ASNSP1, PRR18, PEG10, and RPL26P8 were determined to be independent prognostic factors. The internally validated prognostic risk model, constructed using these five genes, demonstrated its value by revealing a significant difference in patient prognosis between the high-risk and low-risk groups. Specifically, patients in the high-risk group exhibited a considerably worse prognosis than did those in the low-risk group. The high-risk group had a significantly higher proportion of patients over 60 years of age and patients in stage III. Moreover, the tumor immune dysfunction and exclusion (TIDE) score and the expression of human leukocyte antigen (HLA) family genes were all higher in the high-risk group than that in the low-risk group. The allelic state of COL6A6 and the five associated DEGs were identified as novel biomarkers for the diagnosis and prognosis of COAD and may be therapeutic targets in COAD.

Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers.

Background: Rare endocrine cancers such as Adrenocortical Carcinoma (ACC) present a serious diagnostic and prognostication challenge. The knowledge about ACC pathogenesis is incomplete, and patients have limited therapeutic options. Identification of molecular drivers and effective biomarkers is required for timely diagnosis of the disease and stratify patients to offer the most beneficial treatments. In this study we demonstrate how machine learning methods integrating multi-omics data, in combination with system biology tools, can contribute to the identification of new prognostic biomarkers for ACC. Methods: ACC gene expression and DNA methylation datasets were downloaded from the Xena Browser (GDC TCGA Adrenocortical Carcinoma cohort). A highly correlated multi-omics signature discriminating groups of samples was identified with the data integration analysis for biomarker discovery using latent components (DIABLO) method. Additional regulators of the identified signature were discovered using Clarivate CBDD (Computational Biology for Drug Discovery) network propagation and hidden nodes algorithms on a curated network of molecular interactions (MetaBase™). The discriminative power of the multi-omics signature and their regulators was delineated by training a random forest classifier using 55 samples, by employing a 10-fold cross validation with five iterations. The prognostic value of the identified biomarkers was further assessed on an external ACC dataset obtained from GEO (GSE49280) using the Kaplan-Meier estimator method. An optimal prognostic signature was finally derived using the stepwise Akaike Information Criterion (AIC) that allowed categorization of samples into high and low-risk groups. Results: A multi-omics signature including genes, micro RNA's and methylation sites was generated. Systems biology tools identified additional genes regulating the features included in the multi-omics signature. RNA-seq, miRNA-seq and DNA methylation sets of features revealed a high power to classify patients from stages I-II and stages III-IV, outperforming previously identified prognostic biomarkers. Using an independent dataset, associations of the genes included in the signature with Overall Survival (OS) data demonstrated that patients with differential expression levels of 8 genes and 4 micro RNA's showed a statistically significant decrease in OS. We also found an independent prognostic signature for ACC with potential use in clinical practice, combining 9-gene/micro RNA features, that successfully predicted high-risk ACC cancer patients. Conclusion: Machine learning and integrative analysis of multi-omics data, in combination with Clarivate CBDD systems biology tools, identified a set of biomarkers with high prognostic value for ACC disease. Multi-omics data is a promising resource for the identification of drivers and new prognostic biomarkers in rare diseases that could be used in clinical practice.

SCN4B inhibits the progression of lung adenocarcinoma and is associated with better prognosis.

Lung adenocarcinoma (LUAD) is the major type of non-small cell lung cancer with low a survival rate caused by metastasis. SCN4B encoding voltage-gated sodium channel β subunit is regarded as a metastasis-suppressor gene. We aim to explore how SCN4B influences the progression and prognosis of LUAD. The gene expression profiles of 585 LUAD samples in TCGA and GSE31210, GSE116959, and GSE72094 datasets from the GEO database were downloaded for analysis. Differentially expressed genes were obtained through the "limma" package. The "clusterProfiler" package was used to conduct GSEA. Survival analysis was conducted via "survival" and "survminer" packages. Transcription factors regulating SCN4B expression were screened by correlation analysis and further predicted by FIMO. Infiltration of immune cells was analyzed by CIBERSORT. ESTIMATE algorithm was used to evaluate the immune-related scores. SCN4B expressed higher in normal samples than in LUAD samples and higher in female samples than male samples. One hundred and twenty-six pathways were significantly enriched between high and low SCN4B expression groups. Six transcription factors' expressions were positively related to SCN4B expression, and ChIP-seq data from "Cistrome" verified that TAL1 and ERG might bind to the upstream sequence of SCN4B. SCN4B expression was significantly correlated with activated memory CD4 T cells, resting mast cells, and monocytes. TMB status, three scores based on ESTIMATE algorithm, and expression of three immune checkpoints showed significant differences between SCN4B high- and low-expression groups. SCN4B could be considered as an independent prognostic signature of LUAD patients that higher expression represents a better prognosis. SCN4B expresses higher in normal samples, and SCN4B is able to be an independent prognostic signature for LUAD patients. TAL1 and ERG may regulate the expression of SCN4B by binding its upstream sequences. Our research is valuable in improving the effectiveness of treatment in LUAD.

Comprehensive analysis of copper-metabolism-related genes about prognosis and immune microenvironment in osteosarcoma

Despite being significant in various diseases, including cancers, the impact of copper metabolism on osteosarcoma (OS) remains largely unexplored. This study aimed to use bioinformatics analyses to identify a reliable copper metabolism signature that could improve OS patient prognosis prediction, immune landscape understanding, and drug sensitivity. Through nonnegative matrix factorization (NMF) clustering, we revealed distinct prognosis-associated clusters of OS patients based on copper metabolism-related genes (CMRGs), showing differential gene expression linked to immune processes. The risk model, comprising 13 prognostic CMRGs, was established using least absolute shrinkage and selection operator (LASSO) Cox regression, closely associated with the OS microenvironment's immune situation and drug sensitivity. Furthermore, we developed an integrated nomogram, combining the risk score and clinical traits to quantitatively predict OS patient prognosis. The calibration plot, timeROC, and timeROC analyses demonstrated its predictable accuracy and clinical usefulness. Finally, we identified three independent prognostic signatures for OS patients: COX11, AP1B1, and ABCB6. This study confirmed the involvement of CMRGs in OS patient prognosis, immune processes, and drug sensitivity, suggesting their potential as promising prognostic signatures and therapeutic targets for OS.

Cyclin dependent kinase inhibitor 3 (CDKN3) upregulation is associated with unfavorable prognosis in clear cell renal cell carcinoma and shapes tumor immune microenvironment: A bioinformatics analysis.

Cell cycle regulatory proteins plays a pivotal role in the development and progression of many human malignancies. Identification of their biological functions as well as their prognostic utility presents an active field of research. As a continuation of the ongoing efforts to elucidate the molecular characteristics of clear cell renal cell carcinoma (ccRCC); we present a comprehensive bioinformatics study targeting the prognostic and mechanistic role of cyclin-dependent kinase inhibitor 3 (CDKN3) in ccRCC. The ccRCC cohort from the Cancer Genome Atlas Program was accessed through the UCSC Xena browser to obtain CDKN3 mRNA expression data and their corresponding clinicopathological variables. The independent prognostic signature of CDKN3 was evaluated using univariate and multivariate Cox logistic regression analysis. Gene set enrichment analysis and co-expression gene functional annotations were used to discern CDKN3-related altered molecular pathways. The tumor immune microenvironment was evaluated using TIMER 2.0 and gene expression profiling interactive analysis. CDKN3 upregulation is associated with shortened overall survival (hazard ratio [HR] = 2.325, 95% confident interval [CI]: 1.703-3.173, P < .0001) in the Cancer Genome Atlas Program ccRCC cohort. Univariate (HR: 0.426, 95% CI: 0.316-0.576, P < .001) and multivariate (HR: 0.560, 95% CI: 0.409-0.766, P < .001) Cox logistic regression analyses indicate that CDKN3 is an independent prognostic variable of the overall survival. High CDKN3 expression is associated with enrichment within the following pathways including allograph rejection, epithelial-mesenchymal transition, mitotic spindle, inflammatory response, IL-6/JAK/STAT3 signaling, spermatogenesis, TNF-α signaling via NF-kB pathway, complement activation, KRAS signaling, and INF-γ signaling. CDKN3 is also associated with significant infiltration of a wide spectrum of immune cells and correlates remarkably with immune-related genes. CDKN3 is a poor prognostic biomarker in ccRCC that alters many molecular pathways and impacts the tumor immune microenvironment.

Genome instability-derived genes as a novel prognostic signature for lung adenocarcinoma.

Background: An increasing number of patients are being diagnosed with lung adenocarcinoma, but there remains limited progress in enhancing prognostic outcomes and improving survival rates for these patients. Genome instability is considered a contributing factor, as it enables other hallmarks of cancer to acquire functional capabilities, thus allowing cancer cells to survive, proliferate, and disseminate. Despite the importance of genome instability in cancer development, few studies have explored the prognostic signature associated with genome instability for lung adenocarcinoma. Methods: In the study, we randomly divided 397 lung adenocarcinoma patients from The Cancer Genome Atlas database into a training group (n = 199) and a testing group (n = 198). By calculating the cumulative counts of genomic alterations for each patient in the training group, we distinguished the top 25% and bottom 25% of patients. We then compared their gene expressions to identify genome instability-related genes. Next, we used univariate and multivariate Cox regression analyses to identify the prognostic signature. We also performed the Kaplan-Meier survival analysis and the log-rank test to evaluate the performance of the identified prognostic signature. The performance of the signature was further validated in the testing group, in The Cancer Genome Atlas dataset, and in external datasets. We also conducted a time-dependent receiver operating characteristic analysis to compare our signature with established prognostic signatures to demonstrate its potential clinical value. Results: We identified GULPsig, which includes IGF2BP1, IGF2BP3, SMC1B, CLDN6, and LY6K, as a prognostic signature for lung adenocarcinoma patients from 42 genome instability-related genes. Based on the risk score of the risk model with GULPsig, we successfully stratified the patients into high- and low-risk groups according to the results of the Kaplan-Meier survival analysis and the log-rank test. We further validated the performance of GULPsig as an independent prognostic signature and observed that it outperformed established prognostic signatures. Conclusion: We provided new insights to explore the clinical application of genome instability and identified GULPsig as a potential prognostic signature for lung adenocarcinoma patients.

Comprehensive analysis of candidate signatures of long non-coding RNA LINC01116 and related protein-coding genes in patients with hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) is a long-term malignancy that causes high morbidities and mortalities worldwide. Notably, long non-coding RNAs (LncRNAs) have been identified as candidate targets for malignancy treatments.MethodsLncRNA LINC01116 and its Pearson-correlated genes (PCGs) were identified and analyzed in HCC patients. The diagnostic and prognostic value of the lncRNA was evaluated using data from The Cancer Genome Atlas (TCGA). Further, we explored the target drugs of LINC01116 for clinical application. Relationships between immune infiltration and PCGs, methylation and PCGs were explored. The diagnostic potentials were then validated by Oncomine cohorts.ResultsLINC01116 and the PCG OLFML2B are differentially and highly expressed in tumor tissues (both P ≤ 0.050). We found that LINC01116, TMSB15A, PLAU, OLFML2B, and MRC2 have diagnostic potentials (all AUC ≥ 0.700, all P ≤ 0.050) while LINC01116 and TMSB15A have prognostic significance (both adjusted P ≤ 0.050). LINC01116 was enriched in the vascular endothelial growth factor (VEGF) receptor signaling pathway, mesenchyme morphogenesis, etc. After that, candidate target drugs with potential clinical significance were identified: Thiamine, Cromolyn, Rilmenidine, Chlorhexidine, Sulindac_sulfone, Chloropyrazine, and Meprylcaine. Analysis of immune infiltration revealed that MRC2, OLFML2B, PLAU, and TMSB15A are negatively associated with the purity but positively associated with the specific cell types (all P < 0.050). Analysis of promoter methylation demonstrated that MRC2, OLFML2B, and PLAU have differential and high methylation levels in primary tumors (all P < 0.050). Validation results of the differential expressions and diagnostic potential of OLFML2B (Oncomine) were consistent with those obtained in the TCGA cohort (P < 0.050, AUC > 0.700).ConclusionsDifferentially expressed LINC01116 could be a candidate diagnostic and an independent prognostic signature in HCC. Besides, its target drugs may work for HCC therapy via the VEGF receptor signaling pathway. Differentially expressed OLFML2B could be a diagnostic signature involved in HCC via immune infiltrates.

A demonstration based on multi-omics transcriptome sequencing data revealed disulfidptosis heterogeneity within the tumor microenvironment of esophageal squamous cell carcinoma

BackgroundIt is of great concern to identify prognostic signatures for the prediction and prediction of esophageal squamous cell carcinoma (ESCC), which is the lethal pathological type of malignancy.MethodBulk RNA sequencing and scRNA-seq data were retrieved from GSE53624, GSE53622, and GSE188900. Disulfidptosis-related differentially expressed genes (DEGs) were identified between disulfidptosis-high score and disulfidptosis-low score groups. Functional annotation of DEGs were analyzed by Gene Ontology (GO). Consistent clustering and co-expression modules were analyzed, and then constructed a risk score model via multivariate Cox regression analysis. Immune infiltration and immunotherapy response analyses were conducted based on risk score. qRT-PCR, colony formation assay, and flow cytometry analysis were conducted in KYSE-150 and TE-1 cell lines.ResultsSeven genes (CD96, CXCL13, IL2RG, LY96, TPK1, ACAP1, and SOX17) were selected as marker genes. CD96 and SOX17 are independent prognostic signatures for ESCC patients, with a significant correlation with infiltrated immune cells. ESCC patients had worse response to nivolumab in the high-risk group. Through cellular experiments, we found that CD96 expression was associated with apoptosis and cell cycle ESCC cells.ConclusionIn a word, the risk score based on disulfidptosis is associated with prognosis and the immune microenvironment, which may direct immunotherapy of ESCC. The key gene of risk score, namely CD96, plays a role in proliferation and apoptosis in ESCC. We offer an insight into the exploration of the genomic etiology of ESCC for its clinical management.

Unveiling and validation of a disulfidptosis determined prognostic model for osteosarcoma: new insights from prognosis to immunotherapy and chemotherapy

Abstract Objectives Disulfidptosis, a novel form of cell death, has been reported to the be result of high levels of SLC7A11 protein after glucose starvation that led to cell death. In this study, we aimed to illustrate the association between disulfidptosis and osteosarcoma. Methods Patients were collected from TARGET-OS and GSE21257, with a sequence matrix and clinical features. A total of 77 disulfidptosis genes were collected from the literature. A disulfidptosis-related prognostic signature (DSPR) was constructed using univariate Cox analysis, LASSO regression, and risk score computation. Nomograms were established by integrating independent prognostic factors and DSPR signatures. The Student’ t-test or Mann-Whitney U test was used to compare between the two groups. The Log-rank test, univariate and multivariate Cox regression was performed for survival analysis. Results A total of three distinct subtypes (C1, C2, and C3) with varying overall survival prognoses were identified. Comparison between C1 and C3 subtypes revealed 56 differentially-expressed genes, with six genes linked to prognosis. Using LASSO regression, a DSPR signature was constructed, which served as an independent prognostic indicator [hazard ratio (HR)=4.370, 95 % confidence interval (CI): 1.837–10.388, p&lt;0.001], and further validated in an external GSE21257 cohort (HR=4.000, 95 % CI: 1.517–10.183, p=0.004). Nomogram incorporating clinical factors and DSPR signatures showed high predictive accuracy, with AUC values of 0.952 at 1-year, 0.890 at 2-year, and 0.873 at 3-year follow-up. Personalized therapy prediction indicated that low-point patients benefited more from anti-PD-1 immunotherapy, while for high-point patients, chemotherapy was a better treatment option. Conclusions In this study, we identified disulfidptosis-associated genes and unraveled their roles in osteosarcoma prognosis, constructed a prognostic signature, and provided guidance for personalized treatment strategies.

PPP1R81 correlates with the survival and cell proliferation in lower-grade glioma.

The specific functions of PPP1R81 has been elucidated in multiple cancers; however, its role in lower-grade glioma (LGG) remains unknown. In this research, we inspected the specific role of PPP1R81 in LGG. We totally evaluated the expression pattern and prognostic role of PPP1R81 in multitudinous tumors. Subsequently, we systematically examined the connection between PPP1R81 expression and prognosis, clinical characteristics, biological functions, genetic variations, and immunological characteristics in LGG according to the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Altas (CGGA) databases. In vitro experiments were executed to inspect the expression level and specific roles of PPP1R81 in LGG. PPP1R81 was elevated in multiple tumors and was tightly linked to a poor prognosis. LGG with higher expression of PPP1R81 showed poorer prognosis compared with lower expression of PPP1R81. The results of univariate and multivariate Cox regression analyses confirmed that the expression of PPP1R81 was an independent prognostic biomarker of LGG. Immune cell infiltration, immune checkpoint genes (ICPGs), copy number alterations (CNA), and tumor mutation burden (TMB) were also closely associated with PPP1R81 expression in LGG. In vitro experiments demonstrated that PPP1R81 was up-regulated and closely interrelated with cell proliferation and cell cycle in LGG. PPP1R81 was an independent prognostic signature and underlying therapeutic target for patients with LGG.

Comprehensive analysis of the roles of fatty acid transport related proteins in clear cell renal cell carcinoma

ObjectiveThis study aimed to explore the clinical significance of fatty acid transport-related protein (FATRP) in patients with clear cell renal cell carcinoma(ccRCC). MethodsRNA-seq data and corresponding clinical data of ccRCC were obtained from TCGA data portal. Seventeen key FATRP genes were comprehensively investigated using bioinformatics approaches to systematically investigate their expression patterns in ccRCC. In addition, the correlation between the expression levels of these genes and clinicopathological features in ccRCC was further explored. ResultsAmong the 17 key FATRP genes, only FABP5, FABP6, and FABP7 could be regarded as ideal biomarkers for ccRCC, as they were highly expressed in ccRCC tumor tissues, and positively correlates with tumor progression and poor prognosis. FABP6 had the highest copy number variations (CNV) events (63.07 %), and ccRCC patients with FABP6 amplification had a better prognosis than the unaltered group. DNA methylation levels of FABP6 and FABP7 were downregulated in ccRCC tumor tissues compared to those in normal tissues. FABP5 showed the opposite results. Moreover, a novel four FATRP gene (FABP1, FABP5, FABP7, FATP2) and three clinical parameter (age, stage, and grade) prediction model was constructed and that comprised a significant independent prognostic signature. ConclusionsOnly a few FATRP genes are upregulated in ccRCC tumor tissue, and positively correlate with tumor progression and poor prognosis. The accuracy of a single gene of these FATRP genes as predictors of progression and prognosis of ccRCC is limited. The performance of the novel prediction model proposed by this study was much better than that of any single gene.

Development of a redox-related prognostic signature for predicting biochemical-recurrence-free survival of prostate cancer*

Abstract Objective Prostate cancer (PCa) is one of the most common malignancies among elderly males. However, effective prognostic biomarkers are currently lacking. Bioinformatic analysis was used to identify patients at high risk of biochemical recurrence (BCR). Methods In our study, RNA sequencing and clinical data were downloaded from The Cancer Genome Atlas (TCGA) dataset to serve as the training and internal validation sets. The GSE84042 dataset was used as the external validation set. Batch effects were removed and normalized for the two datasets using “sva” package. Univariate Cox, least absolute shrinkage and selection operator (LASSO) Cox, and multivariate Cox regression analyses were successively performed to identify the redox-related gene (RRG) signature. After performing univariate Cox, LASSO Cox, and multivariate Cox regression analyses, a signature consisting of seven RRGs was established to predict BCR of patients with PCa, which included TP53, ADH5, SRRT, SLC24A2, COL1A1, CSF3R, and TEX19. Kaplan-Meier and receiver operating characteristic curve analyses showed good performance for the prognostic signature in the training and validation datasets. Results Univariate and multivariate Cox analyses showed that the RRG signature was an independent prognostic factor for BCR of patients with PCa. Thereafter, the nomogram results revealed that it was able to predict BCR of patients with PCa with high efficiency. Conclusion This study identified an independent prognostic signature and established a nomogram to predict BCR in PCa. This signature can be used to identify patients with PCa with a high risk of BCR, and personalized treatment can be applied.

Inflammatory and Nutritional Scoring System for Predicting Prognosis in Patients with Newly Diagnosed Multiple Myeloma

We aimed to assess the prognostic value of pretreatment inflammatory and nutritional parameters for predicting overall survival (OS) in patients with newly diagnosed multiple myeloma (NDMM), and to build a new scoring system using the most important variables. We retrospectively analyzed baseline clinical and laboratory data for patients with NDMM, who were randomly grouped into training and validation cohorts at a ratio of 8:2. The Inflammatory Nutritional Score (INS) was developed based on the least absolute shrinkage and selection operator (LASSO) Cox regression. The INS and other independent prognostic factors were entered into a multivariate Cox model and merged to generate a nomogram model for predictive optimization. Performance and predictive accuracy were assessed using the concordance index (C-index), calibration plots, and time-dependent receiver operating characteristic (ROC) curves. In total, 442 eligible patients were enrolled. Six inflammatory/nutritional variables, including the Nutritional Risk Index (NRI), body mass index (BMI), neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), platelet-lymphocyte ratio (PLR), and albumin-alkaline phosphatase ratio (AAPR), were integrated to construct the INS using the LASSO Cox model. The predictive nomogram constructed following the multivariate Cox analysis included INS, performance status, lactate dehydrogenase, age, and C-reactive protein. The model exhibited good predictive performance, with a C-index of 0.708 in the training cohort and 0.749 in the validation cohort. Moreover, the calibration curves also demonstrated excellent consistency between predicted and observed survival in both cohorts. In the time-dependent ROC analysis, our nomogram model exhibited better performance than other staging systems for multiple myeloma. The INS represents an independent prognostic signature in patients with NDMM. Our novel nomogram based on INS may aid in predicting survival probability and stratifying risk.

Development of an Independent Prognostic Signature Based on Three Hypoxia-Related Genes for Breast Cancer

Hypoxia was considered to be a prognostic indicator in a variety of solid tumors. This study aims at identifying the hypoxia-related genes (HRGs) in breast cancer (BC) and the feasibility of HRGs as a prognostic indicator. We downloaded the mRNA expression data of BC patients from TCGA and GEO databases. The LASSO Cox regression analysis was applied to screen the hub HRGs to establish a prognostic Risk Score. The independence of Risk Score was assessed by multivariate Cox regression analysis. And the immune checkpoint analysis was also performed. In addition, we also detected the expression level of hub HRGs in MCF-10A cells, MCF-7 cells, and SK-BR-3 cells by RT-qPCR. Three HRGs were identified as hub genes with prognostic value in BC, including CA9, PGK1, and SDC1. The Risk Score constructed by these three genes could efficiently distinguish the prognosis of different BC patients and has been shown to be an independent prognostic indicator. In the high-risk group, patients had lower overall survival and poorer prognosis. In addition, the expression levels of five immune checkpoints (PD1, CTLA4, TIGIT, LAG3, and TIM3) in the high-risk group were significantly higher than those in the low-risk group. Moreover, the expression levels of PGK1 and SDC1 in BC cells were significantly increased. In this study, we established an efficiently model based on three optimal HRGs (CA9, PGK1, and SDC1) could clearly distinguish the prognosis of different BC patients.

Ferroptosis regulator SLC7A11 is a prognostic marker and correlated with PD-L1 and immune cell infiltration in liver hepatocellular carcinoma.

Background: Liver hepatocellular carcinoma (LIHC) is a complicated disease with poor survival and lack of viable treatment options. The roles of ferroptosis and immunotherapy in LIHC are increasingly prominent, but the interplay of ferroptosis with the tumor microenvironment (TME) in LIHC is currently under-investigated. Methods: In this study, we analyzed normal liver tissues and tumor tissues from the TCGA and GTEx databases to obtain differentially expressed ferroptosis-related genes (FRGs). We then clustered LIHC based on the expression levels of selected FRGs and acquired distinct subtypes with significant heterogeneity regarding survival prognoses, PD-L1 expression, and immune cell infiltration. The correlation of those FRGs with TME in LIHC and pan-cancer analysis was also investigated. GO functional annotations and KEGG pathway analyses were performed to investigate the potential reactions of the obtained differentially expressed genes (DEGs). Further external validation was performed using microarrays on the GEO database and the key ferroptosis regulator SLC7A11 expression between LIHC and normal cells was detected by Western blotting. Results: A large proportion of genes were upregulated in the LIHC group. Among three clusters, cluster 3 had the worst prognosis combined with the highest PD-L1 expression and was positively correlated with various immune cells. Subsequently, survival analysis and Cox regression analysis screened out SLC7A11 as an independent prognostic factor in LIHC featured strong PD-L1 expression and unfavorable survival time. We filter out SLC7A11 as an independent prognostic signature in LIHC patients with strongly associated PD-L1 expression and unfavorable survival probability. In the pan-cancer analysis, high expression of SLC7A11 showed poor overall survival in seven cancers, while the correlation between immune checkpoints (ICs) and SLC7A11 varied by cancer type, indicating the potential therapeutic effects of SLC7A11 in cancers other than LIHC. Western blot was further employed to verify the expression of SLC7A11 in LIHC in vitro. Conclusion: Ferroptosis and TME synergistically play key roles in oncogenesis and progression of LIHC, and SLC7A11 can be used as a predictive biomarker for customized immunotherapy.

Identification and validation of an inflammation-related lncRNAs signature for improving outcomes of patients in colorectal cancer.

Background: Colorectal cancer is the fourth most deadly cancer worldwide. Although current treatment regimens have prolonged the survival of patients, the prognosis is still unsatisfactory. Inflammation and lncRNAs are closely related to tumor occurrence and development in CRC. Therefore, it is necessary to establish a new prognostic signature based on inflammation-related lncRNAs to improve the prognosis of patients with CRC. Methods: LASSO-penalized Cox analysis was performed to construct a prognostic signature. Kaplan-Meier curves were used for survival analysis and ROC curves were used to measure the performance of the signature. Functional enrichment analysis was conducted to reveal the biological significance of the signature. The R package “maftool” and GISTIC2.0 algorithm were performed for analysis and visualization of genomic variations. The R package “pRRophetic”, CMap analysis and submap analysis were performed to predict response to chemotherapy and immunotherapy. Results: An effective and independent prognostic signature, IRLncSig, was constructed based on sixteen inflammation-related lncRNAs. The IRLncSig was proved to be an independent prognostic indicator in CRC and was superior to clinical variables and the other four published signatures. The nomograms were constructed based on inflammation-related lncRNAs and detected by calibration curves. All samples were classified into two groups according to the median value, and we found frequent mutations of the TP53 gene in the high-risk group. We also found some significantly amplificated regions in the high-risk group, 8q24.3, 20q12, 8q22.3, and 20q13.2, which may regulate the inflammatory activity of cancer cells in CRC. Finally, we identified chemotherapeutic agents for high-risk patients and found that these patients were more likely to respond to immunotherapy, especially anti-CTLA4 therapy. Conclusion: In short, we constructed a new signature based on sixteen inflammation-related lncRNAs to improve the outcomes of patients in CRC. Our findings have proved that the IRLncSig can be used as an effective and independent marker for predicting the survival of patients with CRC.