Characterization of starvation response-related genes for predicting prognosis in breast cancer.

Breast cancer (BRCA) cells typically exist in nutrient-deficient microenvironments and quickly adapt to states with fluctuating nutrient levels. The tumor microenvironment of starvation is intensely related to metabolism and the malignant progression of BRCA. However, the potential molecular mechanism has not been thoroughly scrutinized. As a result, this study aimed to dissect the prognostic implications of mRNAs involved in the starvation response and construct a signature for forecasting the outcomes of BRCA. In this research, we investigated how starvation could affect BRCA cells' propensities for invasion and migration. The effects of autophagy and glucose metabolism mediated by starved stimulation were examined through transwell assays, western blot, and the detection of glucose concentration. A starvation response-related gene (SRRG) signature was ultimately generated by integrated analysis. The risk score was recognized as an independent risk indicator. The nomogram and calibration curves revealed that the model had excellent prediction accuracy. Functional enrichment analysis indicated this signature was significantly enriched in metabolic-related pathways and energy stress-related biological processes. Furthermore, phosphorylated protein expression of the model core gene EIF2AK3 increased after the stimulus of starvation, and EIF2AK3 may play an essential role in the progression of BRCA in the starved microenvironment. To sum up, we constructed and validated a novel SRRG signature that could accurately predict outcomes and may be developed as a therapeutic target for the precise treatment of BRCA.

N6-methyladenosine regulators are potential prognostic biomarkers for multiple myeloma.

N6-methyladenosine (m6A) regulators play an important role in tumorigenesis; however, their role in multiple myeloma (MM) remains unknown. This study aimed to create an m6A RNA regulators prognostic signature for MM patients. We integrated data from the Multiple Myeloma Research Foundation CoMMpass Study and the Genotype-Tissue Expression database to analyze gene expression profiles of 21 m6A regulators. Consistent clustering analysis was used to identify the clusters of patients with MM having different clinical outcomes. Gene distribution was analyzed using principal component analysis. Next, we generated an mRNA gene signature of m6A regulators using a multivariate logistic regression model with least absolute shrinkage and selection operator. The expressions of m6A regulators, except FMR1, were significantly different in MM samples compared with those in normal samples. The KIAA1429, HNRNPC, FTO, and WTAP expression levels were dramatically downregulated in tumor samples, whereas those of other signatures were remarkably upregulated. Three clusters of patients with MM were identified, and significant differences were found in terms of overall survival (p = .024). A prognostic two-gene signature (KIAA1429 and HNRNPA2B1) was constructed, which had a good prognostic significance using the ROC method (AUC = 0.792). Moreover, the risk score correlated with the infiltration immune cells. In addition, KEGG pathway analysis showed that 16 pathways were dramatically enriched. The m6A signature might be a novel biomarker for predicting the prognosis of patients with MM (p = .002). Our study is the first to explore the potential application value of m6A in MM. These findings may enhance the understanding of the functional organization of m6A in MM and provide new insights into the treatment of MM patients.

Update on the most promising biomarkers of response to immune checkpoint inhibitors in clear cell renal cell carcinoma.

In the last few years, the standard of care for metastatic clear cell renal cell carcinoma (mccRCC) has changed dramatically with the emergence of the immune checkpoint inhibitors (ICI): anti-PD(L)-1 used as a monotherapy or as in combination either with an anti CTLA-4 or with an anti-angiogenic molecule (VEGFR tyrosine kinase inhibitor (TKI)). These combinations are now recommended in first line setting for mccRCC, according to the last European recommendations. In the face of these new therapeutic options, the question of selecting the best treatment arises as well as the optimal sequence. Predictive biomarkers are required to guide the therapeutic choice and provide a personalized treatment for each patient. This narrative review will provide an overview of the main predictive biomarkers assessed in mccRCC treatment, with a particular focus on mRNA panel signatures.

Integrating high-throughput microRNA and mRNA expression data to identify risk mRNA signature for pancreatic cancer prognosis.

Pancreatic cancer is a malignancy of the digestive system characterized by poor prognosis. A number of prognostic messenger RNA (mRNA) signatures have been identified by using the high-throughput expression profiles. MicroRNAs (miRNA) play a critical role in regulating multiple cellular functions. However, no such integrated analysis of miRNAs and mRNAs for studying the prognostic mechanisms of pancreatic cancer has been reported. In this study, we first identified prognostic mRNAs and miRNAs based on The Cancer Genome Atlas datasets, and then performed an enrichment analysis to explore the underlying biological mechanisms involved in pancreatic cancer prognosis at the mRNA level. Furthermore, we performed an integrated analysis of mRNAs and miRNAs to identify prognostic subpathways, which were closely associated with pancreatic cancer genes and tumor hallmarks and involved in hypoxia, oxidative phosphyorylation and xenobiotic metabolisms. Meanwhile, we performed a random walk algorithm based on global network, prognostic mRNAs and miRNAs, and identified top risk mRNAs as the prognostic signature. Finally, an independent testing set was used to confirm the predictive power of the top mRNA signature, and most of these genes involved were known oncogenes. In conclusion, we performed a series of integrated analyses by comprehensively exploring pancreatic cancer prognosis and systematically optimized the prognostic signature for clinical use.

Establishment of a 5-gene risk model related to regulatory T cells for predicting gastric cancer prognosis.

BACKGROUND: Gastric cancer (GC) is one of the high-risk cancers that lacks effective methods for prognosis prediction. Therefore, we searched for immune cells related to the prognosis of GC and studied the role of related genes in GC prognosis. METHODS: In this study, we collected the mRNA data of GC from The Cancer Genome Atlas (TCGA) database and studied the immune cells that were closely related to the prognosis of GC. Spearman correlation analysis was performed to show the association between immune cell-related genes and the differentially expressed genes (DEGs) of GC. Univariate and multivariate Cox regression analyses were conducted on the immune cell-related genes with a high correlation with GC. A prognostic risk score model was constructed and the most significant feature genes were identified. Kaplan-Meier method was then used to compare the overall survival (OS) of patients with high-risk and low-risk, and receiver operating characteristic (ROC) analysis was used to assess the accuracy of the risk model. In addition, GC patients were grouped according to the median expression of the features genes, and survival analysis was further carried out. RESULTS: It was noted that regulatory T cells (Tregs) were significantly correlated with the prognosis of GC, and 172 genes related to Tregs were found to be closely associated with GC. An optimal prognostic risk model was constructed, and a 5-gene (including LRFN4, ADAMTS12, MCEMP1, HP and MUC15) signature-based risk score was established. Survival analysis showed significant difference in OS between low-risk and high-risk samples. ROC analysis results indicated that the risk model had a high accuracy for the prognosis prediction of samples (AUC = 0.717). The results of survival analysis on each feature gene based on expression levels were consistent with the results of multivariate Cox analysis for predicting the risk rate of the 5 genes. CONCLUSION: These results proved that the 5-gene signature-based risk score could be used to predict the survival of GC patients, and these 5 genes were closely related to Tregs. These findings are of great significance for studying the role of immune cells and related immune factors in regulating the prognosis of GC.

A glycolysis-based 4-mRNA signature correlates with the prognosis and cell cycle process in patients with bladder cancer.

BACKGROUND: Bladder cancer is one of the most prevalent malignancies worldwide. However, traditional indicators have limited predictive effects on the clinical outcomes of bladder cancer. The aim of this study was to develop and validate a glycolysis-related gene signature for predicting the prognosis of patients with bladder cancer that have limited therapeutic options. METHODS: mRNA expression profiling was obtained from patients with bladder cancer from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) was conducted to identify glycolytic gene sets that were significantly different between bladder cancer tissues and paired normal tissues. A prognosis-related gene signature was constructed by univariate and multivariate Cox analysis. Kaplan-Meier curves and time-dependent receiver operating characteristic (ROC) curves were utilized to evaluate the signature. A nomogram combined with the gene signature and clinical parameters was constructed. Correlations between glycolysis-related gene signature and molecular characterization as well as cancer subtypes were analyzed. RT-qPCR was applied to analyze gene expression. Functional experiments were performed to determine the role of PKM2 in the proliferation of bladder cancer cells. RESULTS: Using a Cox proportional regression model, we established that a 4-mRNA signature (NUP205, NUPL2, PFKFB1 and PKM) was significantly associated with prognosis in bladder cancer patients. Based on the signature, patients were split into high and low risk groups, with different prognostic outcomes. The gene signature was an independent prognostic indicator for overall survival. The ability of the 4-mRNA signature to make an accurate prognosis was tested in two other validation datasets. GSEA was performed to explore the 4-mRNA related canonical pathways and biological processes, such as the cell cycle, hypoxia, p53 pathway, and PI3K/AKT/mTOR pathway. A heatmap showing the correlation between risk score and cell cycle signature was generated. RT-qPCR revealed the genes that were differentially expressed between normal and cancer tissues. Experiments showed that PKM2 plays essential roles in cell proliferation and the cell cycle. CONCLUSION: The established 4­mRNA signature may act as a promising model for generating accurate prognoses for patients with bladder cancer, but the specific biological mechanism needs further verification.

Identification and validation of a 21-mRNA prognostic signature in diffuse lower-grade gliomas.

PURPOSE: Diffuse low-grade and intermediate-grade gliomas, also known as lower-grade gliomas (LGGs), are a class of central nervous system tumors. Overall survival varies greatly between patients, highlighting the importance of evaluating exact outcomes to facilitate individualized clinical management. We aimed to identify an mRNA-based prognostic signature to predict the survival of patients with LGGs. METHODS: A total of 874 LGGs from two public datasets were included. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to select the most prognostic mRNAs and build a risk score. A nomogram incorporating the risk score and clinical factors was established for individualized survival prediction. The performance of the nomogram was assessed in the training set (329 patients), internal validation set (140 patients), and external validation set (405 patients). RESULTS: 21 most prognostic mRNAs remained following the LASSO Cox regression. The 21-mRNA signature successfully stratified patients into high- and low-risk groups (P < 0.001 for all datasets in Kaplan-Meier analysis). Subsequent gene set enrichment analysis identified 19 essential biological processes in high-risk LGGs. Furthermore, a nomogram incorporating the risk score, age, grade, and 1p/19q status was developed with favorable calibration and high predictive accuracy in the training set and validation sets (C-index: 0.877, 0.878, and 0.812, respectively). CONCLUSION: The 21-mRNA signature has reliable prognostic value for LGGs and might facilitate the effective stratification and individualized management of patients.

Prognostic value of a gene signature in clear cell renal cell carcinoma.

Renal cancer is a common urogenital system malignance. Novel biomarkers could provide more and more critical information on tumor features and patients' prognosis. Here, we performed an integrated analysis on the discovery set and established a three-gene signature to predict the prognosis for clear cell renal cell carcinoma (ccRCC). By constructing a LASSO Cox regression model, a 3-messenger RNA (3-mRNA) signature was identified. Based on the 3-mRNA signature, we divided patients into high- and low-risk groups, and validated this by using three other data sets. In the discovery set, this signature could successfully distinguish between the high- and low-risk patients (hazard ratio (HR), 2.152; 95% confidence interval (CI),1.509-3.069; p < 0.0001). Analysis of internal and two external validation sets yielded consistent results (internal: HR, 2.824; 95% CI, 1.601-4.98; p < 0.001; GSE29609: HR, 3.002; 95% CI, 1.113-8.094; p = 0.031; E-MTAB-3267: HR, 2.357; 95% CI, 1.243-4.468; p = 0.006). Time-dependent receiver operating characteristic (ROC) analysis indicated that the area under the ROC curve at 5 years was 0.66 both in the discovery and internal validation set, while the two external validation sets also suggested good performance of the 3-mRNA signature. Besides that, a nomogram was built and the calibration plots and decision curve analysis indicated the good performance and clinical utility of the nomogram. In conclusion, this 3-mRNA classifier proved to be a useful tool for prognostic evaluation and could facilitate personalized management of ccRCC patients.

Identification of potential key genes and pathways predicting pathogenesis and prognosis for triple-negative breast cancer.

BACKGROUND: Triple negative breast cancer (TNBC) is a specific subtype of breast cancer with a poor prognosis due to its aggressive biological behaviour and lack of therapeutic targets. We aimed to explore some novel genes and pathways related to TNBC prognosis through bioinformatics methods as well as potential initiation and progression mechanisms. METHODS: Breast cancer mRNA data were obtained from The Cancer Genome Atlas database (TCGA). Differential expression analysis of cancer and adjacent cancer, as well as, triple negative breast cancer and non-triple negative breast cancer were performed using R software. The key genes related to the pathogenesis were identified by functional and pathway enrichment analysis and protein-protein interaction network analysis. Based on univariate and multivariate Cox proportional hazards model analyses, a gene signature was established to predict overall survival. Receiver operating characteristic curve was used to evaluate the prognostic performance of our model. RESULTS: Based on mRNA expression profiling of breast cancer patients from the TCGA database, 755 differentially expressed overlapping mRNAs were detected between TNBC/non-TNBC samples and normal tissue. We found eight hub genes associated with the cell cycle pathway highly expressed in TNBC. Additionally, a novel six-gene (TMEM252, PRB2, SMCO1, IVL, SMR3B and COL9A3) signature from the 755 differentially expressed mRNAs was constructed and significantly associated with prognosis as an independent prognostic signature. TNBC patients with high-risk scores based on the expression of the 6-mRNAs had significantly shorter survival times compared to patients with low-risk scores (P < 0.0001). CONCLUSIONS: The eight hub genes we identified might be tightly correlated with TNBC pathogenesis. The 6-mRNA signature established might act as an independent biomarker with a potentially good performance in predicting overall survival.

A novel 4-gene signature for overall survival prediction in lung adenocarcinoma patients with lymph node metastasis.

BACKGROUND: Lung adenocarcinoma (LUAD) patients experiencing lymph node metastasis (LNM) always exhibit poor clinical outcomes. A biomarker or gene signature that could predict survival in these patients would have a substantial clinical impact, allowing for earlier detection of mortality risk and for individualized therapy. METHODS: With the aim to identify a novel mRNA signature associated with overall survival, we analysed LUAD patients with LNM extracted from The Cancer Genome Atlas (TCGA). LASSO Cox regression was applied to build the prediction model. An external cohort was applied to validate the prediction model. RESULTS: We identified a 4-gene signature that could effectively stratify a high-risk subset of these patients, and time-dependent receiver operating characteristic (tROC) analysis indicated that the signature had a powerful predictive ability. Gene Set Enrichment Analysis (GSEA) showed that the high-risk subset was mainly associated with important cancer-related hallmarks. Moreover, a predictive nomogram was established based on the signature integrated with clinicopathological features. Lastly, the signature was validated by an external cohort from Gene Expression Omnibus (GEO). CONCLUSION: In summary, we developed a robust mRNA signature as an independent factor to effectively classify LUAD patients with LNM into low- and high-risk groups, which might provide a basis for personalized treatments for these patients.

Molecular network-based identification of competing endogenous RNAs and mRNA signatures that predict survival in prostate cancer.

BACKGROUND: The aim of the study is described the regulatory mechanisms and prognostic values of differentially expressed RNAs in prostate cancer and construct an mRNA signature that predicts survival. METHODS: The RNA profiles of 499 prostate cancer tissues and 52 non-prostate cancer tissues from TCGA were analyzed. The differential expression of RNAs was examined using the edgeR package. Survival was analyzed by Kaplan-Meier method. microRNA (miRNA), messenger RNA (mRNA), and long non-coding RNA (lncRNA) networks from the miRcode database were constructed, based on the differentially expressed RNAs between non-prostate and prostate cancer tissues. RESULTS: A total of 773 lncRNAs, 1417 mRNAs, and 58 miRNAs were differentially expressed between non-prostate and prostate cancer samples. The newly constructed ceRNA network comprised 63 prostate cancer-specific lncRNAs, 13 miRNAs, and 18 mRNAs. Three of 63 differentially expressed lncRNAs and 1 of 18 differentially expressed mRNAs were significantly associated with overall survival in prostate cancer (P value < 0.05). After the univariate and multivariate Cox regression analyses, 4 mRNAs (HOXB5, GPC2, PGA5, and AMBN) were screened and used to establish a predictive model for the overall survival of patients. Our ROC curve analysis revealed that the 4-mRNA signature performed well. CONCLUSION: These ceRNAs may play a critical role in the progression and metastasis of prostate cancer and are thus candidate therapeutic targets and potential prognostic biomarkers. A novel model that incorporated these candidates was established and might provide more powerful prognostic information in predicting survival in prostate cancer.

Evaluation of a blood miRNA/mRNA signature to follow-up Lu-PRRT therapy for G1/G2 intestinal neuroendocrine tumors.

Background: 177Lu-oxodotreotide peptide receptor therapy (LuPRRT) is an efficient treatment for midgut neuroendocrine tumors (NETs) of variable radiological response. Several clinical, biological, and imaging parameters may be used to establish a relative disease prognosis but none is able to predict early efficacy or toxicities. We investigated expression levels for mRNA and miRNA involved in radiosensitivity and tumor progression searching for correlations related to patient outcome during LuPRRT therapy. Methods: Thirty-five patients received LuPRRT for G1/G2 midgut NETs between May 2019 and September 2021. Peripheral blood samples were collected prior to irradiation, before and 48 h after the second and the fourth LuPRRT, and at 6-month follow-up. Multiple regression analyses and Pearson correlations were performed to identify the miRNA/mRNA signature that will best predict response to LuPRRT. Results: Focusing on four mRNAs and three miRNAs, we identified a miRNA/mRNA signature enabling the early identification of responders to LuPRRT with significant reduced miRNA/mRNA expression after the first LuPRRT administration for patients with progressive disease at 1 year (p < 0.001). The relevance of this signature was reinforced by studying its evolution up to 6 months post-LuPRRT. Moreover, nadir absolute lymphocyte count within the first 2 months after the first LuPRRT administration was significantly related to low miRNA/mRNA expression level (p < 0.05) for patients with progressive disease. Conclusion: We present a pilot study exploring a miRNA/mRNA signature that correlates with early hematologic toxicity and therapeutic response 12 months following LuPRRT. This signature will be tested prospectively in a larger series of patients.

The 'analysis of gene expression and biomarkers for point-of-care decision support in Sepsis' study; temporal clinical parameter analysis and validation of early diagnostic biomarker signatures for severe inflammation andsepsis-SIRS discrimination.

Introduction: Early diagnosis of sepsis and discrimination from SIRS is crucial for clinicians to provide appropriate care, management and treatment to critically ill patients. We describe identification of mRNA biomarkers from peripheral blood leukocytes, able to identify severe, systemic inflammation (irrespective of origin) and differentiate Sepsis from SIRS, in adult patients within a multi-center clinical study. Methods: Participants were recruited in Intensive Care Units (ICUs) from multiple UK hospitals, including fifty-nine patients with abdominal sepsis, eighty-four patients with pulmonary sepsis, forty-two SIRS patients with Out-of-Hospital Cardiac Arrest (OOHCA), sampled at four time points, in addition to thirty healthy control donors. Multiple clinical parameters were measured, including SOFA score, with many differences observed between SIRS and sepsis groups. Differential gene expression analyses were performed using microarray hybridization and data analyzed using a combination of parametric and non-parametric statistical tools. Results: Nineteen high-performance, differentially expressed mRNA biomarkers were identified between control and combined SIRS/Sepsis groups (FC>20.0, p<0.05), termed 'indicators of inflammation' (I°I), including CD177, FAM20A and OLAH. Best-performing minimal signatures e.g. FAM20A/OLAH showed good accuracy for determination of severe, systemic inflammation (AUC>0.99). Twenty entities, termed 'SIRS or Sepsis' (S°S) biomarkers, were differentially expressed between sepsis and SIRS (FC>2·0, p-value<0.05). Discussion: The best performing signature for discriminating sepsis from SIRS was CMTM5/CETP/PLA2G7/MIA/MPP3 (AUC=0.9758). The I°I and S°S signatures performed variably in other independent gene expression datasets, this may be due to technical variation in the study/assay platform.

Characterization of mRNA Signature in Milk Small Extracellular Vesicles from Cattle Infected with Bovine Leukemia Virus.

This study aimed to characterize the mRNA signature of milk small extracellular vesicles (sEVs) from BLV-infected cattle. A total of 23 mRNAs, which showed greater abundance in milk sEVs from BLV-infected cattle compared to those from BLV-uninfected (control) cattle, were identified through microarray analyses conducted in our previous study. To assess the significance of these differences in mRNA abundance, milk was collected from six control cattle and twenty-six cattle infected with BLV. The infected cattle were categorized into two distinct groups based on their proviral loads: a group of eight cattle with low proviral loads (LPVL), characterized by <10,000 copies per 105 white blood cells (WBC), and a group of eighteen cattle with high proviral loads (HPVL), marked by ≥10,000 copies per 105 WBC. The qPCR analysis quantified 7 out of 23 mRNAs, including BoLA, CALB1, IL33, ITGB2, MYOF, TGFBR1, and TMEM156, in the milk sEVs from control cattle, LPVL cattle, and HPVL cattle. Significantly, the average relative expression of CALB1 mRNA in milk sEVs was higher in LPVL cattle compared to HPVL cattle and control cattle (p < 0.05), while it was relatively lower in HPVL cattle compared to LPVL cattle and control cattle (p > 0.05). Likewise, the average relative expression of TMEM156 mRNA in milk sEVs was significantly higher in LPVL cattle compared to HPVL cattle (p < 0.05), and relatively lower in HPVL cattle compared to LPVL cattle and control cattle (p > 0.05). The results indicate distinct patterns of CALB1 and TMEM156 mRNA levels in milk sEVs, with higher levels observed in LPVL cattle and lower levels in HPVL cattle. The current study could provide essential information to comprehend the complexities during the progression of BLV infection and direct the exploration of mRNA biomarkers for monitoring the clinical stage of BLV infection.

A Cell Cycle-Related 13-mRNA Signature to Predict Prognosis in Hepatocellular Carcinoma.

We aimed to propose a cell cycle-related multi/mRNA signature (CCS) for prognosis prediction and uncover new tumor-driver genes for hepatocellular carcinoma (HCC). Cell cycle-related gene sets and HCC samples with mRNA-Seq data were retrieved from public sources. The genes differentially expressed in HCCs relative to normal peritumoral tissues were extracted through statistical analysis. The CCS was constructed by Cox regression analyses. Predictive capacity and clinical practicality of the signature were evaluated and validated. The expression of the function-unknown genes in the CCS was determined by RT-qPCR. Candidate gene TICRR was selected for subsequent validation through functional experiments. A cell cycle-related 13-mRNA signature was generated from the exploratory cohort [The Cancer Genome Atlas (TCGA), n = 371)]. HCC cases were classified as high- vs. low-risk groups per overall survival (OS) [hazard ratio (HR) = 2.699]. Significantly, the CCS exhibited great predictive value for prognosis in three independent cohorts, particularly in GSE76427 cohort [area under the curve (AUC) = 0.835/0.822/0.808/0.821/0.826 at 1/2/3/4/5 years]. The nomogram constructed by integrating clinicopathological features with the CCS indicated high accuracy and practicability. Significant enrichment of tumorigenesis-associated pathways was observed in the high-risk patients by Gene Set Enrichment Analysis (GSEA). RT-qPCR revealed that TICRR was overexpressed in HCC samples. Increased TICRR expression implied poor prognosis in HCC patients. Furthermore, depletion of TICRR in HCC cells decreased cell proliferation and the G1/S transition. In conclusion, the established 13-CCS had efficacy in prognostic prediction of HCC patients. Additionally, TICRR was demonstrated as a tumor-driver gene for this deadly disease.

Identification of mRNA Signature for Predicting Prognosis Risk of Rectal Adenocarcinoma.

Background: The immune system plays a crucial role in rectal adenocarcinoma (READ). Immune-related genes may help predict READ prognoses. Methods: The Cancer Genome Atlas dataset and GSE56699 were used as the training and validation datasets, respectively, and differentially expressed genes (DEGs) were identified. The optimal DEG combination was determined, and the prognostic risk model was constructed. The correlation between optimal DEGs and immune infiltrating cells was evaluated. Results: Nine DEGs were selected for analysis. Moreover, ADAMDEC1 showed a positive correlation with six immune infiltrates, most notably with B cells and dendritic cells. F13A1 was also positively correlated with six immune infiltrates, particularly macrophage and dendritic cells, whereas LGALS9C was negatively correlated with all immune infiltrates except B cells. Additionally, the prognostic risk model was strongly correlated with the actual situation. We retained only three prognosis risk factors: age, pathologic stage, and prognostic risk model. The stratified analysis revealed that lower ages and pathologic stages have a better prognosis with READ. Age and mRNA prognostic factors were the most important factors in determining the possibility of 3- and 5-year survival. Conclusion: In summary, we identified a nine-gene prognosis risk model that is applicable to the treatment of READ. Altogether, characteristics such as the gene signature and age have a strong predictive value for prognosis risk.

A Novel Prognostic Risk Model for Cervical Cancer Based on Immune Checkpoint HLA-G-Driven Differentially Expressed Genes.

Human leukocyte antigen G (HLA-G) is a potential checkpoint molecule that plays a key role in cervical carcinogenesis. The purpose of this study was to construct and validate a prognostic risk model to predict the overall survival (OS) of cervical cancer patients, providing a reference for individualized clinical treatment that may lead to better clinical outcomes. HLA-G-driven differentially expressed genes (DEGs) were obtained from two cervical carcinoma cell lines, namely, SiHa and HeLa, with stable overexpression of HLA-G by RNA sequencing (RNA-seq). The biological functions of these HLA-G-driven DEGs were analysed by GO enrichment and KEGG pathway using the "clusterProfiler" package. The protein-protein interactions (PPIs) were assessed using the STRING database. The prognostic relevance of each DEG was evaluated by univariate Cox regression using the TCGA-CESC dataset. After the TCGA-CESC cohort was randomly divided into training set and testing set, and a prognostic risk model was constructed by LASSO and stepwise multivariate Cox regression analysis in training set and validated in testing set or in different types of cervical cancer set. The predictive ability of the prognostic risk model or nomogram was evaluated by a series of bioinformatics methods. A total of 1108 candidate HLA-G-driven DEGs, including 391 upregulated and 717 downregulated genes, were obtained and were enriched mostly in the ErbB pathway, steroid biosynthesis, and MAPK pathway. Then, an HLA-G-driven DEG signature consisting of the eight most important prognostic genes CD46, LGALS9, PGM1, SPRY4, CACNB3, PLIN2, MSMO1, and DAGLB was identified as a key predictor of cervical cancer. Multivariate Cox regression analysis showed that this signature is an independent risk factor for the overall survival of CESC patients. Kaplan-Meier survival analysis showed that the 5-year overall survival rate is 23.0% and 84.6% for the high-risk and low-risk patients, respectively (P<0.001). The receiver operating characteristic (ROC) curve of this prognostic model with an area under the curve (AUC) was 0.896 for 5 years, which was better than that of other clinical traits. This prognostic risk model was also successfully validated in different subtypes of cervical cancer, including the keratinizing squamous cell carcinoma, non-keratinizing squamous cell carcinoma, squamous cell neoplasms, non-squamous cell neoplasms set. Single-sample gene set enrichment (ssGSEA) algorithm and Tumor Immune Dysfunction and Exclusion (TIDE) analysis confirmed that this signature influence tumour microenvironment and immune checkpoint blockade. A nomogram that integrated risk score, age, clinical stage, histological grade, and pathological type was then built to predict the overall survival of CESC patients and evaluated by calibration curves, AUC, concordance index (C-index) and decision curve analysis (DCA). To summarize, we developed and validated a novel prognostic risk model for cervical cancer based on HLA-G-driven DEGs, and the prognostic signature showed great ability in predicting the overall survival of patients with cervical cancer.

Are mRNA based transcriptomic signatures ready for diagnosing tuberculosis in the clinic? - A review of evidence and the technological landscape.

Advances in discovery and validation of diagnostic, prognostic and treatment-monitoring transcriptomic signatures of tuberculosis (TB) disease could accelerate the goal to end TB. We conducted a review to evaluate whether mRNA transcriptomics technologies are sufficiently mature to develop accurate next-generation TB diagnostic tests. Early studies tended to be limited in sample size, diversity of population groups, sample collection and processing methods, while recent prospective studies have addressed these limitations. Some of the existing signatures could be used for triage; however, high cost and complexity could limit their use. For a confirmatory test, setting an optimal cut-off to maintain specificity across populations and settings is a challenge. mRNA signatures have shown the potential to quantitatively monitor response to treatment. No prognostic signatures can accurately predict progression to active TB over 2 years while short term prediction is possible. The management strategy should be defined for individuals with positive prognostic tests. FUNDING: Development of this manuscript was supported by funding received from the Stop TB Partnership and USAID for the New Diagnostics Working Group. The funders had no role in paper design, article selection and review, interpretation, or writing of the paper.

Development and Verification of a Prognostic Ferroptosis-Related Gene Model in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is the subtype with the worst prognosis of breast cancer. Ferroptosis, a novel iron-dependent programmed cell death, has an increasingly important role in tumorigenesis and development. However, there is still a lack of research on the relationship between ferroptosis-related genes and the prognosis of TNBC. In this study, we obtained the gene expression profile of TNBC patients and matched clinical data from The Cancer Genome Atlas (TCGA) database. Univariate Cox analysis was used to screen out ferroptosis-related genes associated with TNBC prognosis. Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was employed to establish a prognostic prediction model. A 15-ferroptosis-related gene prognostic prediction model was developed, which classified patients into low-risk (LR) or high-risk (HR) groups. Kaplan-Meier analysis results showed that the prognosis of the LR group was better. The receiver operating characteristic (ROC) curve also confirmed the satisfactory predictive ability of this model. Evaluation of the immune microenvironment of TNBC patients in the HR and LR group suggested these 15 ferroptosis-related genes might affect the prognosis of TNBC by regulating the tumor microenvironment. Our prognostic model can provide a theoretical basis for accurate prognosis prediction of TNBC in clinical practice.

An angiogenesis-related long noncoding RNA signature correlates with prognosis in patients with hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most prevalent and lethal cancers worldwide. Neovascularization is closely related to the malignancy of tumors. We constructed a signature of angiogenesis-related long noncoding RNA (lncRNA) to predict the prognosis of patients with HCC. The lncRNA expression matrix of 424 HCC patients was downloaded from The Cancer Genome Atlas (TCGA). First, gene set enrichment analysis (GSEA) was used to distinguish the differentially expressed genes of the angiogenesis genes in liver cancer and adjacent tissues. Next, a signature of angiogenesis-related lncRNAs was constructed using univariate and multivariate analyses, and receiver operating characteristic (ROC) curves were used to assess the accuracy. The signature and relevant clinical information were used to construct the nomogram. A 5-lncRNA signature was highly correlated with overall survival (OS) in HCC patients and performed well in evaluations using the C-index, areas under the curve, and calibration curves. In summary, the 5-lncRNA model can serve as an accurate signature to predict the prognosis of patients with liver cancer, but its mechanism of action must be further elucidated by experiments.