Angiopoietin-2 and angiopoietin-like 4 protein provide prognostic information in patients with suspected acute coronary syndrome

BACKGROUND

Plasma levels of angiopoietin-2 (ANGPT2) and angiopoietin-like 4 protein (ANGPTL4) reflect different pathophysiological aspects of cardiovascular disease. We evaluated their association with outcome in a hospitalized Norwegian patient cohort (n = 871) with suspected acute coronary syndrome (ACS) and validated our results in a similar Argentinean cohort (n = 982).

METHODS

A cox regression model, adjusting for traditional cardiovascular risk factors, was fitted for ANGPT2 and ANGPTL4, respectively, with all-cause mortality and cardiac death within 24 months and all-cause mortality within 60 months as the dependent variables.

RESULTS

At 24 months follow-up, 138 (15.8%) of the Norwegian and 119 (12.1%) of the Argentinian cohort had died, of which 86 and 66 deaths, respectively, were classified as cardiac. At 60 months, a total of 259 (29.7%) and 173 (17.6%) patients, respectively, had died. ANGPT2 was independently associated with all-cause mortality in both cohorts at 24 months [hazard ratio (HR) 1.27 (95% confidence interval (CI), 1.08-1.50) for Norway, and HR 1.57 (95% CI, 1.27-1.95) for Argentina], with similar results at 60 months [HR 1.19 (95% CI, 1.05-1.35) (Norway), and HR 1.56 (95% CI, 1.30-1.88) (Argentina)], and was also significantly associated with cardiac death [HR 1.51 (95% CI, 1.14-2.00)], in the Argentinean population. ANGPTL4 was significantly associated with all-cause mortality in the Argentinean cohort at 24 months [HR 1.39 (95% CI, 1.15-1.68)] and at 60 months [HR 1.43 (95% CI, 1.23-1.67)], enforcing trends in the Norwegian population.

CONCLUSIONS

ANGPT2 and ANGPTL4 were significantly associated with outcome in similar ACS patient cohorts recruited on two continents.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT00521976. ClinicalTrials.gov Identifier: NCT01377402.

Molecular Characterization of Prostate Cancers in the Precision Medicine Era

Prostate cancer (PCa) therapy has been recently revolutionized by the approval of new therapeutic agents in the metastatic setting. However, the optimal therapeutic strategy in such patients should be individualized in the light of prognostic and predictive molecular factors, which have been recently studied: androgen receptor (AR) alterations, PTEN-PI3K-AKT pathway deregulation, homologous recombination deficiency (HRD), mismatch repair deficiency (MMRd), and tumor microenvironment (TME) modifications. In this review, we highlighted the clinical impact of prognostic and predictive molecular factors in PCa patients' outcomes, identifying biologically distinct subtypes. We further analyzed the relevant methods to detect these factors, both on tissue, i.e., immunohistochemistry (IHC) and molecular tests, and blood, i.e., analysis of circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA). Moreover, we discussed the main pros and cons of such techniques, depicting their present and future roles in PCa management, throughout the precision medicine era.

Prognostic Biomarkers and Immunotherapeutic Targets Among CXC Chemokines in Pancreatic Adenocarcinoma

Background

Pancreatic cancer is one of the principal causes of tumor-related death worldwide. CXC chemokines, a subfamily of functional chemotactic peptides, affect the initiation of tumor cells and clinical outcomes in several human malignant tumors. However, the specific biological functions and clinical significance of CXC chemokines in pancreatic cancer have not been clarified.

Methods

Bioinformatics analysis tools and databases, including ONCOMINE, GEPIA2, the Human Protein Atlas, DAVID, GeneMANIA, cBioPortal, STRING, DGidb, MethSurv, TRRUST, SurvExpress, SurvivalMeth, and TIMER, were utilized to clarify the clinical significance and biological functions of CXC chemokine in pancreatic cancer.

Results

Except for CXCL11/12, the transcriptional levels of other CXC chemokines in PAAD tissues were significantly elevated, and the expression level of CXCL16 was the highest among these CXC chemokines. Our findings also suggested that all of the CXC chemokines were linked to tumor-immune dysfunction involving the abundance of immune cell infiltration, and the Cox proportional hazard model confirmed that dendritic and CXCL3/5/7/8/11/17 were significantly associated with the clinical outcome of PAAD patients. Furthermore, increasing expressions of CXCL5/9/10/11/17 were related to unfavorable overall survival (OS), and only CXCL17 was a prognostic factor for disease-free survival (DFS) in PAAD patients. The expression pattern and prognostic power of CXC chemokines were further validated in the independent GSE62452 dataset. For the prognostic value of single CpG of DNA methylation of CXC chemokines in patients with PAAD, we identified 3 CpGs of CXCL1, 2 CpGs of CXCL2, 2 CpGs of CXCL3, 3 CpGs of CXCL4, 10 CpGs of CXCL5, 1 CpG of CXCL6, 1 CpG of CXCL7, 3 CpGs of CXCL12, 3 CpGs of CXCL14, and 5 CpGs of CXCL17 that were significantly associated with prognosis in PAAD patients. Moreover, the prognostic value of CXC chemokine signature in PAAD was explored and tested in two independent cohort, and results indicated that the patients in the low-risk group had a better OS compared with the high-risk group. Survival analysis of the DNA methylation of CXC chemokine signature demonstrated that PAAD patients in the high-risk group had longer survival times.

Conclusions

These findings reveal the novel insights into CXC chemokine expression and their biological functions in the pancreatic cancers, which might serve as accurate prognostic biomarkers and suitable immunotherapeutic targets for patients with pancreatic cancer.

The Expression Profiles and Deregulation of UDP-Glycosyltransferase (UGT) Genes in Human Cancers and Their Association with Clinical Outcomes

The human UDP-glycosyltransferase (UGTs) superfamily has 22 functional enzymes that play a critical role in the metabolism of small lipophilic compounds, including carcinogens, drugs, steroids, lipids, fatty acids, and bile acids. The expression profiles of UGT genes in human cancers and their impact on cancer patient survival remains to be systematically investigated. In the present study, a comprehensive analysis of the RNAseq and clinical datasets of 9514 patients from 33 different TCGA (the Genome Cancer Atlas) cancers demonstrated cancer-specific UGT expression profiles with high interindividual variability among and within individual cancers. Notably, cancers derived from drug metabolizing tissues (liver, kidney, gut, pancreas) expressed the largest number of UGT genes (COAD, KIRC, KIRP, LIHC, PAAD); six UGT genes (1A6, 1A9, 1A10, 2A3, 2B7, UGT8) showed high expression in five or more different cancers. Kaplan-Meier plots and logrank tests revealed that six UGT genes were significantly associated with increased overall survival (OS) rates [UGT1A1 (LUSC), UGT1A6 (ACC), UGT1A7 (ACC), UGT2A3 (KIRC), UGT2B15 (BLCA, SKCM)] or decreased OS rates [UGT2B15 (LGG), UGT8 (UVM)] in specific cancers. Finally, differential expression analysis of 611 patients from 12 TCGA cancers identified 16 UGT genes (1A1, 1A3, 1A6, 1A7, 1A8, 1A9, 1A10, 2A1, 2A3, 2B4, 2B7, 2B11, 2B15, 3A1, 3A2, UGT8) that were up/downregulated in at least one cancer relative to normal tissues. In conclusion, our data show widespread expression of UGT genes in cancers, highlighting the capacity for intratumoural drug metabolism through the UGT conjugation pathway. The data also suggests the potentials for specific UGT genes to serve as prognostic biomarkers or therapeutic targets in cancers.

Clinical and biological determinants of circulating tumor DNA detection and prognostication using a next-generation sequencing panel assay

Circulating tumor DNA (ctDNA) is utilized for molecular profiling of cancers, and is under investigation for a growing number of applications based on the assumption that ctDNA levels faithfully reflect disease burden. Our objective was to investigate whether patient and tumor characteristics may impact ctDNA detection or levels and the prognostic significance of ctDNA levels or mutations. We performed a retrospective cohort analysis of a comprehensively annotated cohort of 561 patients at a National Cancer Institute-designated comprehensive cancer center with advanced solid cancers who underwent ctDNA testing using a commercial targeted next-generation sequencing assay. ctDNA detection in advanced cancers was associated with older age, non-obese body mass index, and diabetes, but not with tumor diameter, volume, lesion number, or other pathological features. Regression models indicate that no more than 14.3% of the variance in ctDNA levels between patients was explained by known clinical factors and disease burden. Even after adjusting for established prognostic factors and tumor burden, ctDNA levels were associated with worse survival among patients without prior systemic therapy, while ctDNA mutations were associated with survival among patients who previously received systemic treatment. These findings uncover clinical factors that affect ctDNA detection in patients with advanced cancers and challenge the convention that ctDNA is a surrogate for tumor burden. Our study also indicates that the prognostic value of ctDNA levels and mutations are independent of tumor burden and dependent on treatment context.

Temporal Muscle Thickness is an Independent Prognostic Biomarker in Patients with Glioma: Analysis of 261 Cases

Purpose

Temporal muscle thickness (TMT) has been proposed as a novel surrogate marker for skeletal muscle mass in head and neck malignancies. This study investigated the TMT prognostic relevance with gliomas and evaluated the influence of TMT values on survival in patients with gliomas of different grades and IDH subtypes.

Methods

The patients’ TMT was measured on contrast-enhanced T1-weighted magnetic resonance images before surgical treatment. Patients were divided into two cohorts based on their median TMT values. The Kaplan–Meier curve was used to compute the overall survival (OS) of different categories and all gliomas. Univariate and multivariate Cox regression analyses were conducted to assess the association between OS and TMT, hematological markers, and other clinical factors in glioma patients. Moreover, the clinical diagnostic efficiency of single and combination biomarkers was evaluated using receiver operating characteristic curve analysis.

Results

We retrospectively analyzed 261 patients with newly diagnosed glioma between November 2016 and May 2020 at Hunan Cancer Hospital. Cox analysis indicated that higher TMT (HR 0.286, P< 0.001) and higher KPS score (HR 0.629, P= 0.012) were protective prognostic factors and IDH wildtype status (HR 2.946, P< 0.001), RDW > 12.6 (HR 1.513, P= 0.036), and NLR > 4 (HR 1.560, P= 0.042) were poor prognostic factors for gliomas. Subsequently, patients with thicker TMT were found to have significantly better overall survival (P<0.001) than patients with thinner TMT among WHO III and WHO IV grade and patients with or without IDH mutation. TMT was considered a better single biomarker than recently prevalent hematological biomarkers for predicting high-grade [0.856 (0.797–0.916)] and IDH- wild-type [0.864 (0.786–0.941)] gliomas.

Conclusion

This study suggests that TMT is a positive biomarker for clinical prognosis in gliomas and that patients with thicker TMT have greater overall survival for gliomas of different grades and IDH subtypes.

Benzo[a]pyrene stimulates miR-650 expression to promote the pathogenesis of fatty liver disease and hepatocellular carcinoma via SOCS3/JAK/STAT3 cascades

Modern diets, which often feature high levels of fat and charcoal-grilled meat, contribute to the pathogenesis of obesity and nonalcoholic fatty liver disease (NAFLD), resulting in liver cancer progression. Benzo(a)pyrene (B[a]P) is a common environmental and foodborne pollutant found in smoke and fire-grilled foods, which can have an adverse effect on human health. Hepatocellular carcinoma (HCC) is the fifth leading cause of cancer and the second leading cause of cancer-related deaths worldwide. The epidemiological studies suggest that both environmental risk factors and chronic liver injury including NAFL are important for HCC development, but the precise mechanisms linking eating habits to hepato-carcinogenesis remain unclear. In the present study, we demonstrated that various miRNAs in B[a]P-exposed tumor cells contribute to tumor metastasis, among which miR-650 could be the most potent inducer. Furthermore, we found that suppressor of cytokine signaling 3 (SOCS3) is directly regulated by miR-650 and its suppression regulates the activation of the Janus kinase/signal transducer and activator of transcription 3 (JAK/STAT3) cascade. Our findings reveal a possible adverse outcome pathway of SOCS3/JAK/STAT3 regulation in B[a]P-induced HCC progress. These results provide a better understanding of the adverse effects of chronic exposure to B[a]P on human health.

Identification and Validation of a Five-Gene Signature Associated With Overall Survival in Breast Cancer Patients

BACKGROUND

Recent years, the global prevalence of breast cancer (BC) was still high and the underlying molecular mechanisms remained largely unknown. The investigation of prognosis-related biomarkers had become an urgent demand.

RESULTS

In this study, gene expression profiles and clinical information of breast cancer patients were downloaded from the TCGA database. The differentially expressed genes (DEGs) were estimated by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. A risk score formula involving five novel prognostic associated biomarkers (EDN2, CLEC3B, SV2C, WT1, and MUC2) were then constructed by LASSO. The prognostic value of the risk model was further confirmed in the TCGA entire cohort and an independent external validation cohort. To explore the biological functions of the selected genes, in vitro assays were performed, indicating that these novel biomarkers could markedly influence breast cancer progression.

CONCLUSIONS

We established a predictive five-gene signature, which could be helpful for a personalized management in breast cancer patients.

Identification of Tumor Microenvironment-Related Prognostic lncRNAs in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most common type of lung cancer and is a severe threat to human health. Although many therapies have been applied to LUAD, the long-term survival rate of patients remains unsatisfactory. We aim to find reliable immune microenvironment-related lncRNA biomarkers to improve LUAD prognosis.

Methods

ESTIMATE analysis was performed to evaluate the degree of immune infiltration of each patient in TAGA LUAD cohort. Correlation analysis was used to identify the immune microenvironment-related lncRNAs. Univariate cox regression analysis, LASSO analysis, and Kaplan Meier analysis were used to construct and validate the prognostic model based on microenvironment-related lncRNAs.

Results

We obtained 1,178 immune microenvironment-related lncRNAs after correlation analysis. One hundred and eighty of them are independent prognostic lncRNAs. Sixteen key lncRNAs were selected by LASSO method. This lncRNA-based model successfully predicted patients’ prognosis in validation cohort, and the risk score was related to pathological stage. Besides, we also found that TP53 had the highest frequency mutation in LUAD, and the mutation of TP53 in the high-risk group, which was identified by our survival model, has a poor prognosis. lncRNA-mRNA co-expression network further suggested that these lncRNAs play a vital role in the prognosis of LUAD.

Conclusion

Here, we filtered 16 key lncRNAs, which could predict the survival of LUAD and may be potential biomarkers and therapeutic targets.

Pathophysiological Roles of Histamine Receptors in Cancer Progression: Implications and Perspectives as Potential Molecular Targets

High levels of histamine and histamine receptors (HRs), including H1R~H4R, are found in many different types of tumor cells and cells in the tumor microenvironment, suggesting their involvement in tumor progression. This review summarizes the latest evidence demonstrating the pathophysiological roles of histamine and its cognate receptors in cancer biology. We also discuss the novel therapeutic approaches of selective HR ligands and their potential prognostic values in cancer treatment. Briefly, histamine is highly implicated in cancer development, growth, and metastasis through interactions with distinct HRs. It also regulates the infiltration of immune cells into the tumor sites, exerting an immunomodulatory function. Moreover, the effects of various HR ligands, including H1R antagonists, H2R antagonists, and H4R agonists, on tumor progression in many different cancer types are described. Interestingly, the expression levels of HR subtypes may serve as prognostic biomarkers in several cancers. Taken together, HRs are promising targets for cancer treatment, and HR ligands may offer novel therapeutic potential, alone or in combination with conventional therapy. However, due to the complexity of the pathophysiological roles of histamine and HRs in cancer biology, further studies are warranted before HR ligands can be introduced into clinical settings.

Immune Activation and Microbial Translocation as Prognostic Biomarkers for AIDS-Related Non-Hodgkin Lymphoma in the AMC-034 Study

PURPOSE

AIDS-related non-Hodgkin lymphoma (ARL) is the most common cancer in HIV-infected individuals in the United States and other countries in which HIV-positive persons have access to effective combination antiretroviral therapy (cART). Our prior work showed that pretreatment/postdiagnosis plasma levels of some cytokines, such as IL6, IL10, and CXCL13, have the potential to serve as indicators of clinical response to treatment and survival in ARL. The aims of this study were to identify novel prognostic biomarkers for response to treatment and/or survival in persons with ARL, including biomarkers of microbial translocation and inflammation.

EXPERIMENTAL DESIGN

We quantified plasma levels of several biomarkers (sCD14, LBP, FABP2, EndoCab IgM, IL18, CCL2/MCP-1, sCD163, IP-10/CXCL10, TARC/CCL17, TNFα, BAFF/BLyS, sTNFRII, sCD44, and sIL2Rα/sCD25) by multiplexed immunometric assays (Luminex) or ELISA in plasma specimens obtained from ARL patients enrolled in the AMC-034 trial, which compared infusional combination chemotherapy (EPOCH: etoposide, vincristine, doxorubicin, cyclophosphamide, and prednisone) with concurrent or sequential rituximab. Plasma was collected prior to the initiation of therapy (n = 57) and after treatment initiation (n = 55).

RESULTS

We found that several biomarkers decreased significantly after treatment, including TNFα, sCD25, LBP, and TARC (CCL17). Moreover, pretreatment plasma levels of BAFF, sCD14, sTNFRII, and CCL2/MCP-1 were univariately associated with overall survival, and pretreatment levels of BAFF, sTNFRII, and CCL2/MCP-1 were also associated with progression-free survival.

CONCLUSIONS

Our results suggest that patients with ARL who responded to therapy had lower pretreatment levels of inflammation and microbial translocation as compared with those who did not respond optimally.

Gastric Cancer Diagnosis: From Imaging Techniques to Biochemical Biomarkers

Gastric cancer (GC) has been found to be the second leading cause of cancer-associated deaths in the world, and is usually detected in the advanced stages. It has been shown that surgery is the major therapeutic approach in the treatment of subjects with GC. Hence, early and fast diagnosis of this malignancy is very important for good subject outcomes. Non-invasive diagnostic platforms, including evolutionary endoscopy and positron emission tomography (PET), are employed for the diagnosis of subjects with GC. Along with imaging techniques, the utilization of biomarkers has emerged as a new diagnosis option for early and fast detection of GC. Multiple lines of evidence have revealed a variety of biomarkers, including microRNAs, exosomes, circulating tumor cells, circular RNAs, cell free DNAs, and various proteins, which could be used as diagnostic biomarkers in patients with GC. Taken together, these findings suggest that the joint application of imaging techniques and different biomarkers could be introduced as a new detection approach in the treatment and screening response to therapy in the subjects with GC. Herein, we have summarized various imaging techniques and biomarkers as powerful tools in the detection of GC.

Prognostic biomarkers of chronic diabetic macular edema treated with a fiuocinolone acetonide intravitreal implant

Background

This study aimed to investigate retinal imaging biomarkers, such as disorganization of the retinal inner layers (DRIL) and/or ellipsoid zone (EZ) disruption by spectral domain optical coherence tomography (SD-OCT), and functional outcomes in eyes treated with 0.2 µg/day of a fluocinolone acetonide intravitreal implant (FAc) after an insufficient response to previous treatments.

Methods

This was a retrospective comparative study of 18 eyes (15 patients) with persistent and/or recurrent diabetic macular edema (DME) treated with FAc. Eyes were divided according to the number of prior intravitreal treatments: group 1 (n = 8) with ≤ 6 injections (early switch) and group 2 (n = 10) with > 6 injections (late switch). Outcomes included percentage of eyes with DRIL and/or EZ disruption at baseline and analysis of the best corrected visual acuity (BCVA) using ETDRS letters, central macular thickness (CMT), DRIL, and EZ disruption at the last observation.

Results

Group 2 revealed a significantly higher percentage of DRIL and/or EZ disruption than group 1 (P < 0.05). At the last observation, group 1 revealed a higher percentage of eyes achieving vision stability/ improvement, gaining ≥ 15 letters, and achieving ≥70 letters (P > 0.05 for all comparisons). The mean BCVA gain was 8.8 and 0.7 letters for groups 1 and 2 (P = 0.397). Both groups revealed a significant mean CMT reduction (> 20% reduction from the baseline value), without a significant statistical difference between them (P = 0.749). After treatment, most eyes from both groups showed resolution of DRIL and EZ disruption.

Conclusions

Patients with DME presenting with a lower percentage of DRIL and/or EZ disruption at baseline had better functional outcomes, supporting the possible benefit of an early switch to FAc after insufficient response to previous treatments. Future randomized studies with a larger patient cohort are warranted to confirm our conclusions.

Limitations of Explainability for Established Prognostic Biomarkers of Prostate Cancer

High-throughput technologies do not only provide novel means for basic biological research but also for clinical applications in hospitals. For instance, the usage of gene expression profiles as prognostic biomarkers for predicting cancer progression has found widespread interest. Aside from predicting the progression of patients, it is generally believed that such prognostic biomarkers also provide valuable information about disease mechanisms and the underlying molecular processes that are causal for a disorder. However, the latter assumption has been challenged. In this paper, we study this problem for prostate cancer. Specifically, we investigate a large number of previously published prognostic signatures of prostate cancer based on gene expression profiles and show that none of these can provide unique information about the underlying disease etiology of prostate cancer. Hence, our analysis reveals that none of the studied signatures has a sensible biological meaning. Overall, this shows that all studied prognostic signatures are merely black-box models allowing sensible predictions of prostate cancer outcome but are not capable of providing causal explanations to enhance the understanding of prostate cancer.

Identification of Potential Prognostic Biomarkers Associated With Cancerometastasis in Skin Cutaneous Melanoma

Skin cutaneous melanoma (SKCM) is a highly aggressive tumor. The mortality and drug resistance among it are high. Thus, exploring predictive biomarkers for prognosis has become a priority. We aimed to find immune cell-based biomarkers for survival prediction. Here 321 genes were differentially expressed in immune-related groups after ESTIMATE analysis and differential analysis. Two hundred nineteen of them were associated with the metastasis of SKCM via weighted gene co-expression network analysis. Twenty-six genes in this module were hub genes. Twelve of the 26 genes were related to overall survival in SKCM patients. After a multivariable Cox regression analysis, we obtained six of these genes (PLA2G2D, IKZF3, MS4A1, ZC3H12D, FCRL3, and P2RY10) that were independent prognostic signatures, and a survival model of them performed excellent predictive efficacy. The results revealed several essential genes that may act as significant prognostic factors of SKCM, which could deepen our understanding of the metastatic mechanisms and improve cancer treatment.

Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer

Simple Summary

Despite recent advancements in lung cancer treatment, individuals with lung cancer have a dismal 5-year survival rate of only 15%. In patients with non-small cell lung cancer (NSCLC), medical images have lately been employed as a valuable marker for predicting overall survival. The primary goal of this study was to develop a risk score based on computed tomography (CT) based radiomics feature signatures that may be used to predict survival in NSCLC patients. After analyzing 577 NSCLC patients from two data sets, we discovered that the risk score model’s prediction ability as a prognostic indicator was superior to other clinical indicators (age, stage, and gender), and the possibility of patient risk stratification with survival was evaluated using a risk score representation of 10 radiomics signatures. According to this study, the risk score generated using CT-based radiomics signatures promises to predict overall survival in NSCLC patients.

Abstract

This study aimed to create a risk score generated from CT-based radiomics signatures that could be used to predict overall survival in patients with non-small cell lung cancer (NSCLC). We retrospectively enrolled three sets of NSCLC patients (including 336, 84, and 157 patients for training, testing, and validation set, respectively). A total of 851 radiomics features for each patient from CT images were extracted for further analyses. The most important features (strongly linked with overall survival) were chosen by pairwise correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and univariate Cox proportional hazard regression. Multivariate Cox proportional hazard model survival analysis was used to create risk scores for each patient, and Kaplan–Meier was used to separate patients into two groups: high-risk and low-risk, respectively. ROC curve assessed the prediction ability of the risk score model for overall survival compared to clinical parameters. The risk score, which developed from ten radiomics signatures model, was found to be independent of age, gender, and stage for predicting overall survival in NSCLC patients (HR, 2.99; 95% CI, 2.27–3.93; p < 0.001) and overall survival prediction ability was 0.696 (95% CI, 0.635–0.758), 0.705 (95% CI, 0.649–0.762), 0.657 (95% CI, 0.589–0.726) (AUC) for 1, 3, and 5 years, respectively, in the training set. The risk score is more likely to have a better accuracy in predicting survival at 1, 3, and 5 years than clinical parameters, such as age 0.57 (95% CI, 0.499–0.64), 0.552 (95% CI, 0.489–0.616), 0.621 (95% CI, 0.544–0.689) (AUC); gender 0.554, 0.546, 0.566 (AUC); stage 0.527, 0.501, 0.459 (AUC), respectively, in 1, 3 and 5 years in the training set. In the training set, the Kaplan–Meier curve revealed that NSCLC patients in the high-risk group had a lower overall survival time than the low-risk group (p < 0.001). We also had similar results that were statistically significant in the testing and validation set. In conclusion, risk scores developed from ten radiomics signatures models have great potential to predict overall survival in NSCLC patients compared to the clinical parameters. This model was able to stratify NSCLC patients into high-risk and low-risk groups regarding the overall survival prediction.

Characterizing the Metabolic and Immune Landscape of Non-small Cell Lung Cancer Reveals Prognostic Biomarkers Through Omics Data Integration

Non-small cell lung cancer (NSCLC) is one of the most common malignancies worldwide. The development of high-throughput single-cell RNA-sequencing (RNA-seq) technology and the advent of multi-omics have provided a solid basis for a systematic understanding of the heterogeneity in cancers. Although numerous studies have revealed the molecular features of NSCLC, it is important to identify and validate the molecular biomarkers related to specific NSCLC phenotypes at single-cell resolution. In this study, we analyzed and validated single-cell RNA-seq data by integrating multi-level omics data to identify key metabolic features and prognostic biomarkers in NSCLC. High-throughput single-cell RNA-seq data, including 4887 cellular gene expression profiles from NSCLC tissues, were analyzed. After pre-processing, the cells were clustered into 12 clusters using the t-SNE clustering algorithm, and the cell types were defined according to the marker genes. Malignant epithelial cells exhibit individual differences in molecular features and intra-tissue metabolic heterogeneity. We found that oxidative phosphorylation (OXPHOS) and glycolytic pathway activity are major contributors to intra-tissue metabolic heterogeneity of malignant epithelial cells and T cells. Furthermore, we constructed T-cell differentiation trajectories and identified several key genes that regulate the cellular phenotype. By screening for genes associated with T-cell differentiation using the Lasso algorithm and Cox risk regression, we identified four prognostic marker genes for NSCLC. In summary, our study revealed metabolic features and prognostic markers of NSCLC at single-cell resolution, which provides novel findings on molecular biomarkers and signatures of cancers.

Serum Biomarker Panel for Diagnosis and Prognosis of Pancreatic Ductal Adenocarcinomas

Background

Patients with pancreatic ductal adenocarcinoma (PDAC) have late diagnosis which results in poor prognosis. Currently, surgical resection is the only option for curative intent. Identifying high-risk features for patients with aggressive PDAC is essential for accurate diagnosis, prognostication, and personalised care due to the disease burden and risk of recurrence despite surgical resection. A panel of three biomarkers identified in tumour tissue (S100A4, Ca125 and Mesothelin) have shown an association with poor prognosis and overall survival. The diagnostic and prognostic value of the serum concentration of this particular biomarker panel for patients with PDAC has not been previously studied.

Methods

Retrospectively collected blood samples of PDAC patients (n =120) and healthy controls (n =80) were evaluated for the serum concentration of select biomarkers - S100A4, S100A2, Ca-125, Ca 19-9 and mesothelin. Statistical analyses were performed for diagnostic and prognostic correlation.

Results

A panel of four biomarkers (S100A2, S100A4, Ca-125 and Ca 19-9) achieved high diagnostic potential (AUROC 0.913). Three biomarkers (S100A4, Ca-125 and Ca 19-9) correlated with poor overall survival in a univariable model (p < 0.05). PDAC patients with abnormal levels of 2 or more biomarkers in their serum demonstrated significantly lower survival compared to patients with abnormal levels of one or less biomarker (p < 0.05).

Conclusion and Impact

The identified biomarker panels have shown the potential to diagnose PDAC patients and stratify patients based on their prognostic outcomes. If independently validated, this may lead to the development of a diagnostic and prognosticating blood test for PDAC.

Adverse Events Induced by PD-1/PD-L1 Inhibitors: A Real-World Single-Centre Experience with a Management-Based Approach

AIM

To assess the efficacy and tolerance of programmed death-1 (PD-1) and PD-ligand 1 (PD-L1) inhibitors and the impact of a standardised management-based protocol in a real-world setting.

PATIENTS AND METHODS

Data from patients who had received anti-PD-(L)1 were collected from our pharmacy database. Clinical response and toxicity were assessed using RECIST criteria and CTCAE version 5.0, respectively. Overall survival (OS) and progression-free survival (PFS) were estimated with the Kaplan-Meier method. Potential prognostic factors were identified using Cox's model.

RESULTS

A total of 196 patients and 201 lines of treatment were included (median age: 66 (range: 38-89) years). Types of cancer included non-small cell lung cancer (73%), transitional cell carcinoma (10%), renal cell carcinoma (6%), small cell lung cancer (5%), head and neck squamous cell carcinoma (4%) and classical Hodgkin's lymphoma (1%). Twenty-five (12%) patients had pre-existing autoimmune conditions. Our standardised management-based protocol included 129 (64%) patients. Objective response rate was 29%, median OS was 10 months (IQR: 7-15) and median PFS was 5 months (IQR: 1-22). Patients with an abnormal baseline complete blood count had a worse OS (HR=2.48 [95% CI: 1.24-4.96]; p=0.0103). Thirty-three (16%) patients experienced severe (grade 3 or 4) immune-related adverse event (irAE). There were three (1%) irAE-related deaths. AEs resolved faster when patients were assessed by an internist before anti-PD-(L)1 initiation (p=0.0205).

CONCLUSION

PD-1 and PD-L1 inhibitors are effective and safe in a real-world setting. Implementation of a standardised management-based protocol with internal medicine specialists is an effective way to optimise irAE management.

Splenic Volume as a Surrogate Marker of Immune Checkpoint Inhibitor Efficacy in Metastatic Non Small Cell Lung Cancer

Simple Summary

Monoclonal antibodies targeting PD1/PD-L1 are game changers in the treatment of advanced non-small cell lung cancer (NSCLC), but biomarkers are lacking. We previously reported the prognostic role of splenic volume in digestive cancer and its correlation with the presence of immunosuppressive cells. The aim of this study was to evaluate the prognostic role of splenic volume in NSCLC patients treated with immune checkpoint inhibitors (ICIs).

Abstract

Monoclonal antibodies targeting PD1/PD-L1 are game changers in advanced non-small cell lung cancer (NSCLC), but biomarkers are lacking. We previously reported the prognostic role of splenic volume in digestive cancer and its correlation with the presence of immunosuppressive cells. The aim of this study was to evaluate the prognostic role of splenic volume in NSCLC patients treated with immune checkpoint inhibitors (ICIs). We conducted a retrospective study of 276 patients receiving ICIs for advanced NSCLC in the Georges François Leclerc Cancer Center. The association between splenic volume at baseline and at two months of therapy and progression-free survival (PFS) during ICI treatment or overall survival (OS) from ICI initiation was evaluated using univariate and multivariable Cox analyses. Splenic volume during treatment and the change in splenic volume were associated with poor PFS (respectively p = 0.02 and p = 0.001) and with OS (respectively p < 1.10⁻³ and p < 1.10⁻³). Baseline splenic volume at the first evaluation was also associated with poor OS (p = 0.001). LDH rate and dNLR were positively correlated with splenic volume, as well as with its evolution. After the adjustment of clinical variables, splenic volumes remained a predictive marker of immunotherapy efficacy. Splenic volume is a prognostic biomarker in patients with advanced NSCLC treated with ICIs.

Novel Prognostic Biomarkers in Gastric Cancer: CGB5, MKNK2, and PAPPA2

Introduction

Gastric cancer is one of the most common malignant tumors of the digestive tract. However, there are no adequate prognostic markers available for this disease. The present study used bioinformatics to identify prognostic markers for gastric cancer that would guide the clinical diagnosis and treatment of this disease.

Materials and Methods

Gene expression data and clinical information of gastric cancer patients along with the gene expression data of 30 healthy samples were downloaded from the TCGA database. The initial screening was performed using the WGCNA method combined with the analysis of differentially expressed genes, which was followed by univariate analysis, multivariate COX regression analysis, and Lasso regression analysis for screening the candidate genes and constructing a prognostic model for gastric cancer. Subsequently, immune cell typing was performed using CIBERSORT to analyze the expression of immune cells in each sample. Finally, we performed laboratory validation of the results of our analyses using immunohistochemical analysis.

Results

After five screenings, it was revealed that only three genes fulfilled all the screening requirements. The survival curves generated by the prognostic model revealed that the survival rate of the patients in the high-risk group was significantly lower compared to the patients in the low-risk group (P-value < 0.001). The immune cell component analysis revealed that the three genes were differentially associated with the corresponding immune cells (P-value < 0.05). The results of immunohistochemistry also support our analysis.

Conclusion

CGB5, MKNK2, and PAPPA2 may be used as novel prognostic biomarkers for gastric cancer.

Pretreatment Plasma IL-6 and YKL-40 and Overall Survival after Surgery for Metastatic Bone Disease of the Extremities

Simple Summary

Estimating postoperative survival in patients undergoing surgery for metastatic bone disease of the extremities is important in order to choose an implant that will outlive the patient. The present study suggests that plasma IL-6, reflecting the inflammatory state of the patient, is predictive for postoperative overall survival (OS).

Abstract

Background: Plasma IL-6 and YKL-40 are prognostic biomarkers for OS in patients with different types of solid tumors, but they have not been studied in patients before surgery of metastatic bone disease (MBD) of the extremities. The aim was to evaluate the prognostic value of plasma IL-6 and YKL-40 in patients undergoing surgery for MBD of the extremities. Patients and Methods: A prospective study included all patients undergoing surgery for MBD in the extremities at a tertiary referral center during the period 2014–2018. Preoperative blood samples from index surgery were included. IL-6 and YKL-40 concentrations in plasma were determined by commercial ELISA. A total of 232 patients (median age 66 years, IQR 58–74; female 51%) were included. Results: Cox regression analysis was performed to identify independent prognostic factors for OS. IL-6 correlated with YKL-40 (rho = 0.46, p < 0.01). In univariate analysis (log₂ continuous variable) IL-6 (HR = 1.26, 95% CI 1.16–1.37), CRP (HR = 1.20, 95% CI 1.12–1.29) and YKL-40 (HR = 1.25, 95% CI 1.15–1.37) were associated with short OS. In multivariable analysis, adjusted for known risk factors for survival, only log₂(IL-6) was independently associated with OS (HR = 1.24, 95% CI 1.08–1.43), whereas CRP and YKL-40 were not. Conclusion: High preoperative plasma IL-6 is an independent biomarker of short OS in patients undergoing surgery for MBD.

Making Patient-Specific Treatment Decisions Using Prognostic Variables and Utilities of Clinical Outcomes

We argue that well-informed patient-specific decision-making may be carried out as three consecutive tasks: (1) estimating key parameters of a statistical model, (2) using prognostic information to convert these parameters into clinically interpretable values, and (3) specifying joint utility functions to quantify risk-benefit trade-offs between clinical outcomes. Using the management of metastatic clear cell renal cell carcinoma as our motivating example, we explain the role of prognostic covariates that characterize between-patient heterogeneity in clinical outcomes. We show that explicitly specifying the joint utility of clinical outcomes provides a coherent basis for patient-specific decision-making.

Immune-related long non-coding RNAs can serve as prognostic biomarkers for clear cell renal cell carcinoma

Background

The immune microenvironment is a critical regulator of clear cell renal cell carcinoma (ccRCC) progression. However, the underlying mechanisms the regulatory role of immune-related long non-coding RNAs (irlncRNAs) in the ccRCC tumor microenvironment (TME) are still obscure. Herein, we investigated prognostics role of irlncRNAs for ccRCC.

Methods

The raw data of patients with ccRCC were downloaded from The Cancer Genome Atlas (TCGA) database, and immune-related genes were obtained from the ImmPort database. First, we investigated the correlation between the immune-related genes and irlncRNAs. Then, we identified the differentially expressed irlncRNA pairs (ILRPs) between normal and cancer tissue samples, and prognostic model was constructed with the differentially expressed ILRPs. We further explored whether the signature risk scores of ILRPs had a considerable impact on immune cell infiltration. Finally, we performed a drug sensitivity analysis based on risk score.

Results

There were 13 upregulated and 40 downregulated irlncRNAs between the ccRCC and normal tissue samples. We further selected the irlncRNAs that significantly affect the prognosis of patients with ccRCC via univariate Cox, lasso regression, and multivariate regression analyses. Twelve ILRPs were used to construct a prognostic signature. The model showed the ILRPs model could be used to assess the prognosis of ccRCC patients. Study of the influence of risk score and clinical characteristics on the prognosis of patients with ccRCC showed risk score to be an independent factor affecting the outcome of ccRCC. We further performed the difference analysis of immune cell abundance between ccRCC and normal tissue samples. The results showed that patients with higher abundance of M0 macrophages, plasma cells, follicular helper T cells, and regulatory T cells (Tregs) had a poor outcome. Finally, we performed a drug sensitivity analysis based on risk score. The results showed that high-risk score patients are sensitive to orafenib, sunitinib, temsirolimus, cisplatin, and gemcitabine.

Conclusions

Our study has developed a novel and reasonable ILPRs model for prognostic prediction, which does not require transcriptional levels to be detected.

Dynein Light Chain Protein Tctex1: A Novel Prognostic Marker and Molecular Mediator in Glioblastoma

Simple Summary

Glioblastoma (GBM) remains one of the deadliest solid cancers, with only a dismal proportion of GBM patients achieving 5-year survival. Thus, it is critical to identify molecular mechanisms that could be targeted by novel therapeutic approaches in this tumor type. Our study identified Tctex1/DYNLT1 as an independent prognostic marker for the overall survival of GBM patients. Importantly, Tctex1 promoted the aggressiveness of GBM cells by enhancing tumor proliferation and invasion. These effects of Tctex1 appeared to be modulated via phosphorylation of retinoblastoma protein (RB) and the release of matrix metalloprotease 2 (MMP2), respectively. As Tctex1 can potentially be inhibited in vivo, our study provides a rationale for novel, individualized therapeutic strategies in GBM patients.

Abstract

The purpose of this study was to determine the role of Tctex1 (DYNLT1, dynein light chain-1) in the pathophysiology of glioblastoma (GBM). To this end, we performed immunohistochemical analyses on tissues from GBM patients (n = 202). Tctex1 was additionally overexpressed in two different GBM cell lines, which were then evaluated in regard to their proliferative and invasive properties. We found that Tctex1 levels were significantly higher in GBM compared to healthy adjacent brain tissues. Furthermore, high Tctex1 expression was significantly associated with the short overall- (p = 0.002, log-rank) and progression-free (p = 0.028, log-rank) survival of GBM patients and was an independent predictor of poor overall survival in multivariate Cox-regression models. In vitro, Tctex1 promoted the metabolic activity, anchorage-independent growth and proliferation of GBM cells. This phenomenon was previously shown to occur via the phosphorylation of retinoblastoma protein (phospho-RB). Here, we found a direct and significant correlation between the levels of Tctex1 and phospho-RB (Ser807/801) in tissues from GBM patients (p = 0.007, Rho = 0.284, Spearman’s rank). Finally, Tctex1 enhanced the invasiveness of GBM cells and the release of pro-invasive matrix metalloprotease 2 (MMP2). These findings indicate that Tctex1 promotes GBM progression and therefore might be a useful therapeutic target in this type of cancer.

TTSurv: Exploring the Multi-Gene Prognosis in Thousands of Tumors

Thoracic malignancies are a common type of cancer and area major global health problem. These complex diseases, including lung cancer, esophageal cancer, and breast cancer, etc. have attracted considerable attention from researchers. Potential gene-cancer associations can be explored by demonstrating the association between clinical data and gene expression data. Emerging evidence suggests that the transcriptome plays a particularly critical role as a diagnostic biomarker in pathology and histology studies. Thus, there is an urgent need to develop a platform that allows users to perform a comprehensive prognostic analysis of thoracic cancers. Here, we developed TTSurv, which aims to correlate coding and noncoding genes with cancers by combining high-throughput data with clinical prognosis. TTSurv focuses on the application of high-throughput data to detect ncRNAs, such as lncRNAs and microRNAs, as novel diagnostic and prognostic biomarkers. For a more comprehensive analysis, a large amount of public expression profile data with clinical follow-up information have been integrated into TTSurv. TTSurv also provides flexible methods such as a minimum p-value algorithm and unsupervised clustering methods that can classify thoracic cancer samples into different risk groups. TTSurv will expand our understanding of ncRNAs in thoracic malignancies and provide new insights into their application as potential prognostic/diagnostic biomarkers.

Senescent T cells: a potential biomarker and target for cancer therapy

The failure of T cells to eradicate tumour cells in the tumour microenvironment is mainly due to the dysfunction of T cells. Senescent T cells, with defects in proliferation and effector functions, accumulate in ageing, chronic viral infections, and autoimmune disorders where antigen stimulation persists. Increasing evidence suggests that inducing T cell senescence is a key strategy used by malignant tumours to evade immune surveillance. In this review, we summarize the general features, functional regulation, and signalling network of senescent T cells in tumour development and highlight their potential as prognostic biomarkers in multiple cancer treatments, including chemotherapy, radiotherapy, and immunotherapy. Moreover, we discuss possible therapeutic strategies for preventing or rejuvenating senescence in tumour-specific T cells. Understanding these critical issues may provide novel strategies to enhance cancer immunotherapy.

N6 -methyladenosine Steers RNA Metabolism and Regulation in Cancer

As one of the most studied ribonucleic acid (RNA) modifications in eukaryotes, N⁶ -methyladenosine (m⁶ A) has been shown to play a predominant role in controlling gene expression and influence physiological and pathological processes such as oncogenesis and tumor progression. Writer and eraser proteins, acting opposite to deposit and remove m⁶ A epigenetic marks, respectively, shape the cellular m⁶ A landscape, while reader proteins preferentially recognize m⁶ A modifications and mediate fate decision of the methylated RNAs, including RNA synthesis, splicing, exportation, translation, and stability. Therefore, RNA metabolism in cells is greatly influenced by these three classes of m⁶ A regulators. Aberrant expression of m⁶ A regulators has been widely reported in various types of cancer, leading to cancer initiation, progression, and drug resistance. The close links between m⁶ A and cancer shed light on the potential use of m⁶ A methylation and its regulators as prognostic biomarkers and drug targets for cancer therapy. Given the notable effects of m⁶ A in reversing chemoresistance and enhancing immune therapy, it is a promising target for combined therapy. Herein, we summarize the recent discoveries on m⁶ A and its regulators, emphasizing their influences on RNA metabolism, their dysregulation and impacts in diverse malignancies, and discuss the clinical implications of m⁶ A modification in cancer.

m6A-Related lncRNAs Are Potential Biomarkers for the Prognosis of Metastatic Skin Cutaneous Melanoma

Background: The incidence of skin cutaneous melanoma (SKCM) has risen more rapidly than any other solid tumor in the past few decades. The median survival for metastatic melanoma is only six to nine months and the 5°years survival rate of patients with conventional therapy is less than 5%. Our aim was to reveal the potential molecular mechanism in m6A modification of lncRNA and provide candidate prognostic biomarkers for metastatic SKCM.

Methods: lncRNAs expression level was obtained by re-annotation in TCGA and CCLE datasets. m6A-related lncRNAs were selected though correlation analysis. Univariate cox regression analysis was used to screen out independent prognostic factors. LASSO Cox regression was performed to construct an m6A-related lncRNA model (m6A-LncM). Univariate survival analysis and ROC curve were used to assess the prognostic efficacy of this model and candidate lncRNAs. Enrichment analysis was used to explore the candidate genes’ functions.

Results: We obtained 1,086 common m6A-related lncRNAs after Pearson correlation analysis in both two datasets. 130 out of the 1,086 lncRNAs are independent prognostic factors. 24 crucial lncRNAs were filtered after LASSO Cox regression analysis. All the m6A-LncM and the 24 lncRNAs were related to overall survival. Stratified survival analysis of m6A-LncM showed that the model retains its prognostic efficacy in recurrence, radiation therapy and other subgroups. Enrichment analysis also found that these lncRNAs were immune associated.

Conclusion: Here, we obtained 24 crucial lncRNAs that may be potential biomarkers to predict survival of metastatic SKCM and may provide a new insight to improve the prognosis of it.

Cervical Cancer Diagnosis: Insights into Biochemical Biomarkers and Imaging Techniques

Cervical malignancy is known as one of the important cancers which is originated from cervix. This malignancy has been observed in women infected with papillomavirus who had regular oral contraceptives, multiple pregnancies, and sexual relations. Early and fast cervical cancer diagnosis is known as two important aspects of cervical cancer therapy. Several investigations indicated that early and fast detection of cervical cancer could be associated with better treatment process and increasing survival rate of patients with this malignancy. Imaging techniques are very important diagnosis tools that could be employed for diagnosis and following responses to therapy in various cervical cancer stages. Multiple lines of evidence indicated that utilization of imaging techniques is related to some limitations (i.e. high cost, and invasive effects). Hence, it seems that along with using imaging techniques, finding and developing new biomarkers could be useful in the diagnosis and treatment of subjects with cervical cancer. Taken together, many studies showed that a variety of biomarkers including, several proteins, mRNAs, microRNAs, exosomes and polymorphisms might be introduced as prognostic, diagnostic and therapeutic biomarkers in cervical cancer therapy. In this review article, we highlighted imaging techniques as well as novel biomarkers for the diagnosis of cervical cancer.

Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential

The pestilential pathogen SARS-CoV-2 has led to a seemingly ceaseless pandemic of COVID-19. The healthcare sector is under a tremendous burden, thus necessitating the prognosis of COVID-19 severity. This in-depth study of plasma proteome alteration provides insights into the host physiological response towards the infection and also reveals the potential prognostic markers of the disease. Using label-free quantitative proteomics, we performed deep plasma proteome analysis in a cohort of 71 patients (20 COVID-19 negative, 18 COVID-19 non-severe, and 33 severe) to understand the disease dynamics. Of the 1200 proteins detected in the patient plasma, 38 proteins were identified to be differentially expressed between non-severe and severe groups. The altered plasma proteome revealed significant dysregulation in the pathways related to peptidase activity, regulated exocytosis, blood coagulation, complement activation, leukocyte activation involved in immune response, and response to glucocorticoid biological processes in severe cases of SARS-CoV-2 infection. Furthermore, we employed supervised machine learning (ML) approaches using a linear support vector machine model to identify the classifiers of patients with non-severe and severe COVID-19. The model used a selected panel of 20 proteins and classified the samples based on the severity with a classification accuracy of 0.84. Putative biomarkers such as angiotensinogen and SERPING1 and ML-derived classifiers including the apolipoprotein B, SERPINA3, and fibrinogen gamma chain were validated by targeted mass spectrometry-based multiple reaction monitoring (MRM) assays. We also employed an in silico screening approach against the identified target proteins for the therapeutic management of COVID-19. We shortlisted two FDA-approved drugs, namely, selinexor and ponatinib, which showed the potential of being repurposed for COVID-19 therapeutics. Overall, this is the first most comprehensive plasma proteome investigation of COVID-19 patients from the Indian population, and provides a set of potential biomarkers for the disease severity progression and targets for therapeutic interventions.

Gene Regulation Network Analysis on Human Prostate Orthografts Highlights a Potential Role for the JMJD6 Regulon in Clinical Prostate Cancer

Simple Summary

Prostate cancer is a very common malignancy worldwide. Treatment resistant prostate cancer poses a big challenge to clinicians and is the second most common cause of premature death in men with cancer. Gene expression analysis has been performed on clinical tumours but to date none of the gene expression-based biomarkers for prostate cancer have been successfully integrated to into clinical practice to improve patient management and treatment choice. We applied a novel laboratory prostate cancer model to mimic clinical hormone responsive and resistant prostate cancer and tested whether a network of genes similarly regulated by transcription factors (gene products that control the expression of target genes) are associated with patient outcome. We identified regulons (networks of genes similarly regulated) from our preclinical prostate cancer models and further evaluated the top ranked JMJD6 gene related regulated network in three independent clinical patient cohorts.

Abstract

Background: Prostate cancer (PCa) is the second most common tumour diagnosed in men. Tumoral heterogeneity in PCa creates a significant challenge to develop robust prognostic markers and novel targets for therapy. An analysis of gene regulatory networks (GRNs) in PCa may provide insight into progressive PCa. Herein, we exploited a graph-based enrichment score to integrate data from GRNs identified in preclinical prostate orthografts and differentially expressed genes in clinical resected PCa. We identified active regulons (transcriptional regulators and their targeted genes) associated with PCa recurrence following radical prostatectomy. Methods: The expression of known transcription factors and co-factors was analysed in a panel of prostate orthografts (n = 18). We searched for genes (as part of individual GRNs) predicted to be regulated by the highest number of transcriptional factors. Using differentially expressed gene analysis (on a per sample basis) coupled with gene graph enrichment analysis, we identified candidate genes and associated GRNs in PCa within the UTA cohort, with the most enriched regulon being JMJD6, which was further validated in two additional cohorts, namely EMC and ICGC cohorts. Cox regression analysis was performed to evaluate the association of the JMJD6 regulon activity with disease-free survival time in the three clinical cohorts as well as compared to three published prognostic gene signatures (TMCC11, BROMO-10 and HYPOXIA-28). Results: 1308 regulons were correlated to transcriptomic data from the three clinical prostatectomy cohorts. The JMJD6 regulon was identified as the top enriched regulon in the UTA cohort and again validated in the EMC cohort as the top-ranking regulon. In both UTA and EMC cohorts, the JMJD6 regulon was significantly associated with cancer recurrence. Active JMJD6 regulon also correlated with disease recurrence in the ICGC cohort. Furthermore, Kaplan–Meier analysis confirmed shorter time to recurrence in patients with active JMJD6 regulon for all three clinical cohorts (UTA, EMC and ICGC), which was not the case for three published prognostic gene signatures (TMCC11, BROMO-10 and HYPOXIA-28). In multivariate analysis, the JMJD6 regulon status significantly predicted disease recurrence in the UTA and EMC, but not ICGC datasets, while none of the three published signatures significantly prognosticate for cancer recurrence. Conclusions: We have characterised gene regulatory networks from preclinical prostate orthografts and applied transcriptomic data from three clinical cohorts to evaluate the prognostic potential of the JMJD6 regulon.

TRIM68, PIKFYVE, and DYNLL2: The Possible Novel Autophagy- and Immunity-Associated Gene Biomarkers for Osteosarcoma Prognosis

Introduction

Osteosarcoma is among the most common orthopedic neoplasms, and currently, there are no adequate biomarkers to predict its prognosis. Therefore, the present study was aimed to identify the prognostic biomarkers for autophagy-and immune-related osteosarcoma using bioinformatics tools for guiding the clinical diagnosis and treatment of this disease.

Materials and Methods

The gene expression and clinical information data were downloaded from the Public database. The genes associated with autophagy were extracted, followed by the development of a logistic regression model for predicting the prognosis of osteosarcoma using univariate and multivariate COX regression analysis and LASSO regression analysis. The accuracy of the constructed model was verified through the ROC curves, calibration plots, and Nomogram plots. Next, immune cell typing was performed using CIBERSORT to analyze the expression of the immune cells in each sample. For the results obtained from the analysis, we used qRT-PCR validation in two strains of human osteosarcoma cells.

Results

The screening process identified a total of three genes that fulfilled all the screening criteria. The survival curves of the constructed prognostic model revealed that patients with the high risk presented significantly lower survival than the patients with low risk. Finally, the immune cell component analysis revealed that all three genes were significantly associated with the immune cells. The expressions of TRIM68, PIKFYVE, and DYNLL2 were higher in the osteosarcoma cells compared to the control cells. Finally, we used human pathological tissue sections to validate the expression of the genes modeled in osteosarcoma and paracancerous tissue.

Conclusion

The TRIM68, PIKFYVE, and DYNLL2 genes can be used as biomarkers for predicting the prognosis of osteosarcoma.

Characterization of the Survival Influential Genes in Carcinogenesis

The genes influencing cancer patient mortality have been studied by survival analysis for many years. However, most studies utilized them only to support their findings associated with patient prognosis: their roles in carcinogenesis have not yet been revealed. Herein, we applied an in silico approach, integrating the Cox regression model with effect size estimated by the Monte Carlo algorithm, to screen survival-influential genes in more than 6000 tumor samples across 16 cancer types. We observed that the survival-influential genes had cancer-dependent properties. Moreover, the functional modules formed by the harmful genes were consistently associated with cell cycle in 12 out of the 16 cancer types and pan-cancer, showing that dysregulation of the cell cycle could harm patient prognosis in cancer. The functional modules formed by the protective genes are more diverse in cancers; the most prevalent functions are relevant for immune response, implying that patients with different cancer types might develop different mechanisms against carcinogenesis. We also identified a harmful set of 10 genes, with potential as prognostic biomarkers in pan-cancer. Briefly, our results demonstrated that the survival-influential genes could reveal underlying mechanisms in carcinogenesis and might provide clues for developing therapeutic targets for cancers.

Using Exome Sequencing to Improve Prediction of FOLFIRINOX First Efficacy for Pancreatic Adenocarcinoma

Simple Summary

Pancreatic ductal adenocarcinoma is one of the deadliest human cancers. The standard treatment for non-resectable tumors is based on usage of FOLFIRINOX (folinic acid, fluorouracil irinotecan oxaliplatin) chemotherapy. The aim of our retrospective study was to identify genomic markers associated with an improved survival in patients treated with FOLFIRINOX using whole exome sequencing (WES). We successfully generated WES analyses in 78 patients. We created and compared several models with clinical or genomic variables and pathways. While the clinical score was associated with overall and progression-free survival, the genomic score and pathways score were associated with the overall survival. The addition of genomic score improved the prediction of prognosis compared to the clinical score alone. Thus, our study showed that WES could provide useful information to predict survival in patients treated with FOLFIRINOX and might be used to select patients who could yield most benefit from FOLFIRINOX treatment.

Abstract

Purpose: The first line treatment of advanced pancreatic ductal adenocarcinoma cancer (PDAC) comprises a FOLFIRINOX regimen for most patients with good performance status. However, no biomarker to predict efficacy is currently available. We investigated whether exome sequencing could be used to predict progression-free and overall survival in patients undergoing FOLFIRINOX for PDAC. Methods: In this single-center observational study, we included 78 patients with advanced PDAC who underwent somatic and germline exome analyses during first line therapy with FOLFIRINOX or gemcitabine. Exome-derived variables associated with outcome were then used in Cox regression models to generate a composite biomarker. Results: Performance status, tumor stage, liver metastasis, and lung metastasis were retained to generate a prognostic clinical score associated with overall and progression-free survival. Clonality, ploidy, and copy number variant (CNV) signatures 1 and 5, as well as gene variants in the calcium, non-homologous end-joining (NHEJ), and spliceosome pathways, were retained to generate a genomic prognostic score. The addition of genomic score improved the prediction of prognosis compared to the clinical score alone. Conclusions: This study underlines that structural and pathway genomic features could be used to predict FOLFIRINOX survival in patients with advanced PDAC.

Identification of DNA-Repair-Related Five-Gene Signature to Predict Prognosis in Patients with Esophageal Cancer

Esophageal cancer (ESCA) is a leading cause of cancer-related mortality, with poor prognosis worldwide. DNA damage repair is one of the hallmarks of cancer. Loss of genomic integrity owing to inactivation of DNA repair genes can increase the risk of cancer progression and lead to poor prognosis. We aimed to identify a novel gene signature related to DNA repair to predict the prognosis of ESCA patients. Based on gene expression profiles of ESCA patients from The Cancer Genome Atlas and gene set enrichment analysis, 102 genes related to DNA repair were identified as candidates. After stepwise Cox regression analysis, we established a five-gene prognostic model comprising DGCR8, POM121, TAF9, UPF3B, and BCAP31. Kaplan-Meier survival analysis confirmed a strong correlation between the prognostic model and survival. Moreover, we verified the clinical value of the prognostic signature under the influence of different clinical parameters. We found that small-molecule drugs (trametinib, selumetinib, and refametinib) could help to improve patient survival. In summary, our study provides a novel and promising prognostic signature based on DNA-repair-related genes to predict survival of patients with ESCA. Systematic data mining provides a theoretical basis for further exploring the molecular pathogenesis of ESCA and identifying therapeutic targets.

Prognostic Stratification of Metastatic Prostate Cancer Patients Treated With Abiraterone and Enzalutamide Through an Integrated Analysis of Circulating Free microRNAs and Clinical Parameters

Androgen Receptor-Targeted Agents (ARTA) have dramatically changed the therapeutic landscape of metastatic Castration-Resistant Prostate Cancer (mCRPC), but 20–40% of these patients progress early after start of ARTA treatment. The present study investigated the potential utility of plasma cell-free microRNAs (cfmiRNAs) as prognostic markers by analyzing a prospective cohort of 31 mCRCP patients treated with abiraterone (N = 10) or enzalutamide (N = 21). Additional potential prognostic factors were extracted from clinical records and outcome was evaluated as overall survival (OS) and progression-free survival (PFS). cfmiRNAs were measured in plasma samples using quantitative real-time RT-PCR. Linear correlation among clinical factors and cfmiRNAs was assessed using the Spearman's rank correlation coefficient. The association with survival was studied using univariate and multivariate Cox proportional hazards models. Continuous variables were dichotomized with the cut points corresponding to the most significant relation with the outcome. Univariate analysis indicated that plasma levels of miR-21-5p, miR-141-3p and miR-223-3p, time to development of castration-resistance (tCRPC), and blood hemoglobin (Hb) levels strongly correlated with both PFS and OS. Multivariate analysis revealed that low plasma levels of miR-21, shorter tCRPC, and lower Hb values were independent factors predicting reduced PFS and OS. These findings suggest that the integrated analysis of cfmiRNAs, tCRPC, and Hb may provide a promising, non-invasive tool for the prognostic stratification of mCRPC patients treated with ARTA.

NPC2 as a Prognostic Biomarker for Glioblastoma Based on Integrated Bioinformatics Analysis and Cytological Experiments

Glioblastoma (GBM) is one of the most common and fatal malignancies worldwide, while its prognostic biomarkers are still being explored. This study aims to identify potential genes with clinical and prognostic significance by integrating bioinformatics analysis and investigating their function in HNSCC. Based on the Single-cell RNA sequencing (scRNA-seq) results of H3K27M-glioma cells, computational bioinformatics methods were employed for selecting prognostic biomarker for GBM. The protein NPC2 (NPC Intracellular Cholesterol Transporter 2), which has been shown to be related to lipoprotein metabolism and innate immune system, was identified to be upregulated in GBM. NPC2 showed a relatively higher expression in GBM samples, and a negative correlation with tumor purity and tumor infiltrating immune cells. Additionally, NPC2 was knocked down in U87-MG and U251 cells line, and cell proliferation and migration capability were evaluated with CCK-8, scratch and transwell assay, respectively. Cytological experiments has shown that NPC2 overexpression inhibited GBM cells proliferation and migration, indicating its important role in GBM progression. This is the first investigation into the prognostic value of NPC2 interact with GBM. The potential molecular factor NPC2 have been identified as a prognostic biomarker for GBM.

CXC Chemokines as Therapeutic Targets and Prognostic Biomarkers in Skin Cutaneous Melanoma Microenvironment

Background

Skin Cutaneous Melanoma (SKCM) is a tumor of the epidermal melanocytes induced by gene activation or mutation. It is the result of the interaction between genetic, constitutional, and environmental factors. SKCM is highly aggressive and is the most threatening skin tumor. The incidence of the disease is increasing year by year, and it is the main cause of death in skin tumors around the world. CXC chemokines in the tumor microenvironment can regulate the transport of immune cells and the activity of tumor cells, thus playing an anti-tumor immunological role and affecting the prognosis of patients. However, the expression level of CXC chemokine in SKCM and its effect on prognosis are still unclear.

Method

Oncomine, UALCAN, GEPIA, STRING, GeneMANIA, cBioPortal, TIMER, TRRUST, DAVID 6.8, and Metascape were applied in our research.

Result

The transcription of CXCL1, CXCL5, CXCL8, CXCL9, CXCL10, and CXCL13 in SKCM tissues were significantly higher than those in normal tissues. The pathological stage of SKCM patients is closely related to the expression of CXCL4, CXCL9, CXCL10, CXCL11, CXCL12, and CXCL13. The prognosis of SKCM patients with low transcription levels of CXCL4, CXCL9, CXCL10, CXCL11, and CXCL13 is better. The differential expression of CXC chemokines is mainly associated with inflammatory response, immune response, and cytokine mediated signaling pathways. Our data indicate that the key transcription factors of CXC chemokines are RELA, NF-κB1 and SP1. The targets of CXC chemokines are mainly LCK, LYN, SYK, MAPK2, MAPK12, and ART. The relationship between CXC chemokine expression and immune cell infiltration in SKCM was closed.

Conclusions

Our research provides a basis for screening SKCM biomarkers, predicting prognosis, and choosing immunotherapy.

Biomarkers for Risk Stratification in Patients With Previously Untreated Follicular Lymphoma Receiving Anti-CD20-based Biological Therapy

Follicular lymphoma (FL) is an indolent B-cell neoplasm of germinal center origin. Standard treatment regimens consist of anti-CD20 therapy with or without chemotherapy. While high response rates to initial therapy are common, patients ultimately relapse or have progressive disease. Clinical risk factors such as the Follicular Lymphoma International Prognostic Index (FLIPI) have been identified, but there is a need for prognostic and predictive biomarkers. We studied markers of lymphoma cells and tumor microenvironment by immunohistochemistry in tissue samples from patients enrolled in 1 of 4 phase 2 trials of anti-CD20-based biological therapy for previously untreated grades 1 to 2 or 3A FL. Results were correlated with progression-free survival (PFS) and PFS status at 24 months. The 4 trials included 238 patients (51.1% male, median age: 55 y) with stage III, IV, or bulky stage II disease. By FLIPI, 24.6% had low-risk, 56.8% had intermediate-risk, and 18.6% had high-risk disease. The outcome differed significantly for patients treated with lenalidomide and rituximab (CALGB 50803) compared with the other 3 trials (median: PFS not reached vs. 3.0 y, hazard ratio=3.47, 95% confidence interval: 2.11-5.72); therefore, data were stratified by clinical trial (CALGB 50803 vs. all others) and adjusted for FLIPI risk group. Among 154 patients with available tissue, interfollicular BCL6 positivity, interfollicular CD10 positivity, and elevated Ki67 proliferation index ≥30% within neoplastic follicles were each associated with inferior PFS and a high risk of the early event by PFS status at 24 months. We identify promising biomarkers for FL risk stratification that warrant further validation in phase 3 trials.

Elafin as a Predictive Biomarker of Acute Skin Graft-Versus-Host Disease After Haploidentical Stem Cell Transplantation Using Post-Transplant High-Dose Cyclophosphamide

Haploidentical hematopoietic stem cell transplantation (haplo-HSCT) has shown favorable results in the treatment of hematological malignancies. Despite the use of post-transplant cyclophosphamide (PTCy), graft versus host disease (GVHD) remains as one of the main complications in this setting. Since the skin appears affected in up to 80% of cases of acute GVHD (aGVHD), its prognosis and diagnosis are essential for the correct management of these patients. Plasma concentration of elafin, an elastase inhibitor produced by keratinocytes, has been described elevated at the diagnosis of skin GVHD, correlated with the grade of GVHD, and associated with an increased risk of death. In this study we explored elafin plasma levels in the largest series reported of T cell-replete haplo-HSCT with PTCy. Plasma samples drawn from 87 patients at days +15 and +30 were analyzed ("discovery cohort"). Elafin levels at days +15 were no associated with chronic GVHD, non-relapse mortality, relapse, therapy-resistant GVHD, or overall survival. In our series, elafin levels at day +30 were not associated with post-transplant complications. On the other hand, elafin plasma levels at day +15 were higher in patients with severe skin aGVHD (21,313 vs.14,974 pg/ml; p = 0.01). Of note, patients with higher elafin plasma levels at day +15 presented a higher incidence of stage III-IV skin aGVHD (HR = 18.9; p < 0.001). These results were confirmed (HR = 20.6; p < 0.001) in an independent group of patients (n = 62), i.e. the "validation cohort." These data suggest that measurement of elafin in patients undergoing haplo-HSCT with PTCy might be useful for an early identification of those patients who are at higher risk of suffering severe skin aGVHD and thus, improve their treatment and prognosis.

Identification of novel prognostic genes of triple-negative breast cancer using meta-analysis and weighted gene co-expressed network analysis

Background

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with high rates of metastasis and recurrence. Conventional clinical treatments are ineffective for it as it lacks therapeutic biomarkers. Figuring out the biomarkers related to TNBC will be beneficial for its clinical treatment and prognosis.

Methods

Five independent datasets downloaded from the Gene Expression Omnibus database were merged to identify differentially expressed genes between TNBC and non-TNBC samples by using the MetaDE.ES method followed by mapping the differentially expressed genes into a protein-protein interaction network. Meanwhile, the weighted gene co-expressed network analysis (WGCNA) of The Cancer Genome Atlas data was performed to screen the hub genes. The gene functional analyses were conducted by Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The correlation between gene expression level and patient overall survival was evaluated by survival analysis.

Results

A total of 11 differentially expressed genes (CDH1, SP1, MYC, FAF2, IFI16, MDM2, AR, DBN1, HSPB1, FLNA, YWHAB) were obtained from the protein-protein interaction network with degree >10. WGCNA revealed 5 hub genes (TPX2, CTPS1, KIF2C, MELK, CDCA8) that were significantly associated with TNBC. Cell cycle, oocyte meiosis, spliceosome were the pathways significantly enriched in these genes according to GO functionally annotated terms and KEGG pathways analysis. The Kaplan-Meier curves showed that the expression levels of HSPB1, IFI16, TPX2 were significantly associated with the survival time of TNBC patients (P<0.05).

Conclusions

A total of 16 genes significantly associated with TNBC were identified by bioinformatic analyses. Among these 16 genes, HSPB1, IFI16, TPX2 might be able to be used as biomarkers of TNBC.

miR-30b-5p up-regulation related to the dismal prognosis for patients with renal cell cancer

The diagnosis of renal cell carcinoma (RCC) is often made late since there is no early symptom, which thus results in dismal patient prognosis. As a result, new biomarkers are urgently needed and efforts should be made to identify their functions in predicting RCC prognosis. microRNAs (miRNAs) are a class of small noncoding RNAs that are about 20-22 nucleotides in length, and they have been demonstrated to function as prognostic markers in numerous tumors. This study aimed to assess the role of miR-30b-5p in predicting the prognosis of RCC postoperatively. In this study, RNA was extracted from 284 formalin-fixed and paraffin-embedded kidney cancer tissue samples. After cDNA synthesis, real-time quantitative PCR (RT-qPCR) was adopted for detecting the relative miR-30b-5p level. Then, the Kaplan-Meier method, Cox regression analysis, and the receiver operating characteristic curve analysis were applied in analyzing the miR-30b-5p effect on the prognosis for patients. Our findings indicated that, following adjustment for age, gender, tumor stage, and tumor size, patients with low miR-30b-5p expression had remarkably longer overall survival. Thus, the miR-30b-5p level might be related to RCC prognosis.

Reshaping preoperative treatment of pancreatic cancer in the era of precision medicine

This review summarises the recent evidence on preoperative therapeutic strategies in pancreatic cancer and discusses the rationale for an imminent need for a personalised therapeutic approach in non-metastatic disease. The molecular diversity of pancreatic cancer and its influence on prognosis and treatment response, combined with the failure of 'all-comer' treatments to significantly impact on patient outcomes, requires a paradigm shift towards a genomic-driven approach. This is particularly important in the preoperative, potentially curable setting, where a personalised treatment allocation has the substantial potential to reduce pancreatic cancer mortality.

Identification of theranostic factors for patients developing metastasis after surgery for early-stage lung adenocarcinoma

Rationale: Lung adenocarcinoma (LUAD) is an aggressive disease with high propensity of metastasis. Among patients with early-stage disease, more than 30% of them may relapse or develop metastasis. There is an unmet medical need to stratify patients with early-stage LUAD according to their risk of relapse/metastasis to guide preventive or therapeutic approaches. In this study, we identified 4 genes that can serve both therapeutic and diagnostic (theranostic) purposes.

Methods: Three independent datasets (GEO, TCGA, and KMPlotter) were used to evaluate gene expression profile of patients with LUAD by unbiased screening approach. Upon significant genes uncovered, functional enrichment analysis was carried out. The predictive power of their expression on patient prognosis were evaluated. Once confirmed their theranostic roles by integrated bioinformatics, we further conducted in vitro and in vivo validation.

Results: We found that four genes (ADAM9, MTHFD2, RRM2, and SLC2A1) were associated with poor patient outcomes with an increased hazard ratio in LUAD. Knockdown of them, both separately and simultaneously, suppressed lung cancer cell proliferation and migration ability in vitro and prolonged survival time in metastatic tumor mouse models. Moreover, these four biomarkers were found to be overexpressed in tumor tissues from LUAD patients, and the total immunohistochemical staining scores correlated with poor prognosis.

Conclusions: These results suggest that these four identified genes could be theranostic biomarkers for stratifying high-risk patients who develop relapse/metastasis in early-stage LUAD. Developing therapeutic approaches for the four biomarkers may benefit early-stage LUAD patients after surgery.

Expression and prognostic analyses of SCAMPs in pancreatic adenocarcinoma

Due to the difficulties in early diagnosis of pancreatic adenocarcinoma (PAAD), many patients fail to receive optimal therapeutic regimens. The Secretory-Carrier-Membrane-Proteins (SCAMPs) are known to be dysregulated in a range of human diseases due to their characterized roles in mammalian cell exocytosis inferred from their functions as integral membrane proteins. However, the expression and prognostic value of SCAMPs in PAAD is poorly characterized. We compared cancer vs. healthy tissue and found that the expression of SCAMPs1-4 was upregulated in PAAD compared to normal tissue. In contrast, SCAMP5 expression was downregulated in PAAD. Moreover, the expression of SCAMPs1-4 was enhanced in PAAD cell lines according to Cancer Cell Line public database. Furthermore, the HPA, GEPIA databases and immunohistochemical analysis from 238 patients suggested that the loss of SCAMP1 led to improved overall survival (OS), whilst lower SCAMP5 levels led to a poorer OS. The univariate and multivariate analysis showed that SCAMP1 and SCAMP5 expression were independent prognostic factors of PAAD. In addition, the cBioPortal for Cancer Genomics, LinkedOmics datasets, and the GEPIA were used to identify the co-expression genes of SCAMP1,5 and the correlation between SCAMPs members. We conclude that SCAMPs 1 and 5 significantly represent promising diagnosis and prognostic biomarkers.

New Prognostic Biomarkers in Metastatic Castration-Resistant Prostate Cancer

Prostate cancer is one of the most frequent cancers in men and is a common cause of cancer-related death. Despite significant progress in the diagnosis and treatment of this tumor, patients who relapse after radical treatments inevitably develop metastatic disease. Patient stratification is therefore key in this type of cancer, and there is an urgent need for prognostic biomarkers that can define patients' risk of cancer-related death. In the last 10 years, multiple prognostic factors have been identified and studied. Here, we review the literature available and discuss the most common aberrant genomic pathways in metastatic castration-resistant prostate cancer shown to have a prognostic relevance in this setting.

Assessment of the Efficacy and Clinical Utility of Different Circulating Tumor Cell (CTC) Detection Assays in Patients with Chemotherapy-Naïve Advanced or Metastatic Non-Small Cell Lung Cancer (NSCLC)

We herein investigated the detection frequency and clinical relevance of circulating tumor cells (CTCs) in chemotherapy-naïve stage IIIB/IV non-small cell lung cancer (NSCLC), by using the CellSearch and real-time CEACAM5mRNA assays. Blood samples from 43 patients were obtained at different time points during first-line chemotherapy. CellSearch revealed the detection of ≥1 CTCs in 41.9%, 40.9%, and 16.7% of patients at baseline, post-1st, and post-2nd treatment cycle, respectively, and of ≥5 CTCs in 11.6%, 9.1%, and 5.6%, respectively. CEACAM5mRNA+ CTCs were detected in 29.3% and 16% of patients pre- and post-treatment, respectively. The positivity concordance between the two assays was 2.2%. CTC-detection by CellSearch (≥5 CTCs: p = 0.004), CEACAM5mRNA (p = 0.010), or by any assay (p = 0.000) was associated with disease progression. Reduced survival was demonstrated for patients harboring ≥5 CTCs (progression-free survival; PFS: p = 0.000; overall survival; OS: p = 0.009), CEACAM5mRNA+ CTCs (PFS: p = 0.043; OS: p = 0.039), and CTCs by any assay (PFS: p = 0.005; OS: p = 0.006, respectively). CTC-detection by any assay independently predicted for increased risk of relapse (hazard ratio; HR: 3.496; p = 0.001) and death (HR: 2.866; p = 0.008). CellSearch-positivity either pre-, post-1st, or post-2nd cycle, was predictive for shorter PFS (p = 0.036) compared to negativity in all time points. Persistent CEACAM5mRNA-positivity pre- and post-treatment was associated with reduced PFS (p = 0.036) and OS (p = 0.026). In conclusion, CTC detection and monitoring using the CellSearch and CEACAM5mRNA assays provides valuable and complementary clinical information for chemo-naïve advanced or metastatic NSCLC.

Semaphorins as emerging clinical biomarkers and therapeutic targets in cancer

Semaphorins are a large family of developmental regulatory signals, characterized by aberrant expression in human cancers. These molecules crucially control cell-cell communication, cell migration, invasion and metastasis, tumor angiogenesis, inflammatory and anti-cancer immune responses. Semaphorins comprise secreted and cell surface-exposed molecules and their receptors are mainly found in the Plexin and Neuropilin families, which are further implicated in a signaling network controlling the tumor microenvironment. Accumulating evidence indicates that semaphorins may be considered as novel clinical biomarkers for cancer, especially for the prediction of patient survival and responsiveness to therapy. Moreover, preclinical experimental studies have demonstrated that targeting semaphorin signaling can interfere with tumor growth and/or metastatic dissemination, suggesting their relevance as novel therapeutic targets in cancer; this has also prompted the development of semaphorin-interfering molecules for application in the clinic. Here we will survey, in diverse human cancers, the current knowledge about the relevance of semaphorin family members, and conceptualize potential lines of future research development in this field.

Identification of miR-499a-5p as a Potential Novel Biomarker for Risk Stratification in Endometrial Cancer

INTRODUCTION

The Cancer Genome Atlas (TCGA) project identified four distinct prognostic groups in endometrial cancer (EC), among which two are correlated with an intermediate prognosis: the MisMatch Repair-deficient (MMRd) and the No Specific Molecular Profile (NSMP) groups. The two groups represent a heterogeneous subset of patients frequently harboring CTNNB1 alterations with distinctive clinicopathologic features. The study aimed to evaluate the miRNA expression in ECs to identify potential biomarkers of prognosis.

METHODS

We analyzed miRNA expression in 72 ECs classified as MMRd or NSMP including 15 ECs with CTNNB1 mutations. In the discovery step, miRNA expression was evaluated in 30 cases through TaqMan miRNA arrays. Subsequently, four miRNAs were validated in the total cohort of ECs. The data were further tested in the TCGA cohort, and correlations with overall survival (OS) and progression-free interval (PFI) were evaluated.

RESULTS

miR-499a-3p and miR-499a-5p resulted to be overexpressed in CTNNB1 mutant EC patients at intermediate risk. Similarly, in the TCGA cohort, miR-499a-3p and miR-499a-5p were differentially expressed between CTNNB1 mutant and wild-type patients (p < 0.0001). NSMP patients with low miR-499a-5p expression showed longer OS (p = 0.03, log-rank test). By combining miR-499a-3p or -5p expression levels with the CTNNB1 status, ECs with CTNNB1 mutation and lower miR-499a-5p expression showed better OS compared with the other subgroups (p = 0.03, log-rank test), among the NSMP patients. Moreover, in a multivariate analysis, combination of wild type CTNNB1 status and high miR-499a-5p expression was indipendently associated with high risk of death [HR (95%CI): 3.53 (1.1-10.5), p = 0.02].

CONCLUSION

Our results suggest that the combination of CTNNB1 status and miR-499a-5p allows a better stratification of NSMP patients and could promote a personalization of the treatment in intermediate-risk patients.