Prediction of survival of diffuse large B-cell lymphoma patients via the expression of three inflammatory genes

Assessment and prognostic significance of a serum cytokine panel in diffuse large B‑cell lymphoma

The objective of the present study was to assess the levels of circulating cytokines in patients with diffuse large B-cell lymphoma (DLBCL), and to examine the associations between the cytokine levels, clinicopathological manifestations and patient prognosis. The study enrolled 49 patients with DLBCL, 11 patients with chronic lymphocytic leukemia/small lymphocytic lymphoma and 67 healthy controls from Zhejiang Provincial People's Hospital (Hangzhou, China) between January 2017 and January 2020. The serum levels of interleukin (IL)-2, IL-4, IL-6, IL-10, IL-17, tumor necrosis factor (TNF)-α and interferon (IFN)-γ were measured using flow cytometry. The IL-6, IL-10 and IFN-γ levels were significantly raised in patients with DLBCL compared with those in the healthy controls (P<0.05). The levels of IL-10 were significantly higher in patients with raised levels of circulating lactate dehydrogenase (P<0.05), while increases in both IL-6 and IL-10 were associated with raised C-reactive protein (CRP) levels, with IL-6 levels positively associated with those of serum CRP (P<0.01; r=0.66). Additionally, International Prognostic Index (IPI) risk stratification of patients with DLBCL was strongly associated with circulating IL-6 and IL-10 levels. Raised IL-6, IL-10 and TNF-α levels were linked with worse short-term treatment efficacies (P<0.05). Moreover, the accuracy of the model predicting short-term treatment response in patients with DLBCL, obtained using the support vector machine algorithm, was 81.63%. It was also found that raised serum IL-6 and IL-10 levels, together with reduced levels of IL-17, were associated with survival of <1 year in patients with DLBCL (P<0.05), although no significant link was found between cytokine levels and long-term overall survival. In conclusion, the serum levels of IL-6, IL-10, IL-17, TNF-α and IFN-γ can potentially serve as biological indicators of DLBCL tumor immune status, and combined application with the IPI score can be a robust prognostic indicator in patients with DLBCL.

Identification of an inflammatory response signature associated with prognostic stratification and drug sensitivity in lung adenocarcinoma

Increasing evidence has confirmed the close connection between inflammatory response and tumorigenesis. However, the relationship between inflammatory response genes (IRGs) and the prognosis of lung adenocarcinoma (LUAD) as well as the response to drug therapy remains poorly investigated. Here, we comprehensively analyzed IRGs RNA expression profiling and clinical features of over 2000 LUAD patients from 12 public datasets. The Cox regression method and LASSO analysis were combined to develop a novel IRG signature for risk stratification and drug efficacy prediction in LUAD patients. Enriched pathways, tumor microenvironment (TME), genomic and somatic mutation landscape in different subgroups were evaluated and compared with each other. This established IRG signature including 11 IRGs (ADM, GPC3, IL7R, NMI, NMURI, PSEN1, PTPRE, PVR, SEMA4D, SERPINE1, SPHK1), could well categorize patients into significantly different prognostic subgroups, and have better predictive in independently assessing survival as compared to a single clinical factor. High IRG scores (IRGS) patients might benefit more from immunotherapy and chemotherapy. Comprehensive analysis uncovered significant differences in enriched pathways, TME, genomic and somatic mutation landscape between the two subgroups. Additionally, integrating the IRGS and TNM stage, a reliable prognostic nomogram was developed to optimize survival prediction, and validated in an independent external dataset for clinical application. Take together, the proposed IRG signature in this study is a promising biomarker for risk stratification and drug efficacy prediction in LUAD patients. This study may be meaningful for explaining the responses of clinical therapeutic drugs and providing new strategies for administrating sufferer of LUAD.

An Inflammation-Related Nine-Gene Signature to Improve Prognosis Prediction of Lung Adenocarcinoma

Background

A novel predictive model was rarely reported based on inflammation-related genes to explore clinical outcomes of lung adenocarcinoma (LUAD) patients.

Methods

Using TCGA database, we screened nine inflammation-related genes with a prognostic value, and LASSO regression was applied for model construction. The predictive value of the prognostic signature developed from inflammation-related genes was assessed by survival assays and multivariate assays. PCA and t-SNE analysis were performed to demonstrate clustering abilities of risk scores.

Results

Thirteen inflammation-related genes (BTG2, CCL20, CD69, DCBLD2, GPC3, IL7R, LAMP3, MMP14, NMUR1, PCDH7, PIK3R5, RNF144B, and TPBG) with prognostic values were finally identified. LASSO regression further screened nine candidates (BTG2, CCL20, CD69, IL7R, MMP14, NMUR1, PCDH7, RNF144B, and TPBG). Then, a prognostic prediction model using the above nine genes was constructed. A reliable clustering ability of risk score was demonstrated by PCA and t-SNE assays in 500 LUAD patients. The survival assays revealed that the overall survivals of the high-risk group were distinctly poorer than those of the low-risk group with 1-, 3-, and 5-year AUC values of 0.695, 0.666, and 0.694, respectively. Finally, multivariate assays demonstrated the scoring system as an independent prognostic factor for overall survival.

Conclusions

Our study shows that the signature of nine inflammation-related genes can be used as a prognostic marker for LUAD.

Three inflammation-related genes could predict risk in prognosis and metastasis of patients with breast cancer

BACKGROUND

Current predictive model is not developed by inflammation-related genes to evaluate clinical outcome of breast cancer patients.

METHODS

With mRNA expression profiling, we identified 3 mRNAs with significant expression between 15 normal samples and 669 breast cancer patients. Using 7 cell lines and 150 paraffin-embedded specimens, we verified the expression pattern by bio-experiments. Then, we constructed a three-mRNA model by Cox regression method and approved its predictive accuracy in both training set (n = 1095) and 4 testing sets (n = 703).

RESULTS

We developed a three-mRNA (TBX21, TGIF2, and CYCS) model to stratify patients into high- and low-risk subgroup with significantly different prognosis. In training set, 5-year OS rate was 84.5% (78.8%-90.5%) vs 73.1% (65.9%-81.2%) for the low- and high-risk group (HR = 1.573 (1.090-2.271); P = 0.016). The predictive value was similar in four independent testing sets (HR>1.600; P < 0.05). This model could assess survival independently with better predictive power compared with single clinicopathological risk factors and any of the three mRNAs. Patients with both low-risk values and any poor prognostic factors had more favorable survival from nonmetastatic status (HR = 1.740 (1.028-2.945), P = 0.039). We established two nomograms for clinical application that integrated this model and another three significant risk factors to forecast survival rates precisely in patients with or without metastasis.

CONCLUSIONS

This model is a dependable tool to predict the disease recurrence precisely and could improve the predictive accuracy of survival probability for breast cancer patients with or without metastasis.

Identifying autophagy gene-associated module biomarkers through construction and analysis of an autophagy-mediated ceRNA‑ceRNA interaction network in colorectal cancer

Autophagy is crucial in cellular homeostasis and has been implicated in the development of malignant tumors. However, the regulatory function of autophagy in cancer remains to be fully elucidated. In the present study, the autophagy-mediated competing endogenous RNA (ceRNA)-ceRNA interaction networks in colorectal cancer (CRC) were constructed by integrating systematically expression profiles of long non-coding RNAs and mRNAs. It was found that a large proportion of autophagy genes were inclined to target hub nodes, including a fraction of autophagy genes, by comparing with other genes within ceRNA networks, and showed preferential interaction with themselves. The present study also revealed that autophagy genes may be used as prognostic markers for cancer therapy. A risk score model based on multivariable Cox regression analysis was then used to capture novel biomarkers in connection with lncRNA for the prognosis of CRC. These biomarkers were confirmed in the test dataset and an additional independent dataset. Furthermore, the prognostic value of biomarkers is independent of conventional clinical factors. These results provide improved understanding of autophagy-mediated ceRNA regulatory mechanisms in CRC and provide novel potential molecular therapeutic targets for the diagnosis and treatment of CRC.

TBX21 predicts prognosis of patients and drives cancer stem cell maintenance via the TBX21-IL-4 pathway in lung adenocarcinoma

Background

The Th1 cell-specific transcription factor TBX21 functions as a regulator of expression of a Th1 cytokine, interferon gamma (IFN-γ). However, the specific function of TBX21 correlated with cancer stemness remains unclear.

Methods

Using univariate and multivariate survival analysis, TBX21was identified as an independent predictive factor and was associated with poor prognosis in 1389 patients with lung adenocarcinoma (LUAD). Its mechanism in the prognosis was explored by functional enrichment analysis and validated in bioexperiments.

Results

In the training and test sets, TBX21 could classify 1389 LUAD patients into high and low-risk groups with significantly different prognosis (P < 0.01). Its prognostic power was independent of other clinical factors including stage, age, gender and smoking status. Functional studies indicated that downregulating TBX21 in lung cancer cells decreased the fraction of cancer stem cells and their sphere and tumor initiation frequency. Furthermore, the study showed that TBX21 activation transduced a TBX21–IL-4 signaling cascade to promote tumor initiation, tumor growth and expression of stemness markers.

Conclusions

These data demonstrated a key role of TBX21 in the maintenance of cancer stemness and that the TBX21–IL-4 pathway is a crucial factor contributing to lung carcinogenesis.

Graphical abstract

TBX21 prognostic model correlated with cancer stemness via TBX21-IL-4 pathway in LUAD patients

Electronic supplementary material

The online version of this article (10.1186/s13287-018-0820-6) contains supplementary material, which is available to authorized users.

Molecular heterogeneity in diffuse large B-cell lymphoma and its implications in clinical diagnosis and treatment

Over half of patients with diffuse large B-cell lymphoma (DLBCL) can be cured by standard R-CHOP treatment (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone). However, the remaining patients are refractory and ultimately succumb to progressive or relapsed disease. During the past decade, there has been significant progress in the understanding of molecular mechanisms in DLBCL, largely owing to collaborative efforts in large-scale gene expression profiling and deep sequencing, which have identified genetic alterations critical in lymphomagenesis through activation of key signaling transduction pathways in DLBCL. These discoveries have not only led to the development of targeted therapies, including several currently in clinical trials, but also laid a solid foundation for the future identification of more effective therapies for patients not curable by R-CHOP. This review summarizes the recent advances in our understanding of the molecular characterization and pathogenesis of DLBCL and new treatment directions.

Siltuximab and hematologic malignancies. A focus in non Hodgkin lymphoma

INTRODUCTION

The role of interleukin-6 (IL-6) in tumorigenesis and in particular in haematological malignancies is crucial. On the basis of the favourable results obtained in the subset of multicentric Castleman disease (MCD), Siltuximab, a chimeric, human-murine, immunoglobulin (Ig) Gk monoclonal antibody directed against human IL-6 has been evaluated in haematological malignancies such as multiple myeloma, myelodisplastic syndromes and non Hodgkin lymphomas. Areas covered: This review discusses available data related to the role of IL-6 as a therapeutic target, the characteristics of Siltuximab in term pharmacokinetics and pharmacodynamics properties and a detailed analysis of the studies involving haematological malignancies with a peculiar focus on non Hodgkin lymphoma. Expert opinion: The results obtained with Siltuximab in haematological malignancies and in particular with non Hodgkin lymphoma are inferior to those obtained in MCD. The complex interaction between malignant clones, inflammatory background and host response could justify this difference. New interesting areas of study are the role of Siltuximab in early phase of multiple myeloma (smoldering multiple myeloma) and if there may be a possible future application in the treatment of Waldenström macroglobulinemia.

Prediction of survival of diffuse large B-cell lymphoma patients via the expression of three inflammatory genes

Currently, several gene-expression signatures that were used to predict survival of diffuse large B-cell lymphoma (DLBCL) patients, showed a restriction on the practical work for lack of convenient operation. In this study, we screened inflammatory genes whose expression correlated with survival of DLBCL and established a predictive model including IL6, IL1A and CSF3 through multivariate Cox regression based on the expression of these three genes. We validated the model at protein level in our clinical serum cohort composed of 101 patients of DLBCL and 50 healthy controls and 534 DLBCL patients at mRNA level from three independent Gene Expression Omnibus (GEO) data sets. We found our model to be independent of the International Prognostic Index (IPI), moreover, it can augment the predictive power of IPI. In summary, our three-gene model is sufficient to predict survival of DLBCL patients via measuring the concentration of three inflammatory cytokines in peripheral blood.