The pan-cancer analysis of gene expression patterns in the context of inflammation

Decoding Systems Biology of Inflammation Signatures in Cancer Pathogenesis: Pan-Cancer Insights from 12 Common Cancers

Chronic inflammation is an important contributor to tumorigenesis in many tissues. However, the underlying mechanisms of inflammatory signaling in the tumor microenvironment are not yet fully understood in various cancers. Therefore, this study aimed to uncover the gene expression signatures of inflammation-associated proteins that lead to tumorigenesis, and with an eye to discovery of potential system biomarkers and novel drug candidates in oncology. Gene expression profiles associated with 12 common cancers (e.g., breast invasive carcinoma, colon adenocarcinoma, liver hepatocellular carcinoma, and prostate adenocarcinoma) from The Cancer Genome Atlas were retrieved and mapped to inflammation-related gene sets. Subsequently, the inflammation-associated differentially expressed genes (i-DEGs) were determined. The i-DEGs common in all cancers were proposed as tumor inflammation signatures (TIS) after pan-cancer analysis. A TIS, consisting of 45 proteins, was evaluated as a potential system biomarker based on its prognostic forecasting and secretion profiles in multiple tissues. In addition, i-DEGs for each cancer type were used as queries for drug repurposing. Narciclasine, parthenolide, and homoharringtonine were identified as potential candidates for drug repurposing. Biomarker candidates in relation to inflammation were identified such as KNG1, SPP1, and MIF. Collectively, these findings inform precision diagnostics development to distinguish individual cancer types, and can also pave the way for novel prognostic decision tools and repurposed drugs across multiple cancers. These new findings and hypotheses warrant further research toward precision/personalized medicine in oncology. Pan-cancer analysis of inflammatory mediators can open up new avenues for innovation in cancer diagnostics and therapeutics.

Inflammatory Biomarkers as Promising Predictors of Prognosis in Cervical Cancer Patients

INTRODUCTION

Increasing evidence demonstrates a crucial role of inflammation in inducing and promoting several cancers. Pro-inflammatory upregulation of cytokines such as IL-6 has been implicated in cervical cancer development and progression through several mechanisms, for example, by inducing platelet production, activation, and aggregation. The aim of the study was to evaluate the effective prognostic impact of inflammatory biomarkers such as platelet count, platelet to lymphocyte ratio (PLR), and IL-6 in cervical cancer patients.

MATERIALS AND METHODS

Between 2016 and 2019, 108 out of 159 patients with cervical cancer have been enrolled. Cutoff level of pretreatment platelet count and PLR was identified by using the ROC curve. IL-6 tumoral and peritumoral expression was analyzed and stratified as low and high (low expression: 0 and +1; marked expression: +2 and +3).

RESULTS

Median follow-up duration was 30 months (range 16-44). Patients with higher platelet counts showed worse DFS and OS (DFS p < 0.001; OS p < 0.001). Cumulative rates of DFS and OS in patients with lower PLR were higher than in patients with higher values of PLR (DFS p = 0.032; OS p < 0.001). Survival analysis showed a better prognosis in patients with lower IL-6 expression (DFS p < 0.001; OS p < 0.001).

CONCLUSION

Nowadays, causal relationship between inflammation, innate immunity, and cancer is more widely accepted. However, many of the molecular and cellular mechanisms mediating this relationship remain unresolved. Ongoing inflammatory response was associated with poor outcomes in cervical cancer patients. A higher pretreatment platelet count and PLR value associated with higher IL-6 tumoral expression could be used to predict poor prognosis in cervical cancer patients.

Dietary isothiocyanates inhibit cancer progression by modulation of epigenome

Cell cycle, growth, survival and metabolism are tightly regulated together and failure in cellular regulation leads to carcinogenesis. Several signaling pathways like the PI3K, WNT, MAPK and NFKb pathway exhibit aberrations in cancer and help achieve hallmark capabilities. Clinical research and in vitro studies have highlighted the role of epigenetic alterations in cancer onset and development. Altered gene expression patterns enabled by changes in DNA methylation, histone modifications and RNA processing have proven roles in cancer hallmark acquisition. The reversible nature of epigenetic processes offers robust therapeutic targets. Dietary bioactive compounds offer a vast compendium of effective therapeutic moieties. Isothiocyanates (ITCs) sourced from cruciferous vegetables demonstrate anti-proliferative, pro-apoptotic, anti-inflammatory, anti-migratory and anti-angiogenic effect against several cancers. ITCs also modulate the redox environment, modulate signaling pathways including PI3K, MAPK, WNT, and NFkB. They also modulate the epigenetic machinery by regulating the expression and activity of DNA methyltransferases, histone modifiers and miRNA. This further enhances their transcriptional modulation of key cellular regulators. In this review, we comprehensively assess the impact of ITCs such as sulforaphane, phenethyl isothiocyanate, benzyl isothiocyanate and allyl isothiocyanate on cancer and document their effect on various molecular targets. Overall, this will facilitate consolidation of the current understanding of the anti-cancer and epigenetic modulatory potential of these compounds and recognize the gaps in literature. Further, we discuss avenues of future research to develop these compounds as potential therapeutic entities.

Platelet-lymphocyte ratio as a potential prognostic factor in gynecologic cancers: a meta-analysis

PURPOSE

Cancer-related inflammation plays an important role in tumor development and progression. Platelet-lymphocyte ratio (PLR) has been studied as a biomarker for prognosis in gynecologic cancers. But, the results of previous studies were controversial, so we performed this meta-analysis.

METHODS

We searched the scientific database of PubMed, Embase, Web of Science, Wanfang, and China National Knowledge Infrastructure (CNKI) using free text and MeSH keywords. Crude HR (hazard ratio) with 95% confidence interval was used to evaluate the risk association between PLR and overall survival (OS) or progression-free survival (PFS) in gynecologic neoplasms.

RESULTS

There totally 23 studies, including 6869 patients who were eligible, most of which are published after 2015 or later. PLR greater than the cut-off was associated with poorer survival prognosis in ovarian cancer [OS: HR 1.80 (95% CI 1.37-2.37), p = 0.000; PFS: HR 1.63 (95% CI 1.38-1.91), p = 0.000] and cervical cancer [OS: HR 1.36 (95% CI 1.10-1.68), p = 0.005; PFS: HR 1.40 (95% CI 1.16-1.70), p = 0.002], but not in endometrial cancer [OS: HR 1.95 (95% CI 0.65-5.84), p = 0.234].

CONCLUSIONS

The current meta-analysis revealed that pretreatment PLR was a simple, promising prognostic indicator for OS and PFS in ovarian and cervical cancers. But, its significance of prognosis did not agree with endometrial neoplasm. However, due to the limited number of original studies, future large-scale studies with more well-designed, high-quality studies are still needed.

Horizontal and vertical integrative analysis methods for mental disorders omics data

In recent biomedical studies, omics profiling has been extensively conducted on various types of mental disorders. In most of the existing analyses, a single type of mental disorder and a single type of omics measurement are analyzed. In the study of other complex diseases, integrative analysis, both vertical and horizontal integration, has been conducted and shown to bring significantly new insights into disease etiology, progression, biomarkers, and treatment. In this article, we showcase the applicability of integrative analysis to mental disorders. In particular, the horizontal integration of bipolar disorder and schizophrenia and the vertical integration of gene expression and copy number variation data are conducted. The analysis is based on the sparse principal component analysis, penalization, and other advanced statistical techniques. In data analysis, integration leads to biologically sensible findings, including the disease-related gene expressions, copy number variations, and their associations, which differ from the “benchmark” analysis. Overall, this study suggests the potential of integrative analysis in mental disorder research.

Integrative Analysis of Cancer Omics Data for Prognosis Modeling

Prognosis modeling plays an important role in cancer studies. With the development of omics profiling, extensive research has been conducted to search for prognostic markers for various cancer types. However, many of the existing studies share a common limitation by only focusing on a single cancer type and suffering from a lack of sufficient information. With potential molecular similarity across cancer types, one cancer type may contain information useful for the analysis of other types. The integration of multiple cancer types may facilitate information borrowing so as to more comprehensively and more accurately describe prognosis. In this study, we conduct marginal and joint integrative analysis of multiple cancer types, effectively introducing integration in the discovery process. For accommodating high dimensionality and identifying relevant markers, we adopt the advanced penalization technique which has a solid statistical ground. Gene expression data on nine cancer types from The Cancer Genome Atlas (TCGA) are analyzed, leading to biologically sensible findings that are different from the alternatives. Overall, this study provides a novel venue for cancer prognosis modeling by integrating multiple cancer types.

Cervical cancer systemic inflammation score: a novel predictor of prognosis

Inflammation contributes to development and progression in a variety of cancers, including cervical cancer. We developed a novel cervical cancer systemic inflammation score (CCSIS) based on the preoperative platelet-to-lymphocyte ratio (PLR) and serum albumin levels. A retrospective analysis of clinical data from 795 patients with operable cervical cancer was then conducted to investigate the prognostic value of CCSIS and its association with the patients' clinicopathological features, overall survival (OS), and disease-free survival (DFS). CCSIS was predictive of OS and DFS. High CCSIS was correlated with more advanced FIGO stages, poor tumor differentiation, and the presence of PLN and LVSI. Both albumin levels and the PLR were independent prognostic indicators for operable cervical cancer. The use of the CCSIS could improve risk stratification and traditional clinicopathological analysis in cervical cancer.

Identification of a multi-cancer gene expression biomarker for cancer clinical outcomes using a network-based algorithm

Cancer types are commonly classified by histopathology and more recently through molecular characteristics such as gene expression, mutations, copy number variations, and epigenetic alterations. These molecular characterizations have led to the proposal of prognostic biomarkers for many cancer types. Nevertheless, most of these biomarkers have been proposed for a specific cancer type or even specific subtypes. Although more challenging, it is useful to identify biomarkers that can be applied for multiple types of cancer. Here, we have used a network-based exploration approach to identify a multi-cancer gene expression biomarker highly connected by ESR1, PRKACA, LRP1, JUN and SMAD2 that can be predictive of clinical outcome in 12 types of cancer from The Cancer Genome Atlas (TCGA) repository. The gene signature of this biomarker is highly supported by cancer literature, biological terms, and prognostic power in other cancer types. Additionally, the signature does not seem to be highly associated with specific mutations or copy number alterations. Comparisons with cancer-type specific and other multi-cancer biomarkers in TCGA and other datasets showed that the performance of the proposed multi-cancer biomarker is superior, making the proposed approach and multi-cancer biomarker potentially useful in research and clinical settings.