Authors: Hao Yang, Xiaoming Zou, Shifeng Yang...

BACKGROUND

The epigenetic regulatory chemical lactate is a product of glycolysis. It can regulate gene expression through histone lactylation, thereby promoting tumor proliferation, metastasis, and immunosuppression.

METHODS

In this study, a lactylation-related model for gastric cancer (GC) was constructed, and its relationships to prognosis, immune cell infiltration, and immunotherapy were investigated. By contrasting normal tissues and tumor tissues, four lactylation-related pathways that were substantially expressed in GC tissues were found in the GSEA database. Six lactylation-related genes were screened for bioinformatic analysis. The GC data sets from the TCGA and GEO databases were downloaded and integrated to perform cluster analysis, and the lactylation related model was constructed by secondary clustering.

RESULTS

The fingding demonstrated that the lactylation score has a strong correlation with the overall survival rate from GC and the progression of GC. Mechanistic experiments showed that abundant immune cell infiltration (macrophages showed the highest degree of infiltration) and increased genetic instability are traits of high lactylation scores. Immune checkpoint inhibitors (ICIs) demonstrated a reduced response rate in GC with high lactylation scores. At the same time, tumors with high lactylation scores had high Tumor Immune Dysfunction and Exclusion scores, which means that they had a higher risk of immune evasion and dysfunction.

DISCUSSION

These findings indicate that the lactylation score can be used to predict the malignant progression and immune evasion of GC. This model also can guide the treatment response to ICIs of GC. The constructed model of the lactate gene is also expected to become a potential therapeutic target for GC and diagnostic marker.

Topics: Humans; Stomach Neoplasms; Prognosis; Immunotherapy; Immunosuppression Therapy; Cluster Analysis

PubMed: 36936929
DOI: 10.3389/fimmu.2023.1149989

Review

Authors: Michael Goodman, Judy S LaKind, Jerald A Fagliano...

Residential clusters of non-communicable diseases are a source of enduring public concern, and at times, controversy. Many clusters reported to public health agencies by concerned citizens are accompanied by expectations that investigations will uncover a cause of disease. While goals, methods and conclusions of cluster studies are debated in the scientific literature and popular press, investigations of reported residential clusters rarely provide definitive answers about disease etiology. Further, it is inherently difficult to study a cluster for diseases with complex etiology and long latency (e.g., most cancers). Regardless, cluster investigations remain an important function of local, state and federal public health agencies. Challenges limiting the ability of cluster investigations to uncover causes for disease include the need to consider long latency, low statistical power of most analyses, uncertain definitions of cluster boundaries and population of interest, and in- and out-migration. A multi-disciplinary Workshop was held to discuss innovative and/or under-explored approaches to investigate cancer clusters. Several potentially fruitful paths forward are described, including modern methods of reconstructing residential history, improved approaches to analyzing spatial data, improved utilization of electronic data sources, advances using biomarkers of carcinogenesis, novel concepts for grouping cases, investigations of infectious etiology of cancer, and "omics" approaches.

Topics: Cluster Analysis; Forecasting; Humans; Neoplasms

PubMed: 24477211
DOI: 10.3390/ijerph110201479

Authors: Shaoshuai Tang, Yunzhi Wang, Rongkui Luo...

Soft tissue sarcoma is a broad family of mesenchymal malignancies exhibiting remarkable histological diversity. We portray the proteomic landscape of 272 soft tissue sarcomas representing 12 major subtypes. Hierarchical classification finds the similarity of proteomic features between angiosarcoma and epithelial sarcoma, and elevated expression of SHC1 in AS and ES is correlated with poor prognosis. Moreover, proteomic clustering classifies patients of soft tissue sarcoma into 3 proteomic clusters with diverse driven pathways and clinical outcomes. In the proteomic cluster featured with the high cell proliferation rate, APEX1 and NPM1 are found to promote cell proliferation and drive the progression of cancer cells. The classification based on immune signatures defines three immune subtypes with distinctive tumor microenvironments. Further analysis illustrates the potential association between immune evasion markers (PD-L1 and CD80) and tumor metastasis in soft tissue sarcoma. Overall, this analysis uncovers sarcoma-type-specific changes in proteins, providing insights about relationships of soft tissue sarcoma.

Topics: Humans; Proteomics; Sarcoma; Hemangiosarcoma; Biomarkers; Cluster Analysis; Soft Tissue Neoplasms; Tumor Microenvironment

PubMed: 38360860
DOI: 10.1038/s41467-024-45306-y

Authors: Mohamed Khalis, Imad Elbadisy, Oumnia Bouaddi...

BACKGROUND

Cancer has become a major health concern due to the increasing morbidity and mortality rates, and its negative social, economic consequences and the heavy financial burden incurred by cancer patients. About 40% of cancers are preventable. The aim of this study was to assess the knowledge, attitudes, and practices regarding cancer prevention, and associated characteristics to inform the development of targeted cancer prevention campaigns and policies.

METHODS

We conducted a cross-sectional survey of adult patients at Mohamed Sekkat and Sidi Othmane Hospitals in Casablanca, Morocco. Data collection was conducted by two trained interviewers who administered the questionnaire in-person in the local language. An unsupervised clustering approach included 17 candidate variables for the cluster analysis. The variables covered a wide range of characteristics, including demographics, health perceptions and attitudes. Survey answers were calculated to compose qualitative ordinal categories, including a cancer attitude score and knowledge score.

RESULTS

The cluster-based analysis showed that participants in cluster 1 had the highest mean attitude score (13.9 ± 2.15) and percentage of individuals with a high level of knowledge (50.8%) whereas the lowest mean attitude score (9.48 ± 2.02) and knowledge level (7.5%.) were found in cluster 3. The participants with the lowest cancer attitude scores and knowledge levels were aged 34 to 47 years old (middle age group), predominantly females, living in rural settings, and were least likely to report health professionals as a source of health information.

CONCLUSIONS

The findings showed that female individuals living in rural settings, belonging to an older age group, who were least likely to use health professionals as an information source had the lowest levels of knowledge and attitudes. These groups are amenable to targeted and tailored interventions aiming to modify their understanding of cancer in order to enhance the outcomes of Morocco's on-going efforts in cancer prevention and control strategies.

Topics: Humans; Morocco; Health Knowledge, Attitudes, Practice; Female; Male; Adult; Neoplasms; Middle Aged; Cluster Analysis; Cross-Sectional Studies; Surveys and Questionnaires; Young Adult; Aged; Adolescent

PubMed: 38824496
DOI: 10.1186/s12885-024-12226-5

Review

Authors: Beverly S Kingsley, Karen L Schmeichel, Carol H Rubin...

The Centers for Disease Control and Prevention (CDC) continues to be aware of the need for response to public concern as well as to state and local agency concern about cancer clusters. In 1990 the CDC published the "Guidelines for Investigating Clusters of Health Events," in which a four-stage process was presented. This document has provided a framework that most state health departments have adopted, with modifications pertaining to their specific situations, available resources, and philosophy concerning disease clusters. The purpose of this present article is not to revise the CDC guidelines; they retain their original usefulness and validity. However, in the past 15 years, multiple cluster studies as well as scientific and technologic developments have affected duster science and response (improvements in cancer registries, a federal initiative in environmental public health tracking, refinement of biomarker technology, cluster identification using geographic information systems software, and the emergence of the Internet). Thus, we offer an addendum for use with the original document. Currently, to address both the needs of state health departments as well as public concern, the CDC now a) provides a centralized, coordinated response system for cancer cluster inquiries, b) supports an electronic cancer cluster listserver, c) maintains an informative web page, and d) provides support to states, ranging from laboratory analysis to epidemiologic assistance and expertise. Response to cancer clusters is appropriate public health action, and the CDC will continue to provide assistance, facilitate communication among states, and foster the development of new approaches in duster science.

Topics: Centers for Disease Control and Prevention, U.S.; Cluster Analysis; Environmental Exposure; Environmental Health; Humans; Neoplasms; United States

PubMed: 17366838
DOI: 10.1289/ehp.9021

Authors: Khong-Loon Tiong, Yu-Wei Lin, Chen-Hsiang Yeang...

Despite the remarkable progress in probing tumor transcriptomic heterogeneity by single-cell RNA sequencing (sc-RNAseq) data, several gaps exist in prior studies. Tumor heterogeneity is frequently mentioned but not quantified. Clustering analyses typically target cells rather than genes, and differential levels of transcriptomic heterogeneity of gene clusters are not characterized. Relations between gene clusters inferred from multiple datasets remain less explored. We provided a series of quantitative methods to analyze cancer sc-RNAseq data. First, we proposed two quantitative measures to assess intra-tumoral heterogeneity/homogeneity. Second, we established a hierarchy of gene clusters from sc-RNAseq data, devised an algorithm to reduce the gene cluster hierarchy to a compact structure, and characterized the gene clusters with functional enrichment and heterogeneity. Third, we developed an algorithm to align the gene cluster hierarchies from multiple datasets to a small number of meta gene clusters. By applying these methods to nine cancer sc-RNAseq datasets, we discovered that cancer cell transcriptomes were more homogeneous within tumors than the accompanying normal cells. Furthermore, many gene clusters from the nine datasets were aligned to two large meta gene clusters, which had high and low heterogeneity and were enriched with distinct functions. Finally, we found the homogeneous meta gene cluster retained stronger expression coherence and associations with survival times in bulk level RNAseq data than the heterogeneous meta gene cluster, yet the combinatorial expression patterns of breast cancer subtypes in bulk level data were not preserved in single-cell data. The inference outcomes derived from nine cancer sc-RNAseq datasets provide insights about the contributing factors for transcriptomic heterogeneity of cancer cells and complex relations between bulk level and single-cell RNAseq data. They demonstrate the utility of our methods to enable a comprehensive characterization of co-expressed gene clusters in a wide range of sc-RNAseq data in cancers and beyond.

Topics: Algorithms; Breast Neoplasms; Cluster Analysis; Female; Humans; Multigene Family; Transcriptome

PubMed: 35665803
DOI: 10.1242/bio.059256

Review

Authors: Asha Mathew, Amit Jiwan Tirkey, Hongjin Li...

OBJECTIVE

The two approaches to symptom-cluster research include grouping symptoms and grouping patients. The objective of this systematic review was to examine the conceptual approaches and methodologies used in symptom-cluster research in patients with head and neck cancer.

DATA SOURCES

Articles were retrieved from electronic databases (CINAHL, MEDLINE via Ovid, APA PsycINFO, Scopus, Embase, and Cochrane Central Register of Controlled Trials-CENTRAL), five grey literature portals, and Google Scholar. Seventeen studies met the eligibility criteria. Eight studies grouped symptoms to identify symptom clusters, of which two used qualitative methods. The number of symptom clusters ranged from two to five, and the number of symptoms in a cluster ranged from 2 to 11. Nine studies grouped patients based on their experiences with multiple symptoms. Cluster analysis and factor analysis were most commonly used. Despite variable names and composition of symptom clusters, synthesis revealed three prominent symptom clusters: general, head and neck cancer-specific, and gastrointestinal. Being female and quality of life were significantly associated with high symptom group or cluster severity. Biological mechanisms were sparsely examined.

CONCLUSION

Symptom cluster research in head and neck cancer is emerging. Consensus on nomenclature of a symptom cluster will facilitate deduction of core clinically relevant symptom clusters in head and neck cancer. Further research is required on understanding patients' subjective experiences, identifying predictors and outcomes, and underlying mechanisms for symptom clusters.

IMPLICATIONS FOR NURSING PRACTICE

Identification of clinically relevant symptom clusters would enable targeted symptom assessment and management strategies, thus improving treatment efficiencies and patient outcomes.

Topics: Cluster Analysis; Female; Head and Neck Neoplasms; Humans; Quality of Life; Symptom Assessment; Syndrome

PubMed: 34483015
DOI: 10.1016/j.soncn.2021.151215

Review

Authors: Michael J Thun, Thomas Sinks

Each year, state and local health departments respond to more than 1,000 inquiries about suspected cancer clusters. Three quarters of these reports involve situations that are clearly not clusters and can be resolved by telephone. For the remainder, follow-up is needed, first to confirm the number of persons affected, their age, type of cancer, dates of diagnosis, and other factors, and then to compare cancer incidence in the affected population with background rates in state tumor registries. In approximately 5% to 15% of the reported situations, formal statistical testing confirms that the number of observed cases exceeds the number expected in a specific area, given the age, sex, and size of the affected population. Even in these instances, however, chance remains a plausible explanation for many clusters, and further epidemiologic investigation almost never identifies the underlying cause of disease with confidence. The few exceptions have involved clusters of extremely rare cancers occurring in well-defined occupational or medical settings, generally involving intense and sustained exposure to an unusual chemical, occupation, infection, or drug. This article discusses the resources and scientific tools currently available to investigate cancer clusters. It also provides a framework for understanding cancer clusters and a realistic appraisal of what cluster investigations can and cannot provide in the context of community expectations.

Topics: Cluster Analysis; Humans; Neoplasms; Physician's Role; Public Health; United States

PubMed: 15371285
DOI: 10.3322/canjclin.54.5.273

Authors: Dun Yuan, Bo-Wen Zheng, Bo-Yv Zheng...

BACKGROUND

A scientific and comprehensive analysis of the current status and trends in the field of cancer-associated fibroblast (CAF) research is worth investigating. This study aims to investigate and visualize the development, research frontiers, and future trends in CAFs both quantitatively and qualitatively based on a bibliometric approach.

METHODS

A total of 5518 publications were downloaded from the Science Citation Index Expanded of Web of Science Core Collection from 1999 to 2021 and identified for bibliometric analysis. Visualized approaches, OriginPro (version 9.8.0.200) and R (version 4.2.0) software tools were used to perform bibliometric and knowledge-map analysis.

RESULTS

The number of publications on CAFs increased each year, and the same tendency was observed in the RRI. Apart from China, the countries with the largest number of publications and the most cited frequency were mainly Western developed countries, especially the USA. Cancers was the journal with the largest number of articles published in CAFs, and Oncology was the most popular research orientation. The most productive author was Lisanti MP, and the University of Texas System was ranked first in the institutions. In addition, the topics of CAFs could be divided into five categories, including tumor classification, prognostic study, oncologic therapies, tumor metabolism and tumor microenvironment.

CONCLUSIONS

This is the first thoroughly scientific bibliometric analysis and visualized study of the global research field on CAFs over the past 20 years. The study may provide benefits for researchers to master CAFs' dynamic evolution and research trends.

Topics: Humans; Cancer-Associated Fibroblasts; China; Cluster Analysis; Knowledge; Neoplasms; Tumor Microenvironment

PubMed: 38031121
DOI: 10.1186/s40001-023-01527-3

Authors: Zhanwei Wang, Dionyssios Katsaros, Junlong Wang...

Host immunity involves various immune cells working in concert to achieve balanced immune response. Host immunity interacts with tumorigenic process impacting disease outcome. Clusters of different immune cells may reveal unique host immunity in relation to breast cancer progression. CIBERSORT algorithm was used to estimate relative abundances of 22 immune cell types in 3 datasets, METABRIC, TCGA, and our study. The cell type data in METABRIC were analyzed for cluster using unsupervised hierarchical clustering (UHC). The UHC results were employed to train machine learning models. Kaplan-Meier and Cox regression survival analyses were performed to assess cell clusters in association with relapse-free and overall survival. Differentially expressed genes by clusters were interrogated with IPA for molecular signatures. UHC analysis identified two distinct immune cell clusters, clusters A (83.2%) and B (16.8%). Memory B cells, plasma cells, CD8 positive T cells, resting memory CD4 T cells, activated NK cells, monocytes, M1 macrophages, and resting mast cells were more abundant in clusters A than B, whereas regulatory T cells and M0 and M2 macrophages were more in clusters B than A. Patients in cluster A had favorable survival. Similar survival associations were also observed in other independent studies. IPA analysis showed that pathogen-induced cytokine storm signaling pathway, phagosome formation, and T cell receptor signaling were related to the cell type clusters. Our finding suggests that different immune cell clusters may indicate distinct immune responses to tumor growth, suggesting their potential for disease management.

Topics: Humans; Female; Breast Neoplasms; Neoplasm Recurrence, Local; Survival Analysis; Cluster Analysis; Machine Learning

PubMed: 37923775
DOI: 10.1038/s41598-023-45932-4