Prognostic value of periostin in multiple solid cancers: A systematic review with meta‐analysis

Yang, Tao; Deng, Zhengdong; Pan, Zemin; Qian, Yawei; Yao, Wei; Wang, Jianming

doi:10.1002/jcp.29184

Cited by 15 publications

(14 citation statements)

References 57 publications

(107 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, one clinical study is currently probing the relevance of SPP1 serum levels for diagnosis of breast cancer (NCT 02895178). Other MCP families await successful clinical trials since the expression of several CCN family members in pancreatic, breast, oral, esophageal and brain cancers (241)(242)(243)(244)(245), TN-C in colorectal, glioma, pancreatic and bladder cancers (196,(246)(247)(248), and POSTN in various solid cancers (249) have all been touted as potential diagnostic and prognostic biomarkers.…”

Section: Mcps As Cancer Biomarkersmentioning

confidence: 99%

The Matrix Revolution: Matricellular Proteins and Restructuring of the Cancer Microenvironment

et al. 2020

View full text Add to dashboard Cite

The extracellular matrix (ECM) surrounding cells is indispensable for regulating their behavior. The dynamics of ECM signaling are tightly controlled throughout growth and development. During tissue remodeling, matricellular proteins (MCPs) are secreted into the ECM. These factors do not serve classical structural roles, but rather regulate matrix proteins and cell-matrix interactions to influence normal cellular functions. In the tumor microenvironment, it is becoming increasingly clear that aberrantly expressed MCPs can support multiple hallmarks of carcinogenesis by interacting with various cellular components that are coupled to an array of downstream signals. Moreover, MCPs also reorganize the biomechanical properties of the ECM to accommodate metastasis and tumor colonization. This realization is stimulating new research on MCPs as reliable and accessible biomarkers in cancer, as well as effective and selective therapeutic targets. Research.

show abstract

Section: Mcps As Cancer Biomarkersmentioning

confidence: 99%

The Matrix Revolution: Matricellular Proteins and Restructuring of the Cancer Microenvironment

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In lung cancer, the soluble fraction of VCAM-1 predicted relapse and lower OS, whereas high POSTN expression was associated with shorter PFS in ovarian cancer patients (Ryner et al, 2015;Zhou et al, 2020). Of note, a recent meta-analysis confirmed the value of POSTN as a biomarker predicting poor outcome in different solid tumors (Yang et al, 2020). Finally, increased THBS-2 predicted decreased OS in CRC patients .…”

Section: Prognostic and Predictive Value Of Cafs Secretomementioning

confidence: 95%

Determinants and Functions of CAFs Secretome During Cancer Progression and Therapy

Linares

Marín-Jiménez

Badia-Ramentol

et al. 2021

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

Multiple lines of evidence are indicating that cancer development and malignant progression are not exclusively epithelial cancer cell-autonomous processes but may also depend on crosstalk with the surrounding tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs) are abundantly represented in the TME and are continuously interacting with cancer cells. CAFs are regulating key mechanisms during progression to metastasis and response to treatment by enhancing cancer cells survival and aggressiveness. The latest advances in CAFs biology are pointing to CAFs-secreted factors as druggable targets and companion tools for cancer diagnosis and prognosis. Especially, extensive research conducted in the recent years has underscored the potential of several cytokines as actionable biomarkers that are currently evaluated in the clinical setting. In this review, we explore the current understanding of CAFs secretome determinants and functions to discuss their clinical implication in oncology.

show abstract

“…7 POSTN is a secreted extracellular matrix protein that regulates important aspects of tumour biology, including cancer cell migration, invasion, metastatic dissemination, and chemoresistance. [36][37][38][39] These biomarkers represent two major axes of the tumour stroma and were used to define stroma classes.…”

Section: Methodsmentioning

confidence: 99%

“…Multivariable Cox regression analysis adjusted for clinicopathological variables, including TNM stage and histologic subtype, showed that the stromal imaging signature remained an independent prognostic factor for both disease-free survival and overall survival in the training cohort and all four validation cohorts (p<0•0001; appendix pp [38][39][40][41][42][43][44].…”

Section: Stroma Classmentioning

confidence: 99%

“…α-SMA is a marker for cancer-associated fibroblasts (an abundant cell type in the stroma), 7 and POSTN is an extracellular matrix protein secreted predominantly by stromal cells. 35 Given the established clinical relevance of these stromal biomarkers, [36][37][38][39][40] we also tested the model's ability to predict prognosis and treatment outcomes, as the secondary goal of the study.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Radiographical assessment of tumour stroma and treatment outcomes using deep learning: a retrospective, multicohort study

Jiang

Liang

Han

et al. 2021

The Lancet Digital Health

View full text Add to dashboard Cite

Background The tumour stroma microenvironment plays an important part in disease progression and its composition can influence treatment response and outcomes. Histological evaluation of tumour stroma is limited by access to tissue, spatial heterogeneity, and temporal evolution. We aimed to develop a radiological signature for non-invasive assessment of tumour stroma and treatment outcomes. MethodsIn this multicentre, retrospective study, we analysed CT images and outcome data of 2209 patients with resected gastric cancer from five independent cohorts recruited from two centres (Nanfang Hospital of Southern Medical University [Guangzhou, China] and Sun Yat-sen University Cancer Center [Guangzhou, China]). Patients with histologically confirmed gastric cancer, at least 15 lymph nodes harvested, preoperative abdominal CT available, and complete clinicopathological and follow-up data were eligible for inclusion. Tumour tissue was collected for patients in the training cohort (321 patients), internal validation cohort one (246 patients), and external validation cohort one (128 patients). Four stroma classes were defined according to the protein expression of α-smooth muscle actin and periostin assessed by immunohistochemistry. The primary objective was to predict the histologically based stroma classes by using preoperative CT images. We trained a deep convolutional neural network model using the training cohort and tested the model in the internal and external validation cohort one. We evaluated the model's association with prognosis in the training cohort, two internal, and two external validation cohorts and compared outcomes of patients who received or did not receive adjuvant chemotherapy. Findings The deep-learning model achieved a high diagnostic accuracy for assessing tumour stroma in both internal validation cohort one (area under the receiver operating characteristic curve [AUC] 0•96-0•98]) and external validation cohort one (AUC 0•89-0•94). The stromal imaging signature was significantly associated with disease-free survival and overall survival in all cohorts (p<0•0001). The predicted stroma classes remained an independent prognostic factor adjusting for clinicopathological variables including tumour size, stage, differentiation, and Lauren histology. In patients with stage II or III disease in predicted stroma classes one and two subgroups, patients who received adjuvant chemotherapy had improved survival compared with those who did not (in those with stage II disease hazard ratio [HR] 0•48 [95% CI 0•29-0•77], p=0•0021; and in those with stage III disease HR 0•70 [0•57-0•85], p=0•00042). However, in the other two subgroups adjuvant chemotherapy was not associated with survival and might even be detrimental in the predicted stroma class 4 subgroup (HR 1•48 [1•08-2•03], p=0•013). Interpretation The deep-learning model could allow for accurate and non-invasive evaluation of tumour stroma from CT images in gastric cancer. The radiographical model predicted chemotherapy outcomes and could be used in combinati...

show abstract

Prognostic value of periostin in multiple solid cancers: A systematic review with meta‐analysis

Cited by 15 publications

References 57 publications

The Matrix Revolution: Matricellular Proteins and Restructuring of the Cancer Microenvironment

The Matrix Revolution: Matricellular Proteins and Restructuring of the Cancer Microenvironment

Determinants and Functions of CAFs Secretome During Cancer Progression and Therapy

Radiographical assessment of tumour stroma and treatment outcomes using deep learning: a retrospective, multicohort study

Contact Info

Product

Resources

About