Prospective molecular mechanism of COL5A1 in breast cancer based on a microarray, RNA sequencing and immunohistochemistry

Identification of ZIC2 as a Potential Biomarker Linked with the Clinical Progression and Immune Infiltration of Oral Cancer: A Multicenter Study

Aim

To investigate the specific expression profile, clinicopathological significance and mechanism of Zic family member 2 (ZIC2) in oral cancer were unclear. Patients and Methods. We explored the expression pattern and clinicopathological significance of ZIC2 in oral cancer through performing in-house tissue microarray and integrated analysis global RNA-seq and microarrays containing large samples. The molecular basis of ZIC2 in oral cancer was further investigated in the aspects of transcription network and immune correlations. We also performed in vitro experiments and calculated drug sensitivity of oral cancer with different ZIC2 expression levels in response to hundreds of compounds.

Results

All data unanimously proved the significant overexpression of ZIC2 in oral cancer. The upregulation of ZIC2 was remarkably associated with the malignant clinical progression of oral cancer. ZIC2 was predicted to be targeted by miRNAs such as miR-3140, miR-4999, and miR-1322. The infiltration level of CD8+ T and central memory cells was positively related to the overexpression of ZIC2. Oral cancer patients with higher ZIC2 expression showed higher drug sensitivity to two compounds including AZD8186 and ERK_2240.

Conclusions

We demonstrated the upregulation of ZIC2 in oral cancer and its promoting effect on the clinical advancement of oral cancer. The potential clinical value of ZIC2 in oral cancer deserves attention.

Tumor-restrictive type III collagen in the breast cancer microenvironment: prognostic and therapeutic implications

Collagen plays a critical role in regulating breast cancer progression and therapeutic resistance. An improved understanding of both the features and drivers of tumor-permissive and -restrictive collagen matrices are critical to improve prognostication and develop more effective therapeutic strategies. In this study, using a combination of in vitro, in vivo and in silico experiments, we show that type III collagen (Col3) plays a tumor-restrictive role in human breast cancer. We demonstrate that Col3-deficient, human fibroblasts produce tumor-permissive collagen matrices that drive cell proliferation and suppress apoptosis in noninvasive and invasive breast cancer cell lines. In human TNBC biopsy samples, we demonstrate elevated deposition of Col3 relative to type I collagen (Col1) in noninvasive compared to invasive regions. Similarly, in silico analyses of over 1000 breast cancer patient biopsies from The Cancer Genome Atlas BRCA cohort revealed that patients with higher Col3:Col1 bulk tumor expression had improved overall, disease-free and progression-free survival relative to those with higher Col1:Col3 expression. Using an established 3D culture model, we show that Col3 increases spheroid formation and induces formation of lumen-like structures that resemble non-neoplastic mammary acini. Finally, our in vivo study shows co-injection of murine breast cancer cells (4T1) with rhCol3-supplemented hydrogels limits tumor growth and decreases pulmonary metastatic burden compared to controls. Taken together, these data collectively support a tumor-suppressive role for Col3 in human breast cancer and suggest that strategies that increase Col3 may provide a safe and effective modality to limit recurrence in breast cancer patients.

Collagen Remodeling along Cancer Progression Providing a Novel Opportunity for Cancer Diagnosis and Treatment

The extracellular matrix (ECM) is a significant factor in cancer progression. Collagens, as the main component of the ECM, are greatly remodeled alongside cancer development. More and more studies have confirmed that collagens changed from a barrier to providing assistance in cancer development. In this course, collagens cause remodeling alongside cancer progression, which in turn, promotes cancer development. The interaction between collagens and tumor cells is complex with biochemical and mechanical signals intervention through activating diverse signal pathways. As the mechanism gradually clears, it becomes a new target to find opportunities to diagnose and treat cancer. In this review, we investigated the process of collagen remodeling in cancer progression and discussed the interaction between collagens and cancer cells. Several typical effects associated with collagens were highlighted in the review, such as fibrillation in precancerous lesions, enhancing ECM stiffness, promoting angiogenesis, and guiding invasion. Then, the values of cancer diagnosis and prognosis were focused on. It is worth noting that several generated fragments in serum were reported to be able to be biomarkers for cancer diagnosis and prognosis, which is beneficial for clinic detection. At a glance, a variety of reported biomarkers were summarized. Many collagen-associated targets and drugs have been reported for cancer treatment in recent years. The new targets and related drugs were discussed in the review. The mass data were collected and classified by mechanism. Overall, the interaction of collagens and tumor cells is complicated, in which the mechanisms are not completely clear. A lot of collagen-associated biomarkers are excavated for cancer diagnosis. However, new therapeutic targets and related drugs are almost in clinical trials, with merely a few in clinical applications. So, more efforts are needed in collagens-associated studies and drug development for cancer research and treatment.

MiR-29b-3p Inhibits Migration and Invasion of Papillary Thyroid Carcinoma by Downregulating COL1A1 and COL5A1

Introduction

MicroRNAs (miRNAs) are small noncoding RNA molecules that regulate genetic expression and are also vital for tumor initiation and development. MiR-29b-3p was found to be involved in regulating various biological processes of tumors, including tumor cell proliferation, metastasis, and apoptosis inhibition; however, the biofunction and molecule-level mechanisms of miR-29b-3p inpapillary thyroid carcinoma (PTC) remain unclear.

Methods

The expression of miR-29b-3p in PTC samples was tested via qRT-PCR. Cellular proliferation was analyzed by CCK-8 and EdU assays, and cellular migratory and invasive abilities were assessed utilizing wound-healing and Transwell assays. In addition, protein expressions of COL1A1, COL5A1, E-cadherin, N-cadherin, Snail, and Vimentin were identified via Western blot (WB) assay. Bioinformatics, qRT-PCR, WB, and dual luciferase reporter assays were completed to identify whether miR-29b-3p targeted COL1A1 and COL5A1. In addition, our team explored the treatment effects of miR-29b-3p on a murine heterograft model.

Results

Our findings revealed that miR-29b-3p proved much more regulated downward in PTC tissue specimens than in adjacent non-cancerous tissues. Meanwhile, decreased expression of miR-29b-3p was strongly related to the TNM stage of PTC patients (p<0.001), while overexpression of miR-29b-3p in PTC cells suppressed cellular migration, invasion, proliferation, and EMT. Conversely, silencing miR-29b-3p yielded the opposite effect. COL1A1 and COL5A1 were affirmed as the target of miR-29b-3p. Additionally, the COL1A1 and COL5A1 were highly expressed in PTC tumor samples than in contrast to neighboring healthy samples. Functional assays revealed that overexpression of COL1A1 or COL5A1 reversed the suppressive role of miR-29b-3p in migration, invasion, and EMT of PTC cells. Finally, miR-29b-3p agomir treatment dramatically inhibited Xenograft tumor growth in the animal model.

Conclusions

These findings document that miR-29b-3p inhibited PTC cells invasion and metastasis by targeting COL1A1 and COL5A1; this study also sparks new ideas for risk assessment and miRNA replacement therapy in PTC.

The Hypoxia-Related Gene COL5A1 Is a Prognostic and Immunological Biomarker for Multiple Human Tumors

Background

Collagen type V alpha 1 chain (COL5A1) is a hypoxia-related gene (a collagen family protein) and participates in the formation of the extracellular matrix. Although some evidence supports a significant role for COL5A1 in the progression of several cancers, a pan-cancer analysis of COL5A1 is not currently available. Herein, we aimed to assess the prognostic value of COL5A1 in 33 human cancers and to investigate its underlying immunological function.

Methods

Through multiple bioinformatics methods, we analyzed the data from Oncomine, TCGA, CCLE, HPA, DNMIVD, and cBioPortal database to explore the potential underlying carcinogenic effect of COL5A1, including the relevance of COL5A1 to the outcome, DNA methylation, tumor microenvironment, immune cells infiltration, and drug sensitivity in 33 human cancers. The effects of COL5A1 on glioma cell proliferation, migration, and invasion were verified in cellular experiments.

Results

Our findings indicated that COL5A1 was expressed at high levels in 13 cancers and was negatively related to the prognosis of 11 cancers. Additionally, COL5A1 was coexpressed with genes encoding the major histocompatibility complex, immune activators, immune suppressors, chemokines, chemokine receptors, mismatch repair genes, and immune checkpoints. We also identified different roles for COL5A1 in the immunocyte infiltration in different cancers. The correlation between COL5A1 and drug sensitivity was found in several cancers. COL5A1 potentially influenced the tumor progression through immune-related pathways, negative regulation of immune system processes, chemokine signaling pathways, JAK-STAT pathways, T cell receptor pathways, lymphocyte migration, and antigen processing and presentation, among other processes.

Conclusions

Based on our study, COL5A1 may be employed as a prognostic marker in different malignancies because of its impact on tumorigenesis and immune cell infiltration and have implications for cancer immune checkpoint inhibitors and chemotherapy.

Type V collagen alpha 1 chain promotes the malignancy of glioblastoma through PPRC1-ESM1 axis activation and extracellular matrix remodeling

Glioblastoma (GBM) is a fatal cancer. Existing therapies do not have significant efficacy for GBM patients. Previous studies have shown that the collagen family is involved in the regulation of the extracellular environment of cancer cells, and these conditions could become an important factor for effective treatment. Therefore, we screened various collagen types and observed that the type V collagen α1 chain (COL5A1) gene plays a pivotal role in GBM. We further examined whether the overexpression of COL5A1 is common in mesenchymal subtypes and is related to the survival rate of GBM patients through several in silico cohorts. In addition, our cohort also showed a consistent trend in COL5A1 protein levels. Most importantly, we validated the cell mobility, metastatic ability and actin polymerization status caused by COL5A1 with two-way models. Based on these results, we established a transcriptomics dataset based on COL5A1. Moreover, PPRC1, GK and ESM1 were predicted by ingenuity pathway analysis (IPA) to be transcription factors or to participate downstream. We investigated the involvement of COL5A1 in extracellular remodeling and the regulation of actin filaments in the metastasis of GBM. Our results indicate that the COL5A1-PPRC1-ESM1 axis may represent a novel therapeutic target in GBM.

Applications of Machine Learning to Predict Cisplatin Resistance in Lung Cancer

Purpose

Lung cancer, mainly lung adenocarcinoma, lung squamous cell carcinoma and small cell lung cancer, has the highest incidence and cancer-related mortality worldwide. Platinum-based chemotherapy plays an important role in the treatment of various lung cancer subtypes, but not all patients benefit from this treatment regimen; thus, it is worth identifying lung cancer patients who are resistant or sensitive to platinum-based therapy.

Methods

The drug response and sequencing data of 170 lung cancer cell lines were downloaded from the Genomics of Drug Sensitivity in Cancer (GDSC) database, and support vector machines (SVMs) and beam search were used to select an optimal gene panel that can predict the sensitivity of cell lines to cisplatin. Then, we used available cell line data to explore the potential mechanisms.

Results

In this work, the drug response and sequencing data of 170 lung cancer cell lines were downloaded from the GDSC database, and SVMs and beam search were used to screen a panel of genes related to lung cancer cell line resistance to cisplatin. A final panel of nine genes (PLXNC1, KIAA0649, SPTBN4, SLC14A2, F13A1, COL5A1, SCN2A, PLEC, and ALMS1) was identified, and achieved an area under the curve (AUC) of 0.873 ± 0.004. The natural logarithm of the half maximal inhibitory concentration (lnIC50) values of the mutant-type (panel-MT) group was significantly higher than that of the wild-type (panel-WT) group, regardless of the lung cancer subtype. The differentially expressed pathways between the two groups may explain this difference.

Conclusion

In this study, we found that a panel of nine genes can accurately predict sensitivity to cisplatin, which may provide individualized treatment recommendations to improve the prognosis of patients with lung cancer.

Prognostic implication of glycolysis related gene signature in non-small cell lung cancer

Abnormal glycolysis is one of the hallmarks of cancer and plays an important role in its development. This study was devoted to identify glycolysis related genes as prognostic biomarkers for non-small cell lung cancer (NSCLC). The mRNA expression profile and clinical follow-up data were obtained using The Cancer Genome Atlas (TCGA) database. The validation set was obtained by bootstrap method of random repeated sampling. A total of 200 glycolysis-related genes were obtained from Gene Set Enrichment Analysis (GSEA) and 46 genes were significantly associated with overall survival (OS). Five genes (PKP2, LDHA, HMMR, COL5A1 and B3GNT3) were eventually identified to calculate risk score of NSCLC patients.

The univariate and multivariate Cox regression analysis indicated that the risk score was an independent prognostic factor (training set: HR=2.126, 95% CI [1.605, 2.815], p<0.001; validation set: HR=2.298, 95%CI [1.450, 3.640], p<0.001). Patients assigned to the high-risk group were associated with poor OS compared with patients in the low-risk group (training set: P=7.946e-06; validation set: P=6.368e-07). Receiver operating characteristic (ROC) curve and stratification analysis also demonstrated the potential prognostic performance. In conclusion, we constructed a novel glycolysis related risk signature which might contribute to predicting the prognosis of NSCLC.

Construction and Validation of a Novel Glycometabolism-Related Gene Signature Predicting Survival in Patients With Ovarian Cancer

Among all fatal gynecological malignant tumors, ovarian cancer has the highest mortality rate. The purpose of this study was to develop a stable and personalized glycometabolism-related prognostic signature to predict the overall survival of ovarian cancer patients. The gene expression profiles and clinical information of ovarian cancer patients were derived from four public GEO datasets, which were divided into training and testing cohorts. Glycometabolism-related genes significantly associated with prognosis were selected. A risk score model was established and validated to evaluate its predictive value. We found 5 genes significantly related to prognosis and established a five-mRNA signature. The five-mRNA signature significantly divided patients into a low-risk group and a high-risk group in the training set and validation set. Survival analysis showed that high risk scores obtained by the model were significantly correlated with adverse survival outcomes and could be regarded as an independent predictor for patients with ovarian cancer. In addition, the five-mRNA signature can predict the overall survival of ovarian cancer patients in different subgroups. In summary, we successfully constructed a model that can predict the prognosis of patients with ovarian cancer, which provides new insights into postoperative treatment strategies, promotes individualized therapy, and provides potential new targets for immunotherapy.

Reciprocal Interplay Between Fibrillar Collagens and Collagen-Binding Integrins: Implications in Cancer Progression and Metastasis

Cancers are complex ecosystems composed of malignant cells embedded in an intricate microenvironment made of different non-transformed cell types and extracellular matrix (ECM) components. The tumor microenvironment is governed by constantly evolving cell-cell and cell-ECM interactions, which are now recognized as key actors in the genesis, progression and treatment of cancer lesions. The ECM is composed of a multitude of fibrous proteins, matricellular-associated proteins, and proteoglycans. This complex structure plays critical roles in cancer progression: it functions as the scaffold for tissues organization and provides biochemical and biomechanical signals that regulate key cancer hallmarks including cell growth, survival, migration, differentiation, angiogenesis, and immune response. Cells sense the biochemical and mechanical properties of the ECM through specialized transmembrane receptors that include integrins, discoidin domain receptors, and syndecans. Advanced stages of several carcinomas are characterized by a desmoplastic reaction characterized by an extensive deposition of fibrillar collagens in the microenvironment. This compact network of fibrillar collagens promotes cancer progression and metastasis, and is associated with low survival rates for cancer patients. In this review, we highlight how fibrillar collagens and their corresponding integrin receptors are modulated during cancer progression. We describe how the deposition and alignment of collagen fibers influence the tumor microenvironment and how fibrillar collagen-binding integrins expressed by cancer and stromal cells critically contribute in cancer hallmarks.

Identification of prognostic biomarkers associated with stromal cell infiltration in muscle-invasive bladder cancer by bioinformatics analyses

Muscle-invasive bladder cancer (MIBC) is one of the common malignant tumors. Patients with MIBC still have high tumor recurrence and progression rates after surgery. Bioinformatics analysis of stromal infiltration-related genes in the tumor microenvironment (TME) of MIBC patients was performed in this study to determine the major stromal cells types and biomarkers for their progression and poor prognosis. The ESTIMATE algorithm was applied to evaluate the stromal score and immune score of samples from MIBC patients in The Cancer Genome Atlas (TCGA) and found that stromal score was closely related to the clinical characteristics of the patients. The Gene Set Enrichment Analysis (GSEA) further revealed that stromal cells were involved in biological processes such as activation of leukocytes and positive regulation of cell migration during MIBC progression, as well as PI3K-Akt, MAPK, and Rap1 signaling pathways. Five hub genes related to prognosis, including ACTA2, COL5A1, DCN, LUM, and PRRX1 were identified by the Weighted Gene Co-Expression Network Analysis (WGCNA), Protein-Protein Interaction (PPI), survival analysis, and Oncomine, Gene Expression Omnibus (GEO) database validation. Besides, we identified five stromal cell types associated with overall survival time, among which chondrocytes and fibroblasts were identified as the major stromal cell types through correlation analysis. Finally, the Receiver Operating Characteristic (ROC) curve and immunohistochemistry were used to verify the diagnostic value and expression of hub genes in different invasive tumors. In summary, we investigated the biological behavior of stromal cells in the TME of MIBC to promote tumor progression obtained hub genes associated with progression and poor prognosis and identified the main stromal cells types in the TME.

Integrated assessment of CDK1 upregulation in thyroid cancer

Cyclin-dependent kinase 1 (CDK1) has a unique role in cell cycle regulation, as it is crucial for cell cycle progression and cell division. The aim of the present study was to use a combination of various detection methods to examine the expression and clinical significance of CDK1 in thyroid cancer (THCA). We used in-house tissue microarrays, immunohistochemistry, public RNA-sequencing, gene microarrays, and meta-analyses to conduct a comprehensive analysis of the role of CDK1 in the occurrence and development of THCA. CDK1 protein expression was notably higher in THCA tissues than in non-cancer tissues as evidenced by the in-house tissue microarrays. The expression of CDK1 protein was also significantly higher in pathologic T3-T4 than in T1-T2 samples. The pooled standardized mean difference (SMD) for CDK1 was 0.71 (95% CI, 0.46-0.95) including a total of 931 THCA and 585 non-cancerous thyroid tissue samples. An aggregation of the immunohistochemistry results and the RNA-sequencing/microarray findings gave a pooled SMD for CDK1 expression of 2.13 (95% CI, 1.30-2.96). The final area under curve (AUC) for the summarized receiver operating characteristic (sROC) was 0.7941 using all 1102 cases of THCA and 672 cases of controls. KEGG analysis with the co-expressed genes of CDK1 in THCA demonstrated the top enriched pathways to be the cell cycle, thyroid hormone synthesis, autoimmune thyroid disease, etc. In summary, we reveal the overexpression of CDK1 in THCA based on multiple detection methods that combine independent cohorts. However, further studies are required to elucidate the molecular mechanisms of CDK1 that promotes the biological aggressiveness of THCA cells.

Identification of potential core genes in metastatic renal cell carcinoma using bioinformatics analysis

Most cases of mRCC without an early finding are not candidates for curative therapies, which may be one of the reasons for the poor patient prognosis. Therefore, candidate markers to diagnose the disease and treatment with high efficiency are urgently demanded. Three datasets of mRNA microarray have been assessed to discover DEGs between tissues from metastatic RCC and RCC. 111 DEGs in total were identified according to the expression profile result of genes in the database of GEO. Enrichment analyses for GO and KEGG have been conducted to reveal the interacting activities in the DEGs. A network of PPI has been established to reveal the interconnection among the DEGs, and we selected 10 hub genes. Subsequently, the disease-free survival rate and total survival rate analysis for the hub genes have been carried out with the method of Kaplan-Meier curve. RCC patients with CDH11, COL3A1, COL5A1, COL5A2, COL6A3 and COL11A1 alteration showed worse overall survival. Nonetheless, RCC patients with CDH11, COL3A1, COL5A1, COL5A2 and COL11A1 alteration showed worse disease-free survival. In the Jones Renal dataset, mRNA levels of 10 hub genes were associated with metastasis, and the gene expression level in patients with mRCC was higher than that in patients without metastasis. COL5A1, COL6A3 and COL11A1 expression levels were remarkably related to RCC patient survival rate using UALCAN. COL5A1, COL6A3 and COL11A1 were positively correlated with each other in RCC. These genes have been recognized as genes with clinical relevance, revealing that they might have important roles in carcinogenesis or development of mRCC.