Collagen type 1 alpha 1 chain is a novel predictive biomarker of poor progression-free survival and chemoresistance in metastatic lung cancer

Differential network analysis reveals the key role of the ECM-receptor pathway in α-particle-induced malignant transformation

Space particle radiation is a major environmental factor in spaceflight, and it is known to cause body damage and even trigger cancer, but with unknown molecular etiologies. To examine these causes, we developed a systems biology approach by focusing on the co-expression network analysis of transcriptomics profiles obtained from single high-dose (SE) and multiple low-dose (ME) α-particle radiation exposures of BEAS-2B human bronchial epithelial cells. First, the differential network and pathway analysis based on the global network and the core modules showed that genes in the ME group had higher enrichment for the extracellular matrix (ECM)-receptor interaction pathway. Then, collagen gene COL1A1 was screened as an important gene in the ME group assessed by network parameters and an expression study of lung adenocarcinoma samples. COL1A1 was found to promote the emergence of the neoplastic characteristics of BEAS-2B cells by both in vitro experimental analyses and in vivo immunohistochemical staining. These findings suggested that the degree of malignant transformation of cells in the ME group was greater than that of the SE, which may be caused by the dysregulation of the ECM-receptor pathway.

COL5A1 overexpression correlates with poor prognosis in human cervical cancer

BACKGROUND

Cervical cancer is the most prevalent malignant tumor in women. This study aims to detect collagen type V α1 chain (COL5A1) expression and its clinical relevance in the prognosis of patients with cervical cancer.

METHODS

Cervical cancer tissues and their paired adjacent normal tissues were prepared for tissue microarray. The expression of COL5A1 protein and the scores of the expression were evaluated by immunohistochemistry (IHC) staining. The prognostic value of COL5A1 was analyzed by R software version 4.2.1 with "survival, survminer, ggplot2" packages and Gene Expression Profiling Interactive Analysis (GEPIA). The cBioPortal database was utilized for the analysis of COL5A1 gene mutations.

RESULTS

COL5A1 protein was overexpressed in human cervical cancer tissues compared to their paired adjacent normal tissues detected by IHC (P < 0.001). High expression of COL5A1 tends to be in elderly patients with cervical cancer. Survival analyses of clinical data of patients with cervical cancer showed that a high level of COL5A1 expression was significantly correlated with shorter overall survival (P = 0.031) and disease-free survival (P = 0.042) of patients. Further analyses of The Cancer Genome Atlas-Cervical Squamous Cell Carcinoma and the GEPIA survival datasets confirmed the association of high COL5A1 expression with poor overall survival of patients (P = 0.040 and P = 0.018, respectively). The analysis of genomic alterations of COL5A1 using the cBioPortal tool revealed that the COL5A1 gene was altered in 4% of cervical cancer patients and COL5A1 corresponding protein alterations with post-translational modifications were hydroxylation.

CONCLUSION

COL5A1 is a tissue biomarker correlated with the poor prognosis of patients with cervical cancer, which may lead to a new clinical application.

The Spatial Extracellular Proteomic Tumor Microenvironment Distinguishes Molecular Subtypes of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) mortality rates continue to increase faster than those of other cancer types due to high heterogeneity, which limits diagnosis and treatment. Pathological and molecular subtyping have identified that HCC tumors with poor outcomes are characterized by intratumoral collagenous accumulation. However, the translational and post-translational regulation of tumor collagen, which is critical to the outcome, remains largely unknown. Here, we investigate the spatial extracellular proteome to understand the differences associated with HCC tumors defined by Hoshida transcriptomic subtypes of poor outcome (Subtype 1; S1; n = 12) and better outcome (Subtype 3; S3; n = 24) that show differential stroma-regulated pathways. Collagen-targeted mass spectrometry imaging (MSI) with the same-tissue reference libraries, built from untargeted and targeted LC-MS/MS was used to spatially define the extracellular microenvironment from clinically-characterized, formalin-fixed, paraffin-embedded tissue sections. Collagen α-1(I) chain domains for discoidin-domain receptor and integrin binding showed distinctive spatial distribution within the tumor microenvironment. Hydroxylated proline (HYP)-containing peptides from the triple helical regions of fibrillar collagens distinguished S1 from S3 tumors. Exploratory machine learning on multiple peptides extracted from the tumor regions could distinguish S1 and S3 tumors (with an area under the receiver operating curve of ≥0.98; 95% confidence intervals between 0.976 and 1.00; and accuracies above 94%). An overall finding was that the extracellular microenvironment has a high potential to predict clinically relevant outcomes in HCC.

Targets in the Tumour Matrisome to Promote Cancer Therapy Response

The extracellular matrix (ECM) is composed of complex fibrillar proteins, proteoglycans, and macromolecules, generated by stromal, immune, and cancer cells. The components and organisation of the matrix evolves as tumours progress to invasive disease and metastasis. In many solid tumours, dense fibrotic ECM has been hypothesised to impede therapy response by limiting drug and immune cell access. Interventions to target individual components of the ECM, collectively termed the matrisome, have, however, revealed complex tumour-suppressor, tumour-promoter, and immune-modulatory functions, which have complicated clinical translation. The degree to which distinct components of the matrisome can dictate tumour phenotypes and response to therapy is the subject of intense study. A primary aim is to identify therapeutic opportunities within the matrisome, which might support a better response to existing therapies. Many matrix signatures have been developed which can predict prognosis, immune cell content, and immunotherapy responses. In this review, we will examine key components of the matrisome which have been associated with advanced tumours and therapy resistance. We have primarily focussed here on targeting matrisome components, rather than specific cell types, although several examples are described where cells of origin can dramatically affect tumour roles for matrix components. As we unravel the complex biochemical, biophysical, and intracellular transduction mechanisms associated with the ECM, numerous therapeutic opportunities will be identified to modify tumour progression and therapy response.

Unveiling Collagen's Role in Breast Cancer: Insights into Expression Patterns, Functions and Clinical Implications

Collagen, the predominant protein constituent of the mammalian extracellular matrix (ECM), comprises a diverse family of 28 members (I-XXVIII). Beyond its structural significance, collagen is implicated in various diseases or cancers, notably breast cancer, where it influences crucial cellular processes including proliferation, metastasis, apoptosis, and drug resistance, intricately shaping cancer progression and prognosis. In breast cancer, distinct collagens exhibit differential expression profiles, with some showing heightened or diminished levels in cancerous tissues or cells compared to normal counterparts, suggesting specific and pivotal biological functions. In this review, we meticulously analyze the expression of individual collagen members in breast cancer, utilizing Transcripts Per Million (TPM) data sourced from the GEPIA2 database. Through this analysis, we identify collagens that deviate from normal expression patterns in breast cancer, providing a comprehensive overview of their expression dynamics, functional roles, and underlying mechanisms. Our findings shed light on recent advancements in understanding the intricate interplay between these aberrantly expressed collagens and breast cancer. This exploration aims to offer valuable insights for the identification of potential biomarkers and therapeutic targets, thereby advancing the prospects of more effective interventions in breast cancer treatment.

Collagen 1A1 (COL1A1) and Collagen11A1(COL11A1) as diagnostic biomarkers in Breast, colorectal and gastric cancers

PURPOSE

Collagen family genes (CFGs) play a significant role in the pathogenesis of cancers. This study aimed to evaluate changes in the expression levels (Els) of CFGs related to epithelial-mesenchymal transition (EMT) and metastasis in gastric (GC), breast (BC), and colorectal (CRC) cancers to introduce these genes as potential diagnostic biomarkers for these three types of cancer.

METHODS

The Cancer Genome Atlas (TCGA) examined ELS changes in CFGs associated with EMT and metastasis to determine their diagnostic value for GC, BC, and CRC. InteractiVenn was used to find genes shared by these three cancers. The biomarker role of CFGs was determined using the receiver operating characteristic (ROC) analysis. GC, BC, and CRC samples were analyzed using the RT-qPCR method to verify the bioinformatics results and evaluate the EL of the selected genes as biomarkers for these cancers.

RESULTS

The in-silico results showed a significant increase in the EL of several CFGs involved in EMT and metastasis in GC, BC, and CRC samples compared to healthy samples. Six common genes (COL11A1, COL12A1, COL1A1, COL1A2, COL5A1, and COL5A2) showed significantly increased in these three cancers, therebysupporting their oncogenic role. Furthermore, the biomarker-related analyses indicated that COL11A1 and COL1A1 were common diagnostic biomarkers for the three cancers. The RT-qPCR method confirmed that the ELs of COL11A1 and COL1A1 in the GC, BC, and CRC samples increased significantly compared to the adjacent normal samples.

CONCLUSION

CFGs in EMT and metastasis of GC, BC, and CRC are strong common diagnostic biomarkers for these cancers.

Integrative bioinformatics analysis reveals ECM and nicotine-related genes in both LUAD and LUSC, but different lung fibrosis-related genes are involved in LUAD and LUSC

There are several bioinformatics studies related to lung cancer, but most of them have mainly focused on either microarray data or RNA-Seq data alone. In this study, we have combined both types of data to identify differentially expressed genes (DEGs) specific to lung cancer subtypes. We obtained six microarray datasets from the GEO and also the expression matrix of LUSC and LUAD from TCGA, which were analyzed by GEO2R tool and GEPIA2, respectively. Enrichment analyses of DEGs were performed using the Enrichr database. Protein module identification was done by MCODE plugin in cytoscape software. We identified 30 LUAD-specific, 17 LUSC-specific, and 17 DEGs shared between LUAD and LUSC. Enrichment analyses revealed that LUSC-specific DEGs are involved in lung fibrosis. In addition, DEGs shared between LUAD and LUSC are involved in extracellular matrix (ECM), nicotine metabolism, and lung fibrosis. We identified lung fibrosis-related genes, including SPP1, MMP9, and CXCL2, involved in both LUAD and LUSC, but SERPINA1 and PLAU genes involved only in LUSC. We also found an important module separately for LUAD-specific, LUSC-specific, and shared DEGs between LUSC and LUAD. S100P, GOLM, AGR2, AK1, TMEM125, SLC2A1, COL1A1, and GHR genes were significantly associated with survival. Our findings suggest that different lung fibrosis-related genes may play roles in LUSC and LUAD. Additionally, nicotine metabolism and ECM remodeling were found to be associated with both LUSC and LUAD, regardless of subtype, emphasizing the role of smoking in the development of lung cancer and ECM in the high aggressiveness and mortality of lung cancer.

Health-related factors and dysregulation of epigenetic related genes in metabolic syndrome trigger finger patients and smoker trigger finger patients: preliminary analysis of patient-derived sample

PURPOSE

To investigate the health-related factors and analyze the expression of epigenetic related genes and inflammatory genes in metabolic syndrome Trigger Finger (TF) and smoker TF.

METHODS

Samples from patients' fingers with symptomatic TF were collected. There were seven groups: healthy control group, carpal tunnel syndrome (as a control for gene expression analysis), TF, diabetic TF, hypertensive TF, dyslipidemic TF and smoker TF. The expression levels of epigenetic related genes and inflammatory genes in metabolic syndrome TF and smoker TF were evaluated by the reverse transcription-polymerase chain reaction (RT-PCR) technique. The Perceived Stress Scale (PSS), Pittsburgh Sleep Quality Index (PSQI) questionnaires, disability of the arm, shoulder and hand (DASH) and numeric pain rating scale were given to the participants to fill out.

RESULTS

There was a significant increase in hand dysfunction in the metabolic TF groups and smoker group compared to the TF group (p < 0.0001). The stress levels of the smoker TF group and TF with hypertension group were significantly increased compared with those in the TF group (p < 0.03) and (p < 0.021), respectively. On the other hand, there was a significant increase in the COL-I, COL-II and TNF-α gene expression of the metabolic TF groups and smoker group (p < 0.0001).

CONCLUSIONS

Health-related factors in the TF tendons was highly associated with the level of inflammation and genetic alteration in TF metabolic syndromes and smoker TF patients. Therefore, further investigation is required to examine the combination of occupational therapy, gene expression, and health-related factors as a promising method of managing TF.

LZTR1 deficiency exerts high metastatic potential by enhancing sensitivity to EMT induction and controlling KLHL12-mediated collagen secretion

Leucine zipper-like transcriptional regulator 1 (LZTR1), a substrate adaptor of Cullin 3 (CUL3)-based E3 ubiquitin ligase, regulates proteostasis of the RAS subfamily. Mutations in LZTR1 have been identified in patients with several types of cancer. However, the role of LZTR1 in tumor metastasis and the target molecules of LZTR1, excluding the RAS subfamily, are not clearly understood. Here, we show that LZTR1 deficiency increases tumor growth and metastasis. In lung adenocarcinoma cells, LZTR1 deficiency induced the accumulation of the RAS subfamily and enhanced cell proliferation, invasion, and xenograft tumor growth. Multi-omics analysis to clarify the pathways related to tumor progression showed that MAPK signaling, epithelial-mesenchymal transition (EMT), and extracellular matrix (ECM) remodeling-related gene ontology terms were enriched in LZTR1 knockout cells. Indeed, LZTR1 deficiency induced high expression of EMT markers under TGF-β1 treatment. Our search for novel substrates that interact with LZTR1 resulted in the discovery of a Kelch-like protein 12 (KLHL12), which is involved in collagen secretion. LZTR1 could inhibit KLHL12-mediated ubiquitination of SEC31A, a component of coat protein complex II (COPII), whereas LZTR1 deficiency promoted collagen secretion. LZTR1-RIT1 and LZTR1-KLHL12 worked independently regarding molecular interactions and did not directly interfere with each other. Further, we found that LZTR1 deficiency significantly increases lung metastasis and promotes ECM deposition around metastatic tumors. Since collagen-rich extracellular matrix act as pathways for migration and facilitate metastasis, increased expression of RAS and collagen deposition may exert synergistic or additive effects leading to tumor progression and metastasis. In conclusion, LZTR1 deficiency exerts high metastatic potential by enhancing sensitivity to EMT induction and promoting collagen secretion. The functional inhibition of KLHL12 by LZTR1 provides important evidence that LZTR1 may be a repressor of BTB-Kelch family members. These results provide clues to the mechanism of LZTR1-deficiency carcinogenesis.

ClearF++: Improved Supervised Feature Scoring Using Feature Clustering in Class-Wise Embedding and Reconstruction

Feature selection methods are essential for accurate disease classification and identifying informative biomarkers. While information-theoretic methods have been widely used, they often exhibit limitations such as high computational costs. Our previously proposed method, ClearF, addresses these issues by using reconstruction error from low-dimensional embeddings as a proxy for the entropy term in the mutual information. However, ClearF still has limitations, including a nontransparent bottleneck layer selection process, which can result in unstable feature selection. To address these limitations, we propose ClearF++, which simplifies the bottleneck layer selection and incorporates feature-wise clustering to enhance biomarker detection. We compare its performance with other commonly used methods such as MultiSURF and IFS, as well as ClearF, across multiple benchmark datasets. Our results demonstrate that ClearF++ consistently outperforms these methods in terms of prediction accuracy and stability, even with limited samples. We also observe that employing the Deep Embedded Clustering (DEC) algorithm for feature-wise clustering improves performance, indicating its suitability for handling complex data structures with limited samples. ClearF++ offers an improved biomarker prioritization approach with enhanced prediction performance and faster execution. Its stability and effectiveness with limited samples make it particularly valuable for biomedical data analysis.

A five-collagen-based risk model in lung adenocarcinoma: prognostic significance and immune landscape

As part of the tumor microenvironment (TME), collagen plays a significant role in cancer fibrosis formation. However, the collagen family expression profile and clinical features in lung adenocarcinoma (LUAD) are poorly understood. The objective of the present work was to investigate the expression pattern of genes from the collagen family in LUAD and to develop a predictive signature based on collagen family. The Cancer Genome Atlas (TCGA) samples were used as the training set, and five additional cohort samples obtained from the Gene Expression Omnibus (GEO) database were used as the validation set. A predictive model based on five collagen genes, including COL1A1, COL4A3, COL5A1, COL11A1, and COL22A1, was created by analyzing samples from the TCGA cohort using LASSO Cox analysis and univariate/multivariable Cox regression. Using Collagen-Risk scores, LUAD patients were then divided into high- and low-risk groups. KM survival analysis showed that collagen signature presented a robust prognostic power. GO and KEGG analyses confirmed that collagen signature was associated with extracellular matrix organization, ECM-receptor interaction, PI3K-Akts and AGE-RAGE signaling activation. High-risk patients exhibited a considerable activation of the p53 pathway and cell cycle, according to GSEA analysis. The Collage-Risk model showed unique features in immune cell infiltration and tumor-associated macrophage (TAM) polarization of the TME. Additionally, we deeply revealed the association of collagen signature with immune checkpoints (ICPs), tumor mutation burden (TMB), and tumor purity. We first constructed a reliable prognostic model based on TME principal component-collagen, which would enable clinicians to treat patients with LUAD more individually.

Construction of an endoplasmic reticulum stress-related signature in lung adenocarcinoma by comprehensive bioinformatics analysis

BACKGROUND

Lung Adenocarcinoma (LUAD) is a major component of lung cancer. Endoplasmic reticulum stress (ERS) has emerged as a new target for some tumor treatments.

METHODS

The expression and clinical data of LUAD samples were downloaded from The Cancer Genome Atlas (TCGA) and The Gene Expression Omnibus (GEO) database, followed by acquiring ERS-related genes (ERSGs) from the GeneCards database. Differentially expressed endoplasmic reticulum stress-related genes (DE-ERSGs) were screened and used to construct a risk model by Cox regression analysis. Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves were plotted to determine the risk validity of the model. Moreover, enrichment analysis of differentially expressed genes (DEGs) between the high- and low- risk groups was conducted to investigate the functions related to the risk model. Furthermore, the differences in ERS status, vascular-related genes, tumor mutation burden (TMB), immunotherapy response, chemotherapy drug sensitivity and other indicators between the high- and low- risk groups were studied. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the mRNA expression levels of prognostic model genes.

RESULTS

A total of 81 DE-ERSGs were identified in the TCGA-LUAD dataset, and a risk model, including HSPD1, PCSK9, GRIA1, MAOB, COL1A1, and CAV1, was constructed by Cox regression analysis. K-M and ROC analyses showed that the high-risk group had a low survival, and the Area Under Curve (AUC) of ROC curves of 1-, 3- and 5-years overall survival was all greater than 0.6. In addition, functional enrichment analysis suggested that the risk model was related to collagen and extracellular matrix. Furthermore, differential analysis showed vascular-related genes FLT1, TMB, neoantigen, PD-L1 protein (CD274), Tumor Immune Dysfunction and Exclusion (TIDE), and T cell exclusion score were significantly different between the high- and low-risk groups. Finally, qRT-PCR results showed that the mRNA expression levels of 6 prognostic genes were consistent with the analysis.

CONCLUSION

A novel ERS-related risk model, including HSPD1, PCSK9, GRIA1, MAOB, COL1A1, and CAV1, was developed and validated, which provided a theoretical basis and reference value for ERS-related fields in the study and treatment of LUAD.

Data-independent acquisition and quantification of extracellular matrix from human lung in chronic inflammation-associated carcinomas

Early events associated with chronic inflammation and cancer involve significant remodeling of the extracellular matrix (ECM), which greatly affects its composition and functional properties. Using lung squamous cell carcinoma (LSCC), a chronic inflammation-associated cancer (CIAC), we optimized a robust proteomic pipeline to discover potential biomarker signatures and protein changes specifically in the stroma. We combined ECM enrichment from fresh human tissues, data-independent acquisition (DIA) strategies, and stringent statistical processing to analyze "Tumor" and matched adjacent histologically normal ("Matched Normal") tissues from patients with LSCC. Overall, 1802 protein groups were quantified with at least two unique peptides, and 56% of those proteins were annotated as "extracellular." Confirming dramatic ECM remodeling during CIAC progression, 529 proteins were significantly altered in the "Tumor" compared to "Matched Normal" tissues. The signature was typified by a coordinated loss of basement membrane proteins and small leucine-rich proteins. The dramatic increase in the stromal levels of SERPINH1/heat shock protein 47, that was discovered using our ECM proteomic pipeline, was validated by immunohistochemistry (IHC) of "Tumor" and "Matched Normal" tissues, obtained from an independent cohort of LSCC patients. This integrated workflow provided novel insights into ECM remodeling during CIAC progression, and identified potential biomarker signatures and future therapeutic targets.

Histone variant H3.3 promotes metastasis in alveolar rhabdomyosarcoma

The relatively quiet mutational landscape of rhabdomyosarcoma (RMS) suggests that epigenetic deregulation could be central to oncogenesis and tumour aggressiveness. Histone variants have long been recognised as important epigenetic regulators of gene expression. However, the role of histone variants in RMS has not been studied hitherto. In this study, we show that histone variant H3.3 is overexpressed in alveolar RMS (ARMS), an aggressive subtype of RMS. Functionally, knockdown of H3F3A, which encodes for H3.3, significantly impairs the ability of ARMS cells to undertake migration and invasion and reduces Rho activation. In addition, a striking reduction in metastatic tumour burden and improved survival is apparent in vivo. Using RNA-sequencing and ChIP-sequencing analyses, we identified melanoma cell adhesion molecule (MCAM/CD146) as a direct downstream target of H3.3. Loss of H3.3 resulted in a reduction in the presence of active marks and an increase in the occupancy of H1 at the MCAM promoter. Cell migration and invasion were rescued in H3F3A-depleted cells through MCAM overexpression. Moreover, we identified G9a, a lysine methyltransferase encoded by EHMT2, as an upstream regulator of H3F3A. Therefore, this study identifies a novel H3.3 dependent axis involved in ARMS metastasis. These findings establish the potential of MCAM as a therapeutic target for high-risk ARMS patients. © 2022 The Pathological Society of Great Britain and Ireland.

Mesenchymal Stem Cells and Selenium Nanoparticles Synergize with Low Dose of Gamma Radiation to Suppress Mammary Gland Carcinogenesis via Regulation of Tumor Microenvironment

Breast cancer is one of the most prevalent and deadliest cancers among women in the world because of its aggressive behavior and inadequate response to conventional therapies. Mesenchymal stem cells (MSCs) combined with green nanomaterials could be an efficient tool in cell cancer therapy. This study examined the curative effects of bone marrow-derived mesenchymal stem cells (BM-MSCs) with selenium nanoparticles (SeNPs) coated with fermented soymilk and a low dose of gamma radiation (LDR) in DMBA-induced mammary gland carcinoma in female rats. DMBA-induced mammary gland carcinoma as marked by an elevation of mRNA level of cancer promoter genes (Serpin and MIF, LOX-1, and COL1A1) and serum level of VEGF, TNF-α, TGF-β, CA15-3, and caspase-3 with the reduction in mRNA level of suppressor gene (FST and ADRP). These deleterious effects were hampered after treatment with BM-MSCs (1 × 10⁶ cells/rat) once and daily administration of SeNPs (20 mg/kg body weight) and exposure once to (0.25 Gy) LDR. Finally, MSCs, SeNPs, and LDR notably modulated the expression of multiple tumor promoters and suppressor genes playing a role in breast cancer induction and suppression.

Policosanol suppresses tumor progression in a gastric cancer xenograft model

Gastric cancer (GC) is the most common cancer worldwide and the third leading cause of cancer death, with the fifth highest incidence. The development of effective chemotherapeutic agents is needed to decrease GC mortality. Policosanol (PC) extracted from Cuban sugar cane wax is a healthy functional food ingredient that helps improve blood cholesterol levels and blood pressure. Its various physiological activities, such as antioxidant, anti-inflammatory, and anticancer activities, have been reported recently. Nevertheless, the therapeutic efficacy of PC in gastric xenograft models is unclear. We aimed to investigate the anticancer effect of PC on human GC SNU-16 cells and a xenograft mouse model. PC significantly inhibited GC cell viability and delayed tumor growth without toxicity in the SNU-16-derived xenograft model. Therefore, we investigated protein expression levels in tumor tissues; the expression levels of Ki-67, a proliferation marker, and cdc2 were decreased. In addition, we performed proteomic analysis and found thirteen differentially expressed proteins. Our results suggested that PC inhibited GC progression via cdc2 suppression and extracellular matrix protein regulation. Notably, our findings might contribute to the development of novel and effective therapeutic strategies for GC.

Construction of a redox-related gene signature for overall survival prediction and immune infiltration in non-small-cell lung cancer

Background: An imbalance in the redox homeostasis has been reported in multiple cancers and is associated with a poor prognosis of disease. However, the prognostic value of redox-related genes in non-small-cell lung cancer (NSCLC) remains unclear.

Methods: RNA sequencing data, DNA methylation data, mutation, and clinical data of NSCLC patients were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Redox-related differentially expressed genes (DEGs) were used to construct the prognostic signature using least absolute shrinkage and selection operator (LASSO) regression analysis. Kaplan–Meier survival curve and receiver operator characteristic (ROC) curve analyses were applied to validate the accuracy of the gene signature. Nomogram and calibration plots of the nomogram were constructed to predict prognosis. Pathway analysis was performed using gene set enrichment analysis. The correlations of risk score with tumor stage, immune infiltration, DNA methylation, tumor mutation burden (TMB), and chemotherapy sensitivity were evaluated. The prognostic signature was validated using GSE31210, GSE26939, and GSE68465 datasets. Real-time polymerase chain reaction (PCR) was used to validate dysregulated genes in NSCLC.

Results: A prognostic signature was constructed using the LASSO regression analysis and was represented as a risk score. The high-risk group was significantly correlated with worse overall survival (OS) (p < 0.001). The area under the ROC curve (AUC) at the 5-year stage was 0.657. The risk score was precisely correlated with the tumor stage and was an independent prognostic factor for NSCLC. The constructed nomogram accurately predicted the OS of patients after 1-, 3-, and 5-year periods. DNA replication, cell cycle, and ECM receptor interaction were the main pathways enriched in the high-risk group. In addition, the high-risk score was correlated with higher TMB, lower methylation levels, increased infiltrating macrophages, activated memory CD4⁺ T cells, and a higher sensitivity to chemotherapy. The signature was validated in GSE31210, GSE26939, and GSE68465 datasets. Real-time PCR validated dysregulated mRNA expression levels in NSCLC.

Conclusions: A prognostic redox-related gene signature was successfully established in NSCLC, with potential applications in the clinical setting.

Proteomic Analysis of Lung Cancer Types—A Pilot Study

Lung cancer is the leading cause of tumor-related mortality, therefore significant effort is directed towards understanding molecular alterations occurring at the origin of the disease to improve current treatment options. The aim of our pilot-scale study was to carry out a detailed proteomic analysis of formalin-fixed paraffin-embedded tissue sections from patients with small cell or non-small cell lung cancer (adenocarcinoma, squamous cell carcinoma, and large cell carcinoma). Tissue surface digestion was performed on relatively small cancerous and tumor-adjacent normal regions and differentially expressed proteins were identified using label-free quantitative mass spectrometry and subsequent statistical analysis. Principal component analysis clearly distinguished cancerous and cancer adjacent normal samples, while the four lung cancer types investigated had distinct molecular profiles and gene set enrichment analysis revealed specific dysregulated biological processes as well. Furthermore, proteins with altered expression unique to a specific lung cancer type were identified and could be the targets of future studies.

Predictive value of collagen in cancer

Cancer is a complex disease and a significant cause of mortality worldwide. Over the course of nearly all cancer types, collagen within the tumor microenvironment influences emergence, progression, and metastasis. This review discusses collagen regulation within the tumor microenvironment, pathological involvement of collagen, and predictive values of collagen and related extracellular matrix components in main cancer types. A survey of predictive tests leveraging collagen assays using clinical cohorts is presented. A conclusion is that collagen has high predictive value in monitoring cancer processes and stratifying by outcomes. New approaches should be considered that continue to define molecular facets of collagen related to cancer.

Blockade of integrin signaling reduces chemotherapy-induced premature senescence in collagen cultured bladder cancer cells

Background

Diminished sensitivity towards chemotherapy remains the major impediment to the clinical treatment of bladder cancer. However, the critical elements in control of chemotherapy resistance remain obscure.

Methods

We adopted improved collagen gels and performed cytotoxicity analysis of doxorubicin (DOX) and mitomycin C (MMC) of bladder cancer cells in a 3D culture system. We then detected the expression of multidrug resistant gene ABCB1, dormancy-associated functional protein chicken ovalbumin upstream-transcription factor 1 (COUPTF1), cell proliferation marker Ki-67, and cellular senescence marker senescence-associated β-galactosidase (SA-β-Gal) in these cells. We further tested the effects of integrin blockade or protein kinase B (AKT) inhibitor on the senescent state of bladder cancer. Also, we examined the tumor growth and survival time of bladder cancer mouse models given the combination treatment of chemotherapeutic agents and integrin α2β1 ligand peptide TFA (TFA).

Results

Collagen gels played a repressive role in bladder cancer cell apoptosis induced by DOX and MMC. In mechanism, collagen activated the integrin β1/AKT cascade to drive bladder cancer cells into a premature senescence state via the p21/p53 pathway, thus attenuating chemotherapy-induced apoptosis. In addition, TFA had the ability to mediate the switch from senescence to apoptosis of bladder cancer cells in xenograft mice. Meanwhile, TFA combined with chemotherapeutic drugs produced a substantial suppression of tumor growth as well as an extension of survival time in vivo.

Conclusions

Based on our finding that integrin β1/AKT acted primarily to impart premature senescence to bladder cancer cells cultured in collagen gel, we suggest that integrin β1 might be a feasible target for bladder cancer eradication.

The Functional Role of Extracellular Matrix Proteins in Cancer

The extracellular matrix (ECM) is highly dynamic as it is constantly deposited, remodeled and degraded to maintain tissue homeostasis. ECM is a major structural component of the tumor microenvironment, and cancer development and progression require its extensive reorganization. Cancerized ECM is biochemically different in its composition and is stiffer compared to normal ECM. The abnormal ECM affects cancer progression by directly promoting cell proliferation, survival, migration and differentiation. The restructured extracellular matrix and its degradation fragments (matrikines) also modulate the signaling cascades mediated by the interaction with cell-surface receptors, deregulate the stromal cell behavior and lead to emergence of an oncogenic microenvironment. Here, we summarize the current state of understanding how the composition and structure of ECM changes during cancer progression. We also describe the functional role of key proteins, especially tenascin C and fibronectin, and signaling molecules involved in the formation of the tumor microenvironment, as well as the signaling pathways that they activate in cancer cells.

The Regulation of Collagen Processing by miRNAs in Disease and Possible Implications for Bone Turnover

This article describes several recent examples of miRNA governing the regulation of the gene expression involved in bone matrix construction. We present the impact of miRNA on the subsequent steps in the formation of collagen type I. Collagen type I is a main factor of mechanical bone stiffness because it constitutes 90–95% of the organic components of the bone. Therefore, the precise epigenetic regulation of collagen formation may have a significant influence on bone structure. We also describe miRNA involvement in the expression of genes, the protein products of which participate in collagen maturation in various tissues and cancer cells. We show how non-collagenous proteins in the extracellular matrix are epigenetically regulated by miRNA in bone and other tissues. We also delineate collagen mineralisation in bones by factors that depend on miRNA molecules. This review reveals the tissue variability of miRNA regulation at different levels of collagen maturation and mineralisation. The functionality of collagen mRNA regulation by miRNA, as proven in other tissues, has not yet been shown in osteoblasts. Several collagen-regulating miRNAs are co-expressed with collagen in bone. We suggest that collagen mRNA regulation by miRNA could also be potentially important in bone metabolism.