COL4A family: potential prognostic biomarkers and therapeutic targets for gastric cancer

The critical role of COL1A1 revealed by integrated bioinformatics analysis of differentially-expressed genes in colorectal cancer and inflammatory bowel disease

PURPOSE

There is an urgent need to identify biomarkers of tumorigenesis for colitis-associated cancer (CAC) as early cancer detection remains crucial for patients with inflammatory bowel disease (IBD). This in silico study examines the relationship between IBD and CAC, with particular regard to differentially-expressed genes (DEGs).

METHODS

Integrated bioinformatics tools and public databases were employed. Data from GEO (GSE102133, GSE48958, GSE9348, GSE83687, GSE138202) were processed using GEOexplorer. DEGs were then functionally annotated with DAVID, SRplot, and integrated analysis via Metascape. Validation used Oncopression and Human Protein Atlas. Survival analysis employed GEPIA2. miRNA interactions were studied via miRTargetLink 2.0. Immune infiltration was analyzed with TIMER 2.0. COL1A1 expression and mutations were examined using cBioPortal, Kaplan-Meier plotter, and DNA methylation was analyzed using MethSurv. Correlation of COL1A1 gene promoter methylation with tissue type and clinical data was performed using the UALCAN database. The ROC analysis of COL1A1 was conducted in the R environment.

RESULTS

Our analysis identified three potential hub genes (ICAM1, LAMC1, and COL1A1), which are overexpressed in IBD and cancer tissues compared to normal tissue, and hence may play a role in CAC. Furthermore, patients with lower COL1A1 expression had longer disease-free survival (p = 0.01) than those with higher expression. Therefore, this gene was chosen for further analysis and identified as the most crucial.

CONCLUSION

COL1A1 reveals significant immunohistochemistry, mutations, and methylation data. Further studies involving machine learning and clinical data are required to validate the results.

Transcriptome-Wide Analysis and Experimental Validation from FFPE Tissue Identifies Stage-Specific Gene Expression Profiles Differentiating Adenoma, Carcinoma In-Situ and Adenocarcinoma in Colorectal Cancer Progression

Colorectal cancer (CRC) progression occurs through three stages: adenoma (pre-cancerous lesion), carcinoma in situ (CIS) and adenocarcinoma, with tumor stage playing a pivotal role in the prognosis and treatment outcomes. Despite therapeutic advancements, the lack of stage-specific biomarkers hinders the development of accurate diagnostic tools and effective therapeutic strategies. This study aims to identify stage-specific gene expression profiles and key molecular mechanisms in CRC providing insights into molecular alterations across disease progression. Our methodological approach integrates the use of absolute gene set enrichment analysis (absGSEA) on formalin-fixed paraffin-embedded (FFPE)-derived transcriptomic data, combined with large-scale clinical validation and experimental confirmation. A comparative whole transcriptomic analysis (RNA-seq) was performed on FFPE samples including adenoma (n = 10), carcinoma in situ (CIS) (n = 8) and adenocarcinoma (n = 11) samples. Using absGSEA, we identified significant cellular pathways and putative molecular biomarkers associated with each stage of CRC progression. Key findings were then validated in a large independent CRC patient cohort (n = 1926), with survival analysis conducted from 1336 patients to assess the prognostic relevance of the candidate biomarkers. The key differentially expressed genes were experimentally validated using real-time PCR (RT-qPCR). Pathway analysis revealed that in CIS, apoptotic processes and Wnt signaling pathways were more prominent than in adenoma samples, while in adenocarcinoma, transcriptional co-regulatory mechanisms and protein kinase activity, which are critical for tumor growth and metastasis, were significantly enriched compared to adenoma. Additionally, extracellular matrix organization pathways were significantly enriched in adenocarcinoma compared to CIS. Distinct gene signatures were identified across CRC stages that differentiate between adenoma, CIS and adenocarcinoma. In adenoma, ARRB1, CTBP1 and CTBP2 were overexpressed, suggesting their involvement in early tumorigenesis, whereas in CIS, RPS3A and COL4A5 were overexpressed, suggesting their involvement in the transition from benign to malignant stage. In adenocarcinoma, COL1A2, CEBPZ, MED10 and PAWR were overexpressed, suggesting their involvement in advanced disease progression. Functional analysis confirmed that ARRB1 and CTBP1/2 were associated with early tumor development, while COL1A2 and CEBPZ were involved in extracellular matrix remodeling and transcriptional regulation, respectively. Experimental validation with RT-qPCR confirmed the differential expression of the candidate biomarkers (ARRB1, RPS3A, COL4A5, COL1A2 and MED10) across the three CRC stages reinforcing their potential as stage-specific biomarkers in CRC progression. These findings provide a foundation to distinguish between the CRC stages and for the development of accurate stage-specific diagnostic and prognostic biomarkers, which helps in the development of more effective therapeutic strategies for CRC.

Integrative bioinformatics analysis of high-throughput sequencing and in vitro functional analysis leads to uncovering key hub genes in esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCA) is a type of cancer that starts in the cells lining the esophagus, the tube connecting the throat to the stomach. It is known for its aggressive nature and poor prognosis. Understanding the key factors that drive this cancer is crucial for developing better diagnostic tools and treatments.

METHODS

Gene expression profiles of ESCA were analyzed using Gene Expression Omnibus (GEO) datasets (GSE23400, GSE29001, GSE92396, and GSE1420) from the GEO database. Differentially expressed genes (DEGs) were identified using the limma package, and a protein-protein interaction (PPI) network was constructed using the STRING database. Hub genes were identified based on the degree method. Further validation was performed through reverse transcription quantitative PCR (RT-qPCR), mutational and copy number variation (CNV) analysis via the cBioPortal database, promoter methylation analysis using the OncoDB and GSCA databases, survival analysis, immune infiltration analysis through the GSCA database, and functional assays, including knockdown of key genes.

RESULTS

We identified four key hub genes, COL3A1, COL4A1, COL5A2, and CXCL8 that play significant roles in ESCA. These genes were highly expressed in ESCA tissues and cell lines, with expression levels significantly (p-value < 0.001) elevated compared to normal controls. Receiver operating characteristic (ROC) curve analysis revealed exceptional diagnostic performance for all four genes, with area under the curve (AUC) values of 1.0, indicating perfect sensitivity and specificity in distinguishing ESCA from normal controls. Mutational analysis revealed that COL3A1 was altered in 67% of ESCA samples, primarily through missense mutations, while COL5A2 exhibited alterations in 50% of the samples, including splice site and missense mutations. Additionally, gene amplification patterns were observed in all four hub genes, further validating their oncogenic potential in ESCA progression. A significant (p-value < 0.05) promoter hypomethylation was detected in these genes, suggesting a potential regulatory role in their expression. Functional assays demonstrated that knocking down COL3A1 and COL4A1 led to decreased cell proliferation, colony formation, and migration, indicating their critical roles in tumor progression. Additionally, these genes were involved in pathways related to the extracellular matrix and immune system modulation.

CONCLUSION

COL3A1, COL4A1, COL5A2, and CXCL8 are crucial in ESCA development and progression, particularly in remodeling the extracellular matrix, modulating the immune system, and promoting metastasis. These findings suggest that these genes could serve as potential biomarkers for diagnosing ESCA and targets for future therapies. Future research should focus on in vivo validation of these findings and clinical testing to assess the therapeutic potential of targeting these genes in ESCA treatment.

Lactylation-related gene signature accurately predicts prognosis and immunotherapy response in gastric cancer

Background

Gastric cancer (GC) is a malignant tumor associated with significant rates of morbidity and mortality. Hence, developing efficient predictive models and directing clinical interventions in GC is crucial. Lactylation of proteins is detected in gastric cancer tumors and is linked to the advancement of gastric cancer.

Methods

The The Cancer Genome Atlas (TCGA) was utilized to analyze the gene expression levels associated with lactylation. A genetic pattern linked to lactylation was created using Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. The predictive ability of the model was evaluated and confirmed in the Gene Expression Omnibus (GEO) cohort, where patients were divided into two risk groups based on their scores. The study examined the relationship between gene expression and the presence of immune cells in the context of immunotherapy treatment. In vitro cytotoxicity assays, ELISA and PD-1 and PD-L1interaction assays were used to assess the expression of PD-L1 while knocking down SLC16A7.

Results

29 predictive lactylation-related genes with differential expression were discovered. A signature consisting of three genes was developed and confirmed. Patients who had higher risk scores experienced worse clinical results. The group with lower risk showed increased Tumor Immune Dysfunction and Exclusion (TIDE) score and greater responsiveness to immunotherapy. The tumor tissues secrete more lactate acid than normal tissues and express more PD-L1 than normal tissues, that is, lactate acid promotes the immune evasion of tumor cells. In GC, the lactylation-related signature showed strong predictive accuracy. Utilizing both anti-lactylation and anti-PD-L1 may prove to be an effective approach for treating GC in clinical settings. We further proved that one of the lactate metabolism related genes, SCL16A7 could promote the expression of PD-L1 in GC cells.

Conclusion

The risk model not only provides a basis for better prognosis in GC patients, but also is a potential prognostic indicator to distinguish the molecular and immune characteristics, and the response from Immune checkpoint inhibitors (ICI) therapy and chemotherapy in GC.

Matrisomics: Beyond the extracellular matrix for unveiling tumor microenvironment

The matrisome, a group of proteins constituting or interacting with the extracellular matrix (ECM), has garnered attention as a potent regulator of cancer progression. An increasing number of studies have focused on cancer matrisome utilizing diverse -omics approaches. Here, we present diverse patterns of matrisomal populations within cancer tissues, exploring recent -omics studies spanning different '-omics' levels (epigenomics, genomics, transcriptomics, and proteomics), as well as newly developed sequencing techniques such as single-cell RNA sequencing and spatial transcriptomics. Some matrisome genes showed uniform patterns of upregulated or downregulated expression across various cancers, while others displayed different expression patterns according to the cancer types. This matrisomal dysregulation in cancer was further examined according to their originating cell type and spatial location in the tumor tissue. Experimental studies were also collected to demonstrate the identified roles of matrisome genes during cancer progression. Interestingly, many studies on cancer matrisome have suggested matrisome genes as effective biomarkers in cancer research. Although the specific mechanisms and clinical applications of cancer matrisome have not yet been fully elucidated, recent techniques and analyses on cancer matrisomics have emphasized their biological importance in cancer progression and their clinical implications in deciding the efficacy of cancer treatment.

Capture of circulating metastatic cancer cell clusters from lung cancer patients can reveal unique genomic profiles and potential anti-metastatic molecular targets: A proof-of-concept study

Metastasis remains the leading cause of cancer deaths worldwide and lung cancer, known for its highly metastatic progression, remains among the most lethal of malignancies. Lung cancer metastasis can selectively spread to multiple different organs, however the genetic and molecular drivers for this process are still poorly understood. Understanding the heterogeneous genomic profile of lung cancer metastases is considered key in identifying therapeutic targets that prevent its spread. Research has identified the key source for metastasis being clusters of cells rather than individual cancer cells. These clusters, known as metastatic cancer cell clusters (MCCCs) have been shown to be 100-fold more tumorigenic than individual cancer cells. Unfortunately, access to these primary drivers of metastases remains difficult and has limited our understanding of their molecular and genomic profiles. Strong evidence in the literature suggests that differentially regulated biological pathways in MCCCs can provide new therapeutic drug targets to help combat cancer metastases. In order to expand research into MCCCs and their role in metastasis, we demonstrate a novel, proof of principle technology, to capture MCCCs directly from patients' whole blood. Our platform can be readily tuned for different solid tumor types by combining a biomimicry-based margination effect coupled with immunoaffinity to isolate MCCCs. Adopting a selective capture approach based on overexpressed CD44 in MCCCs provides a methodology that preferentially isolates them from whole blood. Furthermore, we demonstrate a high capture efficiency of more than 90% when spiking MCCC-like model cell clusters into whole blood. Characterization of the captured MCCCs from lung cancer patients by immunofluorescence staining and genomic analyses, suggests highly differential morphologies and genomic profiles. This study lays the foundation to identify potential drug targets thus unlocking a new area of anti-metastatic therapeutics.

Integrated Analysis Reveals COL4A3 as a Novel Diagnostic and Therapeutic Target in UV-Related Skin Cutaneous Melanoma

Background

High levels of UV exposure are a significant factor that can trigger the onset and progression of SKCM. Moreover, this exposure is closely linked to the malignancy of the tumor and the prognosis of patients. Our objective is to identify a tumor biomarker database associated with UV exposure, which can be utilized for prognostic analysis and diagnosis and treatment of SKCM.

Methods

This study used the weighted gene co-expression network analyses (WGCNA) and gene mutation frequency analyses to screen for UV-related target genes using the GSE59455 and the cancer genome atlas databases (TCGA). The prognostic model was created using Cox regression and least absolute shrinkage and selection operator analyses (LASSCO). Furthermore, in vitro experiments further validated that the overexpression or knockdown of COL4A3 could regulate the proliferation and migration abilities of SKMEL28 and A357 melanoma cells.

Results

A prognostic model was created that included six genes with a high UV-related mutation in SKCM: COL4A3, CHRM2, DSC3, GIMAP5, LAMC2, and PSG7. The model had a strong patient survival correlation (P˂0.001, hazard ratio (HR) = 1.57) and significant predictor (P˂0.001, HR = 3.050). Furthermore, the model negatively correlated with immune cells, including CD8+ T cells (Cor=-0.408, P˂0.001), and M1-type macrophages (Cor=-0.385, P˂0.001), and immune checkpoints, including programmed cell death ligand-1. Moreover, we identified COL4A3 as a molecule with significant predictive functionality. Overexpression of COL4A3 significantly inhibited the proliferation, migration, and invasion abilities of SKMEL28 and A357 melanoma cells, while knockdown of COL4A3 yielded the opposite results. And overexpression of COL4A3 enhanced the inhibitory effects of imatinib on the proliferation, migration, and invasion abilities of SKMEL28 and A357 cells.

Conclusion

The efficacy of the prognostic model was validated by analyzing the prognosis, immune infiltration, and immune checkpoint profiles. COL4A3 stands out as a novel diagnostic and therapeutic target for SKCM, offering new strategies for small-molecule targeted drug therapies.

Identification of potential shared gene signatures between gastric cancer and type 2 diabetes: a data-driven analysis

Background

Gastric cancer (GC) and type 2 diabetes (T2D) contribute to each other, but the interaction mechanisms remain undiscovered. The goal of this research was to explore shared genes as well as crosstalk mechanisms between GC and T2D.

Methods

The Gene Expression Omnibus (GEO) database served as the source of the GC and T2D datasets. The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were utilized to identify representative genes. In addition, overlapping genes between the representative genes of the two diseases were used for functional enrichment analysis and protein-protein interaction (PPI) network. Next, hub genes were filtered through two machine learning algorithms. Finally, external validation was undertaken with data from the Cancer Genome Atlas (TCGA) database.

Results

A total of 292 and 541 DEGs were obtained from the GC (GSE29272) and T2D (GSE164416) datasets, respectively. In addition, 2,704 and 336 module genes were identified in GC and T2D. Following their intersection, 104 crosstalk genes were identified. Enrichment analysis indicated that "ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were mutual pathways. Through the PPI network, 10 genes were identified as candidate hub genes. Machine learning further selected BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 as hub genes.

Conclusion

"ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were revealed as possible crosstalk mechanisms. BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 were identified as shared genes and potential therapeutic targets for people suffering from GC and T2D.

The multifaceted roles of COL4A4 in lung adenocarcinoma: An integrated bioinformatics and experimental study

BACKGROUND

Abnormal expression of collagen IV subunits has been reported in cancers, but the significance is not clear. No study has reported the significance of COL4A4 in lung adenocarcinoma (LUAD).

METHODS

COL4A4 expression data, single-cell sequencing data and clinical data were downloaded from public databases. A range of bioinformatics and experimental methods were adopted to analyze the association of COL4A4 expression with clinical parameters, tumor microenvironment (TME), drug resistance and immunotherapy response, and to investigate the roles and underlying mechanism of COL4A4 in LUAD.

RESULTS

COL4A4 is differentially expressed in most of cancers analyzed, being associated with prognosis, tumor stemness, immune checkpoint gene expression and TME parameters. In LUAD, COL4A4 expression is down-regulated and associated with various TME parameters, response to immunotherapy and drug resistance. LUAD patients with lower COL4A4 have worse prognosis. Knockdown of COL4A4 significantly inhibited the expression of cell-cycle associated genes, and the expression and activation of signaling pathways including JAK/STAT3, p38, and ERK pathways, and induced quiescence in LUAD cells. Besides, it significantly induced the expression of a range of bioactive molecule genes that have been shown to have critical roles in TME remodeling and immune regulation.

CONCLUSIONS

COL4A4 is implicated in the pathogenesis of cancers including LUAD. Its function may be multifaceted. It can modulate the activity of LUAD cells, TME remodeling and tumor stemness, thus affecting the pathological process of LUAD. COL4A4 may be a prognostic molecular marker and a potential therapeutic target.

A Five-gene Signature based on MicroRNA for Predicting Prognosis and Immunotherapy in Stomach Adenocarcinoma

AIMS

We aimed to classify molecular subtypes and establish a prognostic gene signature based on miRNAs for the prognostic prediction and therapeutic response in Stomach adenocarcinoma (STAD).

BACKGROUND

STAD is a common diagnosed gastrointestinal malignancy and its heterogeneity is a big challenge that influences prognosis and precision therapies. Present study was designed to classify molecular subtypes and construct a prognostic gene signature based on miRNAs for the prognostic prediction and therapeutic response in STAD.

OBJECTIVE

The objective of this study is to investigate the molecular subtypes and prognostic model for STAD.

METHODS

A STAD specific miRNA-messenger RNA (mRNA) competing endogenous RNA (ceRNA) network was generated using the RNA-Seq and miRNA expression profiles from The Cancer Genome Atlas (TCGA) database, in which miRNA-related mRNAs were screened. Molecular subtypes were then determined using miRNA-related genes. Through univariate Cox analysis and multivariate regression analysis, a prognostic model was established in GSE84437 Train dataset and validated in GSE84437 Test, TCGA, GSE84437 and GSE66229 datasets. Immunotherapy datasets were employed for assessing the performance of the risk model. Finally, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was applied to validate the expression of hub genes used for the risk score signature.

RESULTS

We constructed a ceRNA network containing 84 miRNAs and 907 mRNAs and determined two molecular subtypes based on 26 genes from the intersection of TCGASTAD and GSE84437 datasets. Subtype S2 had poor prognosis, lower tumor mutational burden, higher immune score and lower response to immunotherapy. Subtype S1 was more sensitive to Sorafenib, Pyrimethamine, Salubrinal, Gemcitabine, Vinorelbine and AKT inhibitor VIII. Next, a five-gene signature was generated and its robustness was validated in Test and external datasets. This risk model also had a good prediction performance in immunotherapy datasets.

CONCLUSION

This study promotes the underlying mechanisms of miRNA-based genes in STAD and offers directions for classification. A five-gene signature accurately predicts the prognosis and helps therapeutic options.

Identification of key genes associated with poor prognosis and neoplasm staging in gastric cancer

BACKGROUND

Gastric cancer (GC) is highly biologically and genetically heterogeneous disease with poor prognosis. Increasing evidence indicates that biomarkers can serve as prediction and clinical intervention. Therefore, it is vital to identify core molecules and pathways participating in the development of GC.

METHODS

In this study, GSE54129, GSE56807, GSE63089, and GSE118916 were used for identified overlapped 75 DEGs. GO and Kyoto Encyclopedia of Genes and Genomes pathway analysis showed DEGs mainly enriched in biological process about collagen-containing extracellular matrix and collagen metabolic. Next, protein-protein interaction network was built and the hub gene was excavated. Clinicopathological features and prognostic value were also evaluated.

RESULTS

Hub genes were shown as below, FN1, COL1A2, COL1A1, COL3A1, COL4A1, COL6A3, COL5A2, SPARC, PDGFRB, COL12A1. Those genes were upregulation in GC and related to the poor prognosis (except COL5A2, P = .73). What is more, high expression indicated worse T stage and tumor, node, metastasis stage in GC patients. Later, the results of 25 GC tumor specimens and 34 normal tissues showed that FN1, COL3A1, COL4A1, SPARC, COL5A2, and COL12A1 were significantly upregulated in cancer samples.

CONCLUSION

Our study systematically explored the core genes and crucial pathways in GC, providing insights into clinical management and individual treatment.

Caveolin-1 promotes glioma progression and maintains its mitochondrial inhibition resistance

BACKGROUND

Glioma is a lethal brain cancer and lacking effective therapies. Challenges include no effective therapeutic target, intra- and intertumoral heterogeneity, inadequate effective drugs, and an immunosuppressive microenvironment, etc. Deciphering the pathogenesis of gliomas and finding out the working mechanisms are urgent and necessary for glioma treatment. Identification of prognostic biomarkers and targeting the biomarker genes will be a promising therapy.

METHODS

From our RNA-sequencing data of the oxidative phosphorylation (OXPHOS)-inhibition sensitive and OXPHOS-resistant cell lines, we found that the scaffolding protein caveolin 1 (CAV1) is highly expressed in the resistant group but not in the sensitive group. By comprehensive analysis of our RNA sequencing data, Whole Genome Bisulfite Sequencing (WGBS) data and public databases, we found that CAV1 is highly expressed in gliomas and its expression is positively related with pathological processes, higher CAV1 predicts shorter overall survival.

RESULTS

Further analysis indicated that (1) the differentiated genes in CAV1-high groups are enriched in immune infiltration and immune response; (2) CAV1 is positively correlated with tumor metastasis markers; (3) the methylation level of CAV1 promoters in glioma group is lower in higher stage than that in lower stage; (4) CAV1 is positively correlated with glioma stemness; (5) higher expression of CAV1 renders the glioma cells' resistant to oxidative phosphorylation inhibitors.

CONCLUSION

Therefore, we identified a key gene CAV1 and deciphered its function in glioma progression and prognosis, proposing that CAV1 may be a therapeutic target for gliomas.

Anatomical landscape of oral squamous cell carcinoma: A single cancer center study in UAE

Objectives

This study aimed to present demographic and clinicopathological aspects of OSCC identified in Pathology service in the UAE over a 13-year period and compare these findings to a cohort of 523 cases of Head and neck squamous cell carcinoma using the Cancer Genome Atlas's cBioPortal database (http://cbioportal.org).

Material and methods

Histological examination of all hematoxylin and eosin-stained slides and assessment of all demographic and clinical information from laboratory records were performed on all OSCC diagnosed between 2005 and 2018.

Results

Males made up 71.4% of the sample of 231 OSCCs that were evaluated. The patients' average age was 55.38 years. The two most prevalent afflicted sites were the anterior two-thirds of the tongue (57.6%) and the cheek (28.1%). The most prevalent site among smokers were the floor of mouth, cheek, and jaw bones. There was a link between tumor size and numerous anatomical subsites that was shown to be highly significant. OSCC in the FOM was associated with a 25% mortality rate. Patients with OSCC of the anterior tongue and cheek had the best prognosis, with only 15.7% and 15.3% of patients dying during follow-up.

Conclusion

The present investigation found a correlation between the diverse clinicopathological characteristics of the various anatomical subsites in OSCC. Different anatomical subsites also displayed varying degrees of gene mutation.

Identification of Novel Candidate Genes for Familial Thyroid Cancer by Whole Exome Sequencing

Thyroid carcinoma (TC) can be classified as medullary (MTC) and non-medullary (NMTC). While most TCs are sporadic, familial forms of MTC and NMTC also exist (less than 1% and 3-9% of all TC cases, respectively). Germline mutations in RET are found in more than 95% of familial MTC, whereas familial NMTC shows a high degree of genetic heterogeneity. Herein, we aimed to identify susceptibility genes for familial NMTC and non-RET MTC by whole exome sequencing in 58 individuals belonging to 18 Spanish families with these carcinomas. After data analysis, 53 rare candidate segregating variants were identified in 12 of the families, 7 of them located in previously TC-associated genes. Although no common mutated genes were detected, biological processes regulating functions such as cell proliferation, differentiation, survival and adhesion were enriched. The reported functions of the identified genes together with pathogenicity and structural predictions, reinforced the candidacy of 36 of them, suggesting new loci related to TC and novel genotype-phenotype correlations. Therefore, our strategy provides clues to possible molecular mechanisms underlying familial forms of MTC and NMTC. These new molecular findings and clinical data of patients may be helpful for the early detection, development of tailored therapies and optimizing patient management.

The pathological significance and potential mechanism of ARHGEF6 in lung adenocarcinoma

BACKGROUND

Emerging evidences suggest that ARHGEF6 is involved in cancers but the exact significance and underlying mechanism are unclear. This study aimed to elucidate the pathological significance and potential mechanism of ARHGEF6 in lung adenocarcinoma (LUAD).

METHODS

Bioinformatics and experimental methods were used to analyze the expression, the clinical significance, the cellular function and potential mechanisms of ARHGEF6 in LUAD.

RESULTS

ARHGEF6 was downregulated in LUAD tumor tissues and correlated negatively with poor prognosis and tumor stemness, positively with the Stromal score, the Immune score and the ESTIMATE score. The expression level of ARHGEF6 was also associated with drug sensitivity, the abundance of immune cells, the expression levels of Immune checkpoint genes and immunotherapy response. Mast cells, T cells and NK cells were the first three cells with the highest expression of ARHGEF6 in LUAD tissues. Overexpression of ARHGEF6 reduced proliferation and migration of LUAD cells and the growth of xenografted tumors, which could be reversed by re-knockdown of ARHGEF6. Results of RNA sequencing revealed that ARHGEF6 overexpression induced significant changes in the expression profile of LUAD cells, and genes encoding uridine 5'-diphosphate-glucuronic acid transferases (UGTs) and extracellular matrix (ECM) components were downregulated.

CONCLUSIONS

ARHGEF6 functions as a tumor suppressor in LUAD and may serve as a new prognostic marker and potential therapeutic target. Regulating tumor microenvironment and immunity, inhibiting the expression of UGTs and ECM components in the cancer cells, and decreasing the stemness of the tumors may among the mechanisms underlying the function of ARHGEF6 in LUAD.

A mutational signature and significantly mutated driver genes associated with immune checkpoint inhibitor response across multiple cancers

Immune checkpoint inhibitor (ICI) treatments dramatically prolong the survival outcomes of several advanced cancers. However, as multiple studies reported, only a subset of patients could benefit from the ICI treatment. In this study, we aim to uncover novel molecular biomarkers predictive of immunotherapy efficacy across multiple cancers. Pre-treatment somatic mutational profiles and immunotherapy clinical information were obtained from 1097 samples of multiple cancers, including melanoma, non-small cell lung cancer (NSCLC), clear cell renal cell carcinoma (ccRCC), bladder carcinoma (BLCA), and head and neck squamous cell carcinoma (HNSCC). Mutational signatures, molecular subtypes, and significantly mutated genes (SMGs) were determined, and their connections with ICI response and outcome were also evaluated. We extracted a total of six mutational signatures across all samples. Among, a mutational signature featured by T > C substitutions was identified to associate with an ICI resistance. A molecular subtype determined based on mutational activities was connected with a significantly improved ICI response rate and outcome. Totaling 50 SMGs were identified, and we observed that patients with COL11A1 or COL4A6 mutations exhibited a superior ICI treatment efficacy than those without such mutations. In this study, we uncovered several novel molecular determinants of cancer immunotherapy response under a multiple-cancer setting, which provides clues for enrolling patients to receive immunotherapy and customizing personalized treatment strategies.

The involvement of collagen family genes in tumor enlargement of gastric cancer

Extracellular matrix (ECM) not only serves as a support for tumor cell but also regulates cell-cell or cell-matrix cross-talks. Collagens are the most abundant proteins in ECM. Several studies have found that certain collagen genes were overexpressed in gastric cancer (GC) tissues and might serve as potential biomarkers and therapeutic targets in GC patients. However, the expression patterns of all collagen family genes in GC tissue and their functions are still not clear. With RNA sequencing (RNA-Seq) data, microarray data, and corresponding clinical data obtained from TCGA, GTEx, and GEO databases, bioinformatics analyses were performed to investigate the correlation between the expression patterns of collagen family genes and GC progression. We found that quite many of the collagen family genes were overexpressed in GC tissues. The increase in mRNA expression of most of these overexpressed collagen genes happened between T1 and T2 stage, which indicates the significance of collagens in tumor enlargement of GC. Notably, the mRNA expressions of these differentially expressed collagens genes were highly positively correlated. The elevated expression of a large number of collagen genes in early T stage might greatly change the composition and structure organization of ECM, contributing to ECM remodeling in GC progression.

MALDI-MSI: A Powerful Approach to Understand Primary Pancreatic Ductal Adenocarcinoma and Metastases

Cancer-related deaths are very commonly attributed to complications from metastases to neighboring as well as distant organs. Dissociate response in the treatment of pancreatic adenocarcinoma is one of the main causes of low treatment success and low survival rates. This behavior could not be explained by transcriptomics or genomics; however, differences in the composition at the protein level could be observed. We have characterized the proteomic composition of primary pancreatic adenocarcinoma and distant metastasis directly in human tissue samples, utilizing mass spectrometry imaging. The mass spectrometry data was used to train and validate machine learning models that could distinguish both tissue entities with an accuracy above 90%. Model validation on samples from another collection yielded a correct classification of both entities. Tentative identification of the discriminative molecular features showed that collagen fragments (COL1A1, COL1A2, and COL3A1) play a fundamental role in tumor development. From the analysis of the receiver operating characteristic, we could further advance some potential targets, such as histone and histone variations, that could provide a better understanding of tumor development, and consequently, more effective treatments.

Identification of Extracellular Matrix Signatures as Novel Potential Prognostic Biomarkers in Lung Adenocarcinoma

The extracellular matrix (ECM) is vital to normal cellular function and has emerged as a key factor in cancer initiation and metastasis. However, the prognostic and oncological values of ECM organization-related genes have not been comprehensively explored in lung adenocarcinoma (LUAD) patients. In this study, we included LUAD samples from The Cancer Genome Atlas (TCGA, training set) and other three validation sets (GSE87340, GSE140343 and GSE115002), then we constructed a three-gene prognostic signature based on ECM organization-related genes. The prognostic signature involving COL4A6, FGA and FSCN1 was powerful and robust in both the training and validation datasets. We further constructed a composite prognostic nomogram to facilitate clinical practice by integrating an ECM organization-related signature with clinical characteristics, including age and TNM stage. Patients with higher risk scores were characterized by proliferation, metastasis and immune hallmarks. It is worth noting that high-risk group showed higher fibroblast infiltration in tumor tissue. Accordingly, factors (IGFBP5, CLCF1 and IL6) reported to be secreted by cancer-associated fibroblasts (CAFs) showed higher expression level in the high-risk group. Our findings highlight the prognostic value of the ECM organization signature in LUAD and provide insights into the specific clinical and molecular features underlying the ECM organization-related signature, which may be important for patient treatment.