TIMP1 promotes cell proliferation and invasion capability of right-sided colon cancers via the FAK/Akt signaling pathway

Bulk and single-cell RNA sequencing analyses coupled with multiple machine learning to develop a glycosyltransferase associated signature in colorectal cancer

BACKGROUND

This study aims to identify key glycosyltransferases (GTs) in colorectal cancer (CRC) and establish a robust prognostic signature derived from GTs.

METHODS

Utilizing the AUCell, UCell, singscore, ssgsea, and AddModuleScore algorithms, along with correlation analysis, we redefined genes related to GTs in CRC at the single-cell RNA level. To improve risk model accuracy, univariate Cox and lasso regression were employed to discover a more clinically subset of GTs in CRC. Subsequently, the efficacy of seven machine learning algorithms for CRC prognosis was assessed, focusing on survival outcomes through nested cross-validation. The model was then validated across four independent external cohorts, exploring variations in the tumor microenvironment (TME), response to immunotherapy, mutational profiles, and pathways of each risk group. Importantly, we identified potential therapeutic agents targeting patients categorized into the high-GARS group.

RESULTS

In our research, we classified CRC patients into distinct subgroups, each exhibiting variations in prognosis, clinical characteristics, pathway enrichments, immune infiltration, and immune checkpoint genes expression. Additionally, we established a Glycosyltransferase-Associated Risk Signature (GARS) based on machine learning. GARS surpasses traditional clinicopathological features in both prognostic power and survival prediction accuracy, and it correlates with higher malignancy levels, providing valuable insights into CRC patients. Furthermore, we explored the association between the risk score and the efficacy of immunotherapy.

CONCLUSION

A prognostic model based on GTs was developed to forecast the response to immunotherapy, offering a novel approach to CRC management.

Repurposing homoharringtonine for thyroid cancer treatment through TIMP1/FAK/PI3K/AKT signaling pathway

Homoharringtonine (HHT), an alkaloid isolated from Cephalotaxus, is an effective anti-leukemia agent and exhibits inhibitory effects in various solid tumors. However, the impacts of HHT treatment on thyroid cancer (TC) remain unclear. Our findings demonstrated that HHT exhibited remarkable anti-TC activity that involved inhibiting cell proliferation, invasion, and migration, as well as inducing apoptosis. Proteomics analysis revealed that the expression of the tissue inhibitor of metalloproteinase 1 (TIMP1) was downregulated in TC cells after HHT treatment. TIMP1 overexpression promoted TC progression and partially reversed the anti-TC effects of HHT, while TIMP1 downregulation inhibited TC progression and enhanced the anti-TC effects of HHT. Furthermore, TIMP1 re-expression attenuated the enhancement of anti-TC effects of HHT induced by TIMP1 knockdown. Mechanistically, HHT exerted anti-TC effects by downregulating TIMP1 expression and then inactivating the FAK/PI3K/AKT signaling pathway. Taken together, our study demonstrated that HHT could inhibit TC progression by inhibiting the TIMP1/FAK/PI3K/AKT signaling pathway.

Integration of single-cell sequencing and bulk RNA-seq to identify and develop a prognostic signature related to colorectal cancer stem cells

The prognosis for patients with colorectal cancer (CRC) remains worse than expected due to metastasis, recurrence, and resistance to chemotherapy. Colorectal cancer stem cells (CRCSCs) play a vital role in tumor metastasis, recurrence, and chemotherapy resistance. However, there are currently no prognostic markers based on CRCSCs-related genes available for clinical use. In this study, single-cell transcriptome sequencing was employed to distinguish cancer stem cells (CSCs) in the CRC microenvironment and analyze their properties at the single-cell level. Subsequently, data from TCGA and GEO databases were utilized to develop a prognostic risk model for CRCSCs-related genes and validate its diagnostic performance. Additionally, functional enrichment, immune response, and chemotherapeutic drug sensitivity of the relevant genes in the risk model were investigated. Lastly, the key gene RPS17 in the risk model was identified as a potential prognostic marker and therapeutic target for further comprehensive studies. Our findings provide new insights into the prognostic treatment of CRC and offer novel perspectives for a systematic and comprehensive understanding of CRC development.

Investigating gene signatures associated with immunity in colon adenocarcinoma to predict the immunotherapy effectiveness using NFM and WGCNA algorithms

Colon adenocarcinoma (COAD), a frequently encountered and highly lethal malignancy of the digestive system, has been the focus of intensive research regarding its prognosis. The intricate immune microenvironment plays a pivotal role in the pathological progression of COAD; nevertheless, the underlying molecular mechanisms remain incompletely understood. This study aims to explore the immune gene expression patterns in COAD, construct a robust prognostic model, and delve into the molecular mechanisms and potential therapeutic targets for COAD liver metastasis, thereby providing critical support for individualized treatment strategies and prognostic evaluation. Initially, we curated a comprehensive dataset by screening 2600 immune-related genes (IRGs) from the ImmPort and InnateDB databases, successfully obtaining a rich data resource. Subsequently, the COAD patient cohort was classified using the non-negative matrix factorization (NMF) algorithm, enabling accurate categorization. Continuing on, utilizing the weighted gene co-expression network analysis (WGCNA) method, we analyzed the top 5000 genes with the smallest p-values among the differentially expressed genes (DEGs) between immune subtypes. Through this rigorous screening process, we identified the gene modules with the strongest correlation to the COAD subpopulation, and the intersection of genes in these modules with DEGs (COAD vs COAD vs Normal colon tissue) is referred to as Differentially Expressed Immune Genes Associated with COAD (DEIGRC). Employing diverse bioinformatics methodologies, we successfully developed a prognostic model (DPM) consisting of six genes derived from the DEIGRC, which was further validated across multiple independent datasets. Not only does this predictive model accurately forecast the prognosis of COAD patients, but it also provides valuable insights for formulating personalized treatment regimens. Within the constructed DPM, we observed a downregulation of CALB2 expression levels in COAD tissues, whereas NOXA1, KDF1, LARS2, GSR, and TIMP1 exhibited upregulated expression levels. These genes likely play indispensable roles in the initiation and progression of COAD and thus represent potential therapeutic targets for patient management. Furthermore, our investigation into the molecular mechanisms and therapeutic targets for COAD liver metastasis revealed associations with relevant processes such as fat digestion and absorption, cancer gene protein polysaccharides, and nitrogen metabolism. Consequently, genes including CAV1, ANXA1, CPS1, EDNRA, and GC emerge as promising candidates as therapeutic targets for COAD liver metastasis, thereby providing crucial insights for future clinical practices and drug development. In summary, this study uncovers the immune gene expression patterns in COAD, establishes a robust prognostic model, and elucidates the molecular mechanisms and potential therapeutic targets for COAD liver metastasis, thereby possessing significant theoretical and clinical implications. These findings are anticipated to offer substantial support for both the treatment and prognosis management of COAD patients.

Integrated analysis of public datasets for the discovery and validation of survival-associated genes in solid tumors

Identifying genes with prognostic significance that can act as biomarkers in solid tumors can help stratify patients and uncover novel therapy targets. Here, our goal was to expand our previous ranking analysis of survival-associated genes in various solid tumors to include colon cancer specimens with available transcriptomic and clinical data. A Gene Expression Omnibus search was performed to identify available datasets with clinical data and raw gene expression measurements. A combined database was set up and integrated into our Kaplan-Meier plotter, making it possible to identify genes with expression changes linked to altered survival. As a demonstration of the utility of the platform, the most powerful genes linked to overall survival in colon cancer were identified using uni- and multivariate Cox regression analysis. The combined colon cancer database includes 2,137 tumor samples from 17 independent cohorts. The most significant genes associated with relapse-free survival with a false discovery rate below 1% in colon cancer carcinoma were RBPMS (hazard rate [HR] = 2.52), TIMP1 (HR = 2.44), and COL4A2 (HR = 2.36). The three strongest genes associated with shorter survival in stage II colon cancer include CSF1R (HR = 2.86), FLNA (HR = 2.88), and TPBG (HR = 2.65). In summary, a new integrated database for colon cancer is presented. A colon cancer analysis subsystem was integrated into our Kaplan-Meier plotter that can be used to mine the entire database (https://www.kmplot.com). The portal has the potential to be employed for the identification and prioritization of promising biomarkers and therapeutic target candidates in multiple solid tumors including, among others, breast, lung, ovarian, gastric, pancreatic, and colon cancers.

Specific lineage transition of tumor-associated macrophages elicits immune evasion of ascitic tumor cells in gastric cancer with peritoneal metastasis

BACKGROUND

Gastric cancer with peritoneal metastasis (PM-GC), recognized as one of the deadliest cancers. However, whether and how the tumor cell-extrinsic tumor microenvironment (TME) is involved in the therapeutic failure remains unknown. Thus, this study systematically assessed the immunosuppressive tumor microenvironment in ascites from patients with PM-GC, and its contribution to dissemination and immune evasion of ascites-disseminated tumor cells (aDTCs).

METHODS

Sixty-three ascites and 43 peripheral blood (PB) samples from 51 patients with PM-GC were included in this study. aDTCs in ascites and circulating tumor cells (CTCs) in paired PB were immunophenotypically profiled. Using single-cell RNA transcriptional sequencing (scRNA-seq), crosstalk between aDTCs and the TME features of ascites was inspected. Further studies on the mechanism underlying aDTCs-immune cells crosstalk were performed on in vitro cultured aDTCs.

RESULTS

Immune cells in ascites interact with aDTCs, prompting their immune evasion. Specifically, we found that the tumor-associated macrophages (TAMs) in ascites underwent a continuum lineage transition from cathepsinhigh (CTShigh) to complement 1qhigh (C1Qhigh) TAM. CTShigh TAM initially attracted the metastatic tumor cells to ascites, thereafter, transitioning terminally to C1Qhigh TAM to trigger overproliferation and immune escape of aDTCs. Mechanistically, we demonstrated that C1Qhigh TAMs significantly enhanced the expression of PD-L1 and NECTIN2 on aDTCs, which was driven by the activation of the C1q-mediated complement pathway.

CONCLUSIONS

For the first time, we identified an immunosuppressive macrophage transition from CTShigh to C1Qhigh TAM in ascites from patients with PM-GC. This may contribute to developing potential TAM-targeted immunotherapies for PM-GC.

Tumor lymphangiogenesis index reveals the immune landscape and immunotherapy response in lung adenocarcinoma

Background

Lymphangiogenesis (LYM) has an important role in tumor progression and is strongly associated with tumor metastasis. However, the clinical application of LYM has not progressed as expected. The potential value of LYM needs to be further developed in lung adenocarcinoma (LUAD) patients.

Methods

The Sequencing data and clinical characteristics of LUAD patients were downloaded from The Cancer Genome Atlas and GEO databases. Multiple machine learning algorithms were used to screen feature genes and develop the LYM index. Immune cell infiltration, immune checkpoint expression, Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and drug sensitivity analysis were used to explore the correlation of LYM index with immune profile and anti-tumor therapy.

Results

We screened four lymphangiogenic feature genes (PECAM1, TIMP1, CXCL5 and PDGFB) to construct LYM index based on multiple machine learning algorithms. We divided LUAD patients into the high LYM index group and the low LYM index group based on the median LYM index. LYM index is a risk factor for the prognosis of LUAD patients. In addition, there was a significant difference in immune profile between high LYM index and low LYM index groups. LUAD patients in the low LYM index group seemed to benefit more from immunotherapy based on the results of TIDE algorithm.

Conclusion

Overall, we confirmed that the LYM index is a prognostic risk factor and a valuable predictor of immunotherapy response in LUAD patients, which provides new evidence for the potential application of LYM.

PANoptosis-related genes function as efficient prognostic biomarkers in colon adenocarcinoma

Background

PANoptosis is a newly discovered cell death type, and tightly associated with immune system activities. To date, the mechanism, regulation and application of PANoptosis in tumor is largely unknown. Our aim is to explore the prognostic value of PANoptosis-related genes in colon adenocarcinoma (COAD).

Methods

Analyzing data from The Cancer Genome Atlas-COAD (TCGA-COAD) involving 458 COAD cases, we concentrated on five PANoptosis pathways from the Molecular Signatures Database (MSigDB) and a comprehensive set of immune-related genes. Our approach involved identifying distinct genetic COAD subtype clusters and developing a prognostic model based on these parameters.

Results

The research successfully identified two genetic subtype clusters in COAD, marked by distinct profiles in PANoptosis pathways and immune-related gene expression. A prognostic model, incorporating these findings, demonstrated significant predictive power for survival outcomes, underscoring the interplay between PANoptosis and immune responses in COAD.

Conclusion

This study enhances our understanding of COAD's genetic framework, emphasizing the synergy between cell death pathways and the immune system. The development of a prognostic model based on these insights offers a promising tool for personalized treatment strategies. Future research should focus on validating and refining this model in clinical settings to optimize therapeutic interventions in COAD.

Glutamate is effective in decreasing opacity formed in galactose-induced cataract model

Although cataract is the leading cause of blindness worldwide, the detailed pathogenesis of cataract remains unclear, and clinically useful drug treatments are still lacking. In this study, we examined the effects of glutamate using an ex vivo model in which rat lens is cultured in a galactose-containing medium to induce opacity formation. After inducing lens opacity formation in galactose medium, glutamate was added, and the opacity decreased when the culture was continued. Next, microarray analysis was performed using samples in which the opacity was reduced by glutamate, and genes whose expression increased with galactose culture and decreased with the addition of glutamate were extracted. Subsequently, STRING analysis was performed on a group of genes that showed variation as a result of quantitative measurement of gene expression by RT-qPCR. The results suggest that apoptosis, oxidative stress, endoplasmic reticulum (ER) stress, cell proliferation, epithelial-mesenchymal transition (EMT), cytoskeleton, and histones are involved in the formation and reduction of opacity. Therefore, glutamate may reduce opacity by inhibiting oxidative stress and its downstream functions, and by regulating the cytoskeleton and cell proliferation.

Prognostic characteristics of T-cell mediated cell killing-related genes in lung adenocarcinoma

Constituted by various heterogeneous cells, the tumor microenvironment (TME) is capable of promoting tumor proliferation, invasion, and metastasis through extensive crosstalk. The pivotal factor influencing the survival time of patients and their response to immunotherapy lies in the intratumoral immune environment. We obtained 112 differential genes related to T cell-mediated tumor killing in LUAD by employing bioinformatics analysis on the basis of the TCGA and TISIDB databases. Then the 6-gene prognostic risk score model (CA9, OIP5, TIMP1, SEC11C, FURIN, and TLR10) was constructed by conducting univariate LASSO as well as multivariate Cox regression analyses. The median risk score was taken as the threshold to classify the samples into two groups. Survival analysis revealed that the low-risk group exhibited a more favorable prognosis. Subsequently, the Cox regression analysis combined with clinical information (age, gender, and pathological stage) and the risk score of LUAD patients demonstrated the potential of this model as an independent prognostic factor. The nomogram established based on clinical information and a risk score in combination with the calibration curve indicated that this model had good predictive ability. Notable enrichment of the differential genes from the high- and low-risk groups was discovered in immune-associated processes or pathways, as shown by the GO and KEGG enrichment analyses. The combined use of single-sample gene enrichment analysis (ssGSEA) and immunophenoscore (IPS) demonstrated heightened immune infiltration and IPS scores in the low-risk group, indicating that immunotherapy was likely to show good efficacy in patients from this group. To sum up, the prognostic model of LUAD constructed based on T-cell-mediated cell killing-related genes was not only capable of screening the prognosis of LUAD patients but was also used for screening those LUAD patients with high sensitivity to immunotherapy. Our study offered novel insights into the clinical treatment and prognostic prediction of LUAD patients.

Unraveling TIMP1: a multifaceted biomarker in colorectal cancer

Background: The pathogenic genes of colorectal cancer (CRC) have not yet been fully elucidated, and there is currently a lack of effective therapeutic targets. This study used bioinformatics methods to explore and experimentally validate the most valuable biomarkers for colorectal cancer and further investigate their potential as targets. Methods: We analyzed differentially expressed genes (DEGs) based on the Gene Expression Omnibus (GEO) dataset and screened out hub genes. ROC curve and univariate Cox analysis of The Cancer Genome Atlas (TCGA) dataset revealed the most diagnostically and prognostically valuable genes. Immunohistochemistry (IHC) experiments were then conducted to validate the expression level of these selected genes in colorectal cancer. Gene set enrichment analysis (GSEA) was performed to evaluate the enriched signaling pathways associated with the gene. Using the CIBERSORT algorithm in R software, we analyzed the immune infiltrating cell abundance in both high and low gene expression groups and examined the gene's correlation with immune cells and immune checkpoints. Additionally, we performed drug sensitivity analysis utilizing the DepMap database, and explored the correlation between gene expression levels and ferroptosis based on the The Cancer Genome Atlas dataset. Results: The study identified a total of 159 DEGs, including 7 hub genes: SPP1, MMP1, CXCL8, CXCL1, TIMP1, MMP3, and CXCL10. Further analysis revealed TIMP1 as the most valuable diagnostic and prognostic biomarker for colorectal cancer, with IHC experiments verifying its high expression. Additionally, GSEA results showed that the high TIMP1 expression group was involved in many cancer signaling pathways. Analysis of the TCGA database revealed a positive correlation between TIMP1 expression and infiltration of macrophages (M0, M1, M2) and neutrophils, as well as the expression of immune checkpoint genes, including CTLA-4 and HAVCR2. Drug sensitivity analysis, conducted using the DepMap database, revealed that colorectal cancer cell lines exhibiting elevated levels of TIMP1 expression were more responsive to certain drugs, such as CC-90003, Pitavastatin, Atuveciclib, and CT7001, compared to those with low levels of TIMP1. Furthermore, TIMP1 expression was positively correlated with that of ferroptosis-related genes, such as GPX4 and HSPA5. Conclusion: TIMP1 can be used as a biomarker for colorectal cancer and is associated with the immunological microenvironment, drug sensitivity, and ferroptosis inhibition in this disease.

Integrated single-cell and bulk RNA sequencing analysis identifies a prognostic signature related to ferroptosis dependence in colorectal cancer

Ferroptosis is an iron-dependent form of cell death induced by lipid oxidation with an essential role in diseases, including cancer. Since prognostic value of ferroptosis-dependent related genes (FDRGs) in colorectal cancer (CRC) remains unclear, we explored the significance of FDRGs in CRC through comprehensive single-cell analysis. We downloaded the GSE161277 dataset for single-cell analyses and calculated the ferroptosis-dependent gene score (FerrScore) for each cell type. According to each cell type-specific median FerrScore, we categorized the cells into low- and high-ferroptosis groups. By analyzing differentially-expressed genes across the two groups, we identified FDRGs. We further screened these prognosis-related genes used to develop a prognostic signature and calculated its correlation with immune infiltration. We also compared immune checkpoint gene efficacy among different risk groups, and qRT-PCR was performed in colorectal normal and cancer cell lines to explore whether the signature genes could be used as clinical prognostic indicators. In total, 523 FDRGs were identified. A prognostic signature including five signature genes was constructed, and patients were divided into two risk groups. The high-risk group had poor survival rates and displayed high levels of immune infiltration. Our newly developed ferroptosis-based prognostic signature possessed a high predictive ability for CRC.

Identification of ferroptosis-related genes in ulcerative colitis: a diagnostic model with machine learning

Background

Ulcerative colitis (UC) is an idiopathic, chronic disorder characterized by inflammation, injury, and disruption of the colonic mucosa. However, there are still many difficulties in the diagnosis and differential diagnosis of UC. An increasing amount of research has shown a connection between ferroptosis and the etiology of UC. Therefore, our study aimed to identify the key genes related to ferroptosis in UC to provide new ideas for diagnosis UC.

Methods

Gene expression profiles of normal and UC samples were extracted from the Gene Expression Omnibus (GEO) database. By combining differentially expressed genes (DEGs), Weighted correlation network analysis (WGCNA) genes, and ferroptosis-related genes, hub genes were identified and then screened using Lasso regression. Based on the key genes, gene ontology (GO) and gene set enrichment analysis (GSEA) analyses were performed. We used NaiveBeyas, Logistic, IBk, and RandomForest algorithms to build a disease diagnosis model using the hub genes. The model was validated using GSE87473 as the validation set.

Results

Gene expression matrices of GSE87466 and GSE75214 were downloaded from the GEO database, including 184 UC patients and 43 control samples. A total of 699 DEGs were obtained. From FerrDb, 565 genes related to ferroptosis were identified. The 1,513 genes with the highest absolute correlation coefficient value in the MEblue module were obtained from WGCNA analysis. Five hub genes (LCN2, MUC1, PARP8, PLIN2, and TIMP1) were identified using the Lasso regression algorithm based on the overlapped DEGs, WGCNA-identified genes, and ferroptosis-related genes. GO and GSEA analyses revealed that 5 hub genes were identified as being involved in the negative regulation of transcription by competitive promoter binding, cellular response to citrate cycle_tca_cycle, cytosolic_dna_sensing pathway, UV-A, and beta-alanine metabolism. The logistic algorithm's values of the area under the curve (AUC)were 1.000 and 0.995 for training and validation cohorts, and sensitivity is 0.962, specificity is 1.000, respectively, as determined by comparing various methods.

Conclusions

The previously described hub genes were identified as being intimately related to ferroptosis in UC and capable of distinguishing UC patients from controls. By detecting the expression of several genes, this model may aid in diagnosing UC and understanding the etiology and treatment of the disease.

siRNA-Mediated Timp1 Silencing Inhibited the Inflammatory Phenotype during Acute Lung Injury

Acute lung injury is a complex cascade process that develops in response to various damaging factors, which can lead to acute respiratory distress syndrome. Within this study, based on bioinformatics reanalysis of available full-transcriptome data of acute lung injury induced in mice and humans by various factors, we selected a set of genes that could serve as good targets for suppressing inflammation in the lung tissue, evaluated their expression in the cells of different origins during LPS-induced inflammation, and chose the tissue inhibitor of metalloproteinase Timp1 as a promising target for suppressing inflammation. We designed an effective chemically modified anti-TIMP1 siRNA and showed that Timp1 silencing correlates with a decrease in the pro-inflammatory cytokine IL6 secretion in cultured macrophage cells and reduces the severity of LPS-induced acute lung injury in a mouse model.

A novel prognostic model based on urea cycle-related gene signature for colorectal cancer

Background

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths in the world. This study aimed to develop a urea cycle (UC)-related gene signature that provides a theoretical foundation for the prognosis and treatment of patients with CRC.

Methods

Differentially expressed UC-related genes in CRC were confirmed using differential analysis and Venn diagrams. Univariate Cox and least absolute shrinkage and selection operator regression analyses were performed to identify UC-related prognostic genes. A UC-related signature was created and confirmed using distinct datasets. Independent prognostic predictors were authenticated using Cox analysis. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts algorithm and Spearman method were applied to probe the linkage between UC-related prognostic genes and tumor immune-infiltrating cells. The Human Protein Atlas database was used to determine the protein expression levels of prognostic genes in CRC and normal tissues. Verification of the expression levels of UC-related prognostic genes in clinical tissue samples was performed using real-time quantitative polymerase chain reaction (qPCR).

Results

A total of 49 DEUCRGs in CRC were mined. Eight prognostic genes (TIMP1, FABP4, MMP3, MMP1, CD177, CA2, S100P, and SPP1) were identified to construct a UC-related gene signature. The signature was then affirmed using an external validation set. The risk score was demonstrated to be a credible independent prognostic predictor using Cox regression analysis. Functional enrichment analysis revealed that focal adhesion, ECM-receptor interaction, IL-17 signaling pathway, and nitrogen metabolism were associated with the UC-related gene signature. Immune infiltration and correlation analyses revealed a significant correlation between UC-related prognostic genes and differential immune cells between the two risk subgroups. Finally, the qPCR results of clinical samples further confirmed the results of the public database.

Conclusion

Taken together, this study authenticated UC-related prognostic genes and developed a gene signature for the prognosis of CRC, which will be of great significance in the identification of prognostic molecular biomarkers, clinical prognosis prediction, and development of treatment strategies for patients with CRC.