Autophagy-related IFNG is a prognostic and immunochemotherapeutic biomarker of COAD patients

Construction of a prognostic signature based on T-helper 17 cells differentiation-related genes for predicting survival and tumor microenvironment in head and neck squamous cell carcinoma

T-helper 17 (Th17) cells significantly influence the onset and advancement of malignancies. This study endeavor focused on delineating molecular classifications and developing a prognostic signature grounded in Th17 cell differentiation-related genes (TCDRGs) using machine learning algorithms in head and neck squamous cell carcinoma (HNSCC). A consensus clustering approach was applied to The Cancer Genome Atlas-HNSCC cohort based on TCDRGs, followed by an examination of differential gene expression using the limma package. Machine learning techniques were utilized for feature selection and model construction, with validation performed using the GSE41613 cohort. The interplay between the predictive marker, immune landscape, immunotherapy response, drug sensitivity, and clinical outcomes was assessed, and a nomogram was constructed. Functional evaluations of TCDRGs were conducted through colony formation, transwell invasion, and wound healing assays. Two distinct HNSCC subtypes with significant differences in prognosis were identified based on 87 TCDRGs, indicating different levels of Th17 cell differentiation. Thirteen differentially expressed TCDRGs were selected and used to create a risk signature, T17I, using the random survival forest algorithm. This signature was associated with grade, chemotherapy, radiotherapy, T stage, and somatic mutations. It was revealed that there were differences in the immune response-related pathways between the high- and low-risk groups. Inflammatory pathways were significantly activated in the low-risk group. The T17I signature was associated with immune infiltration. Specifically, there was a higher infiltration of immune activation cells in the low-risk group, whereas the high-risk group had a higher infiltration of M2 macrophages. In addition, the T17I signature was significantly associated with drug sensitivity. A nomogram combining age, radiotherapy, and the T17I signature accurately predicted the prognosis of patients with HNSCC. Finally, in vitro experiments confirmed that knockdown of LAT gene expression promotes proliferation, metastasis, and invasion of HNSCC cells. In conclusion, this study successfully identified molecular subtypes and constructed a prognostic signature and nomogram based on TCDRGs in HNSCC, which may aid in personalized treatment strategies.

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.

Construction and validation of a colon cancer prognostic model based on tumor mutation burden-related genes

Currently, immunotherapy has entered the clinical diagnosis and treatment guidelines for colon cancer, but existing immunotherapy markers cannot predict the effectiveness of immunotherapy well. This study utilized the TCGA-COAD queue to perform differential gene analysis on high and low-mutation burden samples, and screen differentially expressed genes (DEGs). To explore new molecular markers or predictive models of immunotherapy by using DEGs for NMF classification and prognostic model construction. Through systematic bioinformatics analysis, the TCGA-COAD cohort was successfully divided into high mutation burden subtypes and low mutation burden subtypes by NMF typing using DEGs. The proportion of MSI-H between high mutation burden subtypes was significantly higher than that of low mutation burden subtypes, but there was no significant difference in immunotherapy efficacy between the two subtypes. Drug sensitivity analysis showed significant differences in drug sensitivity between the two subtypes. Subsequently, we constructed a prognostic model using DEGs, which can effectively predict patient survival and immunotherapy outcomes. The prognosis and immunotherapy outcomes of the low-risk group were significantly better than those of the high-risk group. The external dataset validation of the constructed prognostic model using the GSE39582 dataset from the GEO database yielded consistent results. At the same time, we also analyzed the TMB and MSI situation between the high and low-risk groups, and the results showed that there was no significant difference in TMB between the high and low-risk groups, but the proportion of MSI-H in the high-risk group was significantly higher than that in the low-risk group. Finally, we conclude that TMB is not a suitable molecular marker for predicting the efficacy of immunotherapy in colon cancer. The newly constructed prognostic model can effectively differentiate the prognosis of colon cancer patients and predict their immunotherapy efficacy.

Predicting colorectal cancer prognosis based on long noncoding RNAs of disulfidptosis genes

BACKGROUND

A recently hypothesized cause of cell death called disulfidptosis has been linked to the expansion, emigration, and vascular rebuilding of cancer cells. Cancer can be treated by targeting the pathways that trigger cell death.

AIM

To discover the long non-coding RNA of the disulfidaptosis-related lncRNAs (DRLs), prognosis clinical survival, and treat patients with colorectal cancer with medications.

METHODS

Initially, we queried the Cancer Genome Atlas database to collect transcriptome, clinical, and genetic mutation data for colorectal cancer (CRC). Training and testing sets for CRC patient transcriptome data were generated randomly. Key long non-coding RNAs (lncRNAs) related to DRLs were then identified and evaluated using a least absolute shrinkage and selection operator procedure, as well as univariate and multivariate Cox regression models. A prognostic model was then created after risk scoring. Also, Immune infiltration analysis, immune checkpoint analysis, and medication susceptibility analysis were used to investigate the causes of the different prognoses between high and low risk groups. Finally, we validated the differential expression and biomarker potential of risk-predictive lncRNAs through induction using both NCM460 and HT-29 cell lines, as well as a disulfidptosis model.

RESULTS

In this work, eight significant lncRNAs linked to disulfidptosis were found. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of differentially expressed genes between high- and low-risk groups from the prognostic model showed a close relationship with the immune response as well as significant enrichment in neutrophil extracellular trap formation and the IL-17 signaling pathway. Furthermore, significant immune cell variations between the high-risk and low-risk groups were seen, as well as a higher incidence of immunological escape risk in the high-risk group. Finally, Epirubicin, bortezomib, teniposide, and BMS-754807 were shown to have the lowest sensitivity among the four immunotherapy drugs.

CONCLUSION

Our findings emphasizes the role of disulfidptosis in regulating tumor development, therapeutic response, and patient survival in CRC patients. For the clinical treatment of CRC, these important LncRNAs could serve as viable therapeutic targets.

Highly expressed miR-144-3p promotes the proliferation, migration and invasion of colon carcinoma cells by activating the Wnt/β-catenin signaling pathway through targeting SFRP1

Background: To investigate the influence of miR-144-3p on the proliferation, migration and invasion of colon carcinoma by targeting secreted frizzled-related protein 1 (SFRP1) as well as of the Wnt/β-catenin signaling pathway. Methods: Based on the TCGA database, the association between the expression of miR-144-3p and the clinical information and prognosis of patients with colon carcinoma was examined, and SFRP1 was selected as the target gene for further studies based on bioinformatics prediction tools. CCK8 assay, wound healing assay and transwell invasion assay were employed to examine the impact of miR-144-3p on colon carcinoma cells. The regulation of SFRP1 by miR-144-3p was investigated using a dual-luciferase reporter system, and a rescue experiment was conducted to further elucidate whether miR-144-3p promotes the migration of colon carcinoma cells through targeting SFRP1 or not. The Wnt/β-catenin signaling pathway-mediated effect of miR-144-3p in colon carcinoma was finally validated through the targeting of SFRP1. Results: The bioinformatics analysis showed that the miR-144 expression levels were substantially greater in colon carcinoma tissue than in para-carcinoma tissue and were closely with clinical stage and prognosis. The findings obtained from the trial indicated that miR-144-3p substantially expressed in colon carcinoma tissue sample and the colon carcinoma cells, and the overexpressed miR-144-3p boosted the colon carcinoma cells' proliferation, migration and invasion. The results of dual-luciferase reporter gene assay revealed that miR-144-3p targeted SFRP1, and rescue experiment was carried out and its results indicated that miR-144-3p increased colon carcinoma cells' migration through targeting SFRP1. In addition, the molecular axis of miR-144-3p/SFRP1 may over-activate the Wnt/β-catenin signaling pathway. Conclusions: The present study has identified a novel malignant biological behavior, namely the ability of miR-144-3p to enhance the proliferation, migration and invasion of colon carcinoma cells by targeting SFRP1 and activating the Wnt/β-catenin signaling pathway. Consequently, miR-144-3p emerges as a promising diagnostic and therapeutic target for colon carcinoma.

Construction and validation of a novel prognostic signature for cutaneous melanoma based on ferroptosis-related genes

Ferroptosis, a recently uncovered iron-dependent, non-apoptotic cell death process, has been increasingly linked to cancer development. In this study, our objective was to develop a prognostic model centered on ferroptosis-related genes (FRGs) and assess its efficacy as an overall survival (OS) prediction biomarker. We conducted a systematic analysis of cutaneous melanoma (CM) and devised a novel ferroptosis-related prognostic signature (FRGSig) using the TCGA database. An independent dataset from GSE65904 was employed to corroborate the validity of the FRGSig. Both univariate and multivariate Cox proportional hazard regression analyses were utilized to construct a FRGSig composed of five FRGs. mRNA expression and immunohistochemistry (IHC) analysis demonstrated that the expression of FRGSig genes varied between tumor and normal tissues. According to Kaplan-Meier analysis, patients with elevated FRGsig scores faced a worse prognosis. The predictive accuracy of FRGSig was evaluated using the time-dependent receiver operating characteristic curve (ROC), with the area under the curve (AUC) values for 1, 3, and 5 OS at 0.682, 0.711, 0.735 in the TCGA cohort, and 0.662, 0.695, 0.712 in the validation dataset, respectively. Univariate and multivariate Cox regression analyses demonstrated that FRGSig served as an independent prognostic factor. Further analysis revealed a significant relationship between FRGSig and Tumor Mutational Burden (TMB) as well as immune infiltration levels. Gene set enrichment analysis (GSEA) disclosed functional disparities between high- and low-risk groups, suggesting that immune checkpoint-related pathways could be instrumental in the improved prognosis of the low-risk group. Taken together, the FRGSig has potential guidance for prognosis prediction and clinical treatment of CM.