Histone ubiquitination-related gene CUL4B promotes lung adenocarcinoma progression and cisplatin resistance.

Background: The role of the histone ubiquitination-related gene in the cisplatin resistance of lung adenocarcinoma (LUAD) remains an intricate subject. Methods: We accessed transcriptome data of both wild type and cisplatin-resistant cells from the GSE108214 dataset, and garnered transcriptome and clinical data of LUAD patients from The Cancer Genome Atlas (TCGA) database. Utilizing the R software, we analyzed these public datasets in depth. Real-time Quantitative PCR (qPCR) was used to detect the RNA level of CUL4B. Effect of CUL4B on cell proliferation was evaluated using CCK8 and colony formation assay. Effect of CUL4B on cell invasion was evaluated using transwell assay. Cisplatin sensitivity was evaluated by calculating IC50. Results: Our analysis shed light on the significance of the histone ubiquitination-related gene, CUL4B, in relation to cisplatin resistance and the overall survival rates of LUAD patients. Notably, CUL4B was found to be overexpressed in both lung cancer tissues and cells. Meanwhile, in vitro experiments indicated can CUL4B significantly promote the proliferation, invasion and migration of lung cancer cells. Furthermore, suppressing CUL4B expression led to a noticeable reduction in the IC50 value of cisplatin in lung cancer cells. A deep dive into biological enrichment analysis revealed that among patients exhibiting high CUL4B expression, there was a pronounced activation of the G2M checkpoint and the PI3K/AKT/mTOR signaling pathways. Immune microenvironment analysis has revealed that patients with elevated CUL4B expression may exhibit increased infiltration of M2 macrophages, coupled with a reduced infiltration of CD8+ T cells and activated NK cells. Notably, we observed higher CUL4B expression among those who responded positively to immunotherapy. Conclusion: These findings underscore the significance of CUL4B in the resistance to cisplatin in lung cancer, highlighting its potential as a therapeutic target.

Integrative analysis illustrates the role of PCDH7 in lung cancer development, cisplatin resistance, and immunotherapy resistance: an underlying target.

Background: Cisplatin resistance is a common clinical problem in lung cancer. However, the underlying mechanisms have not yet been fully elucidated, highlighting the importance of searching for biological targets. Methods: Bioinformatics analysis is completed through downloaded public data (GSE21656, GSE108214, and TCGA) and specific R packages. The evaluation of cell proliferation ability is completed through CCK8 assay, colony formation, and EdU assay. The evaluation of cell invasion and migration ability is completed through transwell and wound-healing assays. In addition, we evaluated cell cisplatin sensitivity by calculating IC50. Results: Here, we found that PCDH7 may be involved in cisplatin resistance in lung cancer through public database analysis (GSE21656 and GSE108214). Then, a series of in vitro experiments was performed, which verified the cancer-promoting role of PCDH7 in NSCLC. Moreover, the results of IC50 detection showed that PCDH7 might be associated with cisplatin resistance of NSCLC. Next, we investigated the single-cell pattern, biological function, and immune analysis of PCDH7. Importantly, we noticed PCDH7 may regulate epithelial-mesenchymal transition activity, and the local infiltration of CD8+ T and activated NK cells. Furthermore, we noticed that patients with high PCDH7 expression might be more sensitive to bortezomib, docetaxel, and gemcitabine, and resistant to immunotherapy. Finally, a prognosis model based on three PCDH7-derived genes (GPX8, BCAR3, and TNS4) was constructed through a machine learning algorithm, which has good prediction ability on NSCLC patients' survival. Conclusion: Our research has improved the regulatory framework for cisplatin resistance in NSCLC and can provide direction for subsequent related research, especially regarding PCDH7.

Comprehensive analysis of CXCL14 uncovers its role during liver metastasis in colon cancer.

BACKGROUND: The most common cause of death for colon cancer patients is liver metastasis. METHODS: All the data enrolled in this study were downloaded from two public databases, The Cancer Genome Atlas Program, the TCGA-COAD project and Gene Expression Omnibus, GSE41258 project. All the analysis was performed in R software. RESULTS: In our study, we systematically explored the molecules involved in the liver metastasis process of colon cancer. The biological role of these molecules was identified through the GO and KEGG analysis. Moreover, we identified that the molecules SERPINA3, SERPINA1, MMP3, ALDH1A3, PBK and CXCL14 were the independent factors for patients survival. The CXCL14 was selected for further analysis for its most significant P value. Single-cell analysis showed that the CXCL14 was mainly expressed in the fibroblasts. Meanwhile, the biological role of fibroblasts in the colon cancer microenvironment was investigated. Further, the clinical role of CXCL14 in colon cancer was also explored. The result showed that the CXCL14 is a protective factor against colon cancer independent of other clinical parameters like age, gender, clinical stage, and TNM classifications. Then, biological enrichment analysis indicated that the CXCL14 is predominantly involved in the activating of the WNT/ß/catenin pathway, pancreas beta cells, peroxisome and bile acid metabolism. Immune infiltration analysis showed that for the patients with high CXCL14 levels, the plasma B cells, CD8 + T cells, neutrophil and NK cells might infiltrate more, in contrast to B cells, monocyte and macrophages. Furthermore, we found that the patients with low CXCL14 expression might be more sensitive to etoposide, rapamycin and sunitinib. CONCLUSIONS: Our result could improve the understanding of the liver metastasis process in colon cancer. Also, CXCL14 was identified as an underlying therapeutic target for colon cancer.

Characterization and Prognosis of Biological Microenvironment in Lung Adenocarcinoma through a Disulfidptosis-Related lncRNAs Signature.

Background: The role of disulfidptosis-related lncRNAs remains unclear in lung adenocarcinoma. Methods: Analysis in R software was conducted using different R packages, which are based on the public data from The Cancer Genome Atlas (TCGA) database. The transwell assay was used to evaluate the invasion and migration abilities of lung cancer cells. Results: In our study, we identified 1401 lncRNAs significantly correlated with disulfidptosis-related genes (|Cor| > 0.3 and P < 0.05). Then, we constructed a prognosis model consisting of 11 disulfidptosis-related lncRNAs, including AL133445.2, AL442125.1, AC091132.2, AC090948.1, AC020765.2, CASC8, AL606834.1, LINC00707, OGFRP1, U91328.1, and GASAL1. This prognosis model has satisfactory prediction performance. Also, the risk score and clinical information were combined to develop a nomogram. Analyses of biological enrichment and immune-related data were used to identify underlying differences between patients at high-risk and low-risk groups. Moreover, we noticed that the immunotherapy nonresponders have higher risk scores. Meanwhile, patients at a high risk responded more strongly to docetaxel, paclitaxel, and vinblastine. Furthermore, further analysis of the model lncRNA OGFRP1 was conducted, including clinical, immune infiltration, biological enrichment analysis, and a transwell assay. We discovered that by inhibiting OGFRP1, the invasion and migration abilities of lung cancer cells could be remarkably hindered. Conclusion: The results of our study can provide directions for future research in the relevant areas. Moreover, the prognosis signature we identified has the potential for clinical application.

Governor vessel moxibustion for cancer-related fatigue in colorectal patients: a randomized trial.

Objective: This study aimed to investigate the efficacy and mechanism of action of Governor Vessel Moxibustion (GVM) in the treatment of Cancer-Related Fatigue (CRF) in patients who have completed treatment for colorectal cancer. Methods: We randomly assigned 80 CRF patients in a 1:1 ratio to either the experimental group or the control group. During the three-week treatment period, both groups of patients received usual care for CRF provided by professional nurses. The experimental group received additional GVM treatment (three times a week, nine times total). The primary outcome was the mean change in total fatigue score from baseline to the end of treatment, assessed using the Chinese version of the Piper Fatigue Scale. Results: At baseline, the total fatigue scores were 6.20 ± 0.12 in the experimental group and 6.16 ± 0.14 in the control group. At the end of treatment, the total fatigue scores decreased by 2.03 points (32.7% decrease from baseline) in the experimental group and by 0.99 points (15.6% decrease from baseline) in the control group. The absolute reduction in total fatigue scores in the experimental group was 1.04 points higher than in the control group (95% CI, 0.93 to 1.15; P<0.001), corresponding to a relative difference of 17.1% (95% CI, 15.2% to 18.9%; P<0.001). At the end of treatment, the experimental group showed greater reductions in interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) levels compared to the control group. No serious adverse events related to GVM treatment were observed. Conclusion: GVM appears to be safe and effective for alleviating CRF in patients who have completed colorectal cancer treatment, which may be related to the modulation of IL-6 and TNF-α levels. Trial registration: Chinese Clinical Trials Registry: ChiCTR2300069208.

The m6A-regulation and single cell effect pattern in sunitinib resistance on clear cell renal cell carcinoma: Identification and validation of targets.

Background: Sunitinib is the main target drug for clear cell renal cell carcinoma. However, the effect of sunitinib is often limited by acquired drug resistance. Methods: The open-accessed data used in this study were obtained from different online public databases, which were analyzed using the R software. The RNA level of specific genes was detected using quantitative Real-Time PCR. Sunitinib-resistant cell lines were constructed based on protocol get from the previous study. Colony formation and Cell Counting Kit-8 assays were applied to detect cell proliferation ability. Results: In this study, through publicly available data and high-quality analysis, we deeply explored the potential biological mechanisms that affect the resistance of sunitinib. Detailed, data from GSE64052, GSE76068 and The Cancer Genome Atlas were extracted. We identified the IFITM1, IL6, MX2, PCOLCE2, RSAD2 and SLC2A3 were associated with sunitinib resistance. Single-cell analysis, prognosis analysis and m6A regulatory network were conducted to investigate their role. Moreover, the MX2 was selected for further analysis, including its biological role and effect on the ccRCC microenvironment. Interestingly, we noticed that MX2 might be an immune-related gene that could affect the response rate of immunotherapy. Then, in vitro experiments validated the overexpression of MX2 in sunitinib-resistance cells. Colony formation assay indicated that the knockdown of MX2 could remarkably inhibit the proliferation ability of 786-O-Res and Caki-1-Res when exposed to sunitinib. Conclusion: In summary, through publicly available data and high-quality analysis, we deeply explored the potential biological mechanisms that affect the resistance of sunitinib. MX2 was selected for further analysis, including its biological role and effect on the ccRCC microenvironment. Finally, in vitro experiments were used to validate its role in ccRCC.

Machine learning identifies characteristics molecules of cancer associated fibroblasts significantly correlated with the prognosis, immunotherapy response and immune microenvironment in lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is a highly lethal disease with a dramatic pro-fibrocytic response. Cancer-associated fibroblasts (CAFs) have been reported to play a key role in lung adenocarcinoma. Methods: Marker genes of CAFs were obtained from the Cell Marker website. Single sample gene set enrichment analysis (ssGSEA) was used for CAFs quantification. R and GraphPad Prism software were utilized for all analysis. Quantitative real-time PCR (qRT-PCR) was utilized to detect the RNA level of specific molecules. Results: Based on the ssGSEA algorithm and obtained CAFs markers, the LUAD patients with low- and high-CAFs infiltration were successfully identified, which had different response patterns to immunotherapy. Through the machine learning algorithm - LASSO logistic regression, we identified 44 characteristic molecules of CAFs. Furthermore, a prognosis signature consisting of seven characteristic genes was established, which showed great prognosis prediction ability. Additionally, we found that patients in the low-risk group might have better outcomes when receiving immunotherapy of PD-1, but not CTLA4. Also, the biological enrichment analysis revealed that immune response-related pathways were significantly associated with CAFs infiltration. Meanwhile, we investigated the underlying biological and microenvironment difference in patients with high- and low-risk groups. Finally, we identified that AMPD1 might be a novel target for LUAD immunotherapy. Patients with a high level of AMPD1 were correlated with worse responses to immunotherapy. Moreover, immunohistochemistry showed that the protein level of AMPD1 was higher in lung cancer. Results of qRT-PCR demonstrated that AMPD1 was upregulated in A549 cells compared with BEAS-2B. Meanwhile, we found that the knockdown of AMPD4 can significantly reduce the expression of CTLA4 and PDCD1, but not CD274 and PDCD1LG2. Conclusion: We comprehensively explored the role of CAFs and its characteristics molecules in LUAD immunotherapy and developed an effective signature to indicate patients prognosis and immunotherapy response. Moreover, AMPD1 was identified as a novel target for lung cancer immunotherapy.

Research into the biological differences and targets in lung cancer patients with diverse immunotherapy responses.

Background: Immunotherapy has gradually become an important therapy option for lung cancer patients. Methods: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were responsible for all the public data. Results: In our study, we firstly identified 22 characteristic genes of NSCLC immunotherapy response using the machine learning algorithm. Molecule subtyping was then conducted and two patient subtypes were identified Cluster1 and Cluster2. Results showed that Cluster1 patients had a lower TIDE score and were more sensitive to immunotherapy in both TCGA and combined GEO cohorts. Biological enrichment analysis showed that pathways of epithelial-mesenchymal transition (EMT), apical junction, KRAS signaling, myogenesis, G2M checkpoint, E2F targets, WNT/ß-catenin signaling, hedgehog signaling, hypoxia were activated in Cluster2 patients. Genomic instability between Cluster1 and Cluster2 patients was not significantly different. Interestingly, we found that female patients were more adaptable to immunotherapy. Biological enrichment revealed that compared with female patients, pathways of MYC target, G2M checkpoints, mTORC1 signaling, MYC target, E2F target, KRAS signaling, oxidative phosphorylation, mitotic spindle and P53 pathway were activated. Meanwhile, monocytes might have a potential role in affecting NSCLC immunotherapy and underlying mechanism has been explored. Finally, we found that SEC14L3 and APCDD1L were the underlying targets affecting immunotherapy, as well as patients survival. Conclusions: These results can provide direction and guidance for future research focused on NSCLC immunotherapy.

Investigation of M2 macrophage-related gene affecting patients prognosis and drug sensitivity in non-small cell lung cancer: Evidence from bioinformatic and experiments.

Background: The progression process of lung cancer can be accelerated by M2 macrophages. However, genes that affect M2 macrophage polarization remain unidentified. Methods: The Cancer Genome Atlas, Gene Expression Omnibus, and Arrayexpress databases were used to obtain open-access data. The analysis of public data was mostly performed with R studio. The RNA levels of specific genes were detected using quantitative real-time PCR. The proliferation ability of the cells was assessed by CCK8, colony formation, and EdU assays. Results: Based on the multiple datasets, we noticed a poor prognosis in patients with high M2 macrophage infiltration. There were 114 genes differentially expressed between high and low M2 macrophages infiltrated samples, regarded as M2 macrophage-related genes. Subsequently, a prognosis prediction signature consisting of ABHD5, HS3ST2, TM6SF1, CAPZA2, LEPROT, HNMT, and MRO was identified and presented a satisfactory performance. The pathway enrichment results revealed a positive correlation between riskscore and enrichment scores for most immunotherapy-related positive terms. Also, there might be an increase in genomic instability among patients at high risk. Interestingly, low risk patients are most likely to benefit from PD-1 therapy, while high risk patients may benefit from CTLA-4 therapy. Meanwhile, the estimated IC50 of seven drugs differs significantly between two risk groups, including Cisplatin, Docetaxel, Doxorubicin, Gefitinib, Paclitaxel, Sunitinib and Vinorelbine. Moreover, further experiments indicated that HNMT was overexpressed and can enhance the proliferation ability in lung cancer cells. Conclusions: In summary, our study identified the molecules significantly affecting M2 macrophage infiltration and identified a prognosis signature that robustly indicated patients prognosis. Moreover, we validated the cancer-promoting effect of HNMT using in vitro experiments.