-
Journal of Cancer Research and Clinical... Nov 2023As a phosphorylated protein, NOLC1 is mainly located in the nucleus and is highly expressed in a variety of tumors, participating in the regulation of cell proliferation...
BACKGROUND
As a phosphorylated protein, NOLC1 is mainly located in the nucleus and is highly expressed in a variety of tumors, participating in the regulation of cell proliferation and aging. This study further investigated the role of NOLC1 in colorectal cancer tumors, aiming to provide sufficient scientific evidence for the clinical treatment of colorectal cancer.
METHODS
We used TCGA, GEO, TNMplot, GEPIA, and other databases to explore the expression level of NOLC1 in colorectal cancer patients, as well as the correlation between the clinical characteristics of colorectal cancer patients and their expression, and conducted the prognostic analysis. Immunohistofluorescence (IHF) staining verified the analytical results. Subsequently, KEGG and GO enrichment analysis was used to identify the potential molecular mechanism of NOLC1 promoting the occurrence and development of colorectal cancer. The influence of NOLC1 expression on the immune microenvironment of colorectal cancer patients was further investigated using the TIMER database. GDSC database analysis was used to screen out possible anti-colorectal cancer drugs against NOLC1. Finally, we demonstrated the effect of NOLC1 on the activity and migration of colorectal cancer cells by Edu Cell proliferation assay and Wound Healing assay in vitro.
RESULTS
Our results suggest that NOLC1 is overexpressed in colorectal cancer, and that overexpression of NOLC1 is associated with relevant clinical features. NOLC1, as an independent risk factor affecting the prognosis of colorectal cancer patients, can lead to a poor prognosis of colorectal cancer. In addition, NOLC1 may be associated with MCM10, HELLS, NOC3L, and other genes through participating in Wnt signaling pathways and jointly regulate the occurrence and development of colorectal cancer under the influence of the tumor microenvironment and many other influencing factors. Related to NOLC1: Selumetinib, Imatinib, and targeted drugs such as Lapatinib have potential value in the clinical application of colorectal cancer. NOLC1 enhances the proliferation and migration of colorectal cancer cells.
CONCLUSIONS
High expression of NOLC1 as an independent prognostic factor for survival in patients with colorectal cancer. NOLC1 enhances the proliferation and migration of colorectal cancer cells. Further studies and clinical trials are needed to confirm the role of NOLC1 in the development and progression of colorectal cancer.
Topics: Humans; Prognosis; Aging; Cell Proliferation; Colorectal Neoplasms; Databases, Factual; Tumor Microenvironment; Nuclear Proteins; Phosphoproteins
PubMed: 37670166
DOI: 10.1007/s00432-023-05297-7 -
Cancer Research and Treatment Oct 2023Albumin-bilirubin (ALBI) score is a well-known prognostic factor for various diseases, including colorectal cancer (CRC). However, little is known about the significance...
PURPOSE
Albumin-bilirubin (ALBI) score is a well-known prognostic factor for various diseases, including colorectal cancer (CRC). However, little is known about the significance of postoperative ALBI score changes in patients with CRC.
MATERIALS AND METHODS
A total of 723 patients who underwent surgery were enrolled. Preoperative ALBI (ALBI-pre) and postoperative ALBI (ALBI-post) scores were divided into low and high score groups. ALBI-trend was defined as a combination of four groups comprising the low and high ALBI-pre and ALBI-post score groups. Kaplan-Meier survival curves were used to compare the overall survival (OS) between the different ALBI groups. The Cox proportional hazards model was used to examine the independent relevant factors of OS. Stratification performance was compared between the different ALBI groupings using Harrell's concordance index (C-index).
RESULTS
ALBI-pre, ALBI-post, and ALBI-trend score groups were significant prognostic factors of OS in the univariable analysis. However, multivariable analysis showed that ALBI-trend was an independent prognostic factor while ALBI-pre and ALBI-post were not. The C-index of ALBI-trend (0.622; 95% confidence interval [CI], 0.587 to 0.655) was higher than that of ALBI-pre (0.589; 95% CI, 0.557 to 0.621; bootstrap mean difference, 0.033; 95% CI, 0.013 to 0.057) and ALBI-post (0.575; 95% CI, 0.545 to 0.605; bootstrap mean difference, 0.047; 95% CI, 0.024 to 0.074).
CONCLUSION
Combining ALBI-pre and ALBI-post scores is an independent prognostic factor of OS and shows superior predictive power compared to ALBI-pre or ALBI-post alone in patients with CRC.
Topics: Humans; Bilirubin; Clinical Relevance; Colorectal Neoplasms; Prognosis; Retrospective Studies; Serum Albumin
PubMed: 37080608
DOI: 10.4143/crt.2022.1444 -
BMC Women's Health Jan 2024Both mitophagy and long non-coding RNAs (lncRNAs) play crucial roles in ovarian cancer (OC). We sought to explore the characteristics of mitophagy-related gene (MRG) and...
BACKGROUND
Both mitophagy and long non-coding RNAs (lncRNAs) play crucial roles in ovarian cancer (OC). We sought to explore the characteristics of mitophagy-related gene (MRG) and mitophagy-related lncRNAs (MRL) to facilitate treatment and prognosis of OC.
METHODS
The processed data were extracted from public databases (TCGA, GTEx, GEO and GeneCards). The highly synergistic lncRNA modules and MRLs were identified using weighted gene co-expression network analysis. Using LASSO Cox regression analysis, the MRL-model was first established based on TCGA and then validated with four external GEO datasets. The independent prognostic value of the MRL-model was evaluated by Multivariate Cox regression analysis. Characteristics of functional pathways, somatic mutations, immunity features, and anti-tumor therapy related to the MRL-model were evaluated using abundant algorithms, such as GSEA, ssGSEA, GSVA, maftools, CIBERSORT, xCELL, MCPcounter, ESTIMATE, TIDE, pRRophetic and so on.
RESULTS
We found 52 differentially expressed MRGs and 22 prognostic MRGs in OC. Enrichment analysis revealed that MRGs were involved in mitophagy. Nine prognostic MRLs were identified and eight optimal MRLs combinations were screened to establish the MRL-model. The MRL-model stratified patients into high- and low-risk groups and remained a prognostic factor (P < 0.05) with independent value (P < 0.05) in TCGA and GEO. We observed that OC patients in the high-risk group also had the unfavorable survival in consideration of clinicopathological parameters. The Nomogram was plotted to make the prediction results more intuitive and readable. The two risk groups were enriched in discrepant functional pathways (such as Wnt signaling pathway) and immunity features. Besides, patients in the low-risk group may be more sensitive to immunotherapy (P = 0.01). Several chemotherapeutic drugs (Paclitaxel, Veliparib, Rucaparib, Axitinib, Linsitinib, Saracatinib, Motesanib, Ponatinib, Imatinib and so on) were found with variant sensitivity between the two risk groups. The established ceRNA network indicated the underlying mechanisms of MRLs.
CONCLUSIONS
Our study revealed the roles of MRLs and MRL-model in expression, prognosis, chemotherapy, immunotherapy, and molecular mechanism of OC. Our findings were able to stratify OC patients with high risk, unfavorable prognosis and variant treatment sensitivity, thus improving clinical outcomes for OC patients.
Topics: Female; Humans; RNA, Long Noncoding; Mitophagy; Ovarian Neoplasms; Paclitaxel; Axitinib; Prognosis
PubMed: 38218807
DOI: 10.1186/s12905-023-02864-5 -
Frontiers in Endocrinology 2024This study represented the inaugural effort to develop predictive survival nomograms for metastatic soft tissue sarcoma (mSTS) patients in the era of immune checkpoint...
BACKGROUND
This study represented the inaugural effort to develop predictive survival nomograms for metastatic soft tissue sarcoma (mSTS) patients in the era of immune checkpoint inhibitors.
METHOD
From the Surveillance, Epidemiology, and End Results (SEER) program database, we extracted 3078 eligible patients with mSTS between 2016 and 2022. Kaplan-Meier survival analysis, univariate and multivariable Cox analyses, and univariate and multivariable logistic analyses were conducted. Subsequently, predictive nomograms were constructed. Clinical effectiveness was validated using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA) methods.
RESULTS
We used the SEER database to include 3078 eligible patients with mSTS between 2016 and 2022. All the eligible patients were randomly allocated in a ratio of 6:4 and stratified into a training group (n = 1846) and a validation group (n = 1232). In the multivariate Cox analysis, age, race, marital status, pathological grade, histologic subtype, surgery, and chemotherapy were identified as independent prognostic factors. These factors were used to construct the nomogram to predict the 1-, 3-, and 5-year OS of mSTS patients. The C-index for the training cohort and the validation cohort was 0.722(95% confidence interval [CI]: 0.708-0.736), and 0.716(95% CI: 0.698-0.734), respectively. The calibration curves for 1-, 3-, and 5-year OS probability demonstrated excellent calibration between the predicted and the actual survival. The AUC values of the nomogram at 1-, 3-, and 5-year were 0.785, 0.767, and 0.757 in the training cohort, 0.773, 0.754, and 0.751 in the validation cohort, respectively. Furthermore, DCA indicated the favorable clinical utility of the nomogram in both cohorts. The risk stratification system was constructed using the established nomogram, which enhanced prediction accuracy, aided clinicians in identifying high-risk patients and informing treatment decisions.
CONCLUSION
This study marked the inaugural effort in constructing predictive survival nomograms mSTS patients in the era of immune checkpoint inhibitors. The robustly constructed nomograms, alongside actual outcomes, offered valuable insights to inform follow-up management strategies.
Topics: Humans; Nomograms; Sarcoma; Male; Female; Middle Aged; Prognosis; SEER Program; Aged; Adult; Survival Rate; Neoplasm Metastasis
PubMed: 38803474
DOI: 10.3389/fendo.2024.1371910 -
Scientific Reports Nov 2023Anoikis resistance, a notable factor in osteosarcoma, plays a significant role in tumor invasion and metastasis. This study seeks to identify a distinct gene signature...
Anoikis resistance, a notable factor in osteosarcoma, plays a significant role in tumor invasion and metastasis. This study seeks to identify a distinct gene signature that is specifically associated with the anoikis subcluster in osteosarcoma. Clinical, single-cell, and transcriptional data from TARGET and GEO datasets were used to develop a gene signature for osteosarcoma based on the anoikis subcluster. Univariate Cox and LASSO regression analyses were employed. The signature's predictive value was evaluated using time-dependent ROC and Kaplan-Meier analyses. Functional enrichment analyses and drug sensitivity analyses were conducted. Validation of three modular genes was performed using RT-qPCR and Western blotting. Signature (ZNF583, CGNL1, CXCL13) was developed to predict overall survival in osteosarcoma patients, targeting the anoikis subcluster. The signature demonstrated good performance in external validation. Stratification based on the signature revealed significantly different prognoses. The signature was an independent prognostic factor. The low-risk group showed enhanced immune cell infiltration and improved immune function. Drug sensitivity analysis indicated efficacy of chemotherapy agents. Prognostic nomograms incorporating the signature provided greater predictive accuracy and clinical utility. Signatures related to the anoikis subcluster play a significant role in osteosarcoma progression. Incorporating these findings into clinical decision-making can improve osteosarcoma treatment and patient outcomes.
Topics: Humans; Anoikis; Prognosis; Immunotherapy; Osteosarcoma; Bone Neoplasms
PubMed: 37980450
DOI: 10.1038/s41598-023-47367-3 -
A novel NFAT1-IL6/JAK/STAT3 signaling pathway related nomogram predicts overall survival in gliomas.Scientific Reports Jul 2023The NFAT1-mediated IL6/JAK-STAT signaling pathway has been observed to contribute to malignant progression in glioma patients. To predict the overall survival (OS) rate...
The NFAT1-mediated IL6/JAK-STAT signaling pathway has been observed to contribute to malignant progression in glioma patients. To predict the overall survival (OS) rate of these patients, a prognostic model was developed based on this pathway. Two datasets, mRNAseq_325 and mRNAseq_693, were obtained from the China Glioma Genome Atlas (CGGA), excluding some patients with a lack of survival information, resulting in the inclusion of 684 glioma cases. The two groups were randomly divided into training and validation groups to analyze the differential expression of NFAT1 in pan-cancer and investigate the relationship between differential NFAT1 expression and glioma clinicopathological factors and Transcriptional subtypes. A prediction model based on the IL6/JAK/STAT signaling pathway was constructed using the LASSO-COX dimension reduction analysis to predict the OS of glioma patients. Pearson correlation analysis was utilized to identify gene sets associated with patient risk scores and to perform GO and KEGG analyses. NFAT1 is differentially expressed in a variety of cancers and is enriched in the more malignant potential glioma subtypes. It is an independent prognostic factor in glioma patients, and its expression is significantly positively correlated with the IL6/JAK/STAT signalling pathway in glioma patients. The final prediction model incorporating the seven candidate genes together with other prognostic factors showed strong predictive performance in both the training and validation groups. Risk scores of glioma patients were correlated with processes such as NF-κB and protein synthesis in glioma patients. This individualized prognostic model can be used to predict the OS rate of patients with glioma at 1, 2, 3, 5, and 10 years, providing a reference value for the treatment of glioma patients.
Topics: Humans; Nomograms; Interleukin-6; Glioma; Signal Transduction; NF-kappa B; Prognosis; STAT3 Transcription Factor
PubMed: 37452092
DOI: 10.1038/s41598-023-38629-1 -
European Journal of Medical Research Sep 2023Clear cell renal cell carcinoma (ccRCC) is a prevalent cancer in adult urology, often leading to metastasis and poor prognosis. Recently, advances in tumor immunology...
BACKGROUND
Clear cell renal cell carcinoma (ccRCC) is a prevalent cancer in adult urology, often leading to metastasis and poor prognosis. Recently, advances in tumor immunology and aging research have opened up new possibilities for the treatment of kidney cancer. Therefore, the identification of potential targets and prognostic biomarkers for immunotherapy has become increasingly urgent.
METHODS
Using GSE168845 data, we identified immune-aging-associated differentially expressed genes (IAR-DEGs) by intersecting differentially expressed immune-related genes and aging-related genes. The prognostic value of IAR-DEGs was determined via univariate and multivariate Cox regression analysis, revealing KL as an independent prognostic factor for ccRCC. We also investigated the correlation between KL and various immune-related factors, including immune cell infiltration, immune score, immune checkpoint, MSI, and TIED score. To confirm the expression of KL in ccRCC, we conducted qRT-PCR assays on both ccRCC cell lines and clinical tissue samples, and compared KL expression levels between normal kidney cell line (HK-2) and ACHN, a ccRCC cell line. Finally, we assessed KL protein expression levels in tissues using immunohistochemistry (IHC).
RESULTS
In this study, we utilized Venn diagram analysis to identify 17 co-expressed immune-aging related DEGs from GSE168845, import database, and MSigDB database. GO and KEGG analysis revealed that the functions of the 17 IAR-DEGs were primarily related to "aging". Univariate and multivariate Cox analysis validated these 17 genes, and KL was determined to be an independent prognostic factor for ccRCC. The downregulation of KL was observed in ccRCC tissues and was negatively associated with T stage, M stage, pathological stage, and histologic grade (p < 0.05). This downregulation indicated disease deterioration and a shortened overall survival period. Our calibration curves and nomogram demonstrated the excellent predictive potential of KL. GSEA analysis showed that KL gene mediated immune and aging-related pathways, and was significantly correlated with immune infiltration and MS and TIED score. More research has revealed a significant reduction in KL mRNA expression levels in human renal cancer cells, particularly in ccRCC tissues compared to adjacent normal kidney tissues. Moreover, immunohistochemistry data have demonstrated a marked decrease in KL protein expression levels in ccRCC cells when compared to adjacent normal tissues.
CONCLUSIONS
KL was a potential aging-related target for immunotherapy and valid prognostic biomarker for ccRCC patients.
Topics: Adult; Humans; Carcinoma, Renal Cell; Cell Line; Kidney; Kidney Neoplasms; Prognosis
PubMed: 37726833
DOI: 10.1186/s40001-023-01242-z -
Thoracic Cancer Nov 2023The biomarkers of immune checkpoint inhibitors in the treatment of non-small cell lung cancer (NSCLC) patients have limited predictive performance. In this study we...
BACKGROUND
The biomarkers of immune checkpoint inhibitors in the treatment of non-small cell lung cancer (NSCLC) patients have limited predictive performance. In this study we aimed to investigate the feasibility of molecular tumor burden index (mTBI) in circulating tumor DNA (ctDNA) as a predictor for immunotherapy in patients with NSCLC.
METHODS
From February 2017 to November 2020, pretreatment and on-treatment (3~6 weeks after first cycle of immunotherapy) dynamic plasma ctDNA samples from NSCLC patients receiving immune monotherapy or combination therapy were analyzed by targeted capture sequencing of 1021 genes. PyClone was used to infer the mTBI. The impact of pretreatment mTBI on survival outcomes was verified in the POPLAR/OAK trials.
RESULTS
We found that patients without detectable baseline ctDNA had better survival outcomes (median overall survival [OS]: not reached vs. 12.8 months; hazard ratio [HR], 0.15; p = 0.035]). RB1 and SMARCA4 mutations were remarkably associated with worse survival outcomes. Furthermore, lower pretreatment mTBI was associated with superior OS (median: not reached vs. 8.1 months; HR, 0.22; p = 0.024) and PFS (median: 32.9 vs. 5.4 months; HR, 0.35; p = 0.045), but not objective response, which was validated in the POPLAR/OAK cohort, suggesting that baseline mTBI was a prognostic factor for NSCLC immunotherapy. Early dynamic changes of mTBI (ΔmTBI) significantly distinguished responsive patients, and patients with mTBI decrease to more than 68% at the final tumor evaluation had longer OS (median: 38.2 vs. 4.0 months; HR, 0.18; p = 0.017) and PFS (median: not reached vs. 2.3 months; HR, 0.24; p = 0.030).
CONCLUSION
ΔmTBI had a good sensitivity to identify potential beneficial patients based on the best effect CT scans, demonstrating that mTBI dynamics were predictive of benefit from immune checkpoint blockade.
Topics: Humans; Carcinoma, Non-Small-Cell Lung; Prognosis; Lung Neoplasms; Tumor Burden; Biomarkers, Tumor; DNA Helicases; Nuclear Proteins; Transcription Factors
PubMed: 37724484
DOI: 10.1111/1759-7714.15098 -
Clinical and Experimental Medicine Nov 2023Triple negative breast cancer (TNBC) is the most aggressive and malignant subtype in breast cancer. Immunotherapy is a currently promising and effective treatment for...
Triple negative breast cancer (TNBC) is the most aggressive and malignant subtype in breast cancer. Immunotherapy is a currently promising and effective treatment for TNBC, while not all patients are responsive. Therefore, it is necessary to explore novel biomarkers to screen sensitive populations for immunotherapy. All mRNA expression profiles of TNBC from The Cancer Genome Atlas (TCGA) database were clustered into two subgroups by analyzing tumor immune microenvironment (TIME) with single sample gene set enrichment analysis (ssGSEA). A risk score model was constructed based on differently expressed genes (DEGs) identified from two subgroups using Cox and Least Absolute Shrinkage and Selector Operation (LASSO) regression model. And it was validated by Kaplan-Meier analysis and Receiver Operating Characteristic (ROC) analysis in Gene Expression Omnibus (GEO) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases. Multiplex immunofluorescence (mIF) and Immunohistochemical (IHC) staining were performed on clinical TNBC tissue samples. The relationship between risk score and immune checkpoint blockades (ICB) related signatures was further investigated, as well as the biological processes were performed by gene set enrichment analysis (GSEA). We obtained three DEGs positively related to prognosis and infiltrating immune cells in TNBC. Our risk score model could be an independent prognostic factor and the low risk group exhibited a prolonged overall survival (OS). Patients in low risk group were more likely to present a higher immune infiltration and stronger response to immunotherapy. GSEA revealed the model was associated with immune-related pathways. We constructed and validated a novel model based on three prognostic genes related to TIME in TNBC. The model contributed a robust signature that could predict the prognosis in TNBC, especially for the efficacy of immunotherapy.
Topics: Humans; Triple Negative Breast Neoplasms; Tumor Microenvironment; Prognosis; Immunotherapy; Risk Factors
PubMed: 37219794
DOI: 10.1007/s10238-023-01090-5 -
Journal of Ovarian Research Oct 2023The immune system played a multifaceted role in ovarian cancer (OC) and was a significant mediator of ovarian carcinogenesis. Various immune cells and immune gene...
BACKGROUND
The immune system played a multifaceted role in ovarian cancer (OC) and was a significant mediator of ovarian carcinogenesis. Various immune cells and immune gene products played an integrated role in ovarian cancer (OC) progression, proved the significance of the immune microenvironment in prognosis. Therefore, we aimed to establish and validate an immune gene prognostic signature for OC patients' prognosis prediction.
METHODS
Differently expressed Immune-related genes (DEIRGs) were identified in 428 OC and 77 normal ovary tissue specimens from 9 independent GEO datasets. The Cancer Genome Atlas (TCGA) cohort was used as a training cohort, Univariate Cox analysis was used to identify prognostic DEIRGs in TCGA cohort. Then, an immune gene-based risk model for prognosis prediction was constructed using the LASSO regression analysis, and validated the accuracy and stability of the model in 374 and 93 OC patients in TCGA training cohort and International Cancer Genome Consortium (ICGC) validation cohort respectively. Finally, the correlation among risk score model, clinicopathological parameters, and immune cell infiltration were analyzed.
RESULTS
Five DEIRGs were identified to establish the immune gene signature and divided OC patients into the low- and high-risk groups. In TCGA and ICGC datasets, patients in the low-risk group showed a substantially higher survival rate than high-risk group. Receiver operating characteristic (ROC) curves, t-distributed stochastic neighbor embedding (t-SNE) analysis and principal component analysis (PCA) showed the good performance of the risk model. Clinicopathological correlation analysis proved the risk score model could serve as an independent prognostic factor in 2 independent datasets.
CONCLUSIONS
The prognostic model based on immune-related genes can function as a superior prognostic indicator for OC patients, which could provide evidence for individualized treatment and clinical decision making.
Topics: Female; Humans; Ovarian Neoplasms; Prognosis; Carcinogenesis; Risk Assessment; Tumor Microenvironment
PubMed: 37858138
DOI: 10.1186/s13048-023-01289-w