Liver cancer is the fifth most prevalent malignant tumor, while hepatocellular carcinoma represents the most prevalent subtype worldwide. Previous studies have associated the chromobox family, critical components of epigenetic regulatory complexes, with development of many malignancies owing to their role in inhibiting differentiation and promoting proliferation of cancer cells. However, little is known regarding their function in development and progression of hepatocellular carcinoma. In the present study, we analyzed differential expression, prognostic value, immune cell infiltration, and gene pathway enrichment of chromobox family in hepatocellular carcinoma patients. Next, we performed Pearson’s correlation analysis to determine the relationships between chromobox family proteins with tumor-immune infiltration. Results revealed that high expression of CBX1, CBX2, CBX3, CBX6, and CBX8 was associated with poor survival rates of hepatocellular carcinoma patients. These five factors were used to build prognostic gene models using LASSO Cox regression analysis. Results indicated that high expression of CBX2 and CBX3 proteins was significantly associated with poor prognosis for hepatocellular carcinoma patients. The resulting nomogram revealed that CBX3 and T stages were significantly correlated with prognosis of hepatocellular carcinoma patients. Notably, predictive CBX3 was strongly correlated with immune cell infiltration. Furthermore, results from functional enrichment analysis revealed that CBX3 was mainly involved in regulation of methylation of Histone H3-K27. Collectively, these findings suggest that CBX3 could be a biomarker for predicting prognosis of hepatocellular carcinoma patients.

Purpose Serum lipids were reported to be the prognostic factors of various cancers, but their prognostic value in small cell lung cancer (SCLC) patients remains unclear. This study investigated the relationship between lipid profiles and clinical outcomes in extensive-stage (ES) SCLC by establishing a predictive risk classification model. Patients and Methods We retrospectively analyzed the prognostic values of pretreatment serum lipids and their derivatives in patients with a confirmed diagnosis ES-SCLC. Independent factors of progression-free survival (PFS) were determined by univariate and multivariate cox analysis. Then, prognostic nomograms were established, of which predictive performance was evaluated by concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curves, and decision curve analyses (DCA). Results A total of 158 patients was included in this study. Four optimal PFS-related factors, total cholesterol (TC) ≥ 5.30, high-density lipoprotein cholesterol (HDL-C) > 1.30, triglycerides (TG)/HDL-C  > 2.18, and ki67 expression > 70%, were included to construct the predictive nomogram. The C-indexes in training and validation sets were 0.758 and 0.792, respectively. ROC curves, calibration plots, and DCA all suggested favorable discrimination and predictive ability. Besides, the nomogram also performed better predictive ability than ki67 expression. Nomogram-related risk score divided the patients into two groups with significant progression disparities. Conclusion The promising prognostic nomogram based on lipid parameters could help clinicians to conveniently and accurately evaluate the prognosis of ES-SCLC patients and identify high-risk groups, so as to formulate individualized therapeutic regimens and follow-up strategies in time.

Background: Serum lipids were reported to be significant predictive factors in various tumors. In order to develop and validate a nomogram for predicting castration-resistant prostate cancer (CRPC) free survival in advanced hormone-sensitive prostate cancer (HSPC) patients, the goal of this study was to assess the prognostic impact of the lipid profiles. Material and Methods: The follow-up information of 146 CRPC patients who received androgen deprivation therapy as the first and only therapy before progression were retrospectively examined. To evaluate prognostic variables, univariate and multivariate Cox regression analyses were used. The concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curves, and decision curve analyses (DCA) were used to design and evaluate a novel nomogram model. Results: Total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), apolipoprotein B (apoB), N stage and Gleason sum were determined to be independent prognostic markers and were combined to create a nomogram. This nomogram performed well in the customized prediction of CRPC development at 6th, 12th, 18th and 24th month. The C-indexes in training and validation sets were 0.740 and 0.755, respectively. ROC curves, calibration plots, and DCA all suggested favorable discrimination and predictive ability. Besides, the nomogram also performed better predictive ability than N stage and Gleason sum. The Nomogram-related risk score divided the patient population into two groups with significant progression disparities. Conclusions: The established nomogram could aid in identifying the patients at high risk for rapid progression of advanced HSPC, so as to formulate individualized therapeutic regimens and follow-up strategies in time.

BackgroundProstate cancer (PCa) is the second most prevalent malignancy and the fifth cause of cancer-related deaths in men. A crucial challenge is identifying the population at risk of rapid progression from hormone-sensitive PCa (HSPC) to the lethal castration-resistant PCa (CRPC).MethodsWe collected 78 HSPC biopsies and measured their proteomes using pressure cycling technology and a pulsed data-independent acquisition pipeline. The proteomics data and clinical metadata were used to generate models for classifying HSPC patients and predicting the development of each case.ResultsWe quantified 7,961 proteins using the HSPC biopsies. A total of 306 proteins were differentially expressed between patients with a long- or short-term progression to CRPC. Using a random forest model, we identified ten proteins that significantly discriminated long-from short-term cases, which were used to classify PCa patients with an 86% accuracy. Next, two clinical parameters (Gleason sum and total PSA) and five proteins (DPT, ARGEF1, UTP23, CMAS, and ANAPC4) were found to be significantly associated with rapid disease progression. A nomogram model using these seven features was generated for stratifying patients into groups with significant progression disparities (p-value = 5.2 × 10−9).ConclusionWe identified proteins associated with a fast progression to CRPC and an unfavorable prognosis. Based on these proteins, our machine learning and nomogram models stratified HSPC into high- and low-risk groups and predict their prognoses. These tools may aid clinicians in predicting the progression of patients, guiding individualized clinical management and decisions.

Although complex links between heterogeneous nuclear ribonucleoprotein C (HNRNPC) and numerous types of cancer have been shown in both cell and animal models, a comprehensive pan-cancer investigation on the features and activities of HNRNPC is still lacking. Based on the Cancer Genome Atlas and Gene Expression Omnibus datasets, we investigated the possible oncogenic effects of HNRNPC in thirty-three cancers. HNRNPC expression was detected in the majority of cancers, and its expression level was shown to be significantly linked with cancer patient prognosis. HNRNPC increased the phosphorylation of S220, which was detected in various cancers, including ovarian cancer and colon cancer. HNRNPC expression was also shown to be related to cancer-associated cell infiltration, most notably in uveal melanoma, testicular germ cell tumors, and thymoma. Additionally, the signaling pathway for vascular endothelial growth factors and RNA transport were implicated in HNRNPC's functioning processes. In short, HNRNPC may further influence cancer progression through gene mutation, protein phosphorylation, cancer associated fibroblasts infiltration and related molecular pathways. This work was intended to provide a relatively thorough knowledge of the oncogenic activities of HNRNPC across a variety of tumor types by performing a systematic pan-cancer investigation.

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Purpose A prevalent condition with a high probability of recurrence, non-muscle invasive bladder cancer (NMIBC) necessitates lifetime surveillance. In patients with pathologically confirmed NMIBC, our goal was to create a unique nomogram to predict recurrence after transurethral resection of bladder tumor (TURBT). Methods Our institution's 91 NMIBC patients with complete follow-up data between January 2017 and February 2021 were included in the retrospective analysis. The nomogram predicting the 0.5, 1, 2 and 3-year likelihood of recurrence was created using multivariate Cox proportional hazard models to find the significant determinants of recurrence. Using the concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curves, and decision curve analyses (DCA), we internally validated the nomogram. Results The significant factors related to NMIBC recurrence were age, blood platelet count, especially for the urine leukocyte count and mucus filament. The constructed nomogram performed well in the customized prediction of NMIBC recurrence at 6th, 12th, 24th and 36th month, of which the C-index was 0.724. The calibration curve and the ROC curve both validated the prediction accuracy. On DCA, the nomogram presented good net benefit gains across a wide range of threshold probabilities. Furthermore, the Nomogram-related risk score was used to divide the patient population into two groups with significant recurrence disparities. Conclusion For the prediction of NMIBC recurrence, our unique nomogram demonstrated a respectable degree of discriminative capacity, sufficient calibration, and considerable net benefit gain. There will be a need for additional internal and external validation.