Association of Preoperative Serum Carcinoembryonic Antigen and Gastric Cancer Recurrence: A Large Cohort Study

Prognostic value of [68Ga]Ga-FAPI-04 PET in patients with newly diagnosed gastric carcinoma

PURPOSE

Gallium-68-labeled fibroblast activation protein inhibitor ([68Ga]Ga-FAPI) positron emission tomography (PET) has demonstrated excellent diagnostic performance in various malignancies, including gastric carcinoma. However, its prognostic utility is unclear. This study evaluates the prognostic value of [68Ga]Ga-FAPI-04 PET/MRI(CT) in gastric carcinoma.

METHODS

We retrospectively analyzed patients with gastric cancer who underwent [68Ga]Ga-FAPI-04 PET/MRI(CT) between June 2020 and June 2023. Semi-quantitative parameters, including maximum and mean standard uptake value (SUVmax, SUVmean), FAPI-avid tumor volume (FTV), total lesion FAP expression (TLF), tumor to background ratio (TBR), heterogeneity factor (HF) and coefficient of variation (CV) of the primary tumor were measured or calculated. Overall survival (OS) and progression-free survival (PFS) were obtained through follow-up. The relationships between disease prognosis and potential predictors were analyzed， and predictive models were established.

RESULTS

Eighty-six patients (median age 59 years) were included. Thirty-five patients experienced disease progression, and 26 of them died. Univariable analysis revealed SUVmax, FTV, TLF, TBR, HF and CV were significant prognostic factors for both OS and PFS. In multivariate Cox regression analysis, a nomogram model for OS was established, incorporating body mass index (BMI) and CV as independent predictors. The time-dependent C-index of the nomogram model > 0.75 indicates good predictive performance. When predicting PFS, a stratified analysis was performed based on distant metastasis, FTV was an independent prognostic factor among patients without distant metastasis.

CONCLUSION

CV and FTV, derived from [68Ga]Ga-FAPI-04 PET imaging, could serve as independent prognostic factor for OS and PFS in patients with gastric cancer, respectively.

Development and validation of nomograms for predicting the prognosis of early and late recurrence of advanced gastric cancer after radical surgery based on post-recurrence survival

In this study, we aimed to explore the risk factors influencing post-recurrence survival (PRS) of early recurrence (ER) and late recurrence (LR) in stage advanced gastric cancer (AGC) patients after radical surgery, respectively, and to develop predictive models in turn. Medical records of 192 AGC patients who recurred after radical gastrectomy were retrospectively reviewed. They were randomly divided into the training and validation set at a ratio of 2:1. Nomograms were built based on risk factors influencing PRS of ER and LR explored by Cox regression analyses, respectively. Concordance index (C-index) values and calibration curves were used to evaluate predictive power of nomograms. Body mass index < 18.5 kg/m2, prealbumin level < 70.1 mg/L, positive lymph nodes ratio ≥ 0.486 and palliative treatment after recurrence were independent risk factors for the prognosis of ER. In contrast, prealbumin level < 170.1 mg/L, CEA ≥ 18.32 μg/L, tumor diameter ≥ 5.5 cm and palliative treatment after recurrence were independent risk factors for the prognosis of LR. The C-index values were 0.801 and 0.772 for ER and LR in the training set, respectively. The calibration curves of validation set showed a C-index value of 0.744 and 0.676 for ER and LR, respectively. Nomograms which were constructed to predict the prognosis of ER and LR of AGC after surgery showed great predictive power and could provide reference for clinicians' treatment strategies to some extent.

Association between IL-6 and prognosis of gastric cancer: a retrospective study

Background

Gastric cancer (GC) is one of the common and fatal cancers. Even though the Tumor, Node, Metastasis (TNM) staging system is the most classical staging system recognized worldwide, it has been controversial because there are various factors affecting the prognosis of GC patients.

Objectives

The study aims to evaluate the relationship between interleukin-6 (IL-6) and several clinical indicators and construct a prognostic model to better predict the prognosis of GC.

Design

A retrospective study.

Methods

Data of 249 patients with GC diagnosed in GC center of West China Hospital were collected. Clinicopathological characteristics were analyzed to determine whether there were differences between IL-6 HIGH group and IL-6 LOW group. Besides, the association between the two groups and tumor marker levels was clarified. The K-M curves of 3- and 5-year were plotted with log-rank test. Afterward, we conducted univariate and multivariate analysis and a predicting nomogram. Significantly, C-index, and calibration were used to evaluate the value of nomogram in predicting prognosis.

Results

The overall survival of GC in the IL-6 HIGH and IL-6 LOW groups were 47.8 months (95% CI: 42.1-53.4) and 57.9 months (95% CI: 54.1-61.7), respectively, with significant differences (p = 0.0046). Average tumor size of GC (p = 0.000) and nerve invasion (p = 0.018) were statistically significant between two groups. Multivariate analysis revealed that the factors affecting prognosis were IL-6 (<5.51 and ⩾5.51 pg/ml) (Hazard Ratio(HR): 1.665, 95% CI: 1.026-2.703, p = 0.039), N stage (HR: 1.336, 95% CI: 1.106-1.615, p = 0.003), and T stage (HR: 1.268, 95% CI: 0.998-1.611, p = 0.052), which were included in the nomogram with a C-index of 0.71. The current data calculated TNM staging C-index was 0.68, and the p-value for the difference between the two models was 0.08. Internal validation revealed that the predicted overall survival did not differ significantly from the actual observed patient survival.

Conclusion

The differential expression of IL-6 has a tendency to differentiate the prognosis of GC patients. IL-6, N stage, and T stage are independent prognostic factors, and the new survival prognostic model consisting of the above three indicators is better than the classical TNM staging system.

Trial registration

This study is a retrospective study, which does not require clinical registration.

Pretreatment Modified Glasgow Prognostic Score for Predicting Prognosis and Survival in Elderly Patients with Gastric Cancer Treated with Perioperative FLOT

The adverse effects of chemotherapy are more apparent in elderly patients and lead to worse prognosis and mortality. Identifying immunonutritional risk factors is of great importance in terms of treatment effectiveness, prognosis, and mortality in geriatric oncology. The modified Glasgow prognostic score (mGPS) is an immunonutritional index based on serum CRP and albumin levels. In this study, we aimed to investigate the role of mGPS in predicting prognosis and survival in elderly patients with gastric cancer receiving perioperative FLOT treatment. We retrospectively enrolled 71 patients aged over 65 years and grouped them according to their pretreatment mGPS score. Kaplan-Meier and Cox regression analysis showed overall survival was significantly worse in the mGPS 1 and mGPS 2 groups than in the mGPS 0 group (p = 0.005 and p < 0.001, respectively). Compared to the mGPS 0 group, the mGPS 1 group had a 6.25 times greater risk of death (95% CI: 1.61-24.28, p = 0.008), and the mGPS 2 group had a 6.59 times greater risk of death (95% CI: 2.08-20.85, p = 0.001). High BMI was identified as a significant risk factor for being in the mGPS 2 group (OR: 1.20, 95% CI: 1.018-1.425, p = 0.030). In conclusion, elevated pretreatment mGPS was associated with poor overall survival in elderly patients with gastric cancer treated with perioperative FLOT therapy. As such, pretreatment mGPS can be a simple and useful tool to predict mortality in this specific patient group.

An application of raman spectroscopy in combination with machine learning to determine gastric cancer spectroscopy marker

BACKGROUND AND OBJECTIVE

Globally, gastric carcinoma (Gca) ranks fifth in terms of incidence and third in terms of mortality. Higher serum tumor markers (TMs) than those from healthy individuals, led to TMs clinical application as diagnostic biomarkers for Gca. Actually, there is no accurate blood test to diagnose Gca.

METHODS

Raman spectroscopy is applied as an efficient, credible, minimally invasive technique to evaluate the serum TMs levels in blood samples. After curative gastrectomy, serum TMs levels are important in predicting the recurrence of gastric cancer, which must be detected early. The experimentally assesed TMs levels using Raman measurements and ELİSA test were used to develop a prediction model based on machine learning techniques. A total of 70 participants diagnosed with gastric cancer after surgery (n = 26) and healthy (n = 44) were comrpised in this study.

RESULTS

In the Raman spectra of gastric cancer patients, an additional peak at 1182 cm^-1 was observed and, the Raman intensity of amide III, II, I, and CH₂ proteins as well as lipids functional group was higher. Furthermore, Principal Component Analysis (PCA) showed, that it is possible to distinguish between the control and Gca groups using the Raman range between 800 and 1800 cm^-1, as well as between 2700 and 3000 cm^-1. The analysis of Raman spectra dynamics in gastric cancer and healthy patients showed, that the vibrations at 1302 and 1306 cm^-1 were characteristic for cancer patients. In addition, the selected machine learning methods showed classification accuracy of more than 95%, while obtaining an AUROC of 0.98. Such results were obtained using Deep Neural Networks and the XGBoost algorithm.

CONCLUSIONS

The obtained results suggest, that Raman shifts at 1302 and 1306 cm^-1 could be spectroscopic markers of gastric cancer.

Development and validation of a novel nomogram for predicting overall survival in gastric cancer based on inflammatory markers

BACKGROUND

Nearly 66% of occurrences of gastric cancer (GC), which has the second-highest death rate of all cancers, arise in developing countries. In several cancers, the predictive significance of inflammatory markers has been established.

AIM

To identify clinical characteristics and develop a specific nomogram to determine overall survival for GC patients.

METHODS

Nine hundred and four GC patients treated at the First Affiliated Hospital of Anhui Medical University between January 2010 and January 2013 were recruited. Prognostic risk variables were screened for Cox analysis. The C index, receiver operator characteristic (ROC) curve, and decision curve analysis were used to evaluate the nomogram.

RESULTS

Tumor node metastasis stage, carcinoembryonic antigen, systemic immune-inflammation index, and age were identified as independent predictive variables by multivariate analysis. Systemic immune-inflammation index value was superior to that of other inflammatory indicators. The ROC indicated the nomogram had a higher area under the curve than other factors, and its C-index for assessing the validation and training groups of GC patients was extremely reliable.

CONCLUSION

We created a novel nomogram to forecast the prognosis of GC patients following curative gastrectomy based on blood markers and other characteristics. Both surgeons and patients can benefit significantly from this new scoring system.

Treatment Outcome and Safety of the TCX Regimen for Advanced Gastric Cancer: A Prospective Cohort Study

Objective

To evaluate the outcome and safety of the paclitaxel, carboplatin, and capecitabine (TCX) regimen in patients with advanced gastric cancer.

Methods

Advanced gastric cancer patients received the TCX regimen for up to six cycles, which were 3 weeks apart. Paclitaxel (175 mg/m2) was given over a 3-hour infusion, followed by carboplatin in a 1-hour infusion on day 1. Capecitabine (850 mg/m2) was given orally twice daily from day 1 to day 14. Primary endpoints were progression-free survival (PFS) and overall survival (OS).

Results

Among 83 patients at stage IVa and IVb, the median PFS was 9.3 months; 6-month, 1-year, and 2-year PFS were 74.6%, 32.5%, and 14.4%, respectively. The median OS was 17.0 months; 6-month, 1-year, and 2-year OS were 97.5%, 68.7%, and 21.7%, respectively. In the multivariable Cox regression model, higher CEA was associated with poor OS. Common adverse events included hand-food syndrome (77.9%), peripheral neuropathy (63.2%), fatigue (68.7%), and nausea (54.2%).

Conclusion

The TCX regimen provided good survival and a better safety profile. More clinical trials are needed to confirm its treatment efficacy and safety, especially in comparison with other triplet regimens.

Cuprous oxide coated silver/graphitic carbon nitride/cadmium sulfide nanocomposite heterostructure: Specific recognition of carcinoembryonic antigen through sandwich-type mechanism

The development of the effective diagnostic method for the determination of cancer biomarkers is one of the most promising strategies for early clinical diagnosis of cancer. Here, based on the preparation of heterogeneous cuprous oxide coated silver (Ag@Cu₂O) nanocomposites/graphitic carbon nitride (g-C₃N₄)/cadmium sulfide (CdS) nanoarrays structure, a highly sensitive photoelectrochemical (PEC) biosensor for the examination of carcinoembryonic antigen (CEA) has been constructed successfully. The combination of photoactive semiconductor materials g-C₃N₄ and CdS increases the electron transfer rate between them and enhances their photocurrent response, thus greatly increasing the concentration detection range. At the same time, the specific recognition between antigen and antibody is used to form a sandwich structure secondary antibody (Ab₂)/CEA/antibody (Ab₁). And because Ag@Cu₂O has the function of absorbing light and consuming electron donor. Therefore, the successful measurement of CEA was achieved by labeling Ag@Cu₂O on Ab₂ and finally immobilizing it on the sensor to correlate the current reduction with the CEA concentration. The sandwich PEC biosensor proposed by this signal amplification strategy under optimal conditions has good analytical performance for CEA, with a wide linear detection range (from 10^-5 to 1 ng/mL) and a low detection limit of 0.0011 pg/mL. The PEC biosensor constructed by this method showed high sensitivity, excellent anti-interference ability, favourable repeatability, and good stability.

The prediction of survival in Gastric Cancer based on a Robust 13-Gene Signature

Gastric cancer represents a major public health problem. Owing to the great heterogeneity of GC, conventional clinical characteristics are limited in the accurate prediction of individual outcomes and survival. This study aimed to establish a robust gene signature to predict the prognosis of GC based on multiple datasets. Initially, we downloaded raw data from four independent datasets of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and performed univariate Cox proportional hazards regression analysis to identify prognostic genes associated with overall survival (OS) from each dataset. Thirteen common genes from four datasets were screened as candidate prognostic signatures. Then, a risk score model was developed based on this 13‑gene signature and validated by four independent datasets and the entire cohort. Patients with a high-risk score had poorer OS and recurrence-free survival (RFS). Multivariate regression and stratified analysis revealed that the 13-gene signature was not only an independent predictive factor but also associated with recurrence when adjusting for other clinical factors. Furthermore, in the high-risk group, gene set enrichment analysis (GSEA) showed that the mTOR signaling pathway and MAPK signaling pathway were significantly enriched. The present study provided a robust and reliable gene signature for prognostic prediction of both OS and RFS of patients with GC, which may be useful for delivering individualized management of patients.