Introduction
Colon cancer (CC) is globally, one of the most
common malignant tumors in the digestive tract, with adenocarcinoma
accounting for ~95% of cases. According to GLOBOCAN 2022 data,
there were ~1.93 million new cases of colorectal cancer and 904,000
deaths reported globally, with colon cancer accounting for the
majority of cases (1).
CC is the third most common cancer globally and the
second leading cause of cancer-associated mortalities, presenting a
notable challenge to public health. Currently, the standard
treatment for stage I–III CC involves extensive tumor resection
followed by adjuvant chemotherapy, such as 5-fluorouracil/folinic
acid + oxaliplatin or capecitabine + oxaliplatin regimens (2). While this treatment approach has
markedly improved overall survival (OS) rates, notable prognostic
disparities persist in clinical practice. This is particularly
evident in the ongoing debate surrounding adjuvant chemotherapy for
patients with stage II CC. The debate centers on the following: in
contrast to the well-established survival benefit of adjuvant
chemotherapy in stage III colon cancer (10–15% improvement in
5-year OS), the absolute survival benefit in stage II patients
remains very limited (2–3% improvement in 5-year DFS). Furthermore,
reliable biomarkers to identify patients most likely to benefit are
lacking, the criteria for defining high-risk features vary across
different guidelines, and the additional value of oxaliplatin in
high-risk stage II patients remains unproven (3–5).
Although TNM staging is an established tool for risk
stratification based on tumor anatomy and offers certain prognostic
value, it fails to account for key factors such as the nutritional
status, metabolic condition and overall physiological state of the
patient. This limitation hinders its ability to fully reflect tumor
biology and restricts its application in precision medicine
(6,7). Previous studies have highlighted the
important role of nutritional status and body composition
(particularly changes in skeletal muscle mass (SM) in the prognosis
of patients with cancer (8,9). Protein-energy malnutrition impairs
immune function, increases the risk of treatment-associated
toxicity and postoperative complications and markedly reduces
patient survival (10–12). In addition, micronutrients such as
vitamin D, zinc and selenium modulate the tumor microenvironment
through regulation of the barrier-microbiota-inflammation axis,
which consists of three core components: Intestinal barrier
dysfunction, gut microbial dysbiosis and chronic inflammation
(13,14).
In recent years, low skeletal muscle mass (SM) has
attracted notable attention as a clinical indicator that reflects
the overall functional status and nutritional condition of the
patient (15,16). Numerous studies have demonstrated
that low SM is associated with adverse outcomes following surgical
intervention in patients with cancer, including higher rates of
complication, reduced quality of life and reduced OS (17,18).
An important prospective cohort study conducted in China determined
that low SM is an independent predictor of poor postoperative
prognosis in patients with gastrointestinal cancer (19). Notably, a study by Lieffers et
al (20) demonstrated that, for
patients with CC undergoing surgery, low SM was markedly associated
with a prolonged postoperative hospital stay, heightened
chemotherapy toxicity and reduced OS, underscoring its importance
in preoperative risk assessment. While certain studies (21–23)
have suggested an association between preoperative low SM and
unfavorable outcomes in patients with CC, research specifically
targeting stage I–III CC remains relatively limited.
Therefore, the present study aimed to thoroughly
investigate the prognostic value of low SM in patients with stage
I–III CC and clarify its impact on OS. In addition, the present
study constructed a combined prognostic prediction model,
integrating low SM with traditional TNM staging, to assess the
incremental value of low SM in predicting the prognosis of patients
with CC beyond TNM staging alone. Ultimately, the present study
sought to provide clinicians with evidence to stratify patient risk
and guide individualized treatment decisions.
Patients and methods
Patients and inclusion criteria
A retrospective review was performed on 230 patients
with CC who underwent primary tumor resection at Lanzhou University
Second Hospital (Lanzhou, China) between January 2018 and December
2019. None of these patients received neoadjuvant therapy and all
underwent routine abdominal CT scans within 1 month prior to
surgery. The 8th edition of the American Joint Committee on Cancer
(AJCC) staging system was applied, as its publication in 2018
coincided with the initiation of patient data collection for the
present study (24). The inclusion
criteria were as follows: i) Initial diagnosis of CC with
subsequent curative surgery; ii) age >18 years; iii)
postoperative pathological diagnosis of CC with TNM stages I–III;
iv) at least 12 lymph nodes detected in the surgical specimen after
radical surgery; v) availability of complete follow-up and
clinical-pathological records; and vi) availability of complete
preoperative CT imaging data. The exclusion criteria were as
follows: i) Received neoadjuvant therapy prior to surgery; ii)
presence of rectal cancer; iii) history of other malignancies; and
iv) concurrent malignant tumors. The final study cohort consisted
of 230 patients, including 128 males (55.65%) and 102 females
(44.35%). The mean age of the study cohort was 60.00±13.63 years
(range, 33–89 years). Specifically, 112 patients (48.70%) were aged
≤60 years and 118 patients (51.30%) were aged >60 years.
The present study was conducted in accordance with
the Declaration of Helsinki and approved by the Medical Ethics
Committee of Lanzhou University Second Hospital (Lanzhou, China;
approval no. 2025A-638). As a retrospective study, patient privacy
and personal identity information were protected and the Medical
Ethics Committee waived the requirement for informed consent.
Assessment of low SM
A CT scan, routinely performed within 1 month prior
to surgery, utilized parameters including contrast-enhanced or
non-enhanced multi-phase acquisition with a 5 mm slice thickness.
For the assessment of low SM, two adjacent non-contrast CT images
of the third lumbar vertebra (L3) were selected from the same
series (25). ImageJ2 software
(version 2.0.0-rc-11; National Institutes of Health) was used to
measure the total skeletal muscle area (SMA) on the two slices
within the range of −29 to +150 Hounsfield units (26). The SMA was averaged for each
patient. The outlined regions for SMA measurement are shown in
Fig. 1. The skeletal muscle index
(SMI) was defined as SMA divided by the square of height.
According to the European Working Group on
Sarcopenia in Older People 2 (EWGSOP2) consensus (27), sarcopenia requires the presence of
both low muscle mass and reduced muscle strength/function. Given
that the present study was retrospective, preoperative muscle
function measurements (such as handgrip strength) were unavailable.
Consequently, the present assessed low SM instead of full
sarcopenia.
The Global Leadership Initiative on Malnutrition
(GLIM) criteria (28) recognize low
muscle mass as a phenotypic criterion for malnutrition, which can
be evaluated using CT-derived SMI. The GLIM criteria recommend
using ethnicity- and sex-adjusted cut-off values for low muscle
mass. A Chinese population-specific normative L3 SMI database is
currently lacking, primarily due to the following reasons: Most
existing studies are based on regional samples, cutoff values vary
considerably across studies (for example, ranging from 38.89 to
44.77 cm2/m2 for males and from 31.6 to 33.28
cm2/m2 for females), and age-stratified
reference standards have not yet been standardized (29,30).
The present defined low SM as an SMI below the sexspecific lowest
quartile of the present study population. This approach aligns with
the GLIM framework and has been validated in a large prospective
Chinese cohort of patients with digestive tract cancer (19). Specifically, low SM was defined as
SMI <40.6 cm2/m2 for men and <34.9
cm2/m2 for women. According to this
criterion, patients were categorized into low SM and normal SM
groups (Fig. 1).
Data collection
Preoperative demographic and clinical data collected
included age, sex, height, SMI, CEA levels and cancer location.
Tumor location was classified as right-sided or left-sided CC based
on established anatomical and embryological criteria (31). Right-sided CC encompassed the cecum,
ascending colon, hepatic flexure and transverse colon (originating
from the midgut), while left-sided CC included the splenic flexure,
descending colon and sigmoid colon (originating from the hindgut)
(32). Postoperative data comprised
T stage, N stage, differentiation grade, neurovascular invasion,
tumor maximum diameter, Ki67 (%) and cancer staging according to
the AJCC 8th edition staging system. The outcome of interest was
OS, defined as the time from surgery to mortality from any cause.
Patients underwent follow-up within 1 month after surgery and
subsequently every 3 months. The most recent follow-up was
conducted in December 2024.
Statistical analysis
Normality of clinical characteristics was assessed
using the Shapiro-Wilk test. For baseline characteristics,
continuous variables are reported as the mean ± SD and categorical
variables are presented as frequencies and percentages. Continuous
variables were analyzed using independent t-tests or Mann-Whitney U
tests, depending on their normality distribution. Categorical
variables were analyzed using χ2 tests.
Univariate and multivariate Cox regression analyses
were performed to identify potential independent prognostic factors
for OS. Survival analysis involved the construction of Cox
proportional hazards models and Kaplan-Meier analyses. Model
performance was evaluated using Harrell's C statistic and
likelihood ratio P-values were utilized to compare the performance
of risk prediction models.
For risk stratification, samples were divided into
high-risk and low-risk groups based on the median risk score
derived from the respective multivariate Cox regression models (TNM
alone and TNMlow SM). The discriminative ability of this
stratification was assessed using Kaplan-Meier survival curves with
the logrank test. Hazard ratios (HRs) between the two risk groups
were calculated using univariate Cox regression.
Time-dependent receiver operating characteristic
(ROC) analysis was performed to assess the predictive performance
of the models at specific time points (1, 3 and 5 years). The
‘timeROC’ package (version 0.4) (33) in R software was utilized, employing
1,000 bootstrap resamples to estimate the area under the curve
(AUC) and its 95% CIs. The AUC, ranging from 0.5 (no
discrimination) to 1.0 (perfect discrimination), quantified
predictive accuracy. Subgroup analysis was conducted to explore the
impact of low SM within each subgroup. All data analyses were
performed using R software (version 4.2.1; Posit Software, PBC) and
Python (version 3.7.12; Python Software Foundation). P<0.05 was
considered to indicate a statistically significant difference.
Results
Baseline characteristics of
patients
A total of 230 patients with CC who underwent
initial tumor resection at the Second Hospital of Lanzhou
University (Lanzhou, China) were included in the present study. Low
SMI was defined by sex-specific L3 SMI cut-offs, representing the
lowest quartile: 40.6 cm2/m2 for males and
34.9 cm2/m2 for females. Among these
patients, low SMI was identified in 56 individuals (24.34%).
Baseline characteristics are presented in Table I. While patients with low SMI
exhibited no statistically significant difference from those with
normal SMI regarding age, sex, differentiation degree or AJCC
stage, they experienced significantly poorer prognosis, shorter OS
and higher mortality (all, P<0.05).
 | Table I.Baseline characteristics of the
participants. |
Table I.
Baseline characteristics of the
participants.
| Variable | Total (n=230) | Normal SM
(n=174) | Low SM (n=56) | P-value |
|---|
| Age, n (%) |
|
|
| 0.252 |
| ≤60
years | 112 (48.70) | 81 (46.55) | 31 (55.36) |
|
| >60
years | 118 (51.30) | 93 (53.45) | 25 (44.64) |
|
| Sex, n (%) |
|
|
| 0.959 |
|
Female | 102 (44.35) | 77 (44.25) | 25 (44.64) |
|
|
Male | 128 (55.65) | 97 (55.75) | 31 (55.36) |
|
| CEA, n (%) |
|
|
| 0.144 |
| ≤5
µg/l | 114 (49.57) | 91 (52.30) | 23 (41.07) |
|
| >5
µg/l | 116 (50.43) | 83 (47.70) | 33 (58.93) |
|
| Height | 165.72±7.84 | 165.61±7.85 | 166.04±7.85 | 0.728 |
| Tumor location, n
(%) |
|
|
| 0.224 |
| Left
hemicolon | 123 (53.48) | 97 (55.75) | 26 (46.43) |
|
| Right
hemicolon | 107 (46.52) | 77 (44.25) | 30 (53.57) |
|
| Tumor maximum
diameter, n (%) |
|
|
| 0.662 |
| ≤6
cm | 180 (78.26) | 135 (77.59) | 45 (80.36) |
|
| >6
cm | 50 (21.74) | 39 (22.41) | 11 (19.64) |
|
| Neural or vascular
invasion, n (%) |
|
|
| 0.861 |
| No | 84 (36.52) | 63 (36.21) | 21 (37.50) |
|
|
Yes | 146 (63.48) | 111 (63.79) | 35 (62.50) |
|
| Grade, n (%) |
|
|
| 0.156 |
|
Poor/undifferentiated | 39 (16.96) | 26 (14.94) | 13 (23.21) |
|
|
Moderately differentiated | 178 (77.39) | 136 (78.16) | 42 (75.00) |
|
| Well
differentiated | 13 (5.65) | 12 (6.90) | 1 (1.79) |
|
| T stage, n (%) |
|
|
| 0.398 |
|
T1-2 | 46 (20.00) | 37 (21.26) | 9 (16.07) |
|
|
T3-4 | 184 (80.00) | 137 (78.74) | 47 (83.93) |
|
| Lymph node
metastasis, n (%) |
|
|
| 0.886 |
| N0 | 166 (72.17) | 126 (72.41) | 40 (71.43) |
|
|
N1-2 | 64 (27.83) | 48 (27.59) | 16 (28.57) |
|
| TNM stage, n
(%) |
|
|
| 0.798 |
| I | 44 (19.13) | 35 (20.11) | 9 (16.07) |
|
| II | 122 (53.04) | 91 (52.30) | 31 (55.36) |
|
|
III | 64 (27.83) | 48 (27.59%) | 16 (28.57) |
|
| Overall survival,
months | 55.29±23.11 | 57.33±22.07 | 48.95±25.25 | 0.018 |
| Survival status, n
(%) |
|
|
| 0.027 |
|
Survival | 155 (67.39) | 124 (71.26 | 31 (55.36) |
|
|
Mortality | 75 (32.61) | 50 (28.74) | 25 (44.64) |
|
Low SM on OS
Kaplan-Meier survival curve analysis (Fig. 2) indicated that the OS of patients
with low SM was significantly lower compared with that of patients
without low SM (log-rank P<0.001). The risk factors for OS in
patients with stage I–III CC are listed in Table II. Univariate analysis revealed
that CEA >5, lymph node metastasis and low SM were significantly
associated with poorer OS (all P<0.05). Multivariate analysis
demonstrated that lymph node metastasis (N1-2; HR=1.63; 95% CI:
1.01–2.62; P=0.0460) and low SM (HR=1.70; 95% CI: 1.05–2.75;
P=0.0321) were independent predictors of OS.
| Table II.Univariate and multivariate Cox
regression analyses. |
Table II.
Univariate and multivariate Cox
regression analyses.
|
| Univariable
analysis | Multivariable
analysis |
|---|
|
|
|
|
|---|
| Variable | HR (95% CI) | P-value | HR (95% CI) | P-value |
|---|
| Age, years |
|
|
|
|
|
≤60 | 1.00 |
|
|
|
|
>60 | 0.89
(0.56–1.40) | 0.6067 |
|
|
| Sex |
|
|
|
|
|
Female | 1.00 |
|
|
|
|
Male | 0.93
(0.59–1.47) | 0.7691 |
|
|
| CEA, µg/l |
|
|
|
|
| ≤5 | 1.00 | 1.0000 |
|
|
|
>5 | 1.63
(1.03–2.59) | 0.0373 | 1.43
(0.89–2.30) | 0.1363 |
| Tumor location |
|
|
|
|
| Left
hemicolon | 1.00 |
|
|
|
| Right
hemicolon | 1.16
(0.74–1.83) | 0.5152 |
|
|
| Tumor maximum
diameter, cm |
|
|
|
|
| ≤6 | 1.00 |
|
|
|
|
>6 | 1.02
(0.58–1.76) | 0.9562 |
|
|
| Neural or vascular
invasion |
|
|
|
|
| No | 1.00 |
|
|
|
|
Yes | 1.62
(0.98–2.68) | 0.0612 |
|
|
| Grade |
|
|
|
|
|
Poor/undifferentiated | 1.00 |
|
|
|
|
Moderately differentiated | 0.87
(0.48–1.55) | 0.6320 |
|
|
| Well
differentiated | 0.58
(0.17–2.01) | 0.3876 |
|
|
| T stage |
|
|
|
|
|
T1-2 | 1.00 |
|
|
|
|
T3-4 | 1.38
(0.74–2.55) | 0.3105 |
|
|
| Lymph node
metastasis |
|
|
|
|
| N0 | 1.00 | 1.0000 |
|
|
|
N1-2 | 1.76
(1.10–2.80) | 0.0185 | 1.63
(1.01–2.62) | 0.0460 |
| TNM stage |
|
|
|
|
| I | 1.00 |
|
|
|
| II | 1.19
(0.61–2.34) | 0.6141 |
|
|
|
III | 2.00
(1.00–4.02) | 0.0515 |
|
|
| Low SM |
|
|
|
|
| No | 1.00 | 1.0000 |
|
|
|
Yes | 1.76
(1.09–2.85) | 0.0204 | 1.70
(1.05–2.75) | 0.0321 |
Prognostic value of low SM
Within the present study, two Cox proportional
hazards models were developed to assess OS in patients with CC, one
based solely on the TNM staging system and another that
incorporated low SM as an additional prognostic factor. Analysis
revealed that the traditional TNM model yielded a C-index of 0.570
(95% CI: 0.508–0.631), whereas the TNM-low SM model achieved a
higher C-index of 0.604 (95% CI: 0.540–0.669). Therefore, the
inclusion of low SM significantly enhanced the predictive
performance of the traditional TNM model (likelihood ratio
P=0.027), demonstrating that low SM provided significant prognostic
value for OS in patients with stage I–III CC (Table III).
| Table III.Multivariate analysis of two models
in patients with stage I–III disease. |
Table III.
Multivariate analysis of two models
in patients with stage I–III disease.
| Model | Overall survival
index, HR (95% CI) | P-value | Concordance, HR
(95% CI) |
|---|
| TNM-low SM
model |
|
| 0.604
(0.540–0.669) |
| T | 1.13
(0.59–2.16) | 0.7056 |
|
| N | 1.70
(1.05–2.77) | 0.0325 |
|
| Low
SM | 1.75
(1.08–2.83) | 0.0222 |
|
| TNM-model |
|
| 0.570
(0.508–0.631) |
| T | 1.17
(0.61–2.22) | 0.6395 |
|
| N | 1.70
(1.04–2.76) | 0.0332 |
|
To further evaluate the prognostic value of low SM,
patients were stratified into high-risk and low-risk groups using
HRs from the Cox model. The discriminatory performance of these
groups was then assessed using Kaplan-Meier analysis (Fig. 3). The results demonstrated that the
TNM-low SM model (log-rank P=0.001) outperformed the TNM model
(log-rank P=0.017) in identifying high-risk patients. To
comprehensively assess and compare the predictive performance of
the TNM-low SM and TNM models, time-dependent ROC analysis was
conducted at 1, 3 and 5 years (Table
SI). The results showed that the TNM-low SM model outperformed
the TNM model at all time points (Fig.
S1).
Subgroup analysis
Subgroup analyses and interaction tests were further
performed to examine potential effect modifications by factors
(such as age and sex) on the independent association between low SM
and OS (Table IV). Subgroup
analyses consistently demonstrated an increased mortality risk
associated with low SM across all predefined subgroups of patients
with stage I–III CC. While this association was broadly observed,
statistically significant findings for poorer survival were
specifically noted in patients with left-sided CC (HR=2.03; 95% CI:
1.02–4.03; P=0.0423), T3-4 stage disease (HR=1.81; 95% CI:
1.08–3.05; P=0.0244) and TNM stage II (HR=2.18; 95% CI: 1.12–4.27;
P=0.0225). Despite these specific observations, no statistically
significant interaction effects were detected across any subgroups
(all, interaction P>0.05).
| Table IV.Effect of low skeletal muscle mass on
overall survival in patients with stage I–III colon cancer across
different subgroups. |
Table IV.
Effect of low skeletal muscle mass on
overall survival in patients with stage I–III colon cancer across
different subgroups.
| Variable | n | HR (95% CI) | P-value | Interaction
P-value |
|---|
| Age, years |
|
|
| 0.7331 |
|
≤60 | 112 | 1.84
(0.95–3.59) | 0.0714 |
|
|
>60 | 118 | 1.65
(0.82–3.32) | 0.1636 |
|
| Sex |
|
|
| 0.3738 |
|
Female | 102 | 1.37
(0.66–2.87) | 0.3988 |
|
|
Male | 128 | 2.15
(1.14–4.06) | 0.0186 |
|
| CEA, µg/l |
|
|
| 0.1718 |
| ≤5 | 114 | 2.55
(1.21–5.36) | 0.0137 |
|
|
>5 | 116 | 1.28
(0.68–2.41) | 0.4414 |
|
| Tumor location |
|
|
| 0.5301 |
| Left
hemicolon | 123 | 2.03
(1.02–4.03) | 0.0423 |
|
| Right
hemicolon | 107 | 1.50
(0.76–2.94) | 0.2414 |
|
| Tumor maximum
diameter, cm |
|
|
| 0.2733 |
| ≤6 | 180 | 1.56
(0.90–2.69) | 0.1116 |
|
|
>6 | 50 | 2.88
(1.04–7.93) | 0.0411 |
|
| Neural or vascular
invasion |
|
|
| 0.7536 |
| No | 84 | 1.59
(0.64–3.94) | 0.3171 |
|
|
Yes | 146 | 1.89
(1.07–3.32) | 0.0282 |
|
| Histological
grade |
|
|
| 0.6226 |
|
Poor/undifferentiated | 39 | 1.64
(0.57–4.74) | 0.3585 |
|
|
Moderately differentiated | 178 | 1.81
(1.05–3.14) | 0.0338 |
|
| Well
differentiated | 13 | 0.00
(0.00-Inf) | 0.9991 |
|
| T stage |
|
|
| 0.7243 |
|
T1-2 | 46 | 1.40
(0.38–5.17) | 0.6142 |
|
|
T3-4 | 184 | 1.81
(1.08–3.05) | 0.0244 |
|
| Lymph node
metastasis |
|
|
| 0.4618 |
| N0 | 166 | 2.02
(1.11–3.66) | 0.0206 |
|
|
N1-2 | 64 | 1.39
(0.61–3.16) | 0.4291 |
|
| TNM stage |
|
|
| 0.6808 |
| I | 44 | 1.49
(0.39–5.60) | 0.5591 |
|
| II | 122 | 2.18
(1.12–4.27) | 0.0225 |
|
|
III | 64 | 1.39
(0.61–3.16) | 0.4291 |
|
Prognostic value of low SM in stage II
CC
Table V details the
baseline clinical characteristics of patients with stage II CC,
stratified by low SM status. The present study further explored the
prognostic importance of low SM on OS in these patients, utilizing
Kaplan-Meier survival analysis and both unadjusted and multivariate
Cox proportional hazard regression models. The Kaplan-Meier
survival curves (Fig. 4) revealed
that OS for patients with stage II CC in the low SM group was
significantly shorter compared with the normal SM group
(P=0.019).
| Table V.Baseline clinical characteristics of
stage II colon cancer patients by SM status. |
Table V.
Baseline clinical characteristics of
stage II colon cancer patients by SM status.
| Variable | Normal SM
(n=91) | Low SM (n=31) | P-value |
|---|
| Sex, n (%) |
|
| 0.840 |
|
Female | 43 (47.25) | 14 (45.16) |
|
|
Male | 48 (52.75) | 17 (54.84) |
|
| Age, n (%) |
|
| 0.009 |
| ≤60
years | 51 (56.04) | 9 (29.03) |
|
| >60
years | 40 (43.96) | 22 (70.97) |
|
| CEA, n (%) |
|
| 0.254 |
| ≤5
µg/l | 46 (50.55) | 12 (38.71) |
|
| >5
µg/l | 45 (49.45) | 19 (61.29) |
|
| Tumor location, n
(%) |
|
| 0.340 |
|
Left-sided colon | 53 (58.24) | 15 (48.39) |
|
|
Right-sided colon | 38 (41.76) | 16 (51.61) |
|
| Tumor maximum
diameter, n (%) |
|
| 0.064 |
| ≤6
cm | 30 (32.97) | 16 (51.61) |
|
| >6
cm | 61 (67.03) | 15 (48.39) |
|
| Neurovascular
invasion, n (%) |
|
| 0.486 |
| No | 29 (31.87) | 12 (38.71) |
|
|
Yes | 62 (68.13) | 19 (61.29) |
|
| Differentiation
grade, n (%) |
|
| 0.438 |
|
Poor/undifferentiated | 11 (12.09) | 5 (16.13) |
|
|
Moderately differentiated | 76 (83.52) | 26 (83.87) |
|
| Well
differentiated | 4 (4.40) | 0 (0.00) |
|
| Overall survival, n
(%) |
|
| 0.027 |
|
Survival | 69 (75.82) | 17 (54.84) |
|
| Deaths
during follow-up | 22 (24.18) | 14 (45.16) |
|
Among patients with stage II tumors (n=122), Cox
regression analysis revealed that low SM was associated with
shorter OS (HR=2.18; 95% CI: 1.12–4.27; P=0.0225), an association
that remained consistent in the adjusted model after controlling
for sex, age, CEA level, tumor location, differentiation grade,
maximum tumor diameter, neurovascular invasion and T stage
(Table VI). These findings
suggested that low SM may serve as an independent prognostic factor
for OS in patients with stage II CC.
| Table VI.Multivariate analysis of low SM in
patients with stage II colon cancer. |
Table VI.
Multivariate analysis of low SM in
patients with stage II colon cancer.
| Variable | Overall survival,
HR (95% CI) | P-value | Concordance index,
(95% CI) |
|---|
| Unadjusted Cox
regression |
|
| 0.584
(0.507–0.661) |
| Low
SM | 2.18
(1.12–4.27) | 0.0225 |
|
| Multivariable Cox
regression |
|
| 0.649
(0.567–0.730) |
| Low
SM | 2.19
(1.08–4.42) | 0.0289 |
|
|
Sex | 1.14
(0.58–2.23) | 0.7012 |
|
|
Age | 0.87
(0.42–1.80) | 0.7037 |
|
| CEA,
µg/l | 1.36
(0.67–2.75) | 0.3887 |
|
| Tumor
location | 1.18
(0.59–2.35) | 0.6387 |
|
|
Histological grade | 1.16
(0.37–3.64) | 0.7951 |
|
| Tumor
maximum diameter, cm | 1.02
(0.46–2.25) | 0.9593 |
|
| Neural
or vascular invasion | 1.61
(0.74–3.50) | 0.2308 |
|
| T
stage | 1.57
(0.67–3.64) | 0.2976 |
|
Discussion
Low SM is a common metabolic abnormality in patients
with malignant tumors, previously associated with poor outcomes
across a number of cancer types (34,35),
including lung, gastric, pancreatic and CC. Expanding upon this,
the present study investigated the impact of low SM on OS in
patients with stage I–III CC, determining its role as an
independent prognostic factor. The results demonstrated a
significant association between low SM and poorer survival outcomes
in patients with CC, with its impact remaining evident in
multivariate Cox regression analysis (HR=1.70; 95% CI: 1.05–2.75;
P=0.0321). Subgroup analyses further revealed that low SM exerted a
more notable effect on survival in patients with left-sided CC,
T3-4 stage and N1-2 stage disease.
To enhance prognostic predictive capability, the
present study developed a novel TNM-low SM model based on the
existing TNM staging system. This new model exhibited an improved
predictive performance, with higher C-index and AUC values compared
with the standard TNM model. This suggests the marked potential of
incorporating low SM into the prognostic assessment of CC.
Notably, low SM is a common metabolic abnormality in
patients with malignant tumors and previous studies have shown that
it is associated with poor outcomes in a number of cancer types,
including lung, gastric, pancreatic and CC (36–39).
While previous studies have investigated the impact of preoperative
low SM on short- and long-term outcomes in CC, a meta-analysis by
Trejo-Avila et al (40)
further clarified its role as a notable predictor of increased
postoperative complications and reduced survival. An additional
study demonstrated that low SM was an objective and reliable
predictive factor, outperforming current nutritional, functional,
biochemical and clinical indicators in predicting postoperative
complications and cancer recurrence after radical resection for CC
(41). However, to date, research
on its prognostic value in patients with stage I–III CC is
insufficient. Therefore, the findings of the present study may
further support the need for clinical intervention for low SM in
patients with stage I–III CC.
The present study showed that patients with low SM
exhibited significantly lower OS rates compared with those without
(P=0.018) and low SM was identified as an independent risk factor
for OS (HR=1.70; P=0.0321), further demonstrating its reliability
as a prognostic indicator for long-term outcomes in patients with
stage I–III CC. However, the mechanisms through which low SM
influences the long-term prognosis of CC patients are not fully
understood. This may be associated with the chronic inflammatory
state, impaired immune function and reduced antitumor capacity
associated with low SM (42). Low
SM in patients with cancer is hypothesized to result from skeletal
muscle atrophy due to metabolic abnormalities associated with tumor
cachexia (43). Dietary supplements
and appetite stimulants alone are insufficient to reverse this
underlying metabolic abnormality, which may partially explain the
low prognostic role of SM for poor outcomes in patients with cancer
(43).
Patients with low SM often exhibit elevated levels
of systemic inflammatory factors, such as C-reactive protein and
IL-6. These inflammatory markers accelerate tumor progression and
compromise treatment outcomes by stimulating tumor cell
proliferation, suppressing antitumor immune responses and enhancing
tumor resistance via chronic inflammation-driven NF-κB activation
(44,45). In addition, studies (46–48)
have demonstrated a notable association between low SM and elevated
IL-23 levels. This combination effectively predicts prognosis,
suggesting that IL-23 and systemic inflammation may serve key roles
in poor survival outcomes. High levels of inflammation are closely
associated with a poor prognosis in patients with CC. Low SM may
exacerbate this inflammatory state, initiating a cascade of
reactions that further reduce patient survival rates. Furthermore,
low SM has been associated with malnutrition in patients with
cancer, impairing immune function and reducing treatment tolerance.
Given the particular prevalence of malnutrition in patients with
CC, appropriate nutritional supplementation is important in
achieving favorable short-term outcomes and long-term survival
(49,50). Previous studies have shown that
patients with low SM experience a higher incidence of complications
after surgery or chemoradiotherapy, slower postoperative recovery
and difficulties in treatment implementation (18,51).
Despite the present study having not directly analyzed the impact
of treatment modalities on patient outcomes, the present results
indicated poorer survival outcomes for patients with low SM. This
suggested that clinical practice should prioritize screening for
and intervening in low SM to improve patient outcomes.
While the TNM staging system is currently a standard
tool for treatment decision-making and prognosis assessment in CC,
it still presents limitations in individualized prediction
(52). To overcome this limitation,
CC has been classified into molecular subtypes based on gene
expression, aiming to guide treatment decisions more effectively.
However, these classifications currently offer limited practical
value in routine clinical practice (53). Due to complex CC development, a
single genetic marker is unlikely to predict prognosis. Therefore,
international validation analysis, initiated by the International
Immuno-Scoring A study from Consortium (54), assessed the prognostic value of
total and cytotoxic tumor-infiltrating T cell counts in patients
with stage I–III CC, employing a consensus immuno-scoring assay.
Results indicated that patients with high immuno-scores experienced
the lowest 5-year recurrence risk and the assay could effectively
identify patients at a high-risk of tumor recurrence, particularly
in stage II cancer. However, the AUC value for predicting OS in
this model, which combined all clinical variables with the immune
score, remained <0.65. This underscored the need for further
exploration of additional prognostic predictors.
Based upon this, the present study subsequently
constructed both a traditional TNM model and a modified TNM-low SM
model to assess and validate the predictive ability of low SM in
patients with stage I–III CC. The TNM-low SM model demonstrated an
improved predictive performance. The resulting C-index (0.604) was
higher compared with that of the standalone TNM model (0.570).
Furthermore, the AUC values for the TNM-low SM model at 1, 3 and 5
years consistently exceeded those of the TNM model, with the most
significant improvement observed being observed at 3 years (TNM-low
SM model AUC=0.629 vs. TNM model AUC=0.588). These findings
indicated that incorporating low SM factors enhanced the predictive
capability of the model, further validating the role of low SM in
CC prognosis assessment. Kaplan-Meier survival analysis
additionally showed that the TNM-low SM model demonstrated an
improved stratified predictive capability compared with the
standalone TNM model. These results suggested that, in clinical
practice, the TNM-low SM model, by incorporating low SM factors,
may offer more precise survival predictions and effectively
identify patients at a high-risk, thereby aiding individualized
treatment decisions.
Subgroup analysis in the present study further
demonstrated that low SM had a more significant impact on survival
in patients with left-sided CC, as well as those with T3-4 and N1-2
stages. This observation may be associated with known differences
in gut microbiota, tumor microenvironment and metabolic
characteristics among patients with left-sided CC, as previous
studies (55,56) have reported marked biological
differences and long-term prognostic distinctions between
left-sided and right-sided CC (57). A retrospective study indicated that
low SM (15) as assessed by CT,
independently predicts shorter OS and recurrence-free survival in
patients with left-sided colon or rectal cancer after curative
surgery. These results were consistent with the subgroup analysis.
Furthermore, patients with T3-4 and N1-2 stage disease already bear
a greater disease burden and thus low SM may further exacerbate
their survival disadvantage. Therefore, in clinical practice,
prioritizing the screening and management of low SM in these
high-risk subgroups is important in optimizing survival
outcomes.
Although the present study reported the adverse
impact of low SM on OS in patients with stage I–III CC
(particularly those with stage II disease), a number of limitations
must be acknowledged. Firstly, a full diagnosis of sarcopenia,
according to the EWGSOP2 consensus, requires the simultaneous
assessment of both muscle mass and muscle function (including both
grip strength and walking speed). Due to the retrospective design
of the present study, preoperative functional measurements could
not be obtained; thus, the exposure variable in the present study
was low SM rather than comprehensive sarcopenia. Secondly, a number
of body composition components, such as skeletal muscle and
visceral fat, are interrelated through adipose, muscular and
metabolic factors and should not be assessed in isolation. The
present study focused solely on low SM and did not assess visceral
adipose tissue (VAT) at the L3 level or trunk fat, which could have
provided an improved prognostic stratification through combined
phenotypes (such as sarcopenic obesity, defined as low SM combined
with high VAT). This omission was due to limitations in the storage
of imaging data, preventing the quantification of VAT or trunk fat
(58,59). Thirdly, as a retrospective,
single-center analysis, the present study was susceptible to
selection bias. Furthermore, survival outcomes in patients with low
SM who received different treatment regimens (such as adjuvant
chemotherapy or intensive nutritional intervention) were not
analyzed and other potential confounding factors (including
nutritional intake or inflammatory status) were not accounted
for.
To overcome these limitations, future prospective
studies should be conducted in larger, multicenter cohorts,
incorporating functional assessments, standardized CT cut-offs,
comprehensive body composition analysis (including VAT and trunk
fat) and detailed treatment and nutritional data.
In conclusion, low SM may be an independent
prognostic factor for OS in patients with stage I–III CC. When
incorporated into a combined model with TNM staging, it markedly
enhanced prognostic prediction, particularly in patients with stage
II CC, by more effectively identifying high-risk populations.
Therefore, low SM may represent a robust prognostic biomarker to
facilitate risk stratification and individualized treatment.
Supplementary Material
Supporting Data
Supporting Data
Acknowledgements
Not applicable.
Funding
The present study was supported by the Nature Science Foundation
of Gansu Province (grant no. 24JRRA3221), the ‘Cuiying Science and
Technology Innovation’ program (grant no. CY2024-MS-B04) and the
Scientific Research Project of Traditional Chinese Medicine of
Gansu Province (grant no. GZKZ-2024-26).
Availability of data and materials
The data generated in the present study may be
requested from the corresponding author.
Authors' contributions
CM and FC designed the present study, collected the
data and conducted the data analysis. HY conceived the research
idea, supervised the entire research process, participated in the
interpretation and validation of the results, provided critical
oversight of the statistical methods, and was responsible for the
final review, revision and approval of the manuscript. All authors
read and approved the final version of the manuscript. CM and HY
confirm the authenticity of all the raw data.
Ethics approval and consent to
participate
The present study was conducted in accordance with
the Declaration of Helsinki and approved by the Medical Ethics
Committee of the Second Hospital of Lanzhou University (Lanzhou,
China; approval no. 2025 A-638). As the present study was
retrospective and the privacy and personal identity information of
the patients were protected, the need for informed consent was
waived by the Medical Ethics Committee of the Second Hospital of
Lanzhou University.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing
interests.
Glossary
Abbreviations
Abbreviations:
|
HR
|
hazard ratio
|
|
ROC
|
receiver operating characteristic
|
|
AUC
|
area under the curve
|
|
AJCC
|
American Joint Committee on Cancer
|
|
SMA
|
skeletal muscle area
|
|
SMI
|
skeletal muscle index
|
|
OS
|
overall survival
|
References
|
1
|
Bray F, Laversanne M, Sung H, Ferlay J,
Siegel RL, Soerjomataram I and Jemal A: Global cancer statistics
2022: GLOBOCAN estimates of incidence and mortality worldwide for
36 cancers in 185 countries. CA Cancer J Clin. 74:229–263.
2024.PubMed/NCBI
|
|
2
|
André T, Boni C, Navarro M, Tabernero J,
Hickish T, Topham C, Bonetti A, Clingan P, Bridgewater J, Rivera F
and de Gramont A: Improved overall survival with oxaliplatin,
fluorouracil, and leucovorin as adjuvant treatment in stage II or
III colon cancer in the MOSAIC trial. J Clin Oncol. 27:3109–3116.
2009. View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Pericay C, Montagut C, Reina JJ, Melian M,
Alcaide J, Tarazona N, Ruiz-Casado A, González-Flores E, Graña B
and Grávalos C: SEOM-GEMCAD-TTD clinical guidelines for the
adjuvant treatment of colon cancer (2023). Clin Transl Oncol.
26:2812–2825. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Zhou Q, Zhou C, Zhang G, Xia X, Zhou X and
Lang J: Age-related differences in adjuvant chemotherapy use and
outcomes in stage II colon cancer: A retrospective cohort study.
BMC Cancer. 25:15272025. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Erkaya M, Benlice C, Karahan S, Oruc M,
Ozben V, Baca B and Gorgun E: Adjuvant chemotherapy and survival in
MSI-High stages II and III colon cancer: Impact of histopathologic
risk stratification. Ann Surg Oncol. 32:9510–9520. 2025. View Article : Google Scholar : PubMed/NCBI
|
|
6
|
Ruan GT, Song MM, Zhang KP, Xie HL, Zhang
Q, Zhang X, Tang M, Zhang XW, Ge YZ, Yang M, et al: A novel
nutrition-related nomogram for the survival prediction of
colorectal cancer-results from a multicenter study. Nutr Metab
(Lond). 20:22023. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Wozniakova M, Skarda J and Raska M: The
role of tumor microenvironment and immune response in colorectal
cancer development and prognosis. Pathol Oncol Res. 28:16105022022.
View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Song M, Liu Z, Wu F, Nie T, Heng Y, Xu J,
Huang N, Wu X, Cao Y and Hu G: Serum tumor marker and CT body
composition scoring system predicts outcomes in colorectal cancer
surgical patients. Eur Radiol. 34:7596–7608. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Zhang L, Guan J, Ding C, Feng M, Gong L
and Guan W: Muscle loss 6 months after surgery predicts poor
survival of patients with non-metastatic colorectal cancer. Front
Nutr. 9:10470292022. View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Chen MF, Chen YY, Chen WC and Hsieh CC:
The relationship of nutritional status with anticancer immunity and
its prognostic value for head and neck cancer. Mol Carcinog.
62:1388–1398. 2023. View
Article : Google Scholar : PubMed/NCBI
|
|
11
|
Pu R, Man Q, Song S, Jia S, Liu Z, Zhang
X, Zhang J and Song P: The dietary inflammatory index and
sarcopenia in older adults in four chinese provinces: A
cross-sectional study. Nutrients. 17:4782025. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Scarcella M, Scarpellini E, De Rosa S,
Umbrello M, Petroni GM, Monti R, Fusco P, De Robertis E, Commissari
R, Abenavoli L and Tack J: Impact of protein and nutritional
support on the muscular status of critically Ill patients: A pilot,
perspective, and exploratory study. Nutrients. 17:4972025.
View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Dong X, Yang J, He L, Fang H, Wang L, Zhu
J, Xu J, Song K and Xuan Z: The barrier-microbiota-inflammation
axis in colorectal cancer: mechanisms and emerging diagnostic &
therapeutic strategies. Cancers (Basel). 18:5762026. View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Bautista J, Lamas-Maceiras M,
Hidalgo-Tinoco C, Guerra-Guerrero A, Betancourt-Velarde A and
López-Cortés A: Gut microbiome-driven colorectal cancer via immune,
metabolic, neural, and endocrine axes reprogramming. NPJ Biofilms
Microbiomes. 12:212026. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Li Q, An T, Wu J, Lu W, Wang Y, Li J, Yang
L, Chen Y, Lin L and Yang Z: The impact of sarcopenia on the
outcome of patients with left-sided colon and rectal cancer after
curative surgery. BMC Cancer. 23:6402023. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Rasa HK and Erdemir A: Our experience with
left colon and rectal cancer surgery and the impact of preoperative
sarcopenia on complication rates. Cureus. 15:e452092023.PubMed/NCBI
|
|
17
|
Barone M, Baccaro P and Molfino A: An
overview of sarcopenia: focusing on nutritional treatment
approaches. Nutrients. 17:12372025. View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Yang XY and Hu Q: Association between
sarcopenia and postoperative complications in patients undergoing
surgery for gastrointestinal or hepato-pancreatico-biliary cancer.
J Surg Oncol. 128:708–710. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Zhang S, Tan S, Jiang Y, Xi Q, Meng Q,
Zhuang Q, Han Y, Sui X and Wu G: Sarcopenia as a predictor of poor
surgical and oncologic outcomes after abdominal surgery for
digestive tract cancer: A prospective cohort study. Clin Nutr.
38:2881–2888. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Lieffers JR, Bathe OF, Fassbender K,
Winget M and Baracos VE: Sarcopenia is associated with
postoperative infection and delayed recovery from colorectal cancer
resection surgery. Br J Cancer. 107:931–936. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Atuiri C, Ngang IC, Appiah D,
Batchi-Bouyou AL, Matthew KA, Girma AZ, Katumba LS, Mbapah LT and
Bekena S: Pretreatment skeletal muscle index and survival outcomes
in non-metastatic colorectal cancer: A systematic review and
meta-analysis. J Gastrointest Cancer. 56:1432025. View Article : Google Scholar : PubMed/NCBI
|
|
22
|
Miranda AL, da Costa Pereira JP, de Sousa
IM, Ferreira GMC, de Oliveira Bezerra MR, Chaves GV, Maciel FF,
Murad LB, Lima Verde SMM, Maurício SF, et al: Impact of body
composition and muscle health phenotypes on survival outcomes in
colorectal cancer: A multicenter cohort. Sci Rep. 14:318162024.
View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Lee J, Cho JR, Kim DW, Yang IJ, Suh JW, Oh
HK and Kang SB: Clinical impact of preoperative and postoperative
sarcopenia on oncological outcomes in non-metastatic colorectal
cancer. Colorectal Dis. 25:775–786. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Liao X and Zhang D: The 8th Edition
American joint committee on cancer staging for
hepato-pancreato-biliary cancer: A review and update. Arch Pathol
Lab Med. 145:543–553. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Schweitzer L, Geisler C, Pourhassan M,
Braun W, Glüer CC, Bosy-Westphal A and Müller MJ: What is the best
reference site for a single MRI slice to assess whole-body skeletal
muscle and adipose tissue volumes in healthy adults? Am J Clin
Nutr. 102:58–65. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Goodpaster BH, Kelley DE, Thaete FL, He J
and Ross R: Skeletal muscle attenuation determined by computed
tomography is associated with skeletal muscle lipid content. J Appl
Physiol (1985). 89:104–110. 2000. View Article : Google Scholar : PubMed/NCBI
|
|
27
|
Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie
Y, Bruyère O, Cederholm T, Cooper C, Landi F, Rolland Y, Sayer AA,
et al: Sarcopenia: Revised European consensus on definition and
diagnosis. Age Ageing. 48:16–31. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Jensen GL, Cederholm T, Correia MITD,
Gonzalez MC, Fukushima R, Higashiguchi T, de Baptista GA, Barazzoni
R, Blaauw R, Coats AJS, et al: GLIM criteria for the diagnosis of
malnutrition: A consensus report from the global clinical nutrition
community. JPEN J Parenter Enteral Nutr. 43:32–40. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
29
|
Kong M, Geng N, Zhou Y, Lin N, Song W, Xu
M, Li S, Piao Y, Han Z, Guo R, et al: Defining reference values for
low skeletal muscle index at the L3 vertebra level based on
computed tomography in healthy adults: A multicentre study. Clin
Nutr. 41:396–404. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Zeng X, Shi ZW, Yu JJ, Wang LF, Luo YY,
Jin SM, Zhang LY, Tan W, Shi PM, Yu H, et al: Sarcopenia as a
prognostic predictor of liver cirrhosis: A multicentre study in
China. J Cachexia Sarcopenia Muscle. 12:1948–1958. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
Loree JM, Pereira AAL, Lam M, Willauer AN,
Raghav K, Dasari A, Morris VK, Advani S, Menter DG, Eng C, et al:
Classifying colorectal cancer by tumor location rather than
sidedness highlights a continuum in mutation profiles and consensus
molecular subtypes. Clin Cancer Res. 24:1062–1072. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Zhao J and Miao D: Precision oncology in
colorectal cancer: An anatomical revolution through
molecular-clinical integration across colonic subsites. Clin Res
Hepatol Gastroenterol. 49:1026132025. View Article : Google Scholar : PubMed/NCBI
|
|
33
|
Blanche P, Dartigues JF and Jacqmin-Gadda
H: Estimating and comparing time-dependent areas under receiver
operating characteristic curves for censored event times with
competing risks. Stat Med. 32:5381–5397. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
34
|
Ramírez-Giraldo C, Venegas-Sanabria LC,
Pesce A, González-Muñoz A, Van-Londoño I and Isaza-Restrepo A:
Effects of sarcopenia on patient outcomes in gastrointestinal
cancer: An umbrella review of published meta-analyses. J
Gastrointest Cancer. 56:1712025. View Article : Google Scholar : PubMed/NCBI
|
|
35
|
Li S, Wang T, Tong G, Li X, You D and Cong
M: Prognostic impact of sarcopenia on clinical outcomes in
malignancies treated with immune checkpoint inhibitors: A
systematic review and meta-analysis. Front Oncol. 11:7262572021.
View Article : Google Scholar : PubMed/NCBI
|
|
36
|
Kawaguchi Y, Kataoka Y, Okamoto K, Yoden
M, Shiratori T, Kaku R, Ueda K, Ohshio Y, Terashima T and Hanaoka
J: Activin A is deeply involved in the progression of sarcopenia
and leads to poor prognosis in lung cancer patients. Sci Rep.
15:136412025. View Article : Google Scholar : PubMed/NCBI
|
|
37
|
Liu C, An L, Zhang S, Deng S, Wang N and
Tang H: Association between preoperative sarcopenia and prognosis
of pancreatic cancer after curative-intent surgery: A updated
systematic review and meta-analysis. World J Surg Oncol. 22:382024.
View Article : Google Scholar : PubMed/NCBI
|
|
38
|
Şahin MEH, Akbaş F, Yardimci AH and Şahin
E: The effect of sarcopenia and sarcopenic obesity on survival in
gastric cancer. BMC Cancer. 23:9112023. View Article : Google Scholar : PubMed/NCBI
|
|
39
|
Sılay K, Uçar G, Eren T, Selvi Öztorun H,
Yazıcı O and Özdemir N: Could Sarcopenia Be related to chemotherapy
in gastrointestinal cancer? A cross-sectional study including
comprehensive geriatric assessment. J Clin Med. 14:7112025.
View Article : Google Scholar : PubMed/NCBI
|
|
40
|
Trejo-Avila M, Bozada-Gutiérrez K,
Valenzuela-Salazar C, Herrera-Esquivel J and Moreno-Portillo M:
Sarcopenia predicts worse postoperative outcomes and decreased
survival rates in patients with colorectal cancer: A systematic
review and meta-analysis. Int J Colorectal Dis. 36:1077–1096. 2021.
View Article : Google Scholar : PubMed/NCBI
|
|
41
|
Chai VW, Chia M, Cocco A, Bhamidipaty M
and D'Souza B: Sarcopenia is a strong predictive factor of clinical
and oncological outcomes following curative colorectal cancer
resection. ANZ J Surg. 91:E292–E297. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
42
|
Larsson L, Degens H, Li M, Salviati L, Lee
YI, Thompson W, Kirkland JL and Sandri M: Sarcopenia: Aging-related
loss of muscle mass and function. Physiol Rev. 99:427–511. 2019.
View Article : Google Scholar : PubMed/NCBI
|
|
43
|
Martino FK, Zattarin A, Cinquini C,
Toniazzo S, Francini Pesenti F, Stefanelli LF, Cacciapuoti M,
Bettin E, Calò LA and Spinella P: Low-protein diet in elderly
patients with chronic kidney disease stage 4 and 5 in conservative
management: Focus on sarcopenia development. Nutrients.
16:14982024. View Article : Google Scholar : PubMed/NCBI
|
|
44
|
Roxburgh CS and McMillan DC: Role of
systemic inflammatory response in predicting survival in patients
with primary operable cancer. Future Oncol. 6:149–163. 2010.
View Article : Google Scholar : PubMed/NCBI
|
|
45
|
Mao H, Zhao X and Sun SC: NF-κB in
inflammation and cancer. Cell Mol Immunol. 22:811–839. 2025.
View Article : Google Scholar : PubMed/NCBI
|
|
46
|
Hu WH, Chang CD, Liu TT, Chen HH, Hsiao
CC, Kang HY and Chuang JH: Association of sarcopenia and expression
of interleukin-23 in colorectal cancer survival. Clin Nutr.
40:5322–5326. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
47
|
Hu WH, Chen HH, Yen SL, Huang HY, Hsiao CC
and Chuang JH: Increased expression of interleukin-23 associated
with progression of colorectal cancer. J Surg Oncol. 115:208–212.
2017. View Article : Google Scholar : PubMed/NCBI
|
|
48
|
Bano G, Trevisan C, Carraro S, Solmi M,
Luchini C, Stubbs B, Manzato E, Sergi G and Veronese N:
Inflammation and sarcopenia: A systematic review and meta-analysis.
Maturitas. 96:10–15. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
49
|
Gupta A, Gupta E, Hilsden R, Hawel JD,
Elnahas AI, Schlachta CM and Alkhamesi NA: Preoperative
malnutrition in patients with colorectal cancer. Can J Surg.
64:E621–E629. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
50
|
Tan BH and Fearon KC: Cachexia: Prevalence
and impact in medicine. Curr Opin Clin Nutr Metab Care. 11:400–407.
2008.PubMed/NCBI
|
|
51
|
Ryan AM, Prado CM, Sullivan ES, Power DG
and Daly LE: Effects of weight loss and sarcopenia on response to
chemotherapy, quality of life, and survival. Nutrition. 67–68.
1105392019.
|
|
52
|
Hari DM, Leung AM, Lee JH, Sim MS, Vuong
B, Chiu CG and Bilchik AJ: AJCC Cancer Staging Manual 7th edition
criteria for colon cancer: Do the complex modifications improve
prognostic assessment? J Am Coll Surg. 217:181–190. 2013.
View Article : Google Scholar : PubMed/NCBI
|
|
53
|
Weitz J, Koch M, Debus J, Höhler T, Galle
PR and Büchler MW: Colorectal cancer. Lancet. 365:153–165. 2005.
View Article : Google Scholar : PubMed/NCBI
|
|
54
|
Pagès F, Mlecnik B, Marliot F, Bindea G,
Ou FS, Bifulco C, Lugli A, Zlobec I, Rau TT, Berger MD, et al:
International validation of the consensus Immunoscore for the
classification of colon cancer: A prognostic and accuracy study.
Lancet. 391:2128–2139. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
55
|
Abdel Hamid M, Pammer LM, Oberparleiter S,
Günther M, Amann A, Gruber RA, Mair A, Nocera FI, Ormanns S, Zimmer
K, et al: Multidimensional differences of right- and left-sided
colorectal cancer and their impact on targeted therapies. NPJ
Precis Oncol. 9:1162025. View Article : Google Scholar : PubMed/NCBI
|
|
56
|
Boka HJ, Engel RM, Georges C, McMurrick PJ
and Abud HE: Does side matter? Deciphering mechanisms that underpin
side-dependent pathogenesis and therapy response in colorectal
cancer. Mol Cancer. 24:1302025. View Article : Google Scholar : PubMed/NCBI
|
|
57
|
Lee MS, Menter DG and Kopetz S: Right
versus left colon cancer biology: Integrating the consensus
molecular subtypes. J Natl Compr Canc Netw. 15:411–419. 2017.
View Article : Google Scholar : PubMed/NCBI
|
|
58
|
Wang W, Fang F, Wang T, Chen X, Zhao L,
Wang C, Tang Y, Li Q, Tang D and Zhang J: The impact of sarcopenic
obesity on the prevalence and prognosis of non-metastatic
colorectal cancer: A systematic review and meta-analysis. Clin Nutr
ESPEN. 69:492–502. 2025. View Article : Google Scholar : PubMed/NCBI
|
|
59
|
Zhou H, Tian L, Wu Y and Liu S: Computed
tomography-measured body composition can predict long-term outcomes
for stage I–III colorectal cancer patients. Front Oncol.
14:14209172024. View Article : Google Scholar : PubMed/NCBI
|
